[ { "path": ".adr-dir", "content": "docs/source/design\n" }, { "path": ".codespellignore", "content": "filetest" }, { "path": ".gitattributes", "content": "tests/cassettes/**/*.yaml linguist-generated=true\n" }, { "path": ".github/dependabot.yml", "content": "version: 2\nupdates:\n - package-ecosystem: github-actions\n directory: \"/\"\n schedule:\n interval: weekly\n - package-ecosystem: pip\n directory: \"/.github/workflows\"\n schedule:\n interval: weekly\n - package-ecosystem: pip\n directory: \".\"\n schedule:\n interval: daily\n versioning-strategy: increase-if-necessary\n" }, { "path": ".github/pull_request_template.md", "content": "**Related Issue(s):** \n\n- #\n\n\n**Description:**\n\n\n**PR Checklist:**\n\n- [ ] Code is formatted\n- [ ] Tests pass\n- [ ] Changes are added to CHANGELOG.md\n" }, { "path": ".github/workflows/continuous-integration.yml", "content": "name: CI\n\non:\n push:\n branches:\n - main\n pull_request:\n\nconcurrency:\n group: ${{ github.ref }}\n cancel-in-progress: true\n\njobs:\n build:\n name: build\n runs-on: ${{ matrix.os }}\n strategy:\n matrix:\n python-version:\n - \"3.10\"\n - \"3.11\"\n - \"3.12\"\n os:\n - ubuntu-latest\n - windows-latest\n - macos-latest\n steps:\n - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6\n - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0\n with:\n python-version: ${{ matrix.python-version }}\n - name: Sync\n run: uv sync\n - name: Run pre-commit\n run: uv run pre-commit run --all-files\n - name: Run pytest\n run: uv run pytest -Werror -s --block-network --cov pystac_client --cov-report term-missing\n - name: Run coverage\n run: uv run coverage xml\n - uses: codecov/codecov-action@57e3a136b779b570ffcdbf80b3bdc90e7fab3de2 # v6\n with:\n token: ${{ secrets.CODECOV_TOKEN }}\n files: ./coverage.xml\n fail_ci_if_error: false\n\n min-versions:\n name: min-versions\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6\n - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0\n with:\n activate-environment: true\n python-version: \"3.10\"\n - name: Install with min requirements\n run: uv sync --no-dev --resolution=lowest-direct\n - name: Run smoke test\n run: stac-client search https://landsatlook.usgs.gov/stac-server -c landsat-c2l2-sr --max-items 1\n\n pre-release:\n name: pre-release\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6\n - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0\n - name: Sync\n run: uv sync\n - name: Install any pre-releases of pystac\n run: uv pip install -U --pre pystac\n - name: Run pytest\n run: uv run pytest -Werror -s --block-network\n\n upstream:\n name: upstream\n runs-on: ubuntu-latest\n if: github.event_name != 'pull_request'\n steps:\n - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6\n - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0\n - name: Sync\n run: uv sync\n - name: Install pystac from main\n run: uv pip install --force-reinstall git+https://github.com/stac-utils/pystac.git\n - name: Run pytest\n run: uv run pytest -Werror -s --block-network\n\n docs:\n name: docs\n runs-on: ubuntu-latest\n defaults:\n run:\n shell: bash -el {0}\n steps:\n - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6\n - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0\n - name: Install pandoc\n run: sudo apt-get install -y pandoc\n - name: Sync\n run: uv sync --group docs\n - name: Build docs\n run: ./scripts/build-docs\n" }, { "path": ".github/workflows/release.yml", "content": "name: Release\n\non:\n push:\n tags:\n - \"*\"\n\njobs:\n release:\n name: release\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6\n - name: Set up Python 3.11\n uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6\n with:\n python-version: \"3.11\"\n - name: Install release dependencies\n run: |\n python -m pip install --upgrade pip\n pip install build twine\n - name: Build and publish package\n env:\n TWINE_USERNAME: ${{ secrets.PYPI_STACUTILS_USERNAME }}\n TWINE_PASSWORD: ${{ secrets.PYPI_STACUTILS_PASSWORD }}\n run: |\n python -m build\n twine upload dist/*\n" }, { "path": ".gitignore", "content": ".python-version\n**/*.private*\n\n# Ignore everything JetBrains\n.idea\n\n# Created by .ignore support plugin (hsz.mobi)\n### VisualStudio template\n## Ignore Visual Studio temporary files, build results, and\n## files generated by popular Visual Studio add-ons.\n##\n## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore\n\n# User-specific files\n*.rsuser\n*.suo\n*.user\n*.userosscache\n*.sln.docstates\n\n# User-specific files (MonoDevelop/Xamarin Studio)\n*.userprefs\n\n# Mono auto generated files\nmono_crash.*\n\n# Build results\n[Dd]ebug/\n[Dd]ebugPublic/\n[Rr]elease/\n[Rr]eleases/\nx64/\nx86/\n[Ww][Ii][Nn]32/\n[Aa][Rr][Mm]/\n[Aa][Rr][Mm]64/\nbld/\n[Bb]in/\n[Oo]bj/\n[Ll]og/\n[Ll]ogs/\n\n# Visual Studio 2015/2017 cache/options directory\n.vs/\n# Uncomment if you have tasks that create the project's static files in wwwroot\n#wwwroot/\n\n# Visual Studio 2017 auto generated files\nGenerated\\ Files/\n\n# MSTest test Results\n[Tt]est[Rr]esult*/\n[Bb]uild[Ll]og.*\n\n# NUnit\n*.VisualState.xml\nTestResult.xml\nnunit-*.xml\n\n# Build Results of an ATL Project\n[Dd]ebugPS/\n[Rr]eleasePS/\ndlldata.c\n\n# Benchmark Results\nBenchmarkDotNet.Artifacts/\n\n# .NET Core\nproject.lock.json\nproject.fragment.lock.json\nartifacts/\n\n# ASP.NET Scaffolding\nScaffoldingReadMe.txt\n\n# StyleCop\nStyleCopReport.xml\n\n# Files built by Visual Studio\n*_i.c\n*_p.c\n*_h.h\n*.ilk\n*.meta\n*.obj\n*.iobj\n*.pch\n*.pdb\n*.ipdb\n*.pgc\n*.pgd\n*.rsp\n*.sbr\n*.tlb\n*.tli\n*.tlh\n*.tmp\n*.tmp_proj\n*_wpftmp.csproj\n*.log\n*.vspscc\n*.vssscc\n.builds\n*.pidb\n*.svclog\n*.scc\n\n# Chutzpah Test files\n_Chutzpah*\n\n# Visual C++ cache files\nipch/\n*.aps\n*.ncb\n*.opendb\n*.opensdf\n*.sdf\n*.cachefile\n*.VC.db\n*.VC.VC.opendb\n\n# Visual Studio profiler\n*.psess\n*.vsp\n*.vspx\n*.sap\n\n# Visual Studio Trace Files\n*.e2e\n\n# TFS 2012 Local Workspace\n$tf/\n\n# Guidance Automation Toolkit\n*.gpState\n\n# ReSharper is a .NET coding add-in\n_ReSharper*/\n*.[Rr]e[Ss]harper\n*.DotSettings.user\n\n# TeamCity is a build add-in\n_TeamCity*\n\n# DotCover is a Code Coverage Tool\n*.dotCover\n\n# AxoCover is a Code Coverage Tool\n.axoCover/*\n!.axoCover/settings.json\n\n# Coverlet is a free, cross platform Code Coverage Tool\ncoverage*.json\ncoverage*.xml\ncoverage*.info\n\n# Visual Studio code coverage results\n*.coverage\n*.coveragexml\n\n# NCrunch\n_NCrunch_*\n.*crunch*.local.xml\nnCrunchTemp_*\n\n# MightyMoose\n*.mm.*\nAutoTest.Net/\n\n# Web workbench (sass)\n.sass-cache/\n\n# Installshield output folder\n[Ee]xpress/\n\n# DocProject is a documentation generator add-in\nDocProject/buildhelp/\nDocProject/Help/*.HxT\nDocProject/Help/*.HxC\nDocProject/Help/*.hhc\nDocProject/Help/*.hhk\nDocProject/Help/*.hhp\nDocProject/Help/Html2\nDocProject/Help/html\n\n# Click-Once directory\npublish/\n\n# Publish Web Output\n*.[Pp]ublish.xml\n*.azurePubxml\n# Note: Comment the next line if you want to checkin your web deploy settings,\n# but database connection strings (with potential passwords) will be unencrypted\n*.pubxml\n*.publishproj\n\n# Microsoft Azure Web App publish settings. Comment the next line if you want to\n# checkin your Azure Web App publish settings, but sensitive information contained\n# in these scripts will be unencrypted\nPublishScripts/\n\n# NuGet Packages\n*.nupkg\n# NuGet Symbol Packages\n*.snupkg\n# The packages folder can be ignored because of Package Restore\n**/[Pp]ackages/*\n# except build/, which is used as an MSBuild target.\n!**/[Pp]ackages/build/\n# Uncomment if necessary however generally it will be regenerated when needed\n#!**/[Pp]ackages/repositories.config\n# NuGet v3's project.json files produces more ignorable files\n*.nuget.props\n*.nuget.targets\n\n# Microsoft Azure Build Output\ncsx/\n*.build.csdef\n\n# Microsoft Azure Emulator\necf/\nrcf/\n\n# Windows Store app package directories and files\nAppPackages/\nBundleArtifacts/\nPackage.StoreAssociation.xml\n_pkginfo.txt\n*.appx\n*.appxbundle\n*.appxupload\n\n# Visual Studio cache files\n# files ending in .cache can be ignored\n*.[Cc]ache\n# but keep track of directories ending in .cache\n!?*.[Cc]ache/\n\n# Others\nClientBin/\n~$*\n*~\n*.dbmdl\n*.dbproj.schemaview\n*.jfm\n*.pfx\n*.publishsettings\norleans.codegen.cs\n\n# Including strong name files can present a security risk\n# (https://github.com/github/gitignore/pull/2483#issue-259490424)\n#*.snk\n\n# Since there are multiple workflows, uncomment next line to ignore bower_components\n# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622)\n#bower_components/\n\n# RIA/Silverlight projects\nGenerated_Code/\n\n# Backup & report files from converting an old project file\n# to a newer Visual Studio version. Backup files are not needed,\n# because we have git ;-)\n_UpgradeReport_Files/\nBackup*/\nUpgradeLog*.XML\nUpgradeLog*.htm\nServiceFabricBackup/\n*.rptproj.bak\n\n# SQL Server files\n*.mdf\n*.ldf\n*.ndf\n\n# Business Intelligence projects\n*.rdl.data\n*.bim.layout\n*.bim_*.settings\n*.rptproj.rsuser\n*- [Bb]ackup.rdl\n*- [Bb]ackup ([0-9]).rdl\n*- [Bb]ackup ([0-9][0-9]).rdl\n\n# Microsoft Fakes\nFakesAssemblies/\n\n# GhostDoc plugin setting file\n*.GhostDoc.xml\n\n# Node.js Tools for Visual Studio\n.ntvs_analysis.dat\nnode_modules/\n\n# Visual Studio 6 build log\n*.plg\n\n# Visual Studio 6 workspace options file\n*.opt\n\n# Visual Studio 6 auto-generated workspace file (contains which files were open etc.)\n*.vbw\n\n# Visual Studio LightSwitch build output\n**/*.HTMLClient/GeneratedArtifacts\n**/*.DesktopClient/GeneratedArtifacts\n**/*.DesktopClient/ModelManifest.xml\n**/*.Server/GeneratedArtifacts\n**/*.Server/ModelManifest.xml\n_Pvt_Extensions\n\n# Paket dependency manager\n.paket/paket.exe\npaket-files/\n\n# FAKE - F# Make\n.fake/\n\n# CodeRush personal settings\n.cr/personal\n\n# Python Tools for Visual Studio (PTVS)\n__pycache__/\n*.pyc\n\n# Cake - Uncomment if you are using it\n# tools/**\n# !tools/packages.config\n\n# Tabs Studio\n*.tss\n\n# Telerik's JustMock configuration file\n*.jmconfig\n\n# BizTalk build output\n*.btp.cs\n*.btm.cs\n*.odx.cs\n*.xsd.cs\n\n# OpenCover UI analysis results\nOpenCover/\n\n# Azure Stream Analytics local run output\nASALocalRun/\n\n# MSBuild Binary and Structured Log\n*.binlog\n\n# NVidia Nsight GPU debugger configuration file\n*.nvuser\n\n# MFractors (Xamarin productivity tool) working folder\n.mfractor/\n\n# Local History for Visual Studio\n.localhistory/\n\n# BeatPulse healthcheck temp database\nhealthchecksdb\n\n# Backup folder for Package Reference Convert tool in Visual Studio 2017\nMigrationBackup/\n\n# Ionide (cross platform F# VS Code tools) working folder\n.ionide/\n\n# Fody - auto-generated XML schema\nFodyWeavers.xsd\n\n### macOS template\n# General\n.DS_Store\n.AppleDouble\n.LSOverride\n\n# Icon must end with two \\r\nIcon\n\n# Thumbnails\n._*\n\n# Files that might appear in the root of a volume\n.DocumentRevisions-V100\n.fseventsd\n.Spotlight-V100\n.TemporaryItems\n.Trashes\n.VolumeIcon.icns\n.com.apple.timemachine.donotpresent\n\n# Directories potentially created on remote AFP share\n.AppleDB\n.AppleDesktop\nNetwork Trash Folder\nTemporary Items\n.apdisk\n\n### JetBrains template\n# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider\n# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839\n\n\n# CMake\ncmake-build-*/\n\n# File-based project format\n*.iws\n\n# IntelliJ\nout/\n\n# JIRA plugin\natlassian-ide-plugin.xml\n\n# Crashlytics plugin (for Android Studio and IntelliJ)\ncom_crashlytics_export_strings.xml\ncrashlytics.properties\ncrashlytics-build.properties\nfabric.properties\n\n### VirtualEnv template\n# Virtualenv\n# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/\n.Python\n[Bb]in\n[Ii]nclude\n[Ll]ib\n[Ll]ib64\n[Ll]ocal\npyvenv.cfg\n.venv\npip-selfcheck.json\n\n### JupyterNotebooks template\n# gitignore template for Jupyter Notebooks\n# website: http://jupyter.org/\n\n.ipynb_checkpoints\n*/.ipynb_checkpoints/*\n\n# IPython\nprofile_default/\nipython_config.py\n\n# Remove previous ipynb_checkpoints\n# git rm -r .ipynb_checkpoints/\n\n### Python template\n# Byte-compiled / optimized / DLL files\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n\n# IPython\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n### Linux template\n\n# temporary files which can be created if a process still has a handle open of a deleted file\n.fuse_hidden*\n\n# KDE directory preferences\n.directory\n\n# Linux trash folder which might appear on any partition or disk\n.Trash-*\n\n# .nfs files are created when an open file is removed but is still being accessed\n.nfs*\n\n### Windows template\n# Windows thumbnail cache files\nThumbs.db\nThumbs.db:encryptable\nehthumbs.db\nehthumbs_vista.db\n\n# Dump file\n*.stackdump\n\n# Folder config file\n[Dd]esktop.ini\n\n# Recycle Bin used on file shares\n$RECYCLE.BIN/\n\n# Windows Installer files\n*.cab\n*.msi\n*.msix\n*.msm\n*.msp\n\n# Windows shortcuts\n*.lnk\n" }, { "path": ".pre-commit-config.yaml", "content": "# Configuration file for pre-commit (https://pre-commit.com/).\n# Please run `pre-commit run --all-files` when adding or changing entries.\n\nrepos:\n - repo: https://github.com/charliermarsh/ruff-pre-commit\n rev: \"v0.9.6\"\n hooks:\n - id: ruff\n args: [--fix, --exit-non-zero-on-fix]\n - id: ruff-format\n - repo: https://github.com/codespell-project/codespell\n rev: v2.4.1\n hooks:\n - id: codespell\n args: [--ignore-words=.codespellignore]\n types_or: [jupyter, markdown, python, shell]\n - repo: https://github.com/PyCQA/doc8\n rev: v1.1.2\n hooks:\n - id: doc8\n args: [--ignore=D004]\n additional_dependencies:\n - importlib_metadata < 5; python_version == \"3.7\"\n - repo: https://github.com/pre-commit/mirrors-mypy\n rev: v1.15.0\n hooks:\n - id: mypy\n files: \".*\\\\.py$\"\n additional_dependencies:\n - pystac\n - pytest-vcr\n - types-requests\n - types-python-dateutil\n" }, { "path": ".readthedocs.yml", "content": "version: 2\n\nbuild:\n os: ubuntu-24.04\n tools:\n python: \"3.12\"\n jobs:\n pre_create_environment:\n - asdf plugin add uv\n - asdf install uv latest\n - asdf global uv latest\n create_environment:\n - uv venv \"${READTHEDOCS_VIRTUALENV_PATH}\"\n install:\n - UV_PROJECT_ENVIRONMENT=\"${READTHEDOCS_VIRTUALENV_PATH}\" uv sync --group docs --link-mode=copy\n\nsphinx:\n configuration: docs/conf.py\n fail_on_warning: false\n\nformats:\n - pdf\n - htmlzip\n" }, { "path": "CHANGELOG.md", "content": "# Changelog\n\nAll notable changes to this project will be documented in this file.\n\nThe format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)\nand this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).\n\n## [Unreleased]\n\n### Added\n\n- Add comprehensive test coverage for warning context managers (`ignore()` and `strict()`) ([#832](https://github.com/stac-utils/pystac-client/pull/832))\n- Moved -Werror to pyproject.toml ([#841](https://github.com/stac-utils/pystac-client/pull/841))\n\n### Changed\n\n- Make `get_collection` raise if `collection_id` is empty ([#809](https://github.com/stac-utils/pystac-client/pull/809))\n- Update `get_collection` to return None if no collection exists ([#861](https://github.com/stac-utils/pystac-client/pull/861))\n\n### Added\n\n- Add comprehensive test coverage for ConformanceClasses enum ([#834](https://github.com/stac-utils/pystac-client/pull/834))\n\n### Documentation\n\n- Update contributing guide to consistently use `uv` workflow ([#822](https://github.com/stac-utils/pystac-client/pull/822))\n- Fix format script to use `ruff-format` instead of deprecated `black` hook ([#822](https://github.com/stac-utils/pystac-client/pull/822))\n- Add \"How to...\" section to usage documentation with latest datetime search across multiple collections example ([#823](https://github.com/stac-utils/pystac-client/pull/823))\n\n\n## [v0.9.0] - 2025-07-17\n\n### Added\n\n- Coerce cql2 style to match HTTP method using `cql2` library ([#804](https://github.com/stac-utils/pystac-client/pull/804))\n\n### Fixed\n\n- Fix usage documentation of `ItemSearch` ([#790](https://github.com/stac-utils/pystac-client/pull/790))\n- Fix fields argument to CLI ([#797](https://github.com/stac-utils/pystac-client/pull/797))\n- Clarify recursive behaviour of the `get_items` method in the method docstring ([#800](https://github.com/stac-utils/pystac-client/pull/800))\n\n## [v0.8.6] - 2025-02-11\n\n### Changed\n\n- Use [uv](https://docs.astral.sh/uv/) for development ([#784](https://github.com/stac-utils/pystac-client/pull/784))\n- Updated to Python 3.10 syntax with **pyupgrade** ([#783](https://github.com/stac-utils/pystac-client/pull/783/))\n\n### Fixed\n\n- `Client.get_collection` for static catalogs ([#782](https://github.com/stac-utils/pystac-client/pull/782))\n- Permissive collection reading ([#787](https://github.com/stac-utils/pystac-client/pull/787))\n\n## [v0.8.5] - 2024-10-23\n\n### Fixed\n\n- Use urljoin to build hrefs ([#746](https://github.com/stac-utils/pystac-client/pull/746))\n\n## [v0.8.4] - 2024-10-16\n\n### Added\n\n- Support for collection search via `CollectionSearch` class and associated client methods ([#735](https://github.com/stac-utils/pystac-client/pull/735))\n\n### Removed\n\n- Python 3.9 support ([#724](https://github.com/stac-utils/pystac-client/pull/724))\n\n## [v0.8.3] - 2024-07-01\n\n### Fixed\n\n- Actually set `timeout` when initializing `StacApiIO` ([#709](https://github.com/stac-utils/pystac-client/pull/709))\n- Set `_stac_io` on `CollectionClient` when reading ([#709](https://github.com/stac-utils/pystac-client/pull/709))\n\n## [v0.8.2] - 2024-05-30\n\n### Added\n\n- Support for \"Feature\" type `intersects` dictionaries ([#696](https://github.com/stac-utils/pystac-client/pull/696))\n\n## [v0.8.1] - 2024-05-23\n\n### Fixed\n\n- Use singular `include` and `exclude` Field extension key names ([#690](https://github.com/stac-utils/pystac-client/pull/690))\n\n## [v0.8.0] - 2024-05-17\n\n### Fixed\n\n- Update `requires-python` in metadata to require >= 3.9 ([#684](https://github.com/stac-utils/pystac-client/pull/684))\n- Interpret naive datetimes as UTC ([#686](https://github.com/stac-utils/pystac-client/pull/686))\n\n## [v0.7.7] - 2024-04-19\n\n### Changed\n\n- Updated to **pystac** v1.10.0 ([#661](https://github.com/stac-utils/pystac-client/pull/661))\n- Use [uv](https://github.com/astral-sh/uv) for CI ([#663](https://github.com/stac-utils/pystac-client/pull/663))\n- use `APILayoutStrategy` as fallback strategy ([#666](https://github.com/stac-utils/pystac-client/pull/666))\n- Updated grammar in README ([#676](https://github.com/stac-utils/pystac-client/pull/676))\n\n### Fixed\n\n- Respect the `REQUESTS_CA_BUNDLE` and `CURL_CA_BUNDLE` environment variables when sending requests ([#669](https://github.com/stac-utils/pystac-client/pull/669))\n\n## [v0.7.6] - 2024-02-28\n\n### Fixed\n\n- Recursion error in `get_items` ([#608](https://github.com/stac-utils/pystac-client/pull/608))\n- Fixes inconsistent behavior in `CollectionClient.get_items` when Item IDs are supplied ([#639](https://github.com/stac-utils/pystac-client/pull/639))\n- Logic determining if `context.matched` in `ItemSearch.matched` response ([#646](https://github.com/stac-utils/pystac-client/pull/646))\n\n### Removed\n\n- Passing `-q` to the CLI rather than `--query` ([#614](https://github.com/stac-utils/pystac-client/pull/614))\n\n## [v0.7.5] - 2023-09-05\n\n### Fixed\n\n- `--matched` flag in CLI ([#588](https://github.com/stac-utils/pystac-client/pull/588))\n\n## [v0.7.4] - 2023-09-05\n\n### Changed\n\n- Don't provide a `limit` by default, trust the server to know its limits ([#584](https://github.com/stac-utils/pystac-client/pull/584))\n\n## [v0.7.3] - 2023-08-21\n\n### Changed\n\n- Raise more informative errors from CLI ([#554](https://github.com/stac-utils/pystac-client/pull/554))\n- Add `Retry` type hint to `StacApiIO` ([#577](https://github.com/stac-utils/pystac-client/pull/577))\n\n### Fixed\n\n- Updated `get_items` signatures for PySTAC v1.8 ([#559](https://github.com/stac-utils/pystac-client/pull/559), [#579](https://github.com/stac-utils/pystac-client/pull/579))\n\n## [v0.7.2] - 2023-06-23\n\n### Fixed\n\n- Remove troublesome assertion ([#549](https://github.com/stac-utils/pystac-client/pull/549))\n\n## [v0.7.1] - 2023-06-13\n\n### Fixed\n\n- Remove unnecessary `typing_extensions` import ([#541](https://github.com/stac-utils/pystac-client/pull/541))\n\n## [v0.7.0] - 2023-06-12\n\n### Added\n\n- Timeout option added to `Client.open` ([#463](https://github.com/stac-utils/pystac-client/pull/463), [#538](https://github.com/stac-utils/pystac-client/pull/538))\n- Support for fetching catalog queryables ([#477](https://github.com/stac-utils/pystac-client/pull/477))\n- PySTAC Client specific warnings ([#480](https://github.com/stac-utils/pystac-client/pull/480))\n- Support for fetching and merging a selection of queryables ([#511](https://github.com/stac-utils/pystac-client/pull/511))\n- Better error messages for the CLI ([#531](https://github.com/stac-utils/pystac-client/pull/531))\n- `Modifiable` to our public API ([#534](https://github.com/stac-utils/pystac-client/pull/534))\n- `max_retries` parameter to `StacApiIO` ([#532](https://github.com/stac-utils/pystac-client/pull/532))\n\n### Changed\n\n- Switched to Ruff from isort/flake8 ([#457](https://github.com/stac-utils/pystac-client/pull/457))\n- Move to `FutureWarning` from `DeprecationWarning` for item search interface functions that are to be removed ([#464](https://github.com/stac-utils/pystac-client/pull/464))\n- Consolidate contributing docs into one place ([#478](https://github.com/stac-utils/pystac-client/issues/478))\n- Use `pyproject.toml` instead of `setup.py` ([#501](https://github.com/stac-utils/pystac-client/pull/501))\n- Enable Ruff import sorting ([#518](https://github.com/stac-utils/pystac-client/pull/518))\n\n### Fixed\n\n- `query` parameter in GET requests ([#362](https://github.com/stac-utils/pystac-client/pull/362))\n- Double encoding of `intersects` parameter in GET requests ([#362](https://github.com/stac-utils/pystac-client/pull/362))\n- Fix geometry instantiation in item-search-intersects.ipynb ([#484](https://github.com/stac-utils/pystac-client/pull/484))\n- Three tests that were false positives due to out-of-date cassettes ([#491](https://github.com/stac-utils/pystac-client/pull/491))\n- Max items checks when paging ([#492](https://github.com/stac-utils/pystac-client/pull/492))\n- `ItemSearch.url_with_parameters` no longer unquotes the url ([#530](https://github.com/stac-utils/pystac-client/pull/530))\n\n### Removed\n\n- `pystac_client.conformance.CONFORMANCE_URIS` dictionary ([#480](https://github.com/stac-utils/pystac-client/pull/480))\n\n## [v0.6.1] - 2023-03-14\n\n### Changed\n\n- Bumped PySTAC dependency to >= 1.7.0 ([#449](https://github.com/stac-utils/pystac-client/pull/449))\n\n### Fixed\n\n- Fix parse fail when header has multiple '=' characters ([#440](https://github.com/stac-utils/pystac-client/pull/440))\n- `Client.open` and `Client.from_file` now apply `headers`, etc to existing `stac_io` instances (([#439](https://github.com/stac-utils/pystac-client/pull/439)))\n\n## [v0.6.0] - 2023-01-27\n\n### Added\n\n- Python 3.11 support ([#347](https://github.com/stac-utils/pystac-client/pull/347))\n- `request_modifier` to `StacApiIO` to allow for additional authentication mechanisms (e.g. AWS SigV4) ([#372](https://github.com/stac-utils/pystac-client/pull/372))\n- _Authentication_ tutorial, demonstrating how to use to the provided hooks to use both basic and AWS SigV4 authentication ([#372](https://github.com/stac-utils/pystac-client/pull/372))\n- CI checks for Windows and MacOS ([#378](https://github.com/stac-utils/pystac-client/pull/378))\n- Fallback to `STAC API - Item Search` when finding a single item in `CollectionClient` if `STAC API - Features` is not implemented ([#379](https://github.com/stac-utils/pystac-client/pull/379))\n\n### Fixed\n\n- Stop iteration on an empty page ([#338](https://github.com/stac-utils/pystac-client/pull/338))\n- Some mishandled cases for datetime intervals ([#363](https://github.com/stac-utils/pystac-client/pull/363))\n- Collection requests when the Client's url ends in a '/' ([#373](https://github.com/stac-utils/pystac-client/pull/373), [#405](https://github.com/stac-utils/pystac-client/pull/405))\n- Parse datetimes more strictly ([#364](https://github.com/stac-utils/pystac-client/pull/364))\n\n### Removed\n\n- Python 3.7 support ([#347](https://github.com/stac-utils/pystac-client/pull/347))\n\n## [v0.5.1] - 2022-09-19\n\n### Added\n\n- Added `ItemSearch.url_with_parameters` to get a formatted search url ([#304](https://github.com/stac-utils/pystac-client/issues/304))\n\n### Fixed\n\n- Fix variable name in quickstart example ([#316](https://github.com/stac-utils/pystac-client/pull/316))\n- `StacApiIO.write_text_to_href` ([#312](https://github.com/stac-utils/pystac-client/pull/312))\n- Removed mention of STAC_URL in `Client.open` docstring ([#317](https://github.com/stac-utils/pystac-client/pull/317))\n\n## [v0.5.0] - 2022-08-19\n\n### Added\n\n- Added a new keyword `modifier` to various constructors like `Client.open()` ([#259](https://github.com/stac-utils/pystac-client/issues/259))\n\n### Fixed\n\n- Fix type annotation of `Client._stac_io` and avoid implicit re-exports in `pystac_client.__init__.py` ([#249](https://github.com/stac-utils/pystac-client/pull/249))\n- Added `ItemSearch.pages`, `ItemSearch.pages_as_dicts`, `ItemSearch.item_collection`, and `ItemSearch.item_collection_as_dict`\n as replacements for various deprecated methods ([#237](https://github.com/stac-utils/pystac-client/issues/237))\n- Restored the previous behavior of `Client.search()` to return an unlimited number of items by default. ([#273](https://github.com/stac-utils/pystac-client/pull/273))\n\n### Deprecated\n\n- `ItemSearch.item_collections` has been deprecated in favor of `ItemSearch.pages`. ([#237](https://github.com/stac-utils/pystac-client/issues/237))\n\n## [v0.4.0] - 2022-06-08\n\n### Added\n\n- Significantly improved type hints\n- lru_cache to several methods ([#167](https://github.com/stac-utils/pystac-client/pull/167))\n- Direct item GET via ogcapi-features, if conformant ([#166](https://github.com/stac-utils/pystac-client/pull/166))\n- `py.typed` for downstream type checking ([#163](https://github.com/stac-utils/pystac-client/pull/163))\n- Added tutorial for using various geometry objects (e.g., shapely, geojson) as an Item Search intersects argument ([#232](https://github.com/stac-utils/pystac-client/pull/232))\n\n### Changed\n\n- Item Search no longer defaults to returning an unlimited number of result Items from\n its \"items\" methods. The `max_items` parameter now defaults to 100 instead of None.\n Since the `limit` parameter also defaults to 100, in an ideal situation, only one request\n will be made to the server to retrieve all 100 items. Both of these parameters can be\n carefully adjusted upwards to align with the server's capabilities and the expected\n number of search results. ([#208](https://github.com/stac-utils/pystac-client/pull/208))\n- Better error message when trying to search a non-item-search-conforming catalog ([#164](https://github.com/stac-utils/pystac-client/pull/164))\n- Search `filter-lang` defaults to `cql2-json` instead of `cql-json` ([#169](https://github.com/stac-utils/pystac-client/pull/169))\n- Search `filter-lang` will be set to `cql2-json` if the `filter` is a dict, or `cql2-text` if it is a string ([#169](https://github.com/stac-utils/pystac-client/pull/169))\n- Search parameter `intersects` is now typed to only accept a str, dict, or object that implements `__geo_interface__` ([#174](https://github.com/stac-utils/pystac-client/pull/174))\n- Better error message when trying to open a Collection with `Client.open` ([#222](https://github.com/stac-utils/pystac-client/pull/222))\n\n### Deprecated\n\n- Item Search methods `get_items()` and `get_item_collections()` have been renamed to\n `items()` and `item_collections()`. The original methods are now deprecated\n and may be removed as early as v0.5.0. ([#206](https://github.com/stac-utils/pystac-client/pull/206))\n- Item Search methods `get_all_items()` and `get_all_items_as_dict()` are now deprecated,\n and may be removed as early as v0.5.0.\n These have been deprecated because they have the potential to perform a large number\n of requests to the server and instantiate a large number of objects in memory.\n To a user, this is only visible as a large delay in the method call and/or the\n exhaustion of all available memory. The iterator methods `items()` or\n `item_collections()` should be used instead. ([#206](https://github.com/stac-utils/pystac-client/pull/206))\n- CLI parameter `-q` is now deprecated and may be removed as early as v0.5.0. Use `--query` instead. ([#215](https://github.com/stac-utils/pystac-client/pull/215))\n\n## Removed\n\n- Client parameter `require_geojson_link` has been removed. ([#165](https://github.com/stac-utils/pystac-client/pull/165))\n\n### Fixed\n\n- Search query parameter now has correct typing and handles Query Extension JSON format. ([#220](https://github.com/stac-utils/pystac-client/pull/220))\n- Search sortby parameter now has correct typing and handles both GET and POST JSON parameter formats. ([#175](https://github.com/stac-utils/pystac-client/pull/175))\n- Search fields parameter now has correct typing and handles both GET and POST JSON parameter formats. ([#184](https://github.com/stac-utils/pystac-client/pull/184))\n- Use pytest configuration to skip benchmarks by default (instead of a `skip` mark). ([#168](https://github.com/stac-utils/pystac-client/pull/168))\n- Methods retrieving collections incorrectly checked the existence of the OAFeat OpenAPI 3.0 conformance class\n () instead of the `STAC API - Collections`\n () or `STAC API - Features`\n () conformance classes. ([#223](https://github.com/stac-utils/pystac-client/pull/223))\n\n## [v0.3.5] - 2022-05-26\n\n### Fixed\n\n- Search against earth-search v0 failed with message \"object of type 'Link' has no len()\" ([#179](https://github.com/stac-utils/pystac-client/pull/179))\n\n## [v0.3.4] - 2022-05-18\n\n### Changed\n\n- Relaxed media type requirement for search links ([#160](https://github.com/stac-utils/pystac-client/pull/160), [#165](https://github.com/stac-utils/pystac-client/pull/165))\n\n## [v0.3.3] - 2022-04-28\n\n### Added\n\n- Add `--filter-lang` parameter to allow specifying other filter language to be used within the `--filter` parameter ([#140](https://github.com/stac-utils/pystac-client/pull/140))\n- CI checks against minimum versions of all dependencies and any pre-release versions of PySTAC ([#144](https://github.com/stac-utils/pystac-client/pull/144))\n\n### Changed\n\n- Relaxed upper bound on PySTAC dependency ([#144](https://github.com/stac-utils/pystac-client/pull/144))\n- Bumped PySTAC dependency to >= 1.4.0 ([#147](https://github.com/stac-utils/pystac-client/pull/147))\n\n## [v0.3.2] - 2022-01-11\n\n### Added\n\n- `Client.search` accepts an optional `filter_lang` argument for `filter` requests ([#128](https://github.com/stac-utils/pystac-client/pull/128))\n- Added support for filtering the search endpoint using the `media_type` in `Client.open` ([#142](https://github.com/stac-utils/pystac-client/pull/142))\n\n### Fixed\n\n- Values from `parameters` and `headers` arguments to `Client.open` and `Client.from_file` are now also used in requests made from `CollectionClient` instances\n fetched from the same API (([#126](https://github.com/stac-utils/pystac-client/pull/126)))\n- The tests folder is no longer installed as a package.\n\n## [v0.3.1] - 2021-11-17\n\n### Added\n\n- Adds `--block-network` option to all test commands to ensure no network requests are made during unit tests\n ([#119](https://github.com/stac-utils/pystac-client/pull/119))\n- `parameters` argument to `StacApiIO`, `Client.open`, and `Client.from_file` to allow query string parameters to be passed to all requests\n ([#118](https://github.com/stac-utils/pystac-client/pull/118))\n\n### Changed\n\n- Update min PySTAC version to 1.2\n- Default page size limit set to 100 rather than relying on the server default\n- Fetch single collection directly from endpoint in API rather than iterating through children [Issue #114](https://github.com/stac-utils/pystac-client/issues/114)\n\n### Fixed\n\n- `Client.get_collections` raised an exception when API did not publish `/collections` conformance class instead of falling back to using child links\n ([#120](https://github.com/stac-utils/pystac-client/pull/120))\n\n## [v0.3.0] - 2021-09-28\n\n### Added\n\n- Jupyter Notebook tutorials\n- Basic CQL-JSON filtering ([#100](https://github.com/stac-utils/pystac-client/pull/100))\n\n### Changed\n\n- Improved performance when constructing `pystac.ItemCollection` objects.\n- Relax `requests` dependency ([#87](https://github.com/stac-utils/pystac-client/pull/87))\n- Use regular expressions for checking conformance classes ([#97](https://github.com/stac-utils/pystac-client/pull/97))\n- Reorganized documentation, updated all docs\n\n### Fixed\n\n- `ItemSearch` now correctly handles times without a timezone specifier ([#92](https://github.com/stac-utils/pystac-client/issues/92))\n- queries including `gsd` cast to string to float when using shortcut query syntax (i.e., \"key=val\" strings). ([#98](https://github.com/stac-utils/pystac-client/pull/97))\n- Documentation lints ([#108](https://github.com/stac-utils/pystac-client/pull/108))\n\n## [v0.2.0] - 2021-08-04\n\n### Added\n\n- `Client.open` falls back to the `STAC_URL` environment variable if no url is provided as an argument ([#48](https://github.com/stac-utils/pystac-client/pull/48))\n- New Search.get_pages() iterator function to retrieve pages as raw JSON, not as ItemCollections\n- `StacApiIO` class added, subclass from PySTAC `StacIO`. A `StacApiIO` instance is used for all IO for a Client instance, and all requests\n are in a single HTTP session, handle pagination and respects conformance\n- `conformance.CONFORMANCE_CLASSES` dictionary added containing all STAC API Capabilities from stac-api-spec\n- `collections` subcommand to CLI, for saving all Collections in catalog as JSON\n- `Client.get_collections` overrides Catalog to use /collections endpoint if API conforms\n- `Client.get_collection()` for getting specific collection\n- `Client.get_items` and `Client.get_all_items` override Catalog functions to use search endpoint instead of traversing catalog\n\n### Changed\n\n- Update to use PySTAC 1.1.0\n- IO changed to use PySTAC's new StacIO base class.\n- `Search.item_collections()` renamed to `Search.get_item_collections()`\n- `Search.item()` renamed to `Search.get_items()`\n- Conformance is checked by each individual function that requires a particular conformance\n- STAC API testing URLs changed to updated APIs\n- `ItemSearch.get_pages()` function moved to StacApiIO class for general use\n- Logging is now enabled in the CLI in all cases.\n If data are being printed to stdout, logging goes to stderr.\n ([#79](https://github.com/stac-utils/pystac-client/pull/79))\n- Improved logging for GET requests (prints encoded URL)\n\n### Removed\n\n- `get_pages` and `simple_stac_resolver` functions from `pystac_client.stac_io` (The new StacApiIO class understands `Link` objects)\n- `Client.search()` no longer accepts a `next_resolver` argument\n- pystac.extensions modules, which were based on PySTAC's previous extension implementation, replaced in 1.0.0\n- `stac_api_object.StacApiObjectMixin`, replaced with conformance checking in `StacApiIO`\n- PySTAC Collection objects can no longer be passed in as `collections` arguments to the `ItemSearch` class (just pass ids)\n- `Catalog.get_collection_list` (was alias to `get_child_links`) because made assumption about this being an API only. Also redundant with `Catalog.get_collections`\n- `Search.item_collections()`\n- `Search.items()`\n- STAC_URL environment variable in Client.open(). url parameter in Client is now required\n- STAC_URL environment variable in CLI. CLI now has a required positional argument for the URL\n\n### Fixed\n\n- Running `stac-client` with no arguments no longer raises a confusing exception ([#52](https://github.com/stac-utils/pystac-client/pull/52))\n- `Client.get_collections_list` ([#44](https://github.com/stac-utils/pystac-client/issues/44))\n- The regular expression used for datetime parsing ([#59](https://github.com/stac-utils/pystac-client/pull/59))\n- `Client.from_file` now works as expected, using `Client.open` is not required, although it will fetch STAC_URL from an envvar\n\n## [v0.1.1] - 2021-04-16\n\n### Added\n\n- `ItemSearch.items_as_collection` ([#37](https://github.com/stac-utils/pystac-client/pull/37))\n- Documentation [published on ReadTheDocs](https://pystac-client.readthedocs.io/en/latest/) ([#46](https://github.com/stac-utils/pystac-client/pull/46))\n\n### Changed\n\n- CLI: pass in headers as list of KEY=VALUE pairs\n\n### Fixed\n\n- Include headers in STAC_IO ([#38](https://github.com/stac-utils/pystac-client/pull/38))\n- README updated to reflect actual CLI behavior\n\n## [v0.1.0] - 2021-04-14\n\nInitial release.\n\n[Unreleased]: https://github.com/stac-utils/pystac-client/compare/v0.9.0...main\n[v0.9.0]: https://github.com/stac-utils/pystac-client/compare/v0.8.6...v0.9.0\n[v0.8.6]: https://github.com/stac-utils/pystac-client/compare/v0.8.5...v0.8.6\n[v0.8.5]: https://github.com/stac-utils/pystac-client/compare/v0.8.4...v0.8.5\n[v0.8.4]: https://github.com/stac-utils/pystac-client/compare/v0.8.3...v0.8.4\n[v0.8.3]: https://github.com/stac-utils/pystac-client/compare/v0.8.2...v0.8.3\n[v0.8.2]: https://github.com/stac-utils/pystac-client/compare/v0.8.1...v0.8.2\n[v0.8.1]: https://github.com/stac-utils/pystac-client/compare/v0.8.0...v0.8.1\n[v0.8.0]: https://github.com/stac-utils/pystac-client/compare/v0.7.7...v0.8.0\n[v0.7.7]: https://github.com/stac-utils/pystac-client/compare/v0.7.6...v0.7.7\n[v0.7.6]: https://github.com/stac-utils/pystac-client/compare/v0.7.5...v0.7.6\n[v0.7.5]: https://github.com/stac-utils/pystac-client/compare/v0.7.4...v0.7.5\n[v0.7.4]: https://github.com/stac-utils/pystac-client/compare/v0.7.3...v0.7.4\n[v0.7.3]: https://github.com/stac-utils/pystac-client/compare/v0.7.2...v0.7.3\n[v0.7.2]: https://github.com/stac-utils/pystac-client/compare/v0.7.1...v0.7.2\n[v0.7.1]: https://github.com/stac-utils/pystac-client/compare/v0.7.0...v0.7.1\n[v0.7.0]: https://github.com/stac-utils/pystac-client/compare/v0.6.1...v0.7.0\n[v0.6.1]: https://github.com/stac-utils/pystac-client/compare/v0.6.0...v0.6.1\n[v0.6.0]: https://github.com/stac-utils/pystac-client/compare/v0.5.1...v0.6.0\n[v0.5.1]: https://github.com/stac-utils/pystac-client/compare/v0.5.0...v0.5.1\n[v0.5.0]: https://github.com/stac-utils/pystac-client/compare/v0.4.0...v0.5.0\n[v0.4.0]: https://github.com/stac-utils/pystac-client/compare/v0.3.5...v0.4.0\n[v0.3.5]: https://github.com/stac-utils/pystac-client/compare/v0.3.4...v0.3.5\n[v0.3.4]: https://github.com/stac-utils/pystac-client/compare/v0.3.3...v0.3.4\n[v0.3.3]: https://github.com/stac-utils/pystac-client/compare/v0.3.2...v0.3.3\n[v0.3.2]: https://github.com/stac-utils/pystac-client/compare/v0.3.1...v0.3.2\n[v0.3.1]: https://github.com/stac-utils/pystac-client/compare/v0.3.0...v0.3.1\n[v0.3.0]: https://github.com/stac-utils/pystac-client/compare/v0.2.0...v0.3.0\n[v0.2.0]: https://github.com/stac-utils/pystac-client/compare/v0.1.1...v0.2.0\n[v0.1.1]: https://github.com/stac-utils/pystac-client/compare/v0.1.0...v0.1.1\n[v0.1.0]: https://github.com/stac-utils/pystac-client/tree/v0.1.0\n" }, { "path": "LICENSE", "content": "Copyright 2021 Jon Duckworth\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n" }, { "path": "MANIFEST.in", "content": "global-include *.typed\n" }, { "path": "README.md", "content": "# pystac-client\n\n[![CI](https://github.com/stac-utils/pystac-client/actions/workflows/continuous-integration.yml/badge.svg)](https://github.com/stac-utils/pystac-client/actions/workflows/continuous-integration.yml)\n[![Release](https://github.com/stac-utils/pystac-client/actions/workflows/release.yml/badge.svg)](https://github.com/stac-utils/pystac-client/actions/workflows/release.yml)\n[![PyPI version](https://badge.fury.io/py/pystac-client.svg)](https://badge.fury.io/py/pystac-client)\n[![Documentation](https://readthedocs.org/projects/pystac-client/badge/?version=stable)](https://pystac-client.readthedocs.io)\n[![codecov](https://codecov.io/gh/stac-utils/pystac-client/branch/main/graph/badge.svg)](https://codecov.io/gh/stac-utils/pystac-client)\n\nA Python client for working with [STAC](https://stacspec.org/) APIs.\n\n## Installation\n\nPySTAC Client is published to PyPi as [pystac-client](https://pypi.org/project/pystac-client/).\n\nThe only direct Python dependencies of **pystac-client** are [PySTAC](https://pystac.readthedocs.io),\n[requests](https://docs.python-requests.org), and [dateutil](https://dateutil.readthedocs.io).\n\nTo install with pip, run:\n\n```shell\npython -m pip install pystac-client\n```\n\n## Documentation\n\nSee the [documentation page](https://pystac-client.readthedocs.io/en/latest/) for the latest docs.\n\n## Development\n\nSee the [contributing page](https://pystac-client.readthedocs.io/en/latest/contributing.html) for the latest development instructions.\n" }, { "path": "RELEASING.md", "content": "# Releasing\n\n1. Determine the next version.\n We follow [semantic versioning](https://semver.org/).\n2. Create a release branch named `release/vX.Y.Z`, where `X.Y.Z` is the next version.\n3. Update [version.py](pystac_client/version.py) with the new version.\n4. Update all cassettes: `pytest --record-mode rewrite`\n5. Update the pre-commit hook versions: `pre-commit autoupdate`\n6. Update [CHANGELOG.md](CHANGELOG.md).\n 1. Add a new header under \"Unreleased\" with the new version and the date, e.g. `## [vX.Y.Z] - YYYY-MM-DD`.\n 2. Audit the changelog section to ensure all changes are captured.\n 3. Add a link reference for the new version after the Unreleased link reference at the bottom of the file.\n Follow the format from the previous version links.\n7. If necessary, update the versions table in [the documentation](docs/index.rst)\n8. (optional) Build the package locally and inspect its contents: `pip install build && python -m build`\n9. Open a pull request for your `release/vX.Y.Z` branch against the appropriate branch (either `main` or a version branch, e.g. `v0.3`).\n10. After pull request merge, create an annotated tag for your version, e.g. `git tag -a vX.Y.Z`.\n11. Push the tag.\n This will trigger [the Github release workflow](.github/workflows/release.yml) and publish to PyPI.\n12. [Create a new release on Github](https://github.com/stac-utils/pystac-client/releases/new) pointing to the new tag.\n" }, { "path": "docs/_static/custom.css", "content": ".no-sidebar {\n display: none;\n}\n\n.card-text a {\n font-weight: 600;\n color: rgb(255, 26, 26);\n}\n\n.card-header p {\n font-size: large;\n font-weight: 600;\n}\n\n.navbar-brand img {\n height: 50px;\n}\n\n.navbar-brand {\n height: 75px;\n}\n" }, { "path": "docs/api.rst", "content": "API Reference\n=============\n\nThis section is autogenerated from in-line code documentation. It is mostly useful as a\nreference for the various classes, methods, and other objects in the library, but is\nnot intended to function as a starting point for working with ``pystac_client``.\n\nClient\n------\n\nClient is the base PySTAC-Client that inherits from :class:`Catalog `.\nIn addition to the PySTAC functionality, Client allows opening of API URLs,\nunderstanding of conformance, and support for searching and paging through results.\n\n.. autoclass:: pystac_client.Client\n :members:\n :undoc-members:\n :show-inheritance:\n\nCollection Client\n-----------------\n\nClient is the a PySTAC-Client that inherits from\n:class:`Collection `. In addition to the PySTAC functionality,\nCollectionClient allows opening of API URLs, and iterating through items at a search\nendpoint, if supported.\n\n.. autoclass:: pystac_client.CollectionClient\n :members:\n :undoc-members:\n\nCollection Search\n-----------------\n\nThe `CollectionSearch` class represents a search of collections in a STAC API.\n\n.. autoclass:: pystac_client.CollectionSearch\n :members:\n :undoc-members:\n :member-order: bysource\n\nItem Search\n-----------\n\nThe `ItemSearch` class represents a search of a STAC API.\n\n.. autoclass:: pystac_client.ItemSearch\n :members:\n :undoc-members:\n :member-order: bysource\n\n\nSTAC API IO\n-----------\n\nThe StacApiIO class inherits from the :class:`Collection `\nclass and allows for reading over http, such as with REST APIs.\n\n.. autoclass:: pystac_client.stac_api_io.StacApiIO\n :members:\n :undoc-members:\n :show-inheritance:\n\n\nConformance\n-----------\n\n.. automodule:: pystac_client.conformance\n :members:\n :undoc-members:\n :show-inheritance:\n\n\nExceptions\n----------\n\n.. automodule:: pystac_client.exceptions\n :members:\n :undoc-members:\n :show-inheritance:\n\nWarnings\n--------\n\n.. automodule:: pystac_client.warnings\n :members:\n :undoc-members:\n :show-inheritance:\n" }, { "path": "docs/conf.py", "content": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n\nimport re\nimport subprocess\nimport sys\nfrom pathlib import Path\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nsys.path.insert(0, str(Path(__file__).parent.parent.parent.resolve()))\nfrom pystac_client import __version__ # noqa: E402\n\ngit_branch = (\n subprocess.check_output([\"git\", \"rev-parse\", \"--abbrev-ref\", \"HEAD\"])\n .decode(\"utf-8\")\n .strip()\n)\n\n# -- Project information -----------------------------------------------------\n\nproject = \"pystac-client\"\ncopyright = \"2021, Jon Duckworth\"\nauthor = \"Matthew Hanson, Jon Duckworth\"\ngithub_user = \"stac-utils\"\ngithub_repo = \"pystac-client\"\npackage_description = \"A Python client for the STAC and STAC-API specs\"\n\n# The full version, including alpha/beta/rc tags\nversion = re.fullmatch(r\"^(\\d+\\.\\d+\\.\\d).*$\", __version__).group(1) # type: ignore\nrelease = __version__\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.viewcode\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.extlinks\",\n \"sphinxcontrib.fulltoc\",\n \"nbsphinx\",\n \"myst_parser\",\n]\n\nextlinks = {\n \"tutorial\": (\n \"https://github.com/stac-utils/pystac-client/tree/{}/docs/tutorials/%s\".format(\n git_branch\n ),\n \"tutorial\",\n )\n}\n\nnbsphinx_allow_errors = False\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nsource_suffix = [\".rst\", \"*.md\", \"*.ipynb\"]\nexclude_patterns = [\"build/*\"]\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = \"pydata_sphinx_theme\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\nhtml_theme_options = {\n \"icon_links\": [\n {\n \"name\": \"GitHub\",\n \"url\": \"https://github.com/stac-utils/pystac-client\",\n \"icon\": \"fab fa-github-square\",\n },\n {\n \"name\": \"Gitter\",\n \"url\": \"https://gitter.im/SpatioTemporal-Asset-Catalog/\"\n \"python?utm_source=share-link&utm_medium=link&utm_campaign=share-link\",\n \"icon\": \"fab fa-gitter\",\n },\n ],\n \"external_links\": [\n {\"name\": \"STAC Spec\", \"url\": \"https://github.com/radiantearth/stac-spec\"}\n ],\n \"navigation_with_keys\": False,\n # \"navbar_end\": [\"navbar-icon-links.html\", \"search-field.html\"]\n}\n\nhtml_logo = \"_static/STAC-03.png\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path: list[str] = [\"_static\"]\n\n\n# -- Options for intersphinx extension ---------------------------------------\n\nintersphinx_mapping = {\n \"python\": (\"https://docs.python.org/3\", None),\n \"requests\": (\"https://requests.readthedocs.io/en/latest\", None),\n \"pystac\": (\"https://pystac.readthedocs.io/en/latest\", None),\n \"dateutil\": (\"https://dateutil.readthedocs.io/en/stable/\", None),\n}\n\n# -- Options for autodoc extension -------------------------------------------\n\nautodoc_typehints = \"none\"\n" }, { "path": "docs/contributing.rst", "content": "Contributing\n============\n\nA list of issues and ongoing work is available on the PySTAC Client `issues page\n`_. If you want to contribute code, the best\nway is to coordinate with the core developers via an issue or pull request conversation.\n\nDevelopment installation\n^^^^^^^^^^^^^^^^^^^^^^^^\nFork PySTAC Client into your GitHub account. Clone the repo, install `uv\n`_ then install the package in editable mode with development dependencies:\n\n.. code-block:: bash\n\n $ git clone git@github.com:your_user_name/pystac-client.git\n $ cd pystac-client\n $ uv pip install -e . --group dev\n $ uv run pre-commit install\n\nTesting\n^^^^^^^\ntl;dr: Run ``./scripts/test`` to run all tests and linters.\n\nPySTAC Client runs tests using `pytest `_. You can find unit tests in the ``tests/``\ndirectory.\n\nTo run the tests and generate the coverage report:\n\n.. code-block:: bash\n\n $ uv run pytest -v -s --block-network --cov pystac_client --cov-report term-missing\n\nThe PySTAC Client tests use `vcrpy `_ to mock API calls\nwith \"pre-recorded\" API responses. When adding new tests use the ``@pytest.mark.vcr`` decorator\nfunction to indicate ``vcrpy`` should be used. Record the new responses and commit them to the\nrepository.\n\n.. code-block:: bash\n\n $ uv run pytest -v -s --record-mode new_episodes\n $ git add \n $ git commit -a -m 'new test episodes'\n\n\nTo update PySTAC Client to use future versions of STAC API, the existing recorded API responses\nshould be \"re-recorded\":\n\n.. code-block:: bash\n\n $ uv run pytest -v -s --record-mode rewrite --block-network\n $ git commit -a -m 'updated test episodes'\n\n\nCode quality checks\n^^^^^^^^^^^^^^^^^^^\n\n`pre-commit `_ is used to ensure a standard set of formatting and\nlinting is run before every commit. These hooks should be installed with:\n\n.. code-block:: bash\n\n $ uv run pre-commit install\n\nThese can then be run independent of a commit with:\n\n.. code-block:: bash\n\n $ uv run pre-commit run --all-files\n\nPySTAC Client uses\n\n- `ruff `_ for Python code formatting and style checks\n- `codespell `_ to check code for common misspellings\n- `doc8 `_ for style checking on RST files in the docs\n- `mypy `_ for Python type annotation checks\n\nOnce installed you can bypass pre-commit by adding the ``--no-verify`` (or ``-n``)\nflag to Git commit commands, as in ``git commit --no-verify``.\n\nPull Requests\n^^^^^^^^^^^^^\n\nTo make Pull Requests to PySTAC Client, the code must pass linting, formatting, and code tests. To run\nthe entire suit of checks and tests that will be run the GitHub Action Pipeline, use the ``test`` script.\n\n.. code-block:: bash\n\n $ uv run scripts/test\n\nIf automatic formatting is desired (incorrect formatting will cause the GitHub Action to fail),\nuse the format script (which uses ruff) and commit the resulting files:\n\n.. code-block:: bash\n\n $ uv run scripts/format\n $ git commit -a -m 'formatting updates'\n\n\nTo build the documentation, `install Pandoc `_, install the\nPython documentation requirements via uv, then use the ``build-docs`` script:\n\n.. code-block:: bash\n\n $ uv pip install -e . --group docs\n $ uv run scripts/build-docs\n\nCHANGELOG\n^^^^^^^^^\n\nPySTAC Client maintains a\n`changelog `_\nto track changes between releases. All Pull Requests should make a changelog entry unless\nthe change is trivial (e.g. fixing typos) or is entirely invisible to users who may\nbe upgrading versions (e.g. an improvement to the CI system).\n\nFor changelog entries, please link to the PR of that change. This needs to happen in a\nfew steps:\n\n- Make a Pull Request (see above) to PySTAC Client with your changes\n- Record the link to the Pull Request\n- Push an additional commit to your branch with the changelog entry with the link to the\n Pull Request.\n\nFor more information on changelogs and how to write a good entry, see `keep a changelog\n`_.\n\nBenchmark\n^^^^^^^^^\n\nBy default, PySTAC Client benchmarks are skipped during test runs.\nTo run the benchmarks, use the ``--benchmark-only`` flag:\n\n.. code-block:: bash\n\n $ uv run pytest --benchmark-only\n ============================= test session starts ==============================\n platform darwin -- Python 3.9.13, pytest-6.2.4, py-1.10.0, pluggy-0.13.1\n benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)\n rootdir: /Users/gadomski/Code/pystac-client, configfile: pytest.ini\n plugins: benchmark-3.4.1, recording-0.11.0, console-scripts-1.1.0, requests-mock-1.9.3, cov-2.11.1, typeguard-2.13.3\n collected 75 items\n\n tests/test_cli.py ss [ 2%]\n tests/test_client.py ssssssssssssssss [ 24%]\n tests/test_collection_client.py ss [ 26%]\n tests/test_item_search.py ...sssssssssssssssssssssssssssssssssssssssssss [ 88%]\n s [ 89%]\n tests/test_stac_api_io.py ssssssss [100%]\n\n\n --------------------------------------------------------------------------------------- benchmark: 3 tests --------------------------------------------------------------------------------------\n Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations\n -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n test_single_item_search 213.4729 (1.0) 284.8732 (1.0) 254.9405 (1.0) 32.9424 (3.27) 271.0926 (1.0) 58.2907 (4.95) 1;0 3.9225 (1.0) 5 1\n test_single_item 314.6746 (1.47) 679.7592 (2.39) 563.9692 (2.21) 142.7451 (14.18) 609.5605 (2.25) 93.9942 (7.98) 1;1 1.7731 (0.45) 5 1\n test_requests 612.9212 (2.87) 640.5024 (2.25) 625.6871 (2.45) 10.0637 (1.0) 625.1143 (2.31) 11.7822 (1.0) 2;0 1.5982 (0.41) 5 1\n -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n Legend:\n Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.\n OPS: Operations Per Second, computed as 1 / Mean\n ======================== 3 passed, 72 skipped in 11.86s ========================\n\n\nFor more information on running and comparing benchmarks, see the `pytest-benchmark documentation `_.\n" }, { "path": "docs/design/0001-record-architecture-decisions.md", "content": "# 1. Record architecture decisions\n\nDate: 2021-03-01\n\n## Status\n\nAccepted\n\n## Context\n\nWe need to record the architectural decisions made on this project.\n\n## Decision\n\nWe will use Architecture Decision Records, as \n[described by Michael Nygard](http://thinkrelevance.com/blog/2011/11/15/documenting-architecture-decisions).\n\n## Consequences\n\nSee Michael Nygard's article, linked above. For a lightweight ADR toolset, see Nat Pryce's \n[adr-tools](https://github.com/npryce/adr-tools).\n" }, { "path": "docs/design/0002-choose-stac-library.md", "content": "# 2. Choose STAC library\n\nDate: 2021-03-01\n\n## Status\n\nAccepted\n\n## Context\n\nWe would like to use an existing Python library for working with STAC objects as Python objects. Ideally this library \nwill meet the following requirements:\n\n* **Actively maintained** to ensure that it is up-to-date with the latest STAC spec\n* **Well-documented** to reduce the documentation burden for this library\n* **Easily extensible** to allow us to take advantage of existing STAC object functionality\n\nThe 2 most obvious choices for Python STAC clients are \n[`PySTAC`](https://github.com/stac-utils/pystac) and [`sat-stac`](https://github.com/sat-utils/sat-stac). \n\n### `PySTAC`\n\n`PySTAC` is an actively maintained STAC client for Python 3. Its last release was less than 2 months ago and its last \ncommit was less than 2 weeks ago. It has had 15 contributors within the last year and it supports the latest release \ncandidate for the STAC spec (as well as previous releases). `PySTAC` hosts \n[documentation on ReadTheDocs](https://pystac.readthedocs.io/en/latest/) that includes a description of programming \nconcepts related to the library, quickstart instructions, tutorials, and an API reference.\n\n`PySTAC` supports both reading and writing of STAC objects. Extending `PySTAC` classes through inheritance is made a \nbit more difficult by the fact that some class methods (like ``from_dict``) have references to specific classes \n(like ``Catalog``) rather than using the ``cls`` argument. This will require us to overwrite some of these methods to \nimplement inheritance. It has robust support for traversing catalogs using links. There is no existing support for the \nSTAC API spec in `PySTAC` or any related tooling.\n\n### `sat-stac`\n\n`sat-stac` is an actively maintained STAC client for Python 3. Its last release was less than 2 months ago and its \nlast commit was at that same time. It has had 2 contributors within the last year and it supports the latest release \ncandidate for the STAC spec (as well as previous releases). `sat-stac` has installation documented in the GitHub repo's \nmain README and has 2 tutorials in the form of Jupyter Notebooks in that repo. \n\n`sat-stac` supports reading STAC catalogs (support for writing STAC catalogs was removed in v0.4.0). There is also an \nexisting library for working with STAC API - Item Search results called \n[sat-search](https://github.com/sat-utils/sat-search) that uses `sat-stac` as its backend. \n\n## Decision\n\nWe will use `PySTAC` as our \"backend\" for working with STAC objects in Python. While both libraries are well-maintained, \n`PySTAC` has more thorough documentation and seems to be more widely supported through community contribution. \n\n## Consequences\n\nWe will need to create our own implementation of a STAC API - Item Search client since there is not existing tooling \nbased on `PySTAC`. We will be able to point to the hosted documentation for `PySTAC` to guide users through existing \nfunctionality for navigating STAC Catalogs. Special care should be taken to ensure that we do not break any of \n`PySTAC`'s functionality through inheritance.\n" }, { "path": "docs/design/design_decisions.rst", "content": "Design Decisions\n================\n\n`Architectural Design Records (ADRs)\n`__ for major\ndesign decisions related to the library. In general, this library makes an attempt to\nfollow the design patterns laid out in the PySTAC library.\n\n.. toctree::\n :glob:\n :maxdepth: 1\n\n *\n" }, { "path": "docs/index.rst", "content": "PySTAC Client Documentation\n===========================\n\nPySTAC Client is a Python package for working with `STAC `__\nCatalogs and `STAC APIs `__.\nPySTAC Client builds upon PySTAC through higher-level functionality and ability to\naccess STAC API search endpoints.\n\nSTAC Versions\n=============\n\n+---------------+---------------+-----------------------------+\n| pystac-client | STAC spec | STAC API Spec |\n+===============+===============+=============================+\n| 0.9.x | 1.0.x - 1.1.x | 1.0.0-beta.1 - 1.0.0 |\n+---------------+---------------+-----------------------------+\n| 0.8.x | 1.0.x | 1.0.0-beta.1 - 1.0.0 |\n+---------------+---------------+-----------------------------+\n| 0.7.x | 1.0.x | 1.0.0-beta.1 - 1.0.0 |\n+---------------+---------------+-----------------------------+\n| 0.6.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-rc.2 |\n+---------------+---------------+-----------------------------+\n| 0.5.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-rc.1 |\n+---------------+---------------+-----------------------------+\n| 0.4.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-rc.1 |\n+---------------+---------------+-----------------------------+\n| 0.3.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-beta.4 |\n+---------------+---------------+-----------------------------+\n| 0.2.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-beta.2 |\n+---------------+---------------+-----------------------------+\n\nInstallation\n------------\n\n``pystac-client`` requires `Python >=3.10 `__.\n\nTo install with pip, run:\n\n.. code-block:: console\n\n $ python -m pip install pystac-client\n\nThis will install the dependencies :doc:`pystac `,\n:doc:`python-dateutil `, and :doc:`requests `.\n\nAcknowledgements\n----------------\n\nThis package builds upon the great work of the PySTAC library for working with\nSTAC objects in Python. It also uses concepts from the\n`sat-search `__ library for working with STAC\nAPI search endpoints.\n\nTable of Contents\n-----------------\n\n.. toctree::\n :maxdepth: 2\n\n quickstart\n usage\n api\n tutorials\n contributing\n design/design_decisions\n" }, { "path": "docs/quickstart.rst", "content": "Quickstart\n----------\n\nPySTAC Client can be used as either a Command Line Interface (CLI) or a\nPython library.\n\nCLI\n~~~\n\nUse the CLI to quickly perform Item or Collection searches and\noutput or save the results.\n\nThe ``--matched`` switch performs a search with limit=1 so does not get\nany Items, but gets the total number of matches which will be output to\nthe screen (if supported by the STAC API).\n\n.. code-block:: console\n\n $ stac-client search https://earth-search.aws.element84.com/v1 -c sentinel-2-l2a --bbox -72.5 40.5 -72 41 --matched\n 3141 items matched\n\nThe ``--matched`` flag can also be used for collection search to get\nthe total number of collections that match your search terms.\n\n\n.. code-block:: console\n\n $ stac-client collections https://emc.spacebel.be --q sentinel-2 --matched\n 76 collections matched\n\nIf the same URL is to be used over and over, define an environment\nvariable to be used in the CLI call:\n\n.. code-block:: console\n\n $ export STAC_API_URL=https://earth-search.aws.element84.com/v1\n $ stac-client search ${STAC_API_URL} -c sentinel-2-l2a --bbox -72.5 40.5 -72 41 --datetime 2020-01-01/2020-01-31 --matched\n 48 items matched\n\nWithout the ``--matched`` switch, all items will be fetched, paginating\nif necessary. If the ``--max-items`` switch is provided it will stop\npaging once that many items has been retrieved. It then prints all items\nto stdout as an ItemCollection. This can be useful to pipe output to\nanother process such as\n`stac-terminal `__,\n`geojsonio-cli `__, or\n`jq `__.\n\n.. code-block:: console\n\n $ stac-client search ${STAC_API_URL} -c sentinel-2-l2a --bbox -72.5 40.5 -72 41 --datetime 2020-01-01/2020-01-31 | stacterm cal --label platform\n\n.. figure:: images/stacterm-cal.png\n :alt:\n\nIf the ``--save`` switch is provided instead, the results will not be\noutput to stdout, but instead will be saved to the specified file.\n\n.. code-block:: console\n\n $ stac-client search ${STAC_API_URL} -c sentinel-2-l2a --bbox -72.5 40.5 -72 41 --datetime 2020-01-01/2020-01-31 --save items.json\n\nIf the Catalog supports the `Query\nextension `__,\nany Item property can also be included in the search. Rather than\nrequiring the JSON syntax the Query extension uses, pystac-client uses a\nsimpler syntax that it will translate to the JSON equivalent. Note\nhowever that when the simple syntax is used it sends all property values\nto the server as strings, except for ``gsd`` which it casts to\n``float``. This means that if there are extensions in use with numeric\nproperties these will be sent as strings. Some servers may automatically\ncast this to the appropriate data type, others may not.\n\nThe query filter will also accept complete JSON as per the specification.\n\n::\n\n \n\n where operator is one of `>=`, `<=`, `>`, `<`, `=`\n\n Examples:\n eo:cloud_cover<10\n created=2021-01-06\n view:sun_elevation<20\n\nAny number of properties can be included, and each can be included more\nthan once to use additional operators.\n\n.. code-block:: console\n\n $ stac-client search ${STAC_API_URL} -c sentinel-2-l2a --bbox -72.5 40.5 -72 41 --datetime 2020-01-01/2020-01-31 ---query \"eo:cloud_cover<10\" --matched\n 10 items matched\n\n.. code-block:: console\n\n $ stac-client search ${STAC_API_URL} -c sentinel-2-l2a --bbox -72.5 40.5 -72 41 --datetime 2020-01-01/2020-01-31 --query \"eo:cloud_cover<10\" \"eo:cloud_cover>5\" --matched\n 4 items matched\n\n\nCollection searches can also use multiple filters like this example\nsearch for collections that include the term ``\"biomass\"`` and have\na spatial extent that intersects Scandinavia.\n\n.. code-block:: console\n\n $ stac-client collections https://emc.spacebel.be --q biomass --bbox 0.09 54.72 33.31 71.36 --matched\n 43 items matched\n\nSince most STAC APIs have not yet implemented the `collection search \nextension `__, \n``pystac-client`` will perform a limited client-side \nfilter on the full list of collections using only the ``bbox``, \n``datetime``, and ``q`` (free-text search) parameters.\nIn the case that the STAC API does not support collection search, a\nwarning will be displayed to inform you that the filter is being\napplied client-side.\n\n\nPython\n~~~~~~\n\nFirst, create a Client instance configured to use a specific STAC API by the root URL of that API. For this example, we\nwill use `Earth Search `__.\n\n.. code-block:: python\n\n from pystac_client import Client\n\n client = Client.open(\"https://earth-search.aws.element84.com/v1\")\n\nCreate an Item Search instance that represents a search to run. This does not actually run a search yet --\nthat does not happen until a method is called that requires data from the STAC API.\n\n.. code-block:: python\n\n search = client.search(\n max_items=10,\n collections=[\"sentinel-2-c1-l2a\"],\n bbox=[-72.5,40.5,-72,41]\n )\n\nCalling ``matched()`` will send a request to the STAC API and retrieve a single item and metadata about how many Items\nmatch the search criteria.\n\n.. code-block:: python\n\n print(f\"{search.matched()} items found\")\n\nThe ``items()`` iterator method can be used to iterate through all resulting items. This iterator\nhides the pagination behavior that the may occur if there are sufficient results. Be careful with this\nmethod -- you could end up iterating over the entire catalog if ``max_items`` is not set!\n\n.. code-block:: python\n\n for item in search.items():\n print(item.id)\n\nUse ``item_collection()`` to convert all Items from a search into a single `PySTAC\nItemCollection `__.\nThe ``ItemCollection`` can then be saved as a GeoJSON FeatureCollection. This requires retrieving all\nof the results from the search, so it may take some time to retrieve all the paginated responses.\n\n.. code-block:: python\n\n item_collection = search.item_collection()\n item_collection.save_object(\"my_itemcollection.json\")\n\nSome STAC APIs also implement the Collection Search Extension. Earth Search does not, so we use the\nORNL_CLOUD CMR-STAC Catalog instead:\n\n.. code-block:: python\n\n client = Client.open(\"https://cmr.earthdata.nasa.gov/stac/ORNL_CLOUD\")\n collection_search = client.collection_search(\n q=\"rain\",\n )\n print(f\"{collection_search.matched()} collections found\")\n\n\nThe ``collections()`` iterator method can be used to iterate through all\nresulting collections.\n\n.. code-block:: python\n\n for collection in collection_search.collections():\n print(collection.id)\n\n" }, { "path": "docs/tutorials/aoi-coverage.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e06a27bf\",\n \"metadata\": {},\n \"source\": [\n \"# Calculating Coverage Percentage of the AOI by an Item\\n\",\n \"\\n\",\n \"This notebook demonstrates the use of pystac-client to calculate the percentage an Item's geometry that intesects with the area of interest (AOI) specified in the search by the `intersects` parameter.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b65de617\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from typing import Any, Dict\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from pystac.item import Item\\n\",\n \"from shapely.geometry import shape\\n\",\n \"\\n\",\n \"from pystac_client import Client\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"98942e75\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def intersection_percent(item: Item, aoi: Dict[str, Any]) -> float:\\n\",\n \" \\\"\\\"\\\"The percentage that the Item's geometry intersects the AOI. An Item that\\n\",\n \" completely covers the AOI has a value of 100.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" geom_item = shape(item.geometry)\\n\",\n \" geom_aoi = shape(aoi)\\n\",\n \"\\n\",\n \" intersected_geom = geom_aoi.intersection(geom_item)\\n\",\n \"\\n\",\n \" intersection_percent = (intersected_geom.area * 100) / geom_aoi.area\\n\",\n \"\\n\",\n \" return intersection_percent\\n\",\n \"\\n\",\n \"\\n\",\n \"# STAC API root URL\\n\",\n \"URL = \\\"https://planetarycomputer.microsoft.com/api/stac/v1\\\"\\n\",\n \"\\n\",\n \"# geometry of the AOI to search over\\n\",\n \"intersects_geometry = {\\n\",\n \" \\\"type\\\": \\\"Polygon\\\",\\n\",\n \" \\\"coordinates\\\": [\\n\",\n \" [\\n\",\n \" [-73.21, 43.99],\\n\",\n \" [-73.21, 47.05],\\n\",\n \" [-70.12, 47.05],\\n\",\n \" [-70.12, 43.99],\\n\",\n \" [-73.21, 43.99],\\n\",\n \" ]\\n\",\n \" ],\\n\",\n \"}\\n\",\n \"\\n\",\n \"# Create a Client and an ItemSearch representing our search\\n\",\n \"# No search operations will be performed until we call the items() method\\n\",\n \"client = Client.open(URL)\\n\",\n \"item_search = client.search(\\n\",\n \" collections=[\\\"sentinel-2-l2a\\\"], intersects=intersects_geometry, max_items=100\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0b0883fe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(\\n\",\n \" [\\n\",\n \" f\\\"{intersection_percent(item, intersects_geometry):.2f}\\\"\\n\",\n \" for item in item_search.items()\\n\",\n \" ]\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"03254847\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# create a generator that filters to only those Items that intersect more than 5%\\n\",\n \"items_gt_5_percent = (\\n\",\n \" i for i in item_search.items() if intersection_percent(i, intersects_geometry) > 5\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e1de7a09\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Render the AOI and Item results\\n\",\n \"# The green shape is the AOI\\n\",\n \"# The blue shapes are the Item geometries\\n\",\n \"# If there are no blue shapes, adjust the intersection percent filter above\\n\",\n \"# until there are\\n\",\n \"\\n\",\n \"cm = plt.get_cmap(\\\"RdBu\\\")\\n\",\n \"fig, axs = plt.subplots()\\n\",\n \"axs.set_aspect(\\\"equal\\\", \\\"datalim\\\")\\n\",\n \"\\n\",\n \"for item in items_gt_5_percent:\\n\",\n \" xs, ys = shape(item.geometry).exterior.xy\\n\",\n \" axs.fill(xs, ys, alpha=0.5, fc=\\\"b\\\", ec=\\\"none\\\")\\n\",\n \"\\n\",\n \"geom_intersects = shape(intersects_geometry)\\n\",\n \"xs, ys = geom_intersects.exterior.xy\\n\",\n \"axs.fill(xs, ys, alpha=0.5, fc=\\\"g\\\", ec=\\\"none\\\")\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"interpreter\": {\n \"hash\": \"6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3.9.11 ('.venv': venv)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "docs/tutorials/authentication.md", "content": "# Authentication\n\nWhile not integrated into this library directly, pystac-client provides a series of hooks that support a wide variety of authentication mechanisms. These can be used when interacting with stac API implementations behind various authorization walls.\n\n## Basic auth\n\nPystac-client supports HTTP basic authentication by simply exposing the ability to define headers to be used when sending requests. Simply encode the token and provide the header.\n\n```python\nimport base64\nimport pystac_client\n\n# encode credentials\nuser_name = \"yellowbeard\"\npassword = \"yaarg\"\nuserpass = f\"{user_name}:{password}\"\nb64_userpass = base64.b64encode(userpass.encode()).decode()\n\n# create the client\nclient = pystac_client.Client.open(\n url=\"https://planetarycomputer.microsoft.com/api/stac/v1\",\n headers={\n 'Authorization': f\"Basic {b64_userpass}\"\n }\n)\n```\n\n## Token auth\n\nProviding a authentication token can be accomplished using the same mechanism as described above for [basic auth](#basic-auth). Simply provide the token in the `Authorization` header to the client in the same manner.\n\n## AWS SigV4\n\nAccessing a stac api protected by AWS IAM often requires signing the request using [AWS SigV4](https://docs.aws.amazon.com/general/latest/gr/signature-version-4.html). Unlike basic and token authentication, the entire request is part of the signing process. Thus the `Authorization` header cannot be added when the client is created, rather it must be generated and added after the request is fully formed.\n\nPystac-client provides a lower-level hook, the `request_modifier` parameter, which can mutate the request, adding the necessary header after the request has been generated but before it is sent.\n\nThe code cell below demonstrates this, using the `boto3` module.\n\n```python\nimport boto3\nimport botocore.auth\nimport botocore.awsrequest\nimport pystac_client\nimport requests\n\n# Details regarding the private stac api\nregion = \"us-east-1\"\nservice_name = \"execute-api\"\nendpoint_id = \"xxxxxxxx\"\ndeployment_stage = \"dev\"\nstac_api_url = f\"https://{endpoint_id}.{service_name}.{region}.amazonaws.com/{deployment_stage}\"\n\n# load AWS credentials\ncredentials = boto3.Session(region_name=region).get_credentials()\nsigner = botocore.auth.SigV4Auth(credentials, service_name, region)\n\ndef sign_request(request: requests.Request) -> requests.Request:\n \"\"\"Sign the request using AWS SigV4.\n\n Args:\n request (requests.Request): The fully populated request to sign.\n\n Returns:\n requests.Request: The provided request object, with auth header added.\n \"\"\"\n aws_request = botocore.awsrequest.AWSRequest(\n method=request.method,\n url=request.url,\n params=request.params,\n data=request.data,\n headers=request.headers\n )\n signer.add_auth(aws_request)\n request.headers = aws_request.headers\n return request\n\n# create the client\nclient = pystac_client.Client.open(\n url=stac_api_url,\n request_modifier=sign_request\n)\n```\n" }, { "path": "docs/tutorials/cql2-filter.ipynb", "content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"id\": \"e06a27bf\",\n \"metadata\": {},\n \"source\": [\n \"# CQL2 Filtering\\n\",\n \"\\n\",\n \"This notebook demonstrates using pystac-client to filter STAC items with [CQL2](https://docs.ogc.org/is/21-065r2/21-065r2.html) as described in the [STAC API Filter Extension](https://github.com/stac-api-extensions/filter). \\n\",\n \"\\n\",\n \"Note: Not all STAC APIs support the Filter Extension. APIs advertise conformance by including `https://api.stacspec.org/v1.0.0/item-search#filter` in the `conformsTo` attribute of the root API.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b65de617\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"\\n\",\n \"import geopandas as gpd\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"from pystac_client import Client\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c8ac88bb\",\n \"metadata\": {},\n \"source\": [\n \"The first step as always with pystac-client is opening the catalog:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"98942e75\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# STAC API root URL\\n\",\n \"URL = \\\"https://planetarycomputer.microsoft.com/api/stac/v1\\\"\\n\",\n \"\\n\",\n \"catalog = Client.open(URL)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1e16077c\",\n \"metadata\": {},\n \"source\": [\n \"## Initial Search Parameters\\n\",\n \"\\n\",\n \"Here we set up some initial search parameters to use with the `Client.search` function. We are providing a maximum number of items to return (`max_items`), a collection to look within (`collections`), a geometry (`intersects`), and a datetime range (`datetime`).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5e961981\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# AOI around Delfzijl, in the north of The Netherlands\\n\",\n \"geom = {\\n\",\n \" \\\"type\\\": \\\"Polygon\\\",\\n\",\n \" \\\"coordinates\\\": [\\n\",\n \" [\\n\",\n \" [6.42425537109375, 53.174765470134616],\\n\",\n \" [7.344360351562499, 53.174765470134616],\\n\",\n \" [7.344360351562499, 53.67393435835391],\\n\",\n \" [6.42425537109375, 53.67393435835391],\\n\",\n \" [6.42425537109375, 53.174765470134616],\\n\",\n \" ]\\n\",\n \" ],\\n\",\n \"}\\n\",\n \"\\n\",\n \"params = {\\n\",\n \" \\\"max_items\\\": 100,\\n\",\n \" \\\"collections\\\": \\\"landsat-8-c2-l2\\\",\\n\",\n \" \\\"intersects\\\": geom,\\n\",\n \" \\\"datetime\\\": \\\"2018-01-01/2020-12-31\\\",\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d6f1dd5f\",\n \"metadata\": {},\n \"source\": [\n \"## Using Filters\\n\",\n \"\\n\",\n \"In addition to the parameters described above in the following examples we will filter by Item properties (`filter`) using CQL2-JSON. Here is a little function that does the search constructs a `GeoDataFrame` of the results and then plots `datetime` vs `eo:cloud_cover`.\\n\",\n \"\\n\",\n \"Remember that in this whole notebook we are only looking at STAC metadata, there is no part where we are reading the data itself.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8b26e89b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def search_and_plot(filter):\\n\",\n \" search = catalog.search(**params, filter=filter)\\n\",\n \"\\n\",\n \" gdf = gpd.GeoDataFrame.from_features(search.item_collection_as_dict())\\n\",\n \" gdf[\\\"datetime\\\"] = pd.to_datetime(gdf[\\\"datetime\\\"])\\n\",\n \" print(f\\\"Found {len(gdf)} items\\\")\\n\",\n \"\\n\",\n \" gdf.plot.line(x=\\\"datetime\\\", y=\\\"eo:cloud_cover\\\", title=json.dumps(filter))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11afcc19\",\n \"metadata\": {},\n \"source\": [\n \"We can test out the function by passing an empty dict to do no filtering at all.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b6293c11\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"search_and_plot({})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"44d3bc04\",\n \"metadata\": {},\n \"source\": [\n \"## CQL2 Filters\\n\",\n \"\\n\",\n \"We will use `eo:cloud_cover` as an example and filter for all the STAC Items where `eo:cloud_cover <= 10%`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"dfc0e759\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"filter = {\\\"op\\\": \\\"<=\\\", \\\"args\\\": [{\\\"property\\\": \\\"eo:cloud_cover\\\"}, 10]}\\n\",\n \"\\n\",\n \"search_and_plot(filter)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"75e835f1\",\n \"metadata\": {},\n \"source\": [\n \"Next let's look for all the STAC Items where `eo:cloud_cover >= 80%`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9c2f9ca1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"filter = {\\\"op\\\": \\\">=\\\", \\\"args\\\": [{\\\"property\\\": \\\"eo:cloud_cover\\\"}, 80]}\\n\",\n \"\\n\",\n \"search_and_plot(filter)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0ad984bf\",\n \"metadata\": {},\n \"source\": [\n \"We can combine multiple CQL2 statements to express more complicated logic:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"109f673c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"filter = {\\n\",\n \" \\\"op\\\": \\\"and\\\",\\n\",\n \" \\\"args\\\": [\\n\",\n \" {\\\"op\\\": \\\"<=\\\", \\\"args\\\": [{\\\"property\\\": \\\"eo:cloud_cover\\\"}, 60]},\\n\",\n \" {\\\"op\\\": \\\">=\\\", \\\"args\\\": [{\\\"property\\\": \\\"eo:cloud_cover\\\"}, 40]},\\n\",\n \" ],\\n\",\n \"}\\n\",\n \"\\n\",\n \"search_and_plot(filter)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"617c7416\",\n \"metadata\": {},\n \"source\": [\n \"You can see the power of this syntax. Indeed we can replace `datetime` and `intersects` from our original search parameters with a more complex CQL2 statement.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7b0dc965\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"filter = {\\n\",\n \" \\\"op\\\": \\\"and\\\",\\n\",\n \" \\\"args\\\": [\\n\",\n \" {\\\"op\\\": \\\"s_intersects\\\", \\\"args\\\": [{\\\"property\\\": \\\"geometry\\\"}, geom]},\\n\",\n \" {\\\"op\\\": \\\">=\\\", \\\"args\\\": [{\\\"property\\\": \\\"datetime\\\"}, \\\"2018-01-01\\\"]},\\n\",\n \" {\\\"op\\\": \\\"<=\\\", \\\"args\\\": [{\\\"property\\\": \\\"datetime\\\"}, \\\"2020-12-31\\\"]},\\n\",\n \" {\\\"op\\\": \\\"<=\\\", \\\"args\\\": [{\\\"property\\\": \\\"eo:cloud_cover\\\"}, 60]},\\n\",\n \" {\\\"op\\\": \\\">=\\\", \\\"args\\\": [{\\\"property\\\": \\\"eo:cloud_cover\\\"}, 40]},\\n\",\n \" ],\\n\",\n \"}\\n\",\n \"search = catalog.search(max_items=100, collections=\\\"landsat-8-c2-l2\\\", filter=filter)\\n\",\n \"\\n\",\n \"print(f\\\"Found {len(search.item_collection())} items\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"56503c7b\",\n \"metadata\": {},\n \"source\": [\n \"### CQL2 Text\\n\",\n \"\\n\",\n \"The examples above all use CQL2-json but pystac-client also supports passing `filter` as CQL2 text.\\n\",\n \"\\n\",\n \"NOTE: As of right now in pystac-client if you use CQL2 text you need to change the search HTTP method to GET.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5e8f62f5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"search = catalog.search(**params, method=\\\"GET\\\", filter=\\\"eo:cloud_cover<=10\\\")\\n\",\n \"\\n\",\n \"print(f\\\"Found {len(search.item_collection())} items\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b865c1f\",\n \"metadata\": {},\n \"source\": [\n \"Just like CQL2 json, CQL2 text statements can be combined to express more complex logic:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c06f40cf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"search = catalog.search(\\n\",\n \" **params, method=\\\"GET\\\", filter=\\\"eo:cloud_cover<=60 and eo:cloud_cover>=40\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f\\\"Found {len(search.item_collection())} items\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"35cbf612\",\n \"metadata\": {},\n \"source\": [\n \"## Queryables\\n\",\n \"\\n\",\n \"pystac-client provides a method for accessing all the arguments that can be used within CQL2 filters for a particular collection. These are provided as a json schema document, but for readability we are mostly interested in the names of the fields within `properties`.\\n\",\n \"\\n\",\n \"NOTE: When getting the collection, you might notice that we use \\\"landsat-c2-l2\\\" as the collection id rather than \\\"landsat-8-c2-l2\\\". This is because \\\"landsat-8-c2-l2\\\" doesn't actually exist as a collection. It is just used in some places as a collection id on items. This is likely a remnant of some former setup in the Planetary Computer STAC.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"90f1cc6d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"collection = catalog.get_collection(\\\"landsat-c2-l2\\\")\\n\",\n \"queryables = collection.get_queryables()\\n\",\n \"\\n\",\n \"list(queryables[\\\"properties\\\"].keys())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c407ffec\",\n \"metadata\": {},\n \"source\": [\n \"## Read More\\n\",\n \"\\n\",\n \"- For more involved CQL2 examples in a STAC context read the [STAC API Filter Extension Examples](https://github.com/stac-api-extensions/filter?tab=readme-ov-file#examples)\\n\",\n \"\\n\",\n \"- For examples of all the different CQL2 operations take a look at the [playground on the CQL2-rs docs](https://developmentseed.org/cql2-rs/latest/playground/).\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "docs/tutorials/item-search-intersects.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e06a27bf\",\n \"metadata\": {},\n \"source\": [\n \"# Item Search with Intersects\\n\",\n \"\\n\",\n \"This notebook shows the use of pystac-client to perform item search with the `intersects` parameter, to restrict the results to an Area of Interest (AOI).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1e16077c\",\n \"metadata\": {},\n \"source\": [\n \"# Client\\n\",\n \"\\n\",\n \"We first connect to an API by retrieving the root catalog, or landing page, of the API with the `Client.open` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"98942e75\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import json\\n\",\n \"from typing import Any, Dict\\n\",\n \"\\n\",\n \"from pystac_client import Client\\n\",\n \"\\n\",\n \"# STAC API root URL\\n\",\n \"URL = \\\"https://planetarycomputer.microsoft.com/api/stac/v1\\\"\\n\",\n \"\\n\",\n \"client = Client.open(URL)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"62e26114\",\n \"metadata\": {},\n \"source\": [\n \"# Item Search\\n\",\n \"\\n\",\n \"When the Catalog is a STAC API, we have the ability to search for items based on spatio-temporal properties.\\n\",\n \"\\n\",\n \"The STAC API endpoint `/search` accepts a parameter `intersects` that is a GeoJSON Geometry representing the AOI of the search.\\n\",\n \"\\n\",\n \"The `search` method of the pystac_client `Client` class accepts several different types of objects:\\n\",\n \"\\n\",\n \"1. a string representing a GeoJSON geometry\\n\",\n \"2. a dictionary representing a GeoJSON geometry\\n\",\n \"3. any object that implements a ``__geo_interface__`` property, [an informal specification](https://gist.github.com/sgillies/2217756) \\n\",\n \" supported by several libraries for generating a GeoJSON representation from an object. Several prominent libraries support this\\n\",\n \" protocol for their objects that represent geometries, including [Shapely](https://shapely.readthedocs.io), [ArcPy](https://pro.arcgis.com/en/pro-app/2.8/arcpy/get-started/what-is-arcpy-.htm), [PySAL](https://pysal.org/), [geojson](https://github.com/jazzband/geojson), [pyshp](https://pypi.org/project/pyshp/), [descartes](https://docs.descarteslabs.com/), and [pygeoif](https://github.com/cleder/pygeoif)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d27fabf\",\n \"metadata\": {},\n \"source\": [\n \"## Item Search with AOI as a Dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d8af6334\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# AOI around Delfzijl, in northern Netherlands\\n\",\n \"aoi_as_dict: Dict[str, Any] = {\\n\",\n \" \\\"type\\\": \\\"Polygon\\\",\\n\",\n \" \\\"coordinates\\\": [[[6, 53], [7, 53], [7, 54], [6, 54], [6, 53]]],\\n\",\n \"}\\n\",\n \"\\n\",\n \"search = client.search(\\n\",\n \" max_items=25,\\n\",\n \" collections=\\\"aster-l1t\\\",\\n\",\n \" intersects=aoi_as_dict,\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f\\\"AOI as dictionary, found {len(list(search.items()))} items\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9da4956b\",\n \"metadata\": {},\n \"source\": [\n \"## Item Search with AOI as a String\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9a7c9336\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"aoi_as_str: str = json.dumps(aoi_as_dict)\\n\",\n \"\\n\",\n \"search = client.search(\\n\",\n \" max_items=25,\\n\",\n \" collections=\\\"aster-l1t\\\",\\n\",\n \" intersects=aoi_as_str,\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f\\\"AOI as string, found {len(list(search.items()))} items\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f22ffb5a\",\n \"metadata\": {},\n \"source\": [\n \"## Item Search with AOI as a Shapely Geometry Object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a44598ef\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import shapely.geometry\\n\",\n \"\\n\",\n \"aoi_as_shapely_shape = shapely.geometry.shape(aoi_as_dict)\\n\",\n \"\\n\",\n \"search = client.search(\\n\",\n \" max_items=25,\\n\",\n \" collections=\\\"aster-l1t\\\",\\n\",\n \" intersects=aoi_as_shapely_shape,\\n\",\n \")\\n\",\n \"\\n\",\n \"print(\\n\",\n \" \\\"AOI as Shapely Geometry object from shape(), \\\"\\n\",\n \" f\\\"found {len(list(search.items()))} items\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"aoi_as_shapely_polygon = shapely.geometry.Polygon(aoi_as_dict[\\\"coordinates\\\"][0])\\n\",\n \"\\n\",\n \"search = client.search(\\n\",\n \" max_items=25,\\n\",\n \" collections=\\\"aster-l1t\\\",\\n\",\n \" intersects=aoi_as_shapely_polygon,\\n\",\n \")\\n\",\n \"\\n\",\n \"print(\\n\",\n \" \\\"AOI as Shapely Geometry object with Polygon, \\\"\\n\",\n \" f\\\"found {len(list(search.items()))} items\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8f236254\",\n \"metadata\": {},\n \"source\": [\n \"## Item Search with AOI as a \\\"geojson\\\" library object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"84ce4395\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import geojson\\n\",\n \"\\n\",\n \"aoi_as_geojson_polygon = geojson.Polygon(aoi_as_dict[\\\"coordinates\\\"])\\n\",\n \"\\n\",\n \"search = client.search(\\n\",\n \" max_items=25,\\n\",\n \" collections=\\\"aster-l1t\\\",\\n\",\n \" intersects=aoi_as_geojson_polygon,\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f\\\"AOI as geojson Polygon, found {len(list(search.items()))} items\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3fe9c255\",\n \"metadata\": {},\n \"source\": [\n \"## Item Search with AOI as a pygeoif Object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a27dcb72\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import pygeoif\\n\",\n \"\\n\",\n \"aoi_as_pygeoif_polygon = pygeoif.geometry.Polygon(aoi_as_dict[\\\"coordinates\\\"][0])\\n\",\n \"\\n\",\n \"search = client.search(\\n\",\n \" max_items=25,\\n\",\n \" collections=\\\"aster-l1t\\\",\\n\",\n \" intersects=aoi_as_pygeoif_polygon,\\n\",\n \")\\n\",\n \"\\n\",\n \"print(f\\\"AOI as pygeoif Polygon, found {len(list(search.items()))} items\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"interpreter\": {\n \"hash\": \"6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "docs/tutorials/pystac-client-introduction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e06a27bf\",\n \"metadata\": {},\n \"source\": [\n \"# PySTAC-Client Introduction\\n\",\n \"\\n\",\n \"This notebook shows basic use of pystac-client to open an API, iterate through Collections and Items, and perform simple spatio-temporal searches.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b65de617\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# set pystac_client logger to DEBUG to see API calls\\n\",\n \"import logging\\n\",\n \"\\n\",\n \"from pystac_client import Client\\n\",\n \"\\n\",\n \"logging.basicConfig()\\n\",\n \"logger = logging.getLogger(\\\"pystac_client\\\")\\n\",\n \"logger.setLevel(logging.DEBUG)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1e16077c\",\n \"metadata\": {},\n \"source\": [\n \"# Client\\n\",\n \"\\n\",\n \"We first connect to an API by retrieving the root catalog, or landing page, of the API with the `Client.open` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"98942e75\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# STAC API root URL\\n\",\n \"URL = \\\"https://planetarycomputer.microsoft.com/api/stac/v1\\\"\\n\",\n \"\\n\",\n \"# custom headers\\n\",\n \"headers = []\\n\",\n \"\\n\",\n \"cat = Client.open(URL, headers=headers)\\n\",\n \"cat\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f6efbb86\",\n \"metadata\": {},\n \"source\": [\n \"# CollectionClient\\n\",\n \"\\n\",\n \"As with a static catalog the `get_collections` function will iterate through the Collections in the Catalog. Notice that because this is an API it can get all the Collections through a single call, rather than having to fetch each one individually.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"bb7693fb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for collection in cat.get_collections():\\n\",\n \" print(collection)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ebab2724-cab3-4fba-b25b-fdfb4e537014\",\n \"metadata\": {},\n \"source\": [\n \"# Collection Search\\n\",\n \"\\n\",\n \"Sometimes, it can be challenging to identify which collection you want to work with. The `collection_search` method allows you to discover collections by applying search filters that will help you find the specific collection(s) you need. Since many STAC APIs have not implemented the collection search extension, `pystac-client` will perform a limited client-side filter if the API does not conform to the collection search spec.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a23a53ec-5b5f-421d-9f0e-01dbde8c3697\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"collection_search = cat.collection_search(\\n\",\n \" q=\\\"ASTER\\\",\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"90b3d014-9c8f-4c5b-a94e-bfb7f17380ad\",\n \"metadata\": {},\n \"source\": [\n \"The `collections` method lets you iterate through the results of the search so you can inspect the details of matching collections.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"006f13fd-5e58-4f3f-bd5a-707cd830caa1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for result in collection_search.collections():\\n\",\n \" print(result.id, f\\\"{collection.description}\\\", sep=\\\"\\\\n\\\")\\n\",\n \" print(\\\"\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fef20a46\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"collection = cat.get_collection(\\\"aster-l1t\\\")\\n\",\n \"collection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"47a540fe\",\n \"metadata\": {},\n \"source\": [\n \"# Items\\n\",\n \"\\n\",\n \"The main functions for getting items return iterators, where pystac-client will handle retrieval of additional pages when needed. Note that one request is made for the first ten items, then a second request for the next ten.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"17d6de4b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"items = collection.get_items()\\n\",\n \"\\n\",\n \"\\n\",\n \"# flush stdout so we can see the exact order that things happen\\n\",\n \"def get_ten_items(items):\\n\",\n \" for i, item in enumerate(items):\\n\",\n \" print(f\\\"{i}: {item}\\\", flush=True)\\n\",\n \" if i == 9:\\n\",\n \" return\\n\",\n \"\\n\",\n \"\\n\",\n \"print(\\\"First page\\\", flush=True)\\n\",\n \"get_ten_items(items)\\n\",\n \"\\n\",\n \"print(\\\"Second page\\\", flush=True)\\n\",\n \"get_ten_items(items)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"62e26114\",\n \"metadata\": {},\n \"source\": [\n \"# API Search\\n\",\n \"\\n\",\n \"If the Catalog is an API, we have the ability to search for items based on spatio-temporal properties.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d8af6334\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# AOI around Delfzijl, in northern Netherlands\\n\",\n \"geom = {\\n\",\n \" \\\"type\\\": \\\"Polygon\\\",\\n\",\n \" \\\"coordinates\\\": [\\n\",\n \" [\\n\",\n \" [6.42425537109375, 53.174765470134616],\\n\",\n \" [7.344360351562499, 53.174765470134616],\\n\",\n \" [7.344360351562499, 53.67393435835391],\\n\",\n \" [6.42425537109375, 53.67393435835391],\\n\",\n \" [6.42425537109375, 53.174765470134616],\\n\",\n \" ]\\n\",\n \" ],\\n\",\n \"}\\n\",\n \"\\n\",\n \"# limit sets the # of items per page so we can see multiple pages getting fetched\\n\",\n \"search = cat.search(\\n\",\n \" max_items=15,\\n\",\n \" limit=5,\\n\",\n \" collections=\\\"aster-l1t\\\",\\n\",\n \" intersects=geom,\\n\",\n \" datetime=\\\"2000-01-01/2010-12-31\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"items = list(search.items())\\n\",\n \"\\n\",\n \"len(items)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"275f316f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# note that this will work in JupyterLab, but not in a Jupyter Notebook\\n\",\n \"\\n\",\n \"import IPython.display\\n\",\n \"\\n\",\n \"IPython.display.JSON([i.to_dict() for i in items])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"68bf65f2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# this cell can be used in Jupyter Notebook. Use above if using JupyterLab\\n\",\n \"\\n\",\n \"import json\\n\",\n \"import uuid\\n\",\n \"\\n\",\n \"from IPython.display import display_html, display_javascript\\n\",\n \"\\n\",\n \"\\n\",\n \"class RenderJSON(object):\\n\",\n \" def __init__(self, json_data):\\n\",\n \" if isinstance(json_data, dict) or isinstance(json_data, list):\\n\",\n \" self.json_str = json.dumps(json_data)\\n\",\n \" else:\\n\",\n \" self.json_str = json_data\\n\",\n \" self.uuid = str(uuid.uuid4())\\n\",\n \"\\n\",\n \" def _ipython_display_(self):\\n\",\n \" display_html(\\n\",\n \" '
'.format(self.uuid),\\n\",\n \" raw=True,\\n\",\n \" )\\n\",\n \" display_javascript(\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" require([\\\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\\\"],\\n\",\n \" function() {\\n\",\n \" renderjson.set_show_to_level(2);\\n\",\n \" document.getElementById('%s').appendChild(renderjson(%s))\\n\",\n \" });\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" % (self.uuid, self.json_str),\\n\",\n \" raw=True,\\n\",\n \" )\\n\",\n \"\\n\",\n \"\\n\",\n \"RenderJSON([i.to_dict() for i in items])\"\n ]\n }\n ],\n \"metadata\": {\n \"interpreter\": {\n \"hash\": \"6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "docs/tutorials/stac-metadata-viz.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ab31574b\",\n \"metadata\": {},\n \"source\": [\n \"# STAC Metadata Visualizations\\n\",\n \"\\n\",\n \"This notebook illustrates a simple way to display footprints of discovered Items after searching a STAC API, and making simple plots using Pandas and Holoviews. Only the metadata is visualized in these examples through maps and plots. The actual STAC data (i.e., Item Assets) are not accessed.\\n\",\n \"\\n\",\n \"The libraries GeoPandas and hvplot are used for visualizations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3188b5a0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# set pystac_client logger to DEBUG to see API calls\\n\",\n \"import logging\\n\",\n \"\\n\",\n \"from pystac_client import Client\\n\",\n \"\\n\",\n \"logging.basicConfig()\\n\",\n \"logger = logging.getLogger(\\\"pystac_client\\\")\\n\",\n \"logger.setLevel(logging.DEBUG)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1d404982\",\n \"metadata\": {},\n \"source\": [\n \"Define the STAC API to use, along with any custom headers (such as for authentication)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d40ed5c1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# STAC API root URL\\n\",\n \"URL = \\\"https://planetarycomputer.microsoft.com/api/stac/v1\\\"\\n\",\n \"\\n\",\n \"# custom headers\\n\",\n \"headers = []\\n\",\n \"\\n\",\n \"cat = Client.open(URL, headers=headers)\\n\",\n \"cat\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e513e548\",\n \"metadata\": {},\n \"source\": [\n \"## Search\\n\",\n \"\\n\",\n \"Perform a spatio-temporal search of ASTER data for a small AOI in the northern part of The Netherlands between 2000 and 2010.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ae7a3eca\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# AOI around Delfzijl, in northern Netherlands\\n\",\n \"geom = {\\n\",\n \" \\\"type\\\": \\\"Polygon\\\",\\n\",\n \" \\\"coordinates\\\": [\\n\",\n \" [\\n\",\n \" [6.42425537109375, 53.174765470134616],\\n\",\n \" [7.344360351562499, 53.174765470134616],\\n\",\n \" [7.344360351562499, 53.67393435835391],\\n\",\n \" [6.42425537109375, 53.67393435835391],\\n\",\n \" [6.42425537109375, 53.174765470134616],\\n\",\n \" ]\\n\",\n \" ],\\n\",\n \"}\\n\",\n \"\\n\",\n \"# limit sets the # of items per page so we can see multiple pages getting fetched\\n\",\n \"search = cat.search(\\n\",\n \" max_items=50,\\n\",\n \" collections=\\\"aster-l1t\\\",\\n\",\n \" intersects=geom,\\n\",\n \" datetime=\\\"2000-01-01/2010-12-31\\\",\\n\",\n \")\\n\",\n \"\\n\",\n \"# retrieve the items as dictionaries, rather than Item objects\\n\",\n \"items = list(search.items_as_dicts())\\n\",\n \"len(items)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2a23660f\",\n \"metadata\": {},\n \"source\": [\n \"## GeoPandas\\n\",\n \"\\n\",\n \"A GeoDataFrame is constructed from the AOI geometry, in order to make visualizations easy.\\n\",\n \"\\n\",\n \"The STAC Items, which are a GeoJSON FeatureCollection can be converted to a GeoDataFrame.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ed0da77b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from copy import deepcopy\\n\",\n \"\\n\",\n \"import geopandas as gpd\\n\",\n \"import pandas as pd\\n\",\n \"from shapely.geometry import shape\\n\",\n \"\\n\",\n \"\\n\",\n \"# convert a list of STAC Items into a GeoDataFrame\\n\",\n \"def items_to_geodataframe(items):\\n\",\n \" _items = []\\n\",\n \" for i in items:\\n\",\n \" _i = deepcopy(i)\\n\",\n \" _i[\\\"geometry\\\"] = shape(_i[\\\"geometry\\\"])\\n\",\n \" _items.append(_i)\\n\",\n \" gdf = gpd.GeoDataFrame(pd.json_normalize(_items))\\n\",\n \" for field in [\\\"properties.datetime\\\", \\\"properties.created\\\", \\\"properties.updated\\\"]:\\n\",\n \" if field in gdf:\\n\",\n \" gdf[field] = pd.to_datetime(gdf[field])\\n\",\n \" gdf.set_index(\\\"properties.datetime\\\", inplace=True)\\n\",\n \" return gdf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c4bed793\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# convert geometry to a GeoDataFrame\\n\",\n \"aoi_gdf = gpd.GeoDataFrame([{\\\"geometry\\\": shape(geom)}])\\n\",\n \"aoi_gdf\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d1fc8732\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# convert found items to a GeoDataFrame\\n\",\n \"items_gdf = items_to_geodataframe(items)\\n\",\n \"items_gdf.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4d570626\",\n \"metadata\": {},\n \"source\": [\n \"## Plot Geometries on a Map\\n\",\n \"\\n\",\n \"Holoviews is used to display geometries on a map by using `hvplot`. The The `*` Holoviews operator to overlay two plots\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e8fbfad6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import hvplot.pandas # noqa: F401\\n\",\n \"\\n\",\n \"\\n\",\n \"# plot polygons on a map with background tiles.\\n\",\n \"def plot_polygons(data, *args, **kwargs):\\n\",\n \" return data.hvplot.polygons(\\n\",\n \" *args,\\n\",\n \" geo=True,\\n\",\n \" projection=\\\"GOOGLE_MERCATOR\\\",\\n\",\n \" xaxis=None,\\n\",\n \" yaxis=None,\\n\",\n \" frame_width=600,\\n\",\n \" frame_height=600,\\n\",\n \" fill_alpha=0,\\n\",\n \" line_width=4,\\n\",\n \" **kwargs,\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"72094092\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_polygons(aoi_gdf, tiles=\\\"OSM\\\", line_color=\\\"red\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d7d6cfc6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_polygons(items_gdf, tiles=\\\"OSM\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a07e87b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plot_polygons(items_gdf, tiles=\\\"OSM\\\") * plot_polygons(aoi_gdf, line_color=\\\"red\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b0604c84\",\n \"metadata\": {},\n \"source\": [\n \"## Line Plots\\n\",\n \"\\n\",\n \"Numeric STAC metadata can also be plotted, most often this will be plotted vs the Item `datetime`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c198e261\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"items_df = pd.DataFrame(items_gdf)\\n\",\n \"\\n\",\n \"plot_fields = [\\n\",\n \" \\\"properties.eo:cloud_cover\\\",\\n\",\n \" \\\"properties.view:sun_azimuth\\\",\\n\",\n \" \\\"properties.view:sun_elevation\\\",\\n\",\n \"]\\n\",\n \"\\n\",\n \"items_df[plot_fields].hvplot(height=500, width=800).opts(legend_position=\\\"top_right\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"interpreter\": {\n \"hash\": \"6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.16\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "docs/tutorials.rst", "content": ".. _tutorials:\n\nTutorials\n#########\n\nPySTAC-Client Introduction\n--------------------------\n\n- :tutorial:`GitHub version `\n- :ref:`Docs version `\n\nThis tutorial gives an introduction to using pystac-client with a STAC API\n\nSTAC Metadata Visualization\n---------------------------\n\n- :tutorial:`GitHub version `\n- :ref:`Docs version `\n\nThis tutorial gives an introduction to using hvplot to visualize\nSTAC metadata and Item geometries on a map.\n\nCQL2 Filtering\n---------------------------\n\n- :tutorial:`GitHub version `\n- :ref:`Docs version `\n\nThis tutorial gives an introduction to using CQL2-JSON filtering in searches to\nsearch by arbitrary STAC Item properties.\n\nItem Search with Intersects\n-----------------------------------------------------\n\n- :tutorial:`GitHub version `\n- :ref:`Docs version `\n\nThis tutorial demonstrates the use of different Python library\nobject types that can be used for the `intersects`\nsearch parameter.\n\nCalculating Coverage Percentage of the AOI by an Item\n-----------------------------------------------------\n\n- :tutorial:`GitHub version `\n- :ref:`Docs version `\n\nThis tutorial demonstrates the use of pystac-client to calculate the\npercentage an Item's geometry that intesects with the area of interest\n(AOI) specified in the search by the `intersects` parameter.\n\nAuthentication\n--------------\n\n- :tutorial:`GitHub version `\n- :ref:`Docs version `\n\nThis tutorial demontrates different ways the pystac-client can be\nused to access a private stac api, when protected with various\nauthentication mechanisms.\n" }, { "path": "docs/usage.rst", "content": "Usage\n#####\n\nPySTAC-Client (pystac-client) builds upon\n`PySTAC `_ library to add support\nfor STAC APIs in addition to static STAC catalogs. PySTAC-Client can be used with static\nor dynamic (i.e., API) catalogs. Currently, pystac-client does not offer much in the way\nof additional functionality if using with static catalogs, as the additional features\nare for support STAC API endpoints such as `search`. However, in the future it is\nexpected that pystac-client will offer additional convenience functions that may be\nuseful for static and dynamic catalogs alike.\n\nThe most basic implementation of a STAC API is an endpoint that returns a valid STAC\nCatalog, but also contains a ``\"conformsTo\"`` attribute that is a list of conformance\nURIs for the standards that the API supports.\n\nThis section is organized by the classes that are used, which mirror parent classes\nfrom PySTAC:\n\n+------------------+------------+\n| pystac-client | pystac |\n+==================+============+\n| Client | Catalog |\n+------------------+------------+\n| CollectionClient | Collection |\n+------------------+------------+\n\nThe classes offer all of the same functions for accessing and traversing Catalogs as\nin PySTAC. The documentation for pystac-client only includes new functions, it does\nnot duplicate documentation for inherited functions.\n\nClient\n++++++\n\nThe :class:`pystac_client.Client` class is the main interface for working with services\nthat conform to the STAC API spec. This class inherits from the :class:`pystac.Catalog`\nclass and in addition to the methods and attributes implemented by a Catalog, it also\nincludes convenience methods and attributes for:\n\n* Checking conformance to various specs\n* Querying a search endpoint (if the API conforms to the STAC API - Item Search spec)\n* Getting jsonschema of queryables from `/queryables` endpoint (if the API conforms\n to the STAC API - Filter spec)\n\nThe preferred way to interact with any STAC Catalog or API is to create an\n:class:`pystac_client.Client` instance with the ``pystac_client.Client.open`` method\non a root Catalog. This calls the :meth:`pystac.STACObject.from_file` except it\nproperly configures conformance and IO for reading from remote servers.\n\nThe following code creates an instance by making a call to the Microsoft Planetary\nComputer root catalog.\n\n.. code-block:: python\n\n >>> from pystac_client import Client\n >>> catalog = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1')\n >>> catalog.title\n 'Microsoft Planetary Computer STAC API'\n\nSome functions, such as ``Client.search`` will throw an error if the provided\nCatalog/API does not support the required Conformance Class. In other cases,\nsuch as ``Client.get_collections``, API endpoints will be used if the API\nconforms, otherwise it will fall back to default behavior provided by\n:class:`pystac.Catalog`.\n\nWhen a ``Client`` does not conform to a particular Conformance Class, an informative\nwarning is raised. Similarly when falling back to the :class:`pystac.Catalog`\nimplementation a warning is raised. You can control the behavior of these warnings\nusing the standard :py:mod:`warnings` or special context managers :func:`pystac_client.warnings.strict` and\nfrom :func:`pystac_client.warnings.ignore`.\n\nAPI Conformance\n---------------\n\nThis library is intended to work with any STAC static catalog or STAC API. A static\ncatalog will be usable more or less the same as with PySTAC, except that pystac-client\nsupports providing custom headers to API endpoints. (e.g., authenticating\nto an API with a token).\n\nA STAC API is a STAC Catalog that is required to advertise its capabilities in a\n`conformsTo` field and implements the `STAC API - Core` spec along with other\noptional specifications:\n\n* `CORE `__\n* `ITEM_SEARCH `__\n * `FIELDS `__\n * `QUERY `__\n * `SORT `__\n * `CONTEXT `__\n * `FILTER `__\n* `COLLECTIONS `__ (based on\n the `Features Collection section of OGC APO - Features __`)\n* `FEATURES `__ (based on\n `OGC API - Features `__)\n\nThe :meth:`pystac_client.Client.conforms_to` method is used to check conformance\nagainst conformance classes (specs). To check an API for support for a given spec,\npass the `conforms_to` function the name of a :class:`ConformanceClasses`.\n\n.. code-block:: python\n\n >>> catalog.conforms_to(\"ITEM_SEARCH\")\n True\n\nIf the API does not advertise conformance with a particular spec, but it does support\nit you can update `conforms_to` on the client object. For instance in `v0` of earth-search\nthere are no ``\"conformsTo\"`` uris set at all. But they can be explicitly set:\n\n.. code-block:: python\n\n >>> catalog = Client.open(\"https://earth-search.aws.element84.com/v0\")\n :1: NoConformsTo: Server does not advertise any conformance classes.\n >>> catalog.conforms_to(\"ITEM_SEARCH\")\n False\n >>> catalog.add_conforms_to(\"ITEM_SEARCH\")\n\nNote, updating ``\"conformsTo\"`` does not change what the server supports, it just\nchanges PySTAC client's understanding of what the server supports.\n\nConfiguring retry behavior\n--------------------------\n\nBy default, **pystac-client** will retry requests that fail DNS lookup or have timeouts.\nIf you'd like to configure this behavior, e.g. to retry on some ``50x`` responses, you can configure the StacApiIO's session:\n\n.. code-block:: python\n\n from requests.adapters import HTTPAdapter\n from urllib3 import Retry\n\n from pystac_client import Client\n from pystac_client.stac_api_io import StacApiIO\n\n retry = Retry(\n total=5, backoff_factor=1, status_forcelist=[502, 503, 504], allowed_methods=None\n )\n stac_api_io = StacApiIO(max_retries=retry)\n client = Client.open(\n \"https://planetarycomputer.microsoft.com/api/stac/v1\", stac_io=stac_api_io\n )\n\nAutomatically modifying results\n-------------------------------\n\nSome systems, like the `Microsoft Planetary Computer `__,\nhave public STAC metadata but require some `authentication `__\nto access the actual assets.\n\n``pystac-client`` provides a ``modifier`` keyword that can automatically\nmodify the STAC objects returned by the STAC API.\n\n.. code-block:: python\n\n >>> from pystac_client import Client\n >>> import planetary_computer, requests\n >>> catalog = Client.open(\n ... 'https://planetarycomputer.microsoft.com/api/stac/v1',\n ... modifier=planetary_computer.sign_inplace,\n ... )\n >>> item = next(catalog.get_collection(\"sentinel-2-l2a\").get_all_items())\n >>> requests.head(item.assets[\"B02\"].href).status_code\n 200\n\nWithout the modifier, we would have received a 404 error because the asset\nis in a private storage container.\n\n``pystac-client`` expects that the ``modifier`` callable modifies the result\nobject in-place and returns no result. A warning is emitted if your\n``modifier`` returns a non-None result that is not the same object as the\ninput.\n\nHere's an example of creating your own modifier.\nBecause :py:class:`~pystac_client.Modifiable` is a union, the modifier function must handle a few different types of input objects, and care must be taken to ensure that you are modifying the input object (rather than a copy).\nSimplifying this interface is a space for future improvement.\n\n.. code-block:: python\n\n import urllib.parse\n\n import pystac\n\n from pystac_client import Client, Modifiable\n\n\n def modifier(modifiable: Modifiable) -> None:\n if isinstance(modifiable, dict):\n if modifiable[\"type\"] == \"FeatureCollection\":\n new_features = list()\n for item_dict in modifiable[\"features\"]:\n modifier(item_dict)\n new_features.append(item_dict)\n modifiable[\"features\"] = new_features\n else:\n stac_object = pystac.read_dict(modifiable)\n modifier(stac_object)\n modifiable.update(stac_object.to_dict())\n else:\n for key, asset in modifiable.assets.items():\n url = urllib.parse.urlparse(asset.href)\n if not url.query:\n asset.href = urllib.parse.urlunparse(url._replace(query=\"foo=bar\"))\n modifiable.assets[key] = asset\n\n\n client = Client.open(\n \"https://planetarycomputer.microsoft.com/api/stac/v1\", modifier=modifier\n )\n item_search = client.search(collections=[\"landsat-c2-l2\"], max_items=1)\n item = next(item_search.items())\n asset = item.assets[\"red\"]\n assert urllib.parse.urlparse(asset.href).query == \"foo=bar\"\n\n\nUsing custom certificates\n-------------------------\n\nIf you need to use custom certificates in your ``pystac-client`` requests, you can\ncustomize the :class:`StacApiIO` instance before\ncreating your :class:`Client`.\n\n.. code-block:: python\n\n >>> from pystac_client.stac_api_io import StacApiIO\n >>> from pystac_client.client import Client\n >>> stac_api_io = StacApiIO()\n >>> stac_api_io.session.verify = \"/path/to/certfile\"\n >>> client = Client.open(\"https://planetarycomputer.microsoft.com/api/stac/v1\", stac_io=stac_api_io)\n\nCollectionClient\n++++++++++++++++\n\nSTAC APIs may optionally implement a ``/collections`` endpoint as described in the\n`STAC API - Collections spec\n`__. \nThis endpoint allows clients to search or inspect items within a particular collection.\n\n.. code-block:: python\n\n >>> catalog = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1')\n >>> collection = catalog.get_collection(\"sentinel-2-l2a\")\n >>> collection.title\n 'Sentinel-2 Level-2A'\n\n:class:`pystac_client.CollectionClient` overrides :meth:`pystac.Collection.get_items`.\nPySTAC will get items by iterating through all children until it gets to an ``item`` link.\nPySTAC client will use the API endpoint instead: `/collections//items`\n(as long as `STAC API - Item Search spec\n`__ is supported).\n\n.. code-block:: python\n\n >>> item = next(collection.get_items(), None)\n\nNote that calling list on this iterator will take a really long time since it will be retrieving\nevery itme for the whole ``\"sentinel-2-l2a\"`` collection.\n\nCollectionSearch\n++++++++++++++++\n\nSTAC API services may optionally implement a ``/collections`` endpoint as described in the\n`STAC API - Collections spec\n`__.\nThe ``/collections`` endpoint can be extended with the \n`STAC API - Collection Search Extension `__ \nwhich adds the capability to apply filter parameters to the collection-level metadata. \nSee the `Query Parameters and Fields\n`__\nfrom that spec for details on the meaning of each parameter.\n\nThe :meth:`pystac_client.Client.collection_search` method provides an interface for making\nrequests to a service's \"collections\" endpoint. This method returns a\n:class:`pystac_client.CollectionSearch` instance.\n\n.. code-block:: python\n\n >>> from pystac_client import Client\n >>> catalog = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1')\n >>> results = catalog.collection_search(\n ... q=\"biomass\",\n ... datetime=\"2022/..\"\n ... )\n\nInstances of :class:`~pystac_client.CollectionSearch` have a handful of methods for\ngetting matching collections as Python objects. The right method to use depends on\nhow many of the matches you want to consume (a single collection at a time, a\npage at a time, or everything) and whether you want plain Python dictionaries\nrepresenting the collections, or :class:`pystac.Collection` objects.\n\nThe following table shows the :class:`~pystac_client.CollectionSearch` methods for fetching\nmatches, according to which set of matches to return and whether to return them as\n``pystac`` objects or plain dictionaries.\n\n====================== ======================================================= ===============================================================\nMatches to return PySTAC objects Plain dictionaries\n====================== ======================================================= ===============================================================\n**Single collections** :meth:`~pystac_client.CollectionSearch.collections` :meth:`~pystac_client.CollectionSearch.collections_as_dicts`\n**Pages** :meth:`~pystac_client.CollectionSearch.pages` :meth:`~pystac_client.CollectionSearch.pages_as_dicts`\n**Everything** :meth:`~pystac_client.CollectionSearch.collection_list` :meth:`~pystac_client.CollectionSearch.collection_list_as_dict`\n====================== ======================================================= ===============================================================\n\nAdditionally, the ``matched`` method can be used to access result metadata about\nhow many total items matched the query:\n\n* :meth:`CollectionSearch.matched `: returns the number\n of hits (collections) for this search. If the API supports the STAC API Context Extension this\n value will be returned directly from a search result with ``limit=1``. Otherwise ``pystac-client``\n will count the results and return a value with an associated warning.\n\n.. code-block:: python\n\n >>> for collection in results.collections():\n ... print(item.id)\n fia\n modis-13Q1-061\n modis-13A1-061\n sentinel-3-olci-lfr-l2-netcdf\n\nThe :meth:`~pystac_client.CollectionSearch.collections` and related methods handle retrieval of\nsuccessive pages of results\nby finding links with a ``\"rel\"`` type of ``\"next\"`` and parsing them to construct the\nnext request. The default\nimplementation of this ``\"next\"`` link parsing assumes that the link follows the spec for\nan extended STAC link as\ndescribed in the\n`STAC API - Collections: Collection Paging `__\nsection.\n\nAlternatively, the Collections can be returned as a list, where each\nlist is one page of results retrieved from search:\n\n.. code-block:: python\n\n >>> for page in results.pages():\n ... for collection in page.collections():\n ... print(collection.id)\n fia\n modis-13Q1-061\n modis-13A1-061\n sentinel-3-olci-lfr-l2-netcdf\n\nIf you do not need the :class:`pystac.Collection` instances, you can instead use\n:meth:`CollectionSearch.collections_as_dicts `\nto retrieve dictionary representation of the collections, without incurring the cost of\ncreating the Collection objects.\n\n.. code-block:: python\n\n >>> for collection_dict in results.collections_as_dicts():\n ... print(collection_dict[\"id\"])\n fia\n modis-13Q1-061\n modis-13A1-061\n sentinel-3-olci-lfr-l2-netcdf\n\nItemSearch\n++++++++++\n\nSTAC API services may optionally implement a ``/search`` endpoint as describe in the\n`STAC API - Item Search spec\n`__. This\nendpoint allows clients to query STAC Items across the entire service using a variety\nof filter parameters. See the `Query Parameter Table\n`__\nfrom that spec for details on the meaning of each parameter.\n\nThe :meth:`pystac_client.Client.search` method provides an interface for making\nrequests to a service's \"search\" endpoint. This method returns a\n:class:`pystac_client.ItemSearch` instance.\n\n.. code-block:: python\n\n >>> from pystac_client import Client\n >>> catalog = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1')\n >>> results = catalog.search(\n ... max_items=5,\n ... bbox=[-73.21, 43.99, -73.12, 44.05],\n ... datetime=['2019-01-01T00:00:00Z', '2019-01-02T00:00:00Z'],\n ... )\n\nInstances of :class:`~pystac_client.ItemSearch` have a handful of methods for\ngetting matching items as Python objects. The right method to use depends on\nhow many of the matches you want to consume (a single item at a time, a\npage at a time, or everything) and whether you want plain Python dictionaries\nrepresenting the items, or :class:`pystac.Item` objects.\n\nThe following table shows the :class:`~pystac_client.ItemSearch` methods for fetching\nmatches, according to which set of matches to return and whether to return them as\n``pystac`` objects or plain dictionaries.\n\n================= ================================================= =========================================================\nMatches to return PySTAC objects Plain dictionaries\n================= ================================================= =========================================================\n**Single items** :meth:`~pystac_client.ItemSearch.items` :meth:`~pystac_client.ItemSearch.items_as_dicts`\n**Pages** :meth:`~pystac_client.ItemSearch.pages` :meth:`~pystac_client.ItemSearch.pages_as_dicts`\n**Everything** :meth:`~pystac_client.ItemSearch.item_collection` :meth:`~pystac_client.ItemSearch.item_collection_as_dict`\n================= ================================================= =========================================================\n\nAdditionally, the ``matched`` method can be used to access result metadata about\nhow many total items matched the query:\n\n* :meth:`ItemSearch.matched `: returns the number\n of hits (items) for this search if the API supports the STAC API Context Extension.\n Not all APIs support returning a total count, in which case a warning will be issued.\n\n.. code-block:: python\n\n >>> for item in results.items():\n ... print(item.id)\n S2B_OPER_MSI_L2A_TL_SGS__20190101T200120_A009518_T18TXP_N02.11\n MCD43A4.A2019010.h12v04.006.2019022234410\n MCD43A4.A2019009.h12v04.006.2019022222645\n MYD11A1.A2019002.h12v04.006.2019003174703\n MYD11A1.A2019001.h12v04.006.2019002165238\n\nThe :meth:`~pystac_client.ItemSearch.items` and related methods handle retrieval of\nsuccessive pages of results\nby finding links with a ``\"rel\"`` type of ``\"next\"`` and parsing them to construct the\nnext request. The default\nimplementation of this ``\"next\"`` link parsing assumes that the link follows the spec for\nan extended STAC link as\ndescribed in the\n`STAC API - Item Search: Paging `__\nsection. See the :mod:`Paging ` docs for details on how to\ncustomize this behavior.\n\nAlternatively, the Items can be returned within ItemCollections, where each\nItemCollection is one page of results retrieved from search:\n\n.. code-block:: python\n\n >>> for ic in results.pages():\n ... for item in ic.items:\n ... print(item.id)\n S2B_OPER_MSI_L2A_TL_SGS__20190101T200120_A009518_T18TXP_N02.11\n MCD43A4.A2019010.h12v04.006.2019022234410\n MCD43A4.A2019009.h12v04.006.2019022222645\n MYD11A1.A2019002.h12v04.006.2019003174703\n MYD11A1.A2019001.h12v04.006.2019002165238\n\nIf you do not need the :class:`pystac.Item` instances, you can instead use\n:meth:`ItemSearch.items_as_dicts `\nto retrieve dictionary representation of the items, without incurring the cost of\ncreating the Item objects.\n\n.. code-block:: python\n\n >>> for item_dict in results.items_as_dicts():\n ... print(item_dict[\"id\"])\n S2B_OPER_MSI_L2A_TL_SGS__20190101T200120_A009518_T18TXP_N02.11\n MCD43A4.A2019010.h12v04.006.2019022234410\n MCD43A4.A2019009.h12v04.006.2019022222645\n MYD11A1.A2019002.h12v04.006.2019003174703\n MYD11A1.A2019001.h12v04.006.2019002165238\n\nQuery Extension\n---------------\n\nIf the Catalog supports the `Query\nextension `__,\nany Item property can also be included in the search. Rather than\nrequiring the JSON syntax the Query extension uses, pystac-client can use a\nsimpler syntax that it will translate to the JSON equivalent. Note\nhowever that when the simple syntax is used it sends all property values\nto the server as strings, except for ``gsd`` which it casts to\n``float``. This means that if there are extensions in use with numeric\nproperties these will be sent as strings. Some servers may automatically\ncast this to the appropriate data type, others may not.\n\nThe query filter will also accept complete JSON as per the specification.\n\n::\n\n \n\n where operator is one of `>=`, `<=`, `>`, `<`, `=`\n\n Examples:\n eo:cloud_cover<10\n view:off_nadir<50\n platform=landsat-8\n\nAny number of properties can be included, and each can be included more\nthan once to use additional operators.\n\nSort Extension\n---------------\n\nIf the Catalog supports the `Sort\nextension `__,\nthe search request can specify the order in which the results should be sorted with\nthe ``sortby`` parameter. The ``sortby`` parameter can either be a string\n(e.g., ``\"-properties.datetime,+id,collection\"``), a list of strings\n(e.g., ``[\"-properties.datetime\", \"+id\", \"+collection\"]``), or a dictionary representing\nthe POST JSON format of sortby. In the string and list formats, a ``-`` prefix means a\ndescending sort and a ``+`` prefix or no prefix means an ascending sort.\n\n.. code-block:: python\n\n >>> from pystac_client import Client\n >>> results = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1').search(\n ... sortby=\"properties.datetime\"\n ... )\n >>> results = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1').search(\n ... sortby=\"-properties.datetime,+id,+collection\"\n ... )\n >>> results = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1').search(\n ... sortby=[\"-properties.datetime\", \"+id\" , \"+collection\" ]\n ... )\n >>> results = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1').search(\n ... sortby=[\n {\"direction\": \"desc\", \"field\": \"properties.datetime\"},\n {\"direction\": \"asc\", \"field\": \"id\"},\n {\"direction\": \"asc\", \"field\": \"collection\"},\n ]\n ... )\n\nLoading data\n++++++++++++\n\nOnce you've fetched your STAC :class:`Items` with ``pystac-client``, you\nnow can work with the data referenced by your :class:`Assets`. This is\nout of scope for ``pystac-client``, but there's a wide variety of tools and options\navailable, and the correct choices depend on your type of data, your environment, and\nthe type of analysis you're doing.\n\nFor simple workflows, it can be easiest to load data directly using `rasterio\n`_, `fiona `_, and\nsimilar tools. Here is a simple example using **rasterio** to display data from a raster\nfile.\n\n.. code-block:: python\n\n >>> import rasterio.plot.show\n >>> with rasterio.open(item.assets[\"data\"].href) as dataset:\n ... rasterio.plot.show(dataset)\n\nFor larger sets of data and more complex workflows, a common tool for working with a\nlarge number of raster files is `xarray `_, which provides data\nstructures for labelled multi-dimensional arrays. `stackstac\n`_ and `odc-stac `_\nare two similar tools that can load asset data from :class:`Items` or an\n:class:`ItemCollection` into an **xarray**. Here's a simple\nexample from **odc-stac**'s documentation:\n\n.. code-block:: python\n\n >>> catalog = pystac_client.Client.open(...)\n >>> query = catalog.search(...)\n >>> xx = odc.stac.load(\n ... query.get_items(),\n ... bands=[\"red\", \"green\", \"blue\"],\n ... resolution=100,\n ... )\n >>> xx.red.plot.imshow(col=\"time\")\n\n\nSee each packages's respective documentation for more examples and tutorials.\n\n.. _how_to:\n\nHow To\n++++++++++++\n\nGet the latest dataset across multiple collections\n-----------------------------------------\n\nWhen searching for multiple datasets across different collections, each dataset may be derived from a different time period. You may want to get the latest dataset for each collection instead of all available datasets.\n\n.. code-block:: python\n\n from pystac_client import Client\n\n client = Client.open(\"https://planetarycomputer.microsoft.com/api/stac/v1\")\n items = []\n for collection in [\"nasadem\", \"esa-worldcover\", \"jrc-gsw\"]:\n items.append(\n next(\n client.search(\n collections=[collection], max_items=1, sortby=\"-properties.datetime\"\n ).items()\n )\n )\n\nThe result will be a list containing the most recent item from each collection:\n\n >>> items\n [, , ]" }, { "path": "pyproject.toml", "content": "[project]\nname = \"pystac-client\"\ndescription = \"Python library for searching SpatioTemporal Asset Catalog (STAC) APIs.\"\nreadme = \"README.md\"\nauthors = [\n { name = \"Jon Duckworth\", email = \"duckontheweb@gmail.com\" },\n { name = \"Matthew Hanson\", email = \"matt.a.hanson@gmail.com\" },\n]\nmaintainers = [{ name = \"Pete Gadomski\", email = \"pete.gadomski@gmail.com\" }]\nkeywords = [\"pystac\", \"imagery\", \"raster\", \"catalog\", \"STAC\"]\nlicense = { text = \"Apache-2.0\" }\nclassifiers = [\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n \"Operating System :: OS Independent\",\n \"Natural Language :: English\",\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Topic :: Scientific/Engineering :: GIS\",\n \"Topic :: Software Development :: Libraries\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n]\nrequires-python = \">=3.10\"\ndependencies = [\n \"requests>=2.28.2\",\n \"pystac[validation]>=1.10.0\",\n \"python-dateutil>=2.8.2\",\n]\ndynamic = [\"version\"]\n\n[project.scripts]\nstac-client = \"pystac_client.cli:cli\"\n\n[dependency-groups]\ndev = [\n \"codespell~=2.4.0\",\n \"coverage~=7.2\",\n \"cql2>=0.3.7\",\n \"doc8~=2.0.0\",\n \"importlib-metadata~=9.0\",\n \"mypy~=1.2\",\n \"orjson~=3.8\",\n \"pre-commit~=4.0\",\n \"pytest-benchmark~=5.2.0\",\n \"pytest-console-scripts~=1.4.0\",\n \"pytest-cov~=7.0\",\n \"pytest-recording~=0.13\",\n \"pytest~=9.0\",\n \"recommonmark~=0.7.1\",\n \"requests-mock~=1.12\",\n \"ruff==0.15.12\",\n \"tomli~=2.0; python_version<'3.11'\",\n \"types-python-dateutil>=2.8.19,<2.10.0\",\n \"types-requests~=2.33.0\",\n \"urllib3>=2.4\",\n]\ndocs = [\n \"Sphinx~=8.0\",\n \"boto3~=1.26\",\n \"cartopy~=0.21\",\n \"geojson~=3.2.0\",\n \"geopandas~=1.1.1\",\n \"geoviews~=1.9\",\n \"hvplot~=0.12.0\",\n \"ipykernel~=7.0\",\n \"ipython~=8.12\",\n \"jinja2>=3.0,<4.0\",\n \"matplotlib~=3.8\",\n \"myst-parser~=4.0\",\n \"nbsphinx~=0.9\",\n \"pydata-sphinx-theme~=0.13\",\n \"pygeoif~=1.0\",\n \"scipy~=1.10\",\n \"sphinxcontrib-fulltoc~=1.2\",\n]\n\n[project.urls]\nhomepage = \"https://github.com/stac-utils/pystac-client\"\ndocumentation = \"https://pystac-client.readthedocs.io\"\nrepository = \"https://github.com/stac-utils/pystac-client.git\"\nchangelog = \"https://github.com/stac-utils/pystac-client/blob/main/CHANGELOG.md\"\ndiscussions = \"https://github.com/radiantearth/stac-spec/discussions/categories/stac-software\"\n\n[tool.setuptools.packages.find]\ninclude = [\"pystac_client*\"]\nexclude = [\"tests*\"]\n\n[tool.setuptools.dynamic]\nversion = { attr = \"pystac_client.version.__version__\" }\n\n[tool.doc8]\nignore-path = \"docs/_build,docs/tutorials\"\nmax-line-length = 130\n\n[tool.ruff]\nline-length = 88\n\n[tool.ruff.lint]\nignore = [\"E722\", \"E731\"]\nselect = [\"E\", \"F\", \"W\", \"I\"]\n\n[tool.ruff.lint.per-file-ignores]\n\"__init__.py\" = [\"F401\"]\n\"test_item_search.py\" = [\"E501\"]\n\n[tool.pytest.ini_options]\nmarkers = \"vcr: records network activity\"\naddopts = \"--benchmark-skip --block-network -Werror\"\nfilterwarnings = [\n \"ignore::ResourceWarning\",\n \"ignore::pytest.PytestUnraisableExceptionWarning\",\n]\n\n[tool.mypy]\nshow_error_codes = true\nstrict = true\n\n[[tool.mypy.overrides]]\nmodule = [\"jinja2\"]\nignore_missing_imports = true\n\n[build-system]\nrequires = [\"setuptools>=61.0\"]\nbuild-backend = \"setuptools.build_meta\"\n" }, { "path": "pystac_client/__init__.py", "content": "__all__ = [\n \"Client\",\n \"CollectionClient\",\n \"CollectionSearch\",\n \"ConformanceClasses\",\n \"ItemSearch\",\n \"Modifiable\",\n \"__version__\",\n]\n\nfrom pystac_client._utils import Modifiable\nfrom pystac_client.client import Client\nfrom pystac_client.collection_client import CollectionClient\nfrom pystac_client.collection_search import CollectionSearch\nfrom pystac_client.conformance import ConformanceClasses\nfrom pystac_client.item_search import ItemSearch\nfrom pystac_client.version import __version__\n" }, { "path": "pystac_client/_utils.py", "content": "import urllib\nimport warnings\nfrom collections.abc import Callable\nfrom typing import Any, Union\n\nimport pystac\n\nfrom pystac_client.errors import IgnoredResultWarning\n\nModifiable = Union[\n pystac.Collection, pystac.Item, pystac.ItemCollection, dict[Any, Any]\n]\n\n\ndef call_modifier(\n modifier: Callable[[Modifiable], None] | None, obj: Modifiable\n) -> None:\n \"\"\"Calls the user's modifier and validates that the result is None.\"\"\"\n if modifier is None:\n return None\n\n result = modifier(obj)\n if result is not None and result is not obj:\n warnings.warn(\n f\"modifier '{modifier}' returned a result that's being ignored. \"\n \"You should ensure that 'modifier' is operating in-place and use \"\n \"a function that returns 'None' or silence this warning.\",\n IgnoredResultWarning,\n )\n\n\ndef urljoin(href: str, name: str) -> str:\n \"\"\"Joins a path onto an existing href, respecting query strings, etc.\"\"\"\n url = urllib.parse.urlparse(href)\n path = url.path\n if not path.endswith(\"/\"):\n path += \"/\"\n return urllib.parse.urlunparse(url._replace(path=path + name))\n" }, { "path": "pystac_client/cli.py", "content": "import argparse\nimport json\nimport logging\nimport os\nimport re\nimport sys\nimport warnings\nfrom typing import Any\n\nfrom pystac import STACError, STACTypeError\n\nfrom .client import Client\nfrom .conformance import ConformanceClasses\nfrom .item_search import (\n OPS,\n BBoxLike,\n CollectionsLike,\n DatetimeLike,\n FieldsLike,\n FilterLangLike,\n FilterLike,\n IDsLike,\n IntersectsLike,\n QueryLike,\n SortbyLike,\n)\nfrom .version import __version__\nfrom .warnings import (\n DoesNotConformTo,\n FallbackToPystac,\n MissingLink,\n NoConformsTo,\n PystacClientWarning,\n)\n\nlogger = logging.getLogger(__name__)\n\n\ndef search(\n client: Client,\n matched: bool = False,\n save: str | None = None,\n *,\n method: str = \"GET\",\n max_items: int | None = None,\n limit: int | None = None,\n ids: IDsLike | None = None,\n collections: CollectionsLike | None = None,\n bbox: BBoxLike | None = None,\n intersects: IntersectsLike | None = None,\n datetime: DatetimeLike | None = None,\n query: QueryLike | None = None,\n filter: FilterLike | None = None,\n filter_lang: FilterLangLike | None = None,\n sortby: SortbyLike | None = None,\n fields: FieldsLike | None = None,\n) -> int:\n \"\"\"Main function for performing a search\"\"\"\n\n result = client.search(\n method=method,\n max_items=max_items,\n limit=limit,\n ids=ids,\n collections=collections,\n bbox=bbox,\n intersects=intersects,\n datetime=datetime,\n query=query,\n filter=filter,\n filter_lang=filter_lang,\n sortby=sortby,\n fields=fields,\n )\n\n if matched:\n if (nmatched := result.matched()) is not None:\n print(f\"{nmatched} items matched\")\n else:\n raise KeyError(\"'matched' is not supported for this catalog\")\n else:\n feature_collection = result.item_collection_as_dict()\n if save:\n with open(save, \"w\") as f:\n f.write(json.dumps(feature_collection))\n else:\n print(json.dumps(feature_collection))\n return 0\n\n\ndef collections(\n client: Client,\n save: str | None = None,\n matched: bool = False,\n *,\n max_collections: int | None = None,\n limit: int | None = None,\n bbox: BBoxLike | None = None,\n datetime: DatetimeLike | None = None,\n q: str | None = None,\n query: QueryLike | None = None,\n filter: FilterLike | None = None,\n filter_lang: FilterLangLike | None = None,\n sortby: SortbyLike | None = None,\n fields: FieldsLike | None = None,\n) -> int:\n \"\"\"Fetch collections from collections endpoint\"\"\"\n try:\n result = client.collection_search(\n max_collections=max_collections,\n limit=limit,\n bbox=bbox,\n datetime=datetime,\n q=q,\n query=query,\n filter=filter,\n filter_lang=filter_lang,\n sortby=sortby,\n fields=fields,\n )\n if matched:\n if (nmatched := result.matched()) is not None:\n print(f\"{nmatched} collections matched\")\n else:\n raise KeyError(\"'matched' is not supported for this catalog\")\n else:\n collections_dicts = list(result.collections_as_dicts())\n if save:\n with open(save, \"w\") as f:\n f.write(json.dumps(collections_dicts))\n else:\n print(json.dumps(collections_dicts))\n except STACTypeError as e:\n raise STACError(\n f\"The client at {client.self_href} is OK, but one or more of the \"\n \"collections is invalid.\"\n ) from e\n return 0\n\n\ndef add_warnings_behavior(parser: argparse.ArgumentParser) -> None:\n warnings_group = parser.add_argument_group(\"warnings behavior\")\n warnings_group.add_argument(\n \"--error\",\n nargs=\"*\",\n choices=[\n \"no-conforms-to\",\n \"does-not-conform-to\",\n \"missing-link\",\n \"fallback-to-pystac\",\n ],\n help=\"Error on all stac-client warnings or specific warnings.\",\n )\n warnings_group.add_argument(\n \"--ignore\",\n nargs=\"*\",\n choices=[\n \"no-conforms-to\",\n \"does-not-conform-to\",\n \"missing-link\",\n \"fallback-to-pystac\",\n ],\n help=\"Ignore all stac-client warnings or specific warnings.\",\n )\n\n\ndef set_warnings(error: list[str] | None, ignore: list[str] | None) -> None:\n # First set filters on the base class\n if ignore is not None and len(ignore) == 0:\n warnings.filterwarnings(\"ignore\", category=PystacClientWarning)\n if error is not None and len(error) == 0:\n warnings.filterwarnings(\"error\", category=PystacClientWarning)\n\n # Then set filters on any specific classes\n category_options = {\n \"no-conforms-to\": NoConformsTo,\n \"does-not-conform-to\": DoesNotConformTo,\n \"missing-link\": MissingLink,\n \"fallback-to-pystac\": FallbackToPystac,\n }\n if ignore is not None:\n for w in ignore:\n warnings.filterwarnings(\"ignore\", category=category_options[w])\n if error is not None:\n for w in error:\n warnings.filterwarnings(\"error\", category=category_options[w])\n\n\ndef set_conforms_to(\n client: Client, clear: bool, remove: list[str] | None, add: list[str] | None\n) -> None:\n \"\"\"Alters conforms_to settings on client in-place\"\"\"\n if clear:\n client.clear_conforms_to()\n if remove is not None:\n for conformance_class in remove:\n client.remove_conforms_to(conformance_class)\n if add is not None:\n for conformance_class in add:\n client.add_conforms_to(conformance_class)\n\n\ndef parse_args(args: list[str]) -> dict[str, Any]:\n desc = \"STAC Client\"\n dhf = argparse.ArgumentDefaultsHelpFormatter\n parser0 = argparse.ArgumentParser(description=desc)\n parser0.add_argument(\n \"--version\",\n help=\"Print version and exit\",\n action=\"version\",\n version=__version__,\n )\n\n parent = argparse.ArgumentParser(add_help=False)\n parent.add_argument(\"url\", help=\"Root Catalog URL\")\n parent.add_argument(\n \"--logging\", default=\"INFO\", help=\"DEBUG, INFO, WARN, ERROR, CRITICAL\"\n )\n parent.add_argument(\n \"--headers\",\n nargs=\"*\",\n help=\"Additional request headers (KEY=VALUE pairs)\",\n default=None,\n )\n parent.add_argument(\n \"--add-conforms-to\",\n choices=[c.name for c in ConformanceClasses],\n nargs=\"*\",\n help=\"Specify conformance classes to add to client\",\n )\n parent.add_argument(\n \"--remove-conforms-to\",\n choices=[c.name for c in ConformanceClasses],\n nargs=\"*\",\n help=\"Specify conformance classes to remove from client\",\n )\n parent.add_argument(\n \"--clear-conforms-to\",\n default=False,\n action=\"store_true\",\n )\n\n subparsers = parser0.add_subparsers(dest=\"command\")\n\n # collections command\n parser = subparsers.add_parser(\n \"collections\",\n help=\"Get all collections in this Catalog\",\n parents=[parent],\n formatter_class=dhf,\n )\n add_warnings_behavior(parser)\n\n collections_group = parser.add_argument_group(\"collection search options\")\n collections_group.add_argument(\n \"--bbox\", help=\"Bounding box (min lon, min lat, max lon, max lat)\", nargs=4\n )\n collections_group.add_argument(\n \"--datetime\",\n help=\"Single datetime or begin and end datetime (e.g., 2017-01-01/2017-02-15)\",\n )\n collections_group.add_argument(\"--q\", help=\"Free-text search query\")\n collections_group.add_argument(\n \"--query\",\n nargs=\"*\",\n help=f\"Query properties of form KEY=VALUE ({','.join(OPS)} supported)\",\n )\n collections_group.add_argument(\n \"--filter\",\n help=\"Filter on queryables using language specified in filter-lang parameter\",\n )\n collections_group.add_argument(\n \"--filter-lang\",\n help=\"Filter language used within the filter parameter\",\n default=\"cql2-json\",\n )\n collections_group.add_argument(\"--sortby\", help=\"Sort by fields\", nargs=\"*\")\n collections_group.add_argument(\n \"--fields\", help=\"Control what fields get returned\", nargs=\"*\"\n )\n collections_group.add_argument(\"--limit\", help=\"Page size limit\", type=int)\n collections_group.add_argument(\n \"--max-collections\",\n dest=\"max_collections\",\n help=\"Max collections to retrieve from search\",\n type=int,\n )\n\n output_group = parser.add_argument_group(\"output options\")\n output_group.add_argument(\n \"--save\", help=\"Filename to save collections to\", default=None\n )\n output_group.add_argument(\n \"--matched\", help=\"Print number matched\", default=False, action=\"store_true\"\n )\n\n # search command\n parser = subparsers.add_parser(\n \"search\",\n help=\"Perform new search of items\",\n parents=[parent],\n formatter_class=dhf,\n )\n\n search_group = parser.add_argument_group(\"search options\")\n search_group.add_argument(\n \"-c\", \"--collections\", help=\"One or more collection IDs\", nargs=\"*\"\n )\n search_group.add_argument(\n \"--ids\", help=\"One or more Item IDs (ignores other parameters)\", nargs=\"*\"\n )\n search_group.add_argument(\n \"--bbox\", help=\"Bounding box (min lon, min lat, max lon, max lat)\", nargs=4\n )\n search_group.add_argument(\n \"--intersects\", help=\"GeoJSON Feature or geometry (file or string)\"\n )\n search_group.add_argument(\n \"--datetime\",\n help=\"Single datetime or begin and end datetime (e.g., 2017-01-01/2017-02-15)\",\n )\n search_group.add_argument(\n \"--query\",\n nargs=\"*\",\n help=f\"Query properties of form KEY=VALUE ({','.join(OPS)} supported)\",\n )\n search_group.add_argument(\n \"--filter\",\n help=\"Filter on queryables using language specified in filter-lang parameter\",\n )\n search_group.add_argument(\n \"--filter-lang\",\n help=\"Filter language used within the filter parameter\",\n default=\"cql2-json\",\n )\n search_group.add_argument(\"--sortby\", help=\"Sort by fields\", nargs=\"*\")\n search_group.add_argument(\n \"--fields\", help=\"Control what fields get returned\", nargs=\"*\"\n )\n search_group.add_argument(\"--limit\", help=\"Page size limit\", type=int)\n search_group.add_argument(\n \"--max-items\",\n dest=\"max_items\",\n help=\"Max items to retrieve from search\",\n type=int,\n )\n search_group.add_argument(\"--method\", help=\"GET or POST\", default=\"POST\")\n add_warnings_behavior(parser)\n\n output_group = parser.add_argument_group(\"output options\")\n output_group.add_argument(\n \"--matched\", help=\"Print number matched\", default=False, action=\"store_true\"\n )\n output_group.add_argument(\n \"--save\", help=\"Filename to save Item collection to\", default=None\n )\n\n parsed_args = {\n k: v for k, v in vars(parser0.parse_args(args)).items() if v is not None\n }\n if \"command\" not in parsed_args:\n parser0.print_usage()\n return {}\n\n # if intersects is JSON file, read it in\n if \"intersects\" in parsed_args:\n if os.path.exists(parsed_args[\"intersects\"]):\n with open(parsed_args[\"intersects\"]) as f:\n data = json.loads(f.read())\n if data[\"type\"] == \"Feature\":\n parsed_args[\"intersects\"] = data[\"geometry\"]\n elif data[\"type\"] == \"FeatureCollection\":\n logger.warning(\n \"When the input to intersects is a FeatureCollection, \"\n \"only the first feature geometry is used.\"\n )\n parsed_args[\"intersects\"] = data[\"features\"][0][\"geometry\"]\n else:\n parsed_args[\"intersects\"] = data\n\n # if headers provided, parse it\n if \"headers\" in parsed_args:\n new_headers = {}\n splitter = re.compile(\"^([^=]+)=(.+)$\")\n for head in parsed_args[\"headers\"]:\n match = splitter.match(head)\n if match:\n new_headers[match.group(1)] = match.group(2)\n else:\n logger.warning(f\"Unable to parse header {head}\")\n parsed_args[\"headers\"] = new_headers\n\n if \"filter\" in parsed_args:\n if \"json\" in parsed_args[\"filter_lang\"]:\n parsed_args[\"filter\"] = json.loads(parsed_args[\"filter\"])\n\n return parsed_args\n\n\ndef cli() -> int:\n args = parse_args(sys.argv[1:])\n if not args:\n return 1\n\n loglevel = args.pop(\"logging\")\n if args.get(\"save\", False) or args.get(\"matched\", False):\n logging.basicConfig(level=loglevel)\n # quiet loggers\n logging.getLogger(\"urllib3\").propagate = False\n\n cmd = args.pop(\"command\")\n\n try:\n url = args.pop(\"url\")\n headers = args.pop(\"headers\", {})\n\n set_warnings(args.pop(\"error\", None), args.pop(\"ignore\", None))\n\n client = Client.open(url, headers=headers)\n set_conforms_to(\n client,\n args.pop(\"clear_conforms_to\"),\n args.pop(\"remove_conforms_to\", None),\n args.pop(\"add_conforms_to\", None),\n )\n\n if cmd == \"search\":\n return search(client, **args)\n elif cmd == \"collections\":\n return collections(client, **args)\n else:\n logger.error(\n f\"Command '{cmd}' is not a valid command. \"\n \"must be 'search' or 'collections'\",\n )\n return 1\n except Exception as e:\n logger.error(e, exc_info=True)\n return 1\n\n\nif __name__ == \"__main__\":\n return_code = cli()\n if return_code and return_code != 0:\n sys.exit(return_code)\n" }, { "path": "pystac_client/client.py", "content": "import re\nimport warnings\nfrom collections.abc import Callable, Iterator\nfrom functools import lru_cache\nfrom typing import (\n TYPE_CHECKING,\n Any,\n cast,\n)\n\nimport pystac\nimport pystac.utils\nimport pystac.validation\nfrom pystac import CatalogType, Collection\nfrom pystac.layout import APILayoutStrategy, HrefLayoutStrategy\nfrom requests import Request\n\nfrom pystac_client._utils import Modifiable, call_modifier, urljoin\nfrom pystac_client.collection_client import CollectionClient\nfrom pystac_client.collection_search import CollectionSearch\nfrom pystac_client.conformance import ConformanceClasses\nfrom pystac_client.errors import ClientTypeError\nfrom pystac_client.exceptions import APIError\nfrom pystac_client.item_search import (\n BBoxLike,\n CollectionsLike,\n DatetimeLike,\n FieldsLike,\n FilterLangLike,\n FilterLike,\n IDsLike,\n IntersectsLike,\n ItemSearch,\n QueryLike,\n SortbyLike,\n)\nfrom pystac_client.mixins import QUERYABLES_ENDPOINT, QueryablesMixin\nfrom pystac_client.stac_api_io import StacApiIO, Timeout\nfrom pystac_client.warnings import (\n DoesNotConformTo,\n FallbackToPystac,\n NoConformsTo,\n PystacClientWarning,\n)\n\nif TYPE_CHECKING:\n from pystac.item import Item as Item_Type\n\n\nclass Client(pystac.Catalog, QueryablesMixin):\n \"\"\"A Client for interacting with the root of a STAC Catalog or API\n\n Instances of the ``Client`` class inherit from :class:`pystac.Catalog`\n and provide a convenient way of interacting\n with STAC Catalogs OR STAC APIs that conform to the `STAC API spec\n `_.\n In addition to being a valid\n `STAC Catalog\n `_\n APIs that have a ``\"conformsTo\"`` indicate that it supports additional\n functionality on top of a normal STAC Catalog,\n such as searching items (e.g., /search endpoint).\n \"\"\"\n\n _stac_io: StacApiIO | None\n _fallback_strategy: HrefLayoutStrategy = APILayoutStrategy()\n\n def __init__(\n self,\n id: str,\n description: str,\n title: str | None = None,\n stac_extensions: list[str] | None = None,\n extra_fields: dict[str, Any] | None = None,\n href: str | None = None,\n catalog_type: CatalogType = CatalogType.ABSOLUTE_PUBLISHED,\n strategy: HrefLayoutStrategy | None = None,\n *,\n modifier: Callable[[Modifiable], None] | None = None,\n **kwargs: dict[str, Any],\n ):\n super().__init__(\n id,\n description,\n title=title,\n stac_extensions=stac_extensions,\n extra_fields=extra_fields,\n href=href,\n catalog_type=catalog_type,\n strategy=strategy,\n **kwargs,\n )\n self.modifier = modifier\n\n def __repr__(self) -> str:\n return f\"\"\n\n @classmethod\n def open(\n cls,\n url: str,\n headers: dict[str, str] | None = None,\n parameters: dict[str, Any] | None = None,\n ignore_conformance: bool | None = None,\n modifier: Callable[[Modifiable], None] | None = None,\n request_modifier: Callable[[Request], Request | None] | None = None,\n stac_io: StacApiIO | None = None,\n timeout: Timeout | None = None,\n ) -> \"Client\":\n \"\"\"Opens a STAC Catalog or API\n This function will read the root catalog of a STAC Catalog or API\n\n Args:\n url : The URL of a STAC Catalog.\n headers : A dictionary of additional headers to use in all requests\n made to any part of this Catalog/API.\n parameters: Optional dictionary of query string parameters to\n include in all requests.\n ignore_conformance (DEPRECATED) : Ignore any advertised Conformance Classes\n in this Catalog/API. This means that\n functions will skip checking conformance, and may throw an unknown\n error if that feature is\n not supported, rather than a :class:`NotImplementedError`.\n\n .. deprecated:: 0.7.0\n Conformance can be altered rather than ignored using methods like\n :meth:`clear_conforms_to` and :meth:`add_conforms_to`\n\n modifier : A callable that modifies the children collection and items\n returned by this Client. This can be useful for injecting\n authentication parameters into child assets to access data\n from non-public sources.\n\n The callable should expect a single argument, which will be one\n of the following types:\n\n * :class:`pystac.Collection`\n * :class:`pystac.Item`\n * :class:`pystac.ItemCollection`\n * A STAC item-like :class:`dict`\n * A STAC collection-like :class:`dict`\n\n The callable should mutate the argument in place and return ``None``.\n\n ``modifier`` propagates recursively to children of this Client.\n After getting a child collection with, e.g.\n :meth:`Client.get_collection`, the child items of that collection\n will still be signed with ``modifier``.\n request_modifier: A callable that either modifies a `Request` instance or\n returns a new one. This can be useful for injecting Authentication\n headers and/or signing fully-formed requests (e.g. signing requests\n using AWS SigV4).\n\n The callable should expect a single argument, which will be an instance\n of :class:`requests.Request`.\n\n If the callable returns a `requests.Request`, that will be used.\n Alternately, the callable may simply modify the provided request object\n and return `None`.\n stac_io: A `StacApiIO` object to use for I/O requests. Generally, leave\n this to the default. However in cases where customized I/O processing\n is required, a custom instance can be provided here.\n timeout: Optional float or (float, float) tuple following the semantics\n defined by `Requests\n `__.\n\n Return:\n catalog : A :class:`Client` instance for this Catalog/API\n \"\"\"\n client: Client = cls.from_file(\n url,\n headers=headers,\n parameters=parameters,\n modifier=modifier,\n request_modifier=request_modifier,\n stac_io=stac_io,\n timeout=timeout,\n )\n\n if ignore_conformance is not None:\n warnings.warn(\n (\n \"The `ignore_conformance` option is deprecated and will be \"\n \"removed in the next major release. Instead use `set_conforms_to` \"\n \"or `add_conforms_to` to control behavior.\"\n ),\n FutureWarning,\n )\n\n if not client.has_conforms_to():\n warnings.warn(NoConformsTo())\n\n return client\n\n @classmethod\n def from_file( # type: ignore\n cls,\n href: str,\n stac_io: StacApiIO | None = None,\n headers: dict[str, str] | None = None,\n parameters: dict[str, Any] | None = None,\n modifier: Callable[[Modifiable], None] | None = None,\n request_modifier: Callable[[Request], Request | None] | None = None,\n timeout: Timeout | None = None,\n ) -> \"Client\":\n \"\"\"Open a STAC Catalog/API\n\n Returns:\n Client: A Client (PySTAC Catalog) of the root Catalog for this Catalog/API\n \"\"\"\n if stac_io is None:\n stac_io = StacApiIO(\n headers=headers,\n parameters=parameters,\n request_modifier=request_modifier,\n timeout=timeout,\n )\n else:\n stac_io.update(\n headers=headers,\n parameters=parameters,\n request_modifier=request_modifier,\n timeout=timeout,\n )\n\n client: Client = super().from_file(href, stac_io)\n client.modifier = modifier\n\n return client\n\n def has_conforms_to(self) -> bool:\n \"\"\"Whether server contains list of ``\"conformsTo\"`` URIs\"\"\"\n return \"conformsTo\" in self.extra_fields\n\n def get_conforms_to(self) -> list[str]:\n \"\"\"List of ``\"conformsTo\"`` URIs\n\n Return:\n List[str]: List of URIs that the server conforms to\n \"\"\"\n return cast(list[str], self.extra_fields.get(\"conformsTo\", []).copy())\n\n def set_conforms_to(self, conformance_uris: list[str]) -> None:\n \"\"\"Set list of ``\"conformsTo\"`` URIs\n\n Args:\n conformance_uris : URIs indicating what the server conforms to\n \"\"\"\n self.extra_fields[\"conformsTo\"] = conformance_uris\n\n def clear_conforms_to(self) -> None:\n \"\"\"Clear list of ``\"conformsTo\"`` urls\n\n Removes the entire list, so :py:meth:`has_conforms_to` will\n return False after using this method.\n \"\"\"\n self.extra_fields.pop(\"conformsTo\", None)\n\n def add_conforms_to(self, name: str) -> None:\n \"\"\"Add ``\"conformsTo\"`` by name.\n\n Args:\n name : name of :py:class:`ConformanceClasses` keys to add.\n \"\"\"\n conformance_class = ConformanceClasses.get_by_name(name)\n\n if not self.conforms_to(conformance_class):\n self.set_conforms_to([*self.get_conforms_to(), conformance_class.valid_uri])\n\n def remove_conforms_to(self, name: str) -> None:\n \"\"\"Remove ``\"conformsTo\"`` by name.\n\n Args:\n name : name of :py:class:`ConformanceClasses` keys to remove.\n \"\"\"\n conformance_class = ConformanceClasses.get_by_name(name)\n\n self.set_conforms_to(\n [\n uri\n for uri in self.get_conforms_to()\n if not re.match(conformance_class.pattern, uri)\n ]\n )\n\n def conforms_to(self, conformance_class: ConformanceClasses | str) -> bool:\n \"\"\"Checks whether the API conforms to the given standard.\n\n This method only checks\n against the ``\"conformsTo\"`` property from the API landing page and does not\n make any additional calls to a ``/conformance`` endpoint even if the API\n provides such an endpoint.\n\n Args:\n name : name of :py:class:`ConformanceClasses` keys to check\n conformance against.\n\n Return:\n bool: Indicates if the API conforms to the given spec or URI.\n \"\"\"\n if isinstance(conformance_class, str):\n conformance_class = ConformanceClasses.get_by_name(conformance_class)\n\n return any(\n re.match(conformance_class.pattern, uri) for uri in self.get_conforms_to()\n )\n\n @classmethod\n def from_dict(\n cls,\n d: dict[str, Any],\n href: str | None = None,\n root: pystac.Catalog | None = None,\n migrate: bool = False,\n preserve_dict: bool = True,\n modifier: Callable[[Modifiable], None] | None = None,\n ) -> \"Client\":\n try:\n # this will return a Client because we have used a StacApiIO instance\n result = super().from_dict(\n d=d, href=href, root=root, migrate=migrate, preserve_dict=preserve_dict\n )\n except pystac.STACTypeError:\n raise ClientTypeError(\n f\"Could not open Client (href={href}), \"\n f\"expected type=Catalog, found type={d.get('type', None)}\"\n )\n\n result.modifier = modifier\n return result\n\n def _supports_collections(self) -> bool:\n return self.conforms_to(ConformanceClasses.COLLECTIONS) or self.conforms_to(\n ConformanceClasses.FEATURES\n )\n\n def _warn_about_fallback(self, *args: str) -> None:\n if self.has_conforms_to():\n warnings.warn(DoesNotConformTo(*args), stacklevel=2)\n warnings.warn(FallbackToPystac(), stacklevel=2)\n\n def get_merged_queryables(self, collections: list[str]) -> dict[str, Any]:\n \"\"\"Return the set of queryables in common to the specified collections.\n\n Queryables from multiple collections are unioned together, except in the case\n when the same queryable key has a different definition, in which case that key\n is dropped.\n\n Output is a dictionary that can be used in ``jsonshema.validate``\n\n Args:\n collections List[str]: The IDs of the collections to inspect.\n\n Return:\n Dict[str, Any]: Dictionary containing queryable fields\n \"\"\"\n if not collections:\n raise ValueError(\"cannot get_merged_queryables from empty Iterable\")\n\n if not self.conforms_to(ConformanceClasses.FILTER):\n raise DoesNotConformTo(ConformanceClasses.FILTER.name)\n response = self.get_queryables_from(\n self._get_collection_queryables_href(collections[0])\n )\n response.pop(\"$id\")\n addl_props = response.get(\"additionalProperties\", False)\n for collection in collections[1:]:\n resp = self.get_queryables_from(\n self._get_collection_queryables_href(collection)\n )\n\n # additionalProperties is false if any collection doesn't support additional\n # properties\n addl_props &= resp.get(\"additionalProperties\", False)\n\n # drop queryables if their keys match, but the descriptions differ\n for k in set(resp[\"properties\"]).intersection(response[\"properties\"]):\n if resp[\"properties\"][k] != response[\"properties\"][k]:\n resp[\"properties\"].pop(k)\n response[\"properties\"].pop(k)\n response[\"properties\"].update(resp[\"properties\"])\n return response\n\n @lru_cache\n def get_collection(\n self, collection_id: str\n ) -> Collection | CollectionClient | None:\n \"\"\"Get a single collection from this Catalog/API\n\n Args:\n collection_id: The Collection ID to get\n\n Returns:\n Collection | CollectionClient | None: A STAC Collection or None if it does\n not exist.\n\n \"\"\"\n collection: Collection | CollectionClient | None = None\n\n if not collection_id:\n raise ValueError(\"A `collection_id` must be provided.\")\n\n if self._supports_collections():\n assert self._stac_io is not None\n\n url = self._collections_href(collection_id)\n try:\n collection = CollectionClient.from_dict(\n self._stac_io.read_json(url),\n root=self,\n modifier=self.modifier,\n )\n except APIError as err:\n if getattr(err, \"status_code\", None) and err.status_code == 404:\n return None\n else:\n raise err\n else:\n self._warn_about_fallback(\"COLLECTIONS\", \"FEATURES\")\n for collection in super().get_collections():\n if collection.id == collection_id:\n break\n\n if collection:\n call_modifier(self.modifier, collection)\n\n return collection\n\n def get_collections(self) -> Iterator[Collection]:\n \"\"\"Get Collections in this Catalog\n\n Gets the collections from the /collections endpoint if supported,\n otherwise fall back to Catalog behavior of following child links\n\n Return:\n Iterator[Union[Collection, CollectionClient]]: Collections in Catalog/API\n \"\"\"\n collection: Collection | CollectionClient\n\n if self._supports_collections():\n assert self._stac_io is not None\n\n url = self._collections_href()\n for page in self._stac_io.get_pages(url):\n if \"collections\" not in page:\n raise APIError(\"Invalid response from /collections\")\n for col in page[\"collections\"]:\n collection = CollectionClient.from_dict(\n col, root=self, modifier=self.modifier\n )\n call_modifier(self.modifier, collection)\n yield collection\n else:\n self._warn_about_fallback(\"COLLECTIONS\", \"FEATURES\")\n for collection in super().get_collections():\n call_modifier(self.modifier, collection)\n yield collection\n\n def get_items(\n self, *ids: str, recursive: bool | None = None\n ) -> Iterator[\"Item_Type\"]:\n \"\"\"Return all items of this catalog.\n\n Args:\n ids: Zero or more item ids to find.\n recursive: If this client conforms to the ITEM_SEARCH conformance class,\n this is unused and this will always yield items recursively.\n Otherwise, this will only return items recursively if True or None.\n Return:\n Iterator[Item]: Iterator of items whose parent is this\n catalog.\n \"\"\"\n if self.conforms_to(ConformanceClasses.ITEM_SEARCH):\n if recursive is False:\n warnings.warn(\n \"Getting items recursively using the /search endpoint \"\n \"(the recursive argument is being ignored).\",\n PystacClientWarning,\n )\n search = self.search(ids=ids)\n yield from search.items()\n else:\n self._warn_about_fallback(\"ITEM_SEARCH\")\n for item in super().get_items(\n *ids, recursive=recursive is None or recursive\n ):\n call_modifier(self.modifier, item)\n yield item\n\n def get_all_items(self) -> Iterator[\"Item_Type\"]:\n \"\"\"Get all items from this catalog and all subcatalogs. Will traverse\n any subcatalogs recursively, or use the /search endpoint if supported\n\n Returns:\n Iterator[Item]: All items that belong to this catalog, and all\n catalogs or collections connected to this catalog through\n child links.\n \"\"\"\n yield from self.get_items()\n\n def search(\n self,\n *,\n method: str | None = \"POST\",\n max_items: int | None = None,\n limit: int | None = None,\n ids: IDsLike | None = None,\n collections: CollectionsLike | None = None,\n bbox: BBoxLike | None = None,\n intersects: IntersectsLike | None = None,\n datetime: DatetimeLike | None = None,\n query: QueryLike | None = None,\n filter: FilterLike | None = None,\n filter_lang: FilterLangLike | None = None,\n sortby: SortbyLike | None = None,\n fields: FieldsLike | None = None,\n ) -> ItemSearch:\n \"\"\"Query the ``/search`` endpoint using the given parameters.\n\n This method returns an :class:`~pystac_client.ItemSearch` instance. See that\n class's documentation for details on how to get the number of matches and\n iterate over results. The ``url``, ``stac_io``, and ``client`` keywords are\n supplied by this Client instance.\n\n .. warning::\n\n This method is only implemented if the API conforms to the\n `STAC API - Item Search\n `__\n spec *and* contains a link with a ``\"rel\"`` type of ``\"search\"`` in its\n root catalog. If the API does not meet either of these criteria, this\n method will raise a :exc:`NotImplementedError`.\n\n Args:\n method : The HTTP method to use when making a request to the service.\n This must be either ``\"GET\"``, ``\"POST\"``, or\n ``None``. If ``None``, this will default to ``\"POST\"``.\n If a ``\"POST\"`` request receives a ``405`` status for\n the response, it will automatically retry with\n ``\"GET\"`` for all subsequent requests.\n max_items : The maximum number of items to return from the search, even\n if there are more matching results. This client to limit the\n total number of Items returned from the :meth:`items`,\n :meth:`item_collections`, and :meth:`items_as_dicts methods`. The client\n will continue to request pages of items until the number of max items is\n reached. Setting this to ``None`` will allow iteration over a possibly\n very large number of results.\n limit: A recommendation to the service as to the number of items to return\n *per page* of results. Defaults to 100.\n ids: List of one or more Item ids to filter on.\n collections: List of one or more Collection IDs or\n :class:`pystac.Collection` instances. Only Items in one\n of the provided Collections will be searched\n bbox: A list, tuple, or iterator representing a bounding box of 2D\n or 3D coordinates. Results will be filtered\n to only those intersecting the bounding box.\n intersects: A string or dictionary representing a GeoJSON geometry, or\n an object that implements a\n ``__geo_interface__`` property, as supported by several libraries\n including Shapely, ArcPy, PySAL, and\n geojson. Results filtered to only those intersecting the geometry.\n datetime: Either a single datetime or datetime range used to filter results.\n You may express a single datetime using a :class:`datetime.datetime`\n instance, a `RFC 3339-compliant `__\n timestamp, or a simple date string (see below). Instances of\n :class:`datetime.datetime` may be either\n timezone aware or unaware. Timezone aware instances will be converted to\n a UTC timestamp before being passed\n to the endpoint. Timezone unaware instances are assumed to represent UTC\n timestamps. You may represent a\n datetime range using a ``\"/\"`` separated string as described in the\n spec, or a list, tuple, or iterator\n of 2 timestamps or datetime instances. For open-ended ranges, use either\n ``\"..\"`` (``'2020-01-01:00:00:00Z/..'``,\n ``['2020-01-01:00:00:00Z', '..']``) or a value of ``None``\n (``['2020-01-01:00:00:00Z', None]``).\n\n If using a simple date string, the datetime can be specified in\n ``YYYY-mm-dd`` format, optionally truncating\n to ``YYYY-mm`` or just ``YYYY``. Simple date strings will be expanded to\n include the entire time period, for example:\n\n - ``2017`` expands to ``2017-01-01T00:00:00Z/2017-12-31T23:59:59Z``\n - ``2017-06`` expands to ``2017-06-01T00:00:00Z/2017-06-30T23:59:59Z``\n - ``2017-06-10`` expands to\n ``2017-06-10T00:00:00Z/2017-06-10T23:59:59Z``\n\n If used in a range, the end of the range expands to the end of that\n day/month/year, for example:\n\n - ``2017/2018`` expands to\n ``2017-01-01T00:00:00Z/2018-12-31T23:59:59Z``\n - ``2017-06/2017-07`` expands to\n ``2017-06-01T00:00:00Z/2017-07-31T23:59:59Z``\n - ``2017-06-10/2017-06-11`` expands to\n ``2017-06-10T00:00:00Z/2017-06-11T23:59:59Z``\n\n query: List or JSON of query parameters as per the STAC API `query`\n extension\n filter: JSON of query parameters as per the STAC API `filter` extension\n filter_lang: Language variant used in the filter body. If `filter` is a\n dictionary or not provided, defaults\n to 'cql2-json'. If `filter` is a string, defaults to `cql2-text`.\n sortby: A single field or list of fields to sort the response by\n fields: A list of fields to include in the response. Note this may\n result in invalid STAC objects, as they may not have required fields.\n Use `items_as_dicts` to avoid object unmarshalling errors.\n\n Returns:\n search : An ItemSearch instance that can be used to iterate through Items.\n\n Raises:\n NotImplementedError: If the API does not conform to the `Item Search spec\n `__\n or does not have a link with\n a ``\"rel\"`` type of ``\"search\"``.\n \"\"\"\n\n if not self.conforms_to(ConformanceClasses.ITEM_SEARCH):\n raise DoesNotConformTo(\n \"ITEM_SEARCH\", \"There is no fallback option available for search.\"\n )\n\n return ItemSearch(\n url=self._search_href(),\n method=method,\n max_items=max_items,\n client=self,\n limit=limit,\n ids=ids,\n collections=collections,\n bbox=bbox,\n intersects=intersects,\n datetime=datetime,\n query=query,\n filter=filter,\n filter_lang=filter_lang,\n sortby=sortby,\n fields=fields,\n modifier=self.modifier,\n )\n\n def collection_search(\n self,\n *,\n max_collections: int | None = None,\n limit: int | None = None,\n bbox: BBoxLike | None = None,\n datetime: DatetimeLike | None = None,\n q: str | None = None,\n query: QueryLike | None = None,\n filter: FilterLike | None = None,\n filter_lang: FilterLangLike | None = None,\n sortby: SortbyLike | None = None,\n fields: FieldsLike | None = None,\n ) -> CollectionSearch:\n \"\"\"Query the ``/collections`` endpoint using the given parameters.\n\n This method returns an :class:`~pystac_client.CollectionSearch` instance. See\n that class's documentation for details on how to get the number of matches and\n iterate over results. The ``url``, `stac_io``, and ``client`` keywords are\n supplied by this Client instance.\n\n .. warning::\n\n This method is fully implemented if the API conforms to the\n `STAC API - Collection Search Extension\n `__\n spec.\n\n If the API does not conform to the Collection Search Extension but\n conforms to `STAC API - Collections `__\n (has a ``\"rel\"`` type of ``\"data\"`` in its root catalog), a limited\n client-side collection filtering process will be used. If the API does not\n meet either of these criteria, this method will raise a\n :exc:`NotImplementedError`.\n\n Client-side filtering will only use the ``bbox``, ``datetime``, and ``q``\n (free-text search) parameters.\n\n Args:\n max_collections : The maximum number of collections to return from the\n search, even if there are more matching results. This client to limit\n the total number of Collections returned from the :meth:`collections`,\n :meth:`collection_list`, and :meth:`collections_as_dicts methods`. The\n client will continue to request pages of collections until the number of\n max collections is reached. Setting this to ``None`` will allow\n iteration over a possibly very large number of results.\n limit: A recommendation to the service as to the number of items to return\n *per page* of results. Defaults to 100.\n bbox: A list, tuple, or iterator representing a bounding box of 2D\n or 3D coordinates. Results will be filtered\n to only those intersecting the bounding box.\n datetime: Either a single datetime or datetime range used to filter results.\n You may express a single datetime using a :class:`datetime.datetime`\n instance, a `RFC 3339-compliant `__\n timestamp, or a simple date string (see below). Instances of\n :class:`datetime.datetime` may be either\n timezone aware or unaware. Timezone aware instances will be converted to\n a UTC timestamp before being passed\n to the endpoint. Timezone unaware instances are assumed to represent UTC\n timestamps. You may represent a\n datetime range using a ``\"/\"`` separated string as described in the\n spec, or a list, tuple, or iterator\n of 2 timestamps or datetime instances. For open-ended ranges, use either\n ``\"..\"`` (``'2020-01-01:00:00:00Z/..'``,\n ``['2020-01-01:00:00:00Z', '..']``) or a value of ``None``\n (``['2020-01-01:00:00:00Z', None]``).\n\n If using a simple date string, the datetime can be specified in\n ``YYYY-mm-dd`` format, optionally truncating\n to ``YYYY-mm`` or just ``YYYY``. Simple date strings will be expanded to\n include the entire time period, for example:\n\n - ``2017`` expands to ``2017-01-01T00:00:00Z/2017-12-31T23:59:59Z``\n - ``2017-06`` expands to ``2017-06-01T00:00:00Z/2017-06-30T23:59:59Z``\n - ``2017-06-10`` expands to\n ``2017-06-10T00:00:00Z/2017-06-10T23:59:59Z``\n\n If used in a range, the end of the range expands to the end of that\n day/month/year, for example:\n\n - ``2017/2018`` expands to\n ``2017-01-01T00:00:00Z/2018-12-31T23:59:59Z``\n - ``2017-06/2017-07`` expands to\n ``2017-06-01T00:00:00Z/2017-07-31T23:59:59Z``\n - ``2017-06-10/2017-06-11`` expands to\n ``2017-06-10T00:00:00Z/2017-06-11T23:59:59Z``\n\n q: Free-text search query. See the `STAC API - Free Text Extension\n Spec `__ for\n details.\n query: List or JSON of query parameters as per the STAC API `query`\n extension\n filter: JSON of query parameters as per the STAC API `filter` extension\n filter_lang: Language variant used in the filter body. If `filter` is a\n dictionary or not provided, defaults\n to 'cql2-json'. If `filter` is a string, defaults to `cql2-text`.\n sortby: A single field or list of fields to sort the response by\n fields: A list of fields to include in the response. Note this may\n result in invalid STAC objects, as they may not have required fields.\n Use `items_as_dicts` to avoid object unmarshalling errors.\n\n Returns:\n collection_search : A CollectionSearch instance that can be used to iterate\n through Collections.\n\n Raises:\n NotImplementedError: If the API does not conform to either the `STAC API -\n Collection Search spec `__\n or the `STAC API - Collections spec `__.\n \"\"\"\n\n if not (\n self.conforms_to(ConformanceClasses.COLLECTION_SEARCH)\n or self.conforms_to(ConformanceClasses.COLLECTIONS)\n ) and any([bbox, datetime, q, query, filter, sortby, fields]):\n raise DoesNotConformTo(\n \"COLLECTION_SEARCH or COLLECTIONS\",\n \"there is no fallback option available for search.\",\n )\n\n return CollectionSearch(\n url=self._collections_href(),\n client=self,\n max_collections=max_collections,\n limit=limit,\n bbox=bbox,\n datetime=datetime,\n q=q,\n query=query,\n filter=filter,\n filter_lang=filter_lang,\n sortby=sortby,\n fields=fields,\n modifier=self.modifier,\n )\n\n def get_search_link(self) -> pystac.Link | None:\n \"\"\"Returns this client's search link.\n\n Searches for a link with rel=\"search\" and either a GEOJSON or JSON media type.\n\n Returns:\n Optional[pystac.Link]: The search link, or None if there is not one found.\n \"\"\"\n return next(\n (\n link\n for link in self.links\n if link.rel == \"search\"\n and (\n link.media_type == pystac.MediaType.GEOJSON\n or link.media_type == pystac.MediaType.JSON\n )\n ),\n None,\n )\n\n def _search_href(self) -> str:\n search_link = self.get_search_link()\n href = self._get_href(\"search\", search_link, \"search\")\n return href\n\n def _collections_href(self, collection_id: str | None = None) -> str:\n data_link = self.get_single_link(\"data\")\n href = self._get_href(\"data\", data_link, \"collections\")\n if collection_id is not None:\n return urljoin(href, collection_id)\n return href\n\n def _get_collection_queryables_href(self, collection_id: str | None = None) -> str:\n href = self._collections_href(collection_id)\n return urljoin(href, QUERYABLES_ENDPOINT)\n" }, { "path": "pystac_client/collection_client.py", "content": "from __future__ import annotations\n\nimport warnings\nfrom collections.abc import Callable, Iterator\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Optional,\n cast,\n)\n\nimport pystac\nfrom pystac.layout import APILayoutStrategy, HrefLayoutStrategy\n\nfrom pystac_client._utils import Modifiable, call_modifier\nfrom pystac_client.conformance import ConformanceClasses\nfrom pystac_client.exceptions import APIError\nfrom pystac_client.item_search import ItemSearch\nfrom pystac_client.mixins import QueryablesMixin\nfrom pystac_client.stac_api_io import StacApiIO\nfrom pystac_client.warnings import FallbackToPystac\n\nif TYPE_CHECKING:\n from pystac.item import Item as Item_Type\n\n from pystac_client import Client\n\n\nclass CollectionClient(pystac.Collection, QueryablesMixin):\n modifier: Callable[[Modifiable], None]\n _stac_io: StacApiIO\n _fallback_strategy: HrefLayoutStrategy = APILayoutStrategy()\n\n def __init__(\n self,\n id: str,\n description: str,\n extent: pystac.Extent,\n title: str | None = None,\n stac_extensions: list[str] | None = None,\n href: str | None = None,\n extra_fields: dict[str, Any] | None = None,\n catalog_type: pystac.CatalogType | None = None,\n license: str = \"proprietary\",\n keywords: list[str] | None = None,\n providers: list[pystac.Provider] | None = None,\n summaries: pystac.Summaries | None = None,\n assets: dict[str, pystac.Asset] | None = None,\n strategy: HrefLayoutStrategy | None = None,\n *,\n modifier: Callable[[Modifiable], None] | None = None,\n **kwargs: dict[str, Any],\n ):\n super().__init__(\n id,\n description,\n extent,\n title,\n stac_extensions,\n href,\n extra_fields,\n catalog_type,\n license,\n keywords,\n providers,\n summaries,\n assets,\n strategy,\n **kwargs,\n )\n # error: Cannot assign to a method [assignment]\n # https://github.com/python/mypy/issues/2427\n setattr(self, \"modifier\", modifier)\n\n @classmethod\n def from_dict(\n cls,\n d: dict[str, Any],\n href: str | None = None,\n root: pystac.Catalog | Client | None = None,\n migrate: bool = False,\n preserve_dict: bool = True,\n modifier: Callable[[Modifiable], None] | None = None,\n ) -> CollectionClient:\n result = super().from_dict(d, href, root, migrate, preserve_dict)\n # error: Cannot assign to a method [assignment]\n # https://github.com/python/mypy/issues/2427\n setattr(result, \"modifier\", modifier)\n return result\n\n def __repr__(self) -> str:\n return f\"\"\n\n def set_root(self, root: pystac.Catalog | Client | None) -> None:\n # hook in to set_root and use it for setting _stac_io\n super().set_root(root=root)\n if root is None:\n raise ValueError(\"`CollectionClient.root` must be set\")\n elif root._stac_io is not None and isinstance(root._stac_io, StacApiIO):\n self._stac_io = root._stac_io\n else:\n raise ValueError(\"`CollectionClient.root` must be a valid `Client` object\")\n\n def get_root(self) -> Client:\n from pystac_client.client import Client\n\n root = super().get_root()\n if root is None or not isinstance(root, Client):\n raise ValueError(\n \"`CollectionClient.root` is not have a valid `Client` object.\"\n )\n return root\n\n def conforms_to(self, conformance_class: ConformanceClasses | str) -> bool:\n root = self.get_root()\n return root.conforms_to(conformance_class)\n\n def get_items(self, *ids: str, recursive: bool = False) -> Iterator[Item_Type]:\n \"\"\"Return all items in this Collection or specific items.\n\n If the Collection contains a link of with a `rel` value of `items`,\n that link will be used to iterate through items. Otherwise, the default\n PySTAC behavior is assumed.\n\n Args:\n ids: Items ids to retrieve\n recursive: If True, search every child collection as well as this one.\n\n Return:\n Iterator[Item]: Iterator of items whose parent is this catalog.\n \"\"\"\n if recursive is True:\n yield from super().get_items(*ids, recursive=True)\n else:\n root = self.get_root()\n if root.conforms_to(ConformanceClasses.ITEM_SEARCH):\n url = root._search_href() if ids else self._items_href()\n\n search = ItemSearch(\n url=url,\n method=\"GET\",\n client=root,\n ids=ids,\n collections=[self.id],\n modifier=self.modifier,\n )\n yield from search.items()\n else:\n root._warn_about_fallback(\"ITEM_SEARCH\")\n for item in super().get_items(*ids):\n call_modifier(self.modifier, item)\n yield item\n\n def get_item(self, id: str, recursive: bool = False) -> Item_Type | None:\n \"\"\"Returns an item with a given ID.\n\n If the collection conforms to\n [ogcapi-features](https://github.com/radiantearth/stac-api-spec/blob/738f4837ac6bea041dc226219e6d13b2c577fb19/ogcapi-features/README.md),\n this will use the `/collections/{collectionId}/items/{featureId}`.\n If not, and the collection conforms to [item\n search](https://github.com/radiantearth/stac-api-spec/blob/2d3c0cf644af9976eecbf32aec77b9a137268e12/item-search/README.md),\n this will use `/search?ids={featureId}&collections={collectionId}`.\n Otherwise, the default PySTAC behavior is used.\n\n Args:\n id : The ID of the item to find.\n recursive : If True, search this catalog and all children for the\n item; otherwise, only search the items of this catalog. Defaults\n to False.\n\n Return:\n Item or None: The item with the given ID, or None if not found.\n \"\"\"\n if not recursive:\n root = self.get_root()\n if root.conforms_to(ConformanceClasses.FEATURES) and self._stac_io:\n url = f\"{self._items_href().rstrip('/')}/{id}\"\n try:\n obj = self._stac_io.read_stac_object(url, root=self)\n item = cast(Optional[pystac.Item], obj)\n except APIError as err:\n if getattr(err, \"status_code\", None) and err.status_code == 404:\n return None\n else:\n raise err\n elif root.conforms_to(ConformanceClasses.ITEM_SEARCH) and self._stac_io:\n item_search = ItemSearch(\n url=root._search_href(),\n method=\"GET\",\n client=root,\n ids=[id],\n collections=[self.id],\n modifier=self.modifier,\n )\n item = next(item_search.items(), None)\n else:\n root._warn_about_fallback(\"FEATURES\", \"ITEM_SEARCH\")\n item = super().get_item(id, recursive=False)\n else:\n warnings.warn(FallbackToPystac())\n item = super().get_item(id, recursive=True)\n\n if item:\n call_modifier(self.modifier, item)\n\n return item\n\n def _items_href(self) -> str:\n link = self.get_single_link(\"items\")\n href = self._get_href(\"items\", link, \"items\")\n return href\n" }, { "path": "pystac_client/collection_search.py", "content": "import warnings\nfrom collections.abc import Callable, Iterator\nfrom datetime import datetime, timezone\nfrom functools import lru_cache\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Optional,\n)\n\nfrom pystac import Collection, Extent\n\nfrom pystac_client._utils import Modifiable, call_modifier\nfrom pystac_client.conformance import ConformanceClasses\nfrom pystac_client.free_text import sqlite_text_search\nfrom pystac_client.item_search import (\n BaseSearch,\n BBox,\n BBoxLike,\n Datetime,\n DatetimeLike,\n FieldsLike,\n FilterLangLike,\n FilterLike,\n QueryLike,\n SortbyLike,\n)\nfrom pystac_client.stac_api_io import StacApiIO\nfrom pystac_client.warnings import DoesNotConformTo, PystacClientWarning\n\nif TYPE_CHECKING:\n from pystac_client import client as _client\n\n\nTemporalInterval = tuple[Optional[datetime], Optional[datetime]]\n\n\ndef temporal_intervals_overlap(\n interval1: TemporalInterval,\n interval2: TemporalInterval,\n) -> bool:\n start1, end1 = interval1\n start2, end2 = interval2\n dtmin = datetime.min.replace(tzinfo=timezone.utc)\n dtmax = datetime.max.replace(tzinfo=timezone.utc)\n\n return (start2 or dtmin) <= (end1 or dtmax) and (start1 or dtmin) <= (end2 or dtmax)\n\n\ndef bboxes_overlap(bbox1: BBox, bbox2: BBox) -> bool:\n xmin1, ymin1, xmax1, ymax1 = bbox1\n xmin2, ymin2, xmax2, ymax2 = bbox2\n\n return xmin1 <= xmax2 and xmin2 <= xmax1 and ymin1 <= ymax2 and ymin2 <= ymax1\n\n\ndef _extent_matches(\n extent: Extent,\n bbox: BBox | None = None,\n temporal_interval_str: Datetime | None = None,\n) -> tuple[bool, bool]:\n bbox_overlaps = not bbox or (\n any(\n bboxes_overlap(bbox, tuple(collection_bbox))\n for collection_bbox in extent.spatial.bboxes\n )\n )\n\n # check for overlap between the provided temporal interval and the collection's\n # temporal extent\n collection_temporal_extent = extent.temporal\n\n # process the user-provided temporal interval\n search_temporal_interval = (\n temporal_interval_str.split(\"/\") if temporal_interval_str else []\n )\n\n # replace .. in open intervals with actual strings\n if search_temporal_interval:\n if search_temporal_interval[0] == \"..\":\n search_temporal_interval[0] = datetime.min.replace(\n tzinfo=timezone.utc\n ).isoformat()\n if search_temporal_interval[1] == \"..\":\n search_temporal_interval[1] = datetime.max.replace(\n tzinfo=timezone.utc\n ).isoformat()\n\n datetime_overlaps = not temporal_interval_str or (\n any(\n temporal_intervals_overlap(\n (\n datetime.fromisoformat(\n search_temporal_interval[0].replace(\"Z\", \"+00:00\")\n ),\n datetime.fromisoformat(\n search_temporal_interval[1].replace(\"Z\", \"+00:00\")\n ),\n ),\n (\n collection_temporal_interval[0],\n collection_temporal_interval[1],\n ),\n )\n for collection_temporal_interval in collection_temporal_extent.intervals\n )\n )\n return bbox_overlaps, datetime_overlaps\n\n\ndef collection_matches(\n collection_dict: dict[str, Any],\n bbox: BBox | None = None,\n temporal_interval_str: Datetime | None = None,\n q: str | None = None,\n) -> bool:\n # check for overlap between provided bbox and the collection's spatial extent\n try:\n extent = Extent.from_dict(collection_dict.get(\"extent\", {}))\n except Exception:\n warnings.warn(\n f\"Unable to parse extent from collection={collection_dict.get('id', None)}\",\n PystacClientWarning,\n )\n bbox_overlaps = True\n datetime_overlaps = True\n else:\n bbox_overlaps, datetime_overlaps = _extent_matches(\n extent, bbox, temporal_interval_str\n )\n\n # check for overlap between the provided free-text search query (q) and the\n # collection's title, description, and keywords\n text_fields: dict[str, str] = {\n key: text\n for key, text in collection_dict.items()\n if text and key in [\"title\", \"description\", \"keywords\"]\n }\n\n if text_fields.get(\"keywords\"):\n text_fields[\"keywords\"] = \", \".join(text_fields[\"keywords\"])\n\n text_overlaps = not q or sqlite_text_search(q, text_fields)\n\n return bbox_overlaps and datetime_overlaps and text_overlaps\n\n\nclass CollectionSearch(BaseSearch):\n \"\"\"Represents a deferred query to a STAC collection search endpoint as described in\n the `STAC API - Collection Search extension\n `__.\n\n Due to potential conflicts between the collection-search and transactions extensions\n pystac_client will only send GET requests to the /collections endpoint.\n\n No request is sent to the API until a method is called to iterate\n through the resulting STAC Collections, either :meth:`CollectionSearch.collections`,\n or :meth:`CollectionSearch.collections_as_dicts`.\n\n All parameters except `url``, ``method``, ``max_collections``, and ``client``\n correspond to query parameters\n described in the `STAC API - Collection Extension: Query Parameters Table\n `__\n docs. Please refer to those docs for details on how these parameters filter search\n results.\n\n Args:\n url: The URL to the search page of the STAC API.\n max_collections : The maximum number of collections to return from the\n search, even if there are more matching results. This client to limit\n the total number of Collections returned from the :meth:`collections`,\n :meth:`collection_list`, and :meth:`collections_as_dicts methods`. The\n client will continue to request pages of collections until the number of\n max collections is reached. Setting this to ``None`` will allow\n iteration over a possibly very large number of results.\n stac_io: An instance of StacIO for retrieving results. Normally comes\n from the Client that returns this CollectionSearch client: An instance of a\n root Client used to set the root on resulting Collections.\n client: An instance of Client for retrieving results. This is normally populated\n by the client that returns this CollectionSearch instance.\n limit: A recommendation to the service as to the number of collections to return\n *per page* of results. Defaults to 100.\n bbox: A list, tuple, or iterator representing a bounding box of 2D\n or 3D coordinates. Results will be filtered\n to only those intersecting the bounding box.\n datetime: Either a single datetime or datetime range used to filter results.\n You may express a single datetime using a :class:`datetime.datetime`\n instance, a `RFC 3339-compliant `__\n timestamp, or a simple date string (see below). Instances of\n :class:`datetime.datetime` may be either\n timezone aware or unaware. Timezone aware instances will be converted to\n a UTC timestamp before being passed\n to the endpoint. Timezone unaware instances are assumed to represent UTC\n timestamps. You may represent a\n datetime range using a ``\"/\"`` separated string as described in the spec,\n or a list, tuple, or iterator\n of 2 timestamps or datetime instances. For open-ended ranges, use either\n ``\"..\"`` (``'2020-01-01:00:00:00Z/..'``,\n ``['2020-01-01:00:00:00Z', '..']``) or a value of ``None``\n (``['2020-01-01:00:00:00Z', None]``).\n\n If using a simple date string, the datetime can be specified in\n ``YYYY-mm-dd`` format, optionally truncating\n to ``YYYY-mm`` or just ``YYYY``. Simple date strings will be expanded to\n include the entire time period, for example:\n\n - ``2017`` expands to ``2017-01-01T00:00:00Z/2017-12-31T23:59:59Z``\n - ``2017-06`` expands to ``2017-06-01T00:00:00Z/2017-06-30T23:59:59Z``\n - ``2017-06-10`` expands to ``2017-06-10T00:00:00Z/2017-06-10T23:59:59Z``\n\n If used in a range, the end of the range expands to the end of that\n day/month/year, for example:\n\n - ``2017/2018`` expands to\n ``2017-01-01T00:00:00Z/2018-12-31T23:59:59Z``\n - ``2017-06/2017-07`` expands to\n ``2017-06-01T00:00:00Z/2017-07-31T23:59:59Z``\n - ``2017-06-10/2017-06-11`` expands to\n ``2017-06-10T00:00:00Z/2017-06-11T23:59:59Z``\n q: Free-text search query. See the `STAC API - Free Text Extension\n Spec `__ for\n syntax.\n query: List or JSON of query parameters as per the STAC API `query` extension\n filter: JSON of query parameters as per the STAC API `filter` extension\n filter_lang: Language variant used in the filter body. If `filter` is a\n dictionary or not provided, defaults\n to 'cql2-json'. If `filter` is a string, defaults to `cql2-text`.\n sortby: A single field or list of fields to sort the response by\n fields: A list of fields to include in the response. Note this may\n result in invalid STAC objects, as they may not have required fields.\n Use `collections_as_dicts` to avoid object unmarshalling errors.\n modifier : A callable that modifies the children collection and items\n returned by this Client. This can be useful for injecting\n authentication parameters into child assets to access data\n from non-public sources.\n\n The callable should expect a single argument, which will be one\n of the following types:\n\n * :class:`pystac.Collection`\n * :class:`pystac.Item`\n * :class:`pystac.ItemCollection`\n * A STAC item-like :class:`dict`\n * A STAC collection-like :class:`dict`\n\n The callable should mutate the argument in place and return ``None``.\n\n ``modifier`` propagates recursively to children of this Client.\n After getting a child collection with, e.g.\n :meth:`Client.get_collection`, the child items of that collection\n will still be signed with ``modifier``.\n \"\"\"\n\n _stac_io: StacApiIO\n _collection_search_extension_enabled: bool\n _collection_search_free_text_enabled: bool\n\n def __init__(\n self,\n url: str,\n *,\n max_collections: int | None = None,\n stac_io: StacApiIO | None = None,\n client: Optional[\"_client.Client\"] = None,\n limit: int | None = None,\n bbox: BBoxLike | None = None,\n datetime: DatetimeLike | None = None,\n query: QueryLike | None = None,\n filter: FilterLike | None = None,\n filter_lang: FilterLangLike | None = None,\n sortby: SortbyLike | None = None,\n fields: FieldsLike | None = None,\n q: str | None = None,\n modifier: Callable[[Modifiable], None] | None = None,\n collection_search_extension_enabled: bool = False,\n collection_search_free_text_enabled: bool = False,\n ):\n super().__init__(\n url=url,\n method=\"GET\",\n max_items=max_collections,\n stac_io=stac_io,\n client=client,\n limit=limit,\n bbox=bbox,\n datetime=datetime,\n query=query,\n filter=filter,\n filter_lang=filter_lang,\n sortby=sortby,\n fields=fields,\n q=q,\n modifier=modifier,\n )\n\n if client and client._stac_io is not None and stac_io is None:\n self._stac_io = client._stac_io\n self._collection_search_extension_enabled = client.conforms_to(\n ConformanceClasses.COLLECTION_SEARCH\n )\n self._collection_search_free_text_enabled = client.conforms_to(\n ConformanceClasses.COLLECTION_SEARCH_FREE_TEXT\n )\n if any([bbox, datetime, q, query, filter, sortby, fields]):\n if not self._collection_search_extension_enabled:\n warnings.warn(\n DoesNotConformTo(\n \"COLLECTION_SEARCH\",\n \"Filtering will be performed client-side where only bbox, \"\n \"datetime, and q arguments are supported\",\n )\n )\n self._validate_client_side_args()\n else:\n if not self._collection_search_free_text_enabled:\n warnings.warn(\n DoesNotConformTo(\n \"COLLECTION_SEARCH#FREE_TEXT\",\n \"Free-text search is not enabled for collection search\"\n \"Free-text filters will be applied client-side.\",\n )\n )\n\n else:\n self._stac_io = stac_io or StacApiIO()\n self._collection_search_extension_enabled = (\n collection_search_extension_enabled\n )\n self._collection_search_free_text_enabled = (\n collection_search_free_text_enabled\n )\n self._validate_client_side_args()\n\n def _validate_client_side_args(self) -> None:\n \"\"\"Client-side filtering only supports the bbox, datetime, and q parameters.\"\"\"\n args = {key for key, value in self._parameters.items() if value is not None}\n extras = args - {\"bbox\", \"datetime\", \"q\", \"limit\", \"max_items\"}\n\n if extras:\n raise ValueError(\n \"Only the limit, max_collections, bbox, datetime, and q arguments are \"\n \"supported for client-side filtering but these extra arguments were \"\n \"provided: \" + \",\".join(extras)\n )\n\n @lru_cache(1)\n def matched(self) -> int | None:\n \"\"\"Return number matched for search\n\n Returns the value from the `numberMatched` or `context.matched` field.\n Not all APIs will support counts in which case a warning will be issued\n\n Returns:\n int: Total count of matched collections. If counts are not supported `None`\n is returned.\n \"\"\"\n found = None\n iter = self.pages_as_dicts()\n page = next(iter, None)\n\n if not page:\n return 0\n\n # if collection search and free-text are fully supported, try reading a value\n # from the search result context\n if (\n self._collection_search_extension_enabled\n and self._collection_search_free_text_enabled\n ):\n if \"context\" in page:\n found = page[\"context\"].get(\"matched\", None)\n elif \"numberMatched\" in page:\n found = page[\"numberMatched\"]\n\n if not found:\n count = len(page[\"collections\"])\n\n for page in iter:\n count += len(page[\"collections\"])\n\n found = count\n\n return found\n\n # ------------------------------------------------------------------------\n # Result sets\n # ------------------------------------------------------------------------\n # By collection\n def collections(self) -> Iterator[Collection]:\n \"\"\"Iterator that yields :class:`pystac.Collection` instances for each collection\n matching the given search parameters.\n\n Yields:\n Collection : each Collection matching the search criteria\n \"\"\"\n for collection in self.collections_as_dicts():\n # already signed in collections_as_dicts\n yield Collection.from_dict(\n collection, root=self.client, preserve_dict=False\n )\n\n @lru_cache(1)\n def collections_as_dicts(self) -> Iterator[dict[str, Any]]:\n \"\"\"Iterator that yields :class:`dict` instances for each collection matching\n the given search parameters.\n\n Yields:\n Collection : each Collection matching the search criteria\n \"\"\"\n for page in self.pages_as_dicts():\n yield from page.get(\"collections\", [])\n\n # ------------------------------------------------------------------------\n # By Page\n def pages(self) -> Iterator[list[Collection]]:\n \"\"\"Iterator that yields lists of Collection objects. Each list is\n a page of results from the search.\n\n Yields:\n List[Collection] : a group of Collections matching the search criteria\n \"\"\"\n if isinstance(self._stac_io, StacApiIO):\n for page in self.pages_as_dicts():\n # already signed in pages_as_dicts\n yield [\n Collection.from_dict(\n collection, preserve_dict=False, root=self.client\n )\n for collection in page[\"collections\"]\n ]\n\n def pages_as_dicts(self) -> Iterator[dict[str, Any]]:\n \"\"\"Iterator that yields :class:`dict` instances for each page\n of results from the search.\n\n Yields:\n Dict : a group of collections matching the search\n criteria as a feature-collection-like dictionary.\n \"\"\"\n if isinstance(self._stac_io, StacApiIO):\n num_collections = 0\n for page in self._stac_io.get_pages(\n self.url, self.method, self.get_parameters()\n ):\n call_modifier(self.modifier, page)\n collections = page.get(\"collections\", [])\n page_has_collections = len(collections) > 0\n\n # apply client-side filter if the collection search extension\n # is not enabled in the API\n if not self._collection_search_extension_enabled:\n args = {\n \"bbox\": self._parameters.get(\"bbox\"),\n \"temporal_interval_str\": self._parameters.get(\"datetime\"),\n \"q\": self._parameters.get(\"q\"),\n }\n collections = [\n collection\n for collection in filter(\n lambda x: collection_matches(x, **args),\n collections,\n )\n ]\n\n # apply client-side free-text filter if free-text extension is not\n # enabled in the API\n elif not self._collection_search_free_text_enabled:\n if q := self._parameters.get(\"q\"):\n collections = [\n collection\n for collection in filter(\n lambda x: collection_matches(x, q=q),\n collections,\n )\n ]\n if collections:\n num_collections += len(collections)\n if self._max_items and num_collections > self._max_items:\n # Slice the features down to make sure we hit max_collections\n page[\"collections\"] = collections[\n 0 : -(num_collections - self._max_items)\n ]\n else:\n page[\"collections\"] = collections\n\n yield page\n if self._max_items and num_collections >= self._max_items:\n return\n # if there were collections on this page but they got filtered out keep\n # going\n elif page_has_collections:\n continue\n else:\n return\n\n # ------------------------------------------------------------------------\n # Everything\n\n @lru_cache(1)\n def collection_list(self) -> list[Collection]:\n \"\"\"\n Get the matching collections as a list of :py:class:`pystac.Collection` objects.\n\n Return:\n List[Collection]: The list of collections\n \"\"\"\n # Bypass the cache here, so that we can pass __preserve_dict__\n # without mutating what's in the cache.\n collection_list = self.collections_as_dicts.__wrapped__(self)\n # already signed in collections_as_dicts\n return [\n Collection.from_dict(collection, preserve_dict=False, root=self.client)\n for collection in collection_list\n ]\n\n @lru_cache(1)\n def collection_list_as_dict(self) -> dict[str, Any]:\n \"\"\"\n Get the matching collections as a dict.\n\n The dictionary will have a single key:\n\n 1. ``'collections'`` with the value being a list of dictionaries\n for the matching collections.\n\n Return:\n Dict : A dictionary with the list of matching collections\n \"\"\"\n collections = []\n for page in self.pages_as_dicts():\n for collection in page[\"collections\"]:\n collections.append(collection)\n\n return {\"collections\": collections}\n" }, { "path": "pystac_client/conformance.py", "content": "from __future__ import annotations\n\nimport re\nfrom enum import Enum\n\n\nclass ConformanceClasses(Enum):\n \"\"\"Enumeration class for Conformance Classes\"\"\"\n\n # defined conformance classes regexes\n CORE = \"/core\"\n COLLECTIONS = \"/collections\"\n\n # this is ogcapi-features instead of just features for historical reasons,\n # even thought this is a STAC API conformance class\n FEATURES = \"/ogcapi-features\"\n ITEM_SEARCH = \"/item-search\"\n\n CONTEXT = \"/item-search#context\"\n FIELDS = \"/item-search#fields\"\n SORT = \"/item-search#sort\"\n QUERY = \"/item-search#query\"\n FILTER = \"/item-search#filter\"\n\n # collection search\n COLLECTION_SEARCH = \"/collection-search\"\n COLLECTION_SEARCH_FREE_TEXT = \"/collection-search#free-text\"\n\n @classmethod\n def get_by_name(cls, name: str) -> ConformanceClasses:\n for member in cls:\n if member.name == name.upper():\n return member\n raise ValueError(\n f\"Invalid conformance class '{name}'. Options are: {list(cls)}\"\n )\n\n def __str__(self) -> str:\n return f\"{self.name}\"\n\n def __repr__(self) -> str:\n return str(self)\n\n @property\n def valid_uri(self) -> str:\n return f\"https://api.stacspec.org/v1.0.*{self.value}\"\n\n @property\n def pattern(self) -> re.Pattern[str]:\n return re.compile(\n rf\"{re.escape('https://api.stacspec.org/v1.0.')}(.*){re.escape(self.value)}\"\n )\n" }, { "path": "pystac_client/errors.py", "content": "class ClientTypeError(Exception):\n \"\"\"Raised when trying to open a Client on a non-catalog STAC Object.\"\"\"\n\n pass\n\n\nclass IgnoredResultWarning(RuntimeWarning):\n \"\"\"\n Warning raised when a 'modifier' callable returns a result.\n \"\"\"\n" }, { "path": "pystac_client/exceptions.py", "content": "from requests import Response\n\n\nclass APIError(Exception):\n \"\"\"Raised when unexpected server error.\"\"\"\n\n status_code: int | None\n\n @classmethod\n def from_response(cls, response: Response) -> \"APIError\":\n error = cls(response.text)\n error.status_code = response.status_code\n return error\n\n\nclass ParametersError(Exception):\n \"\"\"Raised when invalid parameters are used in a query\"\"\"\n" }, { "path": "pystac_client/free_text.py", "content": "\"\"\"Client-side free-text search filtering as described in `OGC API - Features - Part 9:\nText Search `__\n\nUses the `SQLite FTS5 Extension `__ to implement\nfree-text search filtering.\n\"\"\"\n\nimport re\nimport sqlite3\n\n\ndef parse_query_for_sqlite(q: str) -> str:\n \"\"\"Translate an OGC Features API free-text search query into the SQLite text search\n syntax\n \"\"\"\n # separate out search terms, quoted exact phrases, commas, and exact phrases\n tokens = [token.strip() for token in re.findall(r'\"[^\"]*\"|,|[\\(\\)]|[^,\\s\\(\\)]+', q)]\n\n # special characters that need to be escaped or quoted for sqlite fts5\n special_chars = set(\"-@&:^~<>=\")\n\n for i, token in enumerate(tokens):\n if token.startswith(\"+\"):\n tokens[i] = token[1:].strip()\n elif token.startswith(\"-\"):\n tokens[i] = \"NOT \" + token[1:].strip()\n elif token == \",\":\n tokens[i] = \"OR\"\n elif any(char in token for char in special_chars):\n # Escape any existing double quotes in the token\n escaped_token = token.replace('\"', '\"\"')\n tokens[i] = f'\"{escaped_token}\"'\n\n return \" \".join(tokens)\n\n\ndef sqlite_text_search(q: str, text_fields: dict[str, str]) -> bool:\n \"\"\"Perform a free-text search against a set of text fields for a single\n collection to determine if that collection matches the query.\n\n Creates an in-memory SQLite database with a single table and a single row\n then runs the MATCH query to determine if the row matches the search\n criteria.\n \"\"\"\n column_clause = \", \".join(text_fields.keys())\n value_clause = \", \".join([\"?\" for _ in text_fields.keys()])\n\n with sqlite3.connect(\":memory:\") as conn: # Use an in-memory database\n cursor = conn.cursor()\n\n cursor.execute(\n f\"\"\"\n CREATE VIRTUAL TABLE collections USING fts5({column_clause});\n \"\"\"\n )\n\n cursor.execute(\n f\"\"\"\n INSERT INTO collections ({column_clause}) VALUES ({value_clause});\n \"\"\",\n tuple(text_fields.values()),\n )\n\n cursor.execute(\n f\"\"\"\n SELECT COUNT(*)\n FROM collections WHERE collections MATCH '{parse_query_for_sqlite(q)}';\n \"\"\"\n )\n\n return bool(cursor.fetchone()[0])\n" }, { "path": "pystac_client/item_search.py", "content": "import json\nimport re\nimport warnings\nfrom abc import ABC\nfrom collections.abc import Callable, Iterable, Iterator, Mapping\nfrom copy import deepcopy\nfrom datetime import datetime as datetime_\nfrom datetime import timezone\nfrom functools import lru_cache\nfrom itertools import chain\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Optional,\n Protocol,\n Union,\n)\n\nfrom dateutil.relativedelta import relativedelta\nfrom dateutil.tz import tzutc\nfrom pystac import Collection, Item, ItemCollection\nfrom requests import Request\n\nfrom pystac_client._utils import Modifiable, call_modifier\nfrom pystac_client.conformance import ConformanceClasses\nfrom pystac_client.stac_api_io import StacApiIO\nfrom pystac_client.warnings import DoesNotConformTo\n\nif TYPE_CHECKING:\n from pystac_client import client as _client\n\nDATETIME_REGEX = re.compile(\n r\"^(?P\\d{4})(-(?P\\d{2})(-(?P\\d{2})\"\n r\"(?P([Tt])\\d{2}:\\d{2}:\\d{2}(\\.\\d+)?\"\n r\"(?P[Zz]|([-+])(\\d{2}):(\\d{2}))?)?)?)?$\"\n)\n\n\nclass GeoInterface(Protocol):\n @property\n def __geo_interface__(self) -> dict[str, Any]: ...\n\n\nDatetimeOrTimestamp = Optional[Union[datetime_, str]]\nDatetime = str\nDatetimeLike = Union[\n DatetimeOrTimestamp,\n tuple[DatetimeOrTimestamp, DatetimeOrTimestamp],\n list[DatetimeOrTimestamp],\n Iterator[DatetimeOrTimestamp],\n]\n\nBBox = tuple[float, ...]\nBBoxLike = Union[BBox, list[float], Iterator[float], str]\n\nCollections = tuple[str, ...]\nCollectionsLike = Union[list[str], Iterator[str], str]\n\nIDs = tuple[str, ...]\nIDsLike = Union[IDs, str, list[str], Iterator[str]]\n\nIntersects = dict[str, Any]\nIntersectsLike = Union[str, GeoInterface, Intersects]\n\nQuery = dict[str, Any]\nQueryLike = Union[Query, list[str]]\n\nFilterLangLike = str\nFilterLike = Union[dict[str, Any], str]\n\nSortby = list[dict[str, str]]\nSortbyLike = Union[Sortby, str, list[str]]\n\nFields = dict[str, list[str]]\nFieldsLike = Union[Fields, str, list[str]]\n\n# these cannot be reordered or parsing will fail!\nOP_MAP = {\n \">=\": \"gte\",\n \"<=\": \"lte\",\n \"=\": \"eq\",\n \"<>\": \"neq\",\n \">\": \"gt\",\n \"<\": \"lt\",\n}\n\nOPS = list(OP_MAP.keys())\n\n\n# from https://gist.github.com/angstwad/bf22d1822c38a92ec0a9#gistcomment-2622319\ndef dict_merge(\n dct: dict[Any, Any], merge_dct: dict[Any, Any], add_keys: bool = True\n) -> dict[Any, Any]:\n \"\"\"Recursive dict merge.\n\n Inspired by :meth:``dict.update()``, instead of\n updating only top-level keys, dict_merge recurses down into dicts nested\n to an arbitrary depth, updating keys. The ``merge_dct`` is merged into\n ``dct``. This version will return a copy of the dictionary and leave the original\n arguments untouched. The optional argument ``add_keys``, determines whether keys\n which are present in ``merge_dict`` but not ``dct`` should be included in the new\n dict.\n\n Args:\n dct (dict) onto which the merge is executed\n merge_dct (dict): dct merged into dct\n add_keys (bool): whether to add new keys\n\n Return:\n dict: updated dict\n \"\"\"\n dct = dct.copy()\n if not add_keys:\n merge_dct = {k: merge_dct[k] for k in set(dct).intersection(set(merge_dct))}\n\n for k, v in merge_dct.items():\n if k in dct and isinstance(dct[k], dict) and isinstance(merge_dct[k], Mapping):\n dct[k] = dict_merge(dct[k], merge_dct[k], add_keys=add_keys)\n else:\n dct[k] = merge_dct[k]\n\n return dct\n\n\nclass BaseSearch(ABC):\n _stac_io: StacApiIO\n\n def __init__(\n self,\n url: str,\n *,\n method: str | None = \"POST\",\n max_items: int | None = None,\n stac_io: StacApiIO | None = None,\n client: Optional[\"_client.Client\"] = None,\n limit: int | None = None,\n ids: IDsLike | None = None,\n collections: CollectionsLike | None = None,\n bbox: BBoxLike | None = None,\n intersects: IntersectsLike | None = None,\n datetime: DatetimeLike | None = None,\n query: QueryLike | None = None,\n filter: FilterLike | None = None,\n filter_lang: FilterLangLike | None = None,\n sortby: SortbyLike | None = None,\n fields: FieldsLike | None = None,\n modifier: Callable[[Modifiable], None] | None = None,\n q: str | None = None,\n ):\n self.url = url\n self.client = client\n\n self._max_items = max_items\n if self._max_items is not None and limit is not None:\n limit = min(limit, self._max_items)\n\n if limit is not None and (limit < 1 or limit > 10000):\n raise Exception(f\"Invalid limit of {limit}, must be between 1 and 10,000\")\n\n self.method = method\n self.modifier = modifier\n\n params = {\n \"limit\": limit,\n \"bbox\": self._format_bbox(bbox),\n \"datetime\": self._format_datetime(datetime),\n \"ids\": self._format_ids(ids),\n \"collections\": self._format_collections(collections),\n \"intersects\": self._format_intersects(intersects),\n \"query\": self._format_query(query),\n \"filter\": self._format_filter(method, filter_lang, filter),\n \"filter-lang\": self._format_filter_lang(method, filter, filter_lang),\n \"sortby\": self._format_sortby(sortby),\n \"fields\": self._format_fields(fields),\n \"q\": q,\n }\n\n self._parameters: dict[str, Any] = {\n k: v for k, v in params.items() if v is not None\n }\n\n def get_parameters(self) -> dict[str, Any]:\n if self.method == \"POST\":\n return self._parameters\n elif self.method == \"GET\":\n return self._clean_params_for_get_request()\n else:\n raise Exception(f\"Unsupported method {self.method}\")\n\n def _clean_params_for_get_request(self) -> dict[str, Any]:\n params = deepcopy(self._parameters)\n if \"bbox\" in params:\n params[\"bbox\"] = \",\".join(map(str, params[\"bbox\"]))\n if \"ids\" in params:\n params[\"ids\"] = \",\".join(params[\"ids\"])\n if \"collections\" in params:\n params[\"collections\"] = \",\".join(params[\"collections\"])\n if \"intersects\" in params:\n params[\"intersects\"] = json.dumps(\n params[\"intersects\"], separators=(\",\", \":\")\n )\n if \"query\" in params:\n params[\"query\"] = json.dumps(params[\"query\"], separators=(\",\", \":\"))\n if \"sortby\" in params:\n params[\"sortby\"] = self._sortby_dict_to_str(params[\"sortby\"])\n if \"fields\" in params:\n params[\"fields\"] = self._fields_dict_to_str(params[\"fields\"])\n if \"filter\" in params and isinstance(params[\"filter\"], dict):\n params[\"filter\"] = json.dumps(params[\"filter\"])\n return params\n\n def url_with_parameters(self) -> str:\n \"\"\"Returns the search url with parameters, appropriate for a GET request.\n\n Examples:\n\n >>> search = ItemSearch(\n ... url=\"https://planetarycomputer.microsoft.com/api/stac/v1/search\",\n ... collections=[\"cop-dem-glo-30\"],\n ... bbox=[88.214, 27.927, 88.302, 28.034],\n ... )\n >>> assert (\n ... search.url_with_parameters()\n ... == \"https://planetarycomputer.microsoft.com/api/stac/v1/search?\"\n ... \"limit=100&bbox=88.214,27.927,88.302,28.034&collections=cop-dem-glo-30\"\n ... )\n\n Returns:\n str: The search url with parameters.\n \"\"\"\n params = self._clean_params_for_get_request()\n request = Request(\"GET\", self.url, params=params)\n url = request.prepare().url\n if url is None:\n raise ValueError(\"Could not construct a full url\")\n return url\n\n def _format_query(self, value: QueryLike | None) -> dict[str, Any] | None:\n if value is None:\n return None\n\n if self.client and not self.client.conforms_to(ConformanceClasses.QUERY):\n warnings.warn(DoesNotConformTo(\"QUERY\"))\n\n if isinstance(value, dict):\n return value\n elif isinstance(value, list):\n query: dict[str, Any] = {}\n for q in value:\n if isinstance(q, str):\n try:\n query = dict_merge(query, json.loads(q))\n except json.decoder.JSONDecodeError:\n for op in OPS:\n parts = q.split(op)\n if len(parts) == 2:\n param = parts[0]\n val: str | float = parts[1]\n if param == \"gsd\":\n val = float(val)\n query = dict_merge(query, {parts[0]: {OP_MAP[op]: val}})\n break\n else:\n raise Exception(\"Unsupported query format, must be a List[str].\")\n else:\n raise Exception(\"Unsupported query format, must be a Dict or List[str].\")\n\n return query\n\n @staticmethod\n def _format_filter_lang(\n method: str | None,\n _filter: FilterLike | None,\n value: FilterLangLike | None,\n ) -> str | None:\n if _filter is None:\n return None\n\n if value is not None:\n return value\n\n if method == \"GET\":\n return \"cql2-text\"\n\n if method == \"POST\":\n return \"cql2-json\"\n\n return None\n\n def _format_filter(\n self,\n method: str | None,\n filter_lang: FilterLangLike | None,\n value: FilterLike | None,\n ) -> FilterLike | None:\n if not value:\n return None\n\n if self.client and not self.client.conforms_to(ConformanceClasses.FILTER):\n warnings.warn(DoesNotConformTo(\"FILTER\"))\n\n if method == \"GET\" and isinstance(value, str):\n return value\n\n if method == \"POST\" and isinstance(value, dict):\n return value\n\n # if filter_lang is specified, do not coerce\n if filter_lang is not None:\n return value\n\n try:\n import cql2\n\n if isinstance(value, dict):\n expr = cql2.parse_json(json.dumps(value))\n else:\n # could be cql2-text or stringified cql2-json\n expr = cql2.Expr(value)\n\n except ImportError as e:\n raise ValueError(\n \"Unless you specify ``filter_lang`` pystac-client will try to convert \"\n \"the filter to cql2-text or cql2-json based on the HTTP method \"\n \"provided.\\n\"\n \"Resolve this error by installing ``cql2``: ``pip install cql2``\"\n ) from e\n\n if method == \"GET\":\n return str(expr.to_text())\n\n if method == \"POST\":\n return dict(expr.to_json())\n\n return value\n\n @staticmethod\n def _format_bbox(value: BBoxLike | None) -> BBox | None:\n if value is None:\n return None\n\n if isinstance(value, str):\n bbox = tuple(map(float, value.split(\",\")))\n else:\n bbox = tuple(map(float, value))\n\n return bbox\n\n @staticmethod\n def _to_utc_isoformat(dt: datetime_) -> str:\n if dt.tzinfo is not None:\n dt = dt.astimezone(timezone.utc)\n dt = dt.replace(tzinfo=None)\n return f\"{dt.isoformat('T')}Z\"\n\n def _to_isoformat_range(\n self,\n component: DatetimeOrTimestamp,\n ) -> tuple[str, str | None]:\n \"\"\"Converts a single DatetimeOrTimestamp into one or two Datetimes.\n\n This is required to expand a single value like \"2017\" out to the whole\n year. This function returns two values. The first value is always a\n valid Datetime. The second value can be None or a Datetime. If it is\n None, this means that the first value was an exactly specified value\n (e.g. a `datetime.datetime`). If the second value is a Datetime, then\n it will be the end of the range at the resolution of the component,\n e.g. if the component were \"2017\" the second value would be the last\n second of the last day of 2017.\n \"\"\"\n if component is None:\n return \"..\", None\n elif isinstance(component, str):\n if component == \"..\":\n return component, None\n elif component == \"\":\n return \"..\", None\n\n match = DATETIME_REGEX.match(component)\n if not match:\n raise Exception(f\"invalid datetime component: {component}\")\n elif match.group(\"remainder\"):\n if match.group(\"tz_info\"):\n return component, None\n else:\n return f\"{component}Z\", None\n else:\n year = int(match.group(\"year\"))\n optional_month = match.group(\"month\")\n optional_day = match.group(\"day\")\n\n if optional_day is not None:\n start = datetime_(\n year,\n int(optional_month),\n int(optional_day),\n 0,\n 0,\n 0,\n tzinfo=tzutc(),\n )\n end = start + relativedelta(days=1, seconds=-1)\n elif optional_month is not None:\n start = datetime_(year, int(optional_month), 1, 0, 0, 0, tzinfo=tzutc())\n end = start + relativedelta(months=1, seconds=-1)\n else:\n start = datetime_(year, 1, 1, 0, 0, 0, tzinfo=tzutc())\n end = start + relativedelta(years=1, seconds=-1)\n return self._to_utc_isoformat(start), self._to_utc_isoformat(end)\n else:\n return self._to_utc_isoformat(component), None\n\n def _format_datetime(self, value: DatetimeLike | None) -> Datetime | None:\n if value is None:\n return None\n elif isinstance(value, datetime_):\n return self._to_utc_isoformat(value)\n elif isinstance(value, str):\n components = value.split(\"/\")\n else:\n components = list(value) # type: ignore\n\n if not components:\n return None\n elif len(components) == 1:\n if components[0] is None:\n raise Exception(\"cannot create a datetime query with None\")\n start, end = self._to_isoformat_range(components[0])\n if end is not None:\n return f\"{start}/{end}\"\n else:\n return start\n elif len(components) == 2:\n if all(c is None for c in components):\n raise Exception(\"cannot create a double open-ended interval\")\n start, _ = self._to_isoformat_range(components[0])\n backup_end, end = self._to_isoformat_range(components[1])\n return f\"{start}/{end or backup_end}\"\n else:\n raise Exception(\n \"too many datetime components \"\n f\"(max=2, actual={len(components)}): {value}\"\n )\n\n @staticmethod\n def _format_collections(value: CollectionsLike | None) -> Collections | None:\n def _format(c: Any) -> Collections:\n if isinstance(c, str):\n return (c,)\n if isinstance(c, Iterable):\n return tuple(map(lambda x: _format(x)[0], c))\n\n return (c.id,)\n\n if value is None:\n return None\n if isinstance(value, str):\n return tuple(map(lambda x: _format(x)[0], value.split(\",\")))\n if isinstance(value, Collection):\n return _format(value)\n\n return _format(value)\n\n @staticmethod\n def _format_ids(value: IDsLike | None) -> IDs | None:\n if value is None or isinstance(value, (tuple, list)) and not value:\n # We can't just check for truthiness here because of the Iterator[str] case\n return None\n elif isinstance(value, str):\n # We could check for str in the first branch, but then we'd be checking\n # for str twice #microoptimizations\n if value:\n return tuple(value.split(\",\"))\n else:\n return None\n else:\n return tuple(value)\n\n def _format_sortby(self, value: SortbyLike | None) -> Sortby | None:\n if value is None:\n return None\n\n if self.client and not self.client.conforms_to(ConformanceClasses.SORT):\n warnings.warn(DoesNotConformTo(\"SORT\"))\n\n if isinstance(value, str):\n return [self._sortby_part_to_dict(part) for part in value.split(\",\")]\n\n if isinstance(value, list):\n if value and isinstance(value[0], str):\n return [self._sortby_part_to_dict(str(v)) for v in value]\n elif value and isinstance(value[0], dict):\n return value # type: ignore\n\n raise Exception(\n \"sortby must be of type None, str, List[str], or List[Dict[str, str]\"\n )\n\n @staticmethod\n def _sortby_part_to_dict(part: str) -> dict[str, str]:\n if part.startswith(\"-\"):\n return {\"field\": part[1:], \"direction\": \"desc\"}\n elif part.startswith(\"+\"):\n return {\"field\": part[1:], \"direction\": \"asc\"}\n else:\n return {\"field\": part, \"direction\": \"asc\"}\n\n @staticmethod\n def _sortby_dict_to_str(sortby: Sortby) -> str:\n return \",\".join(\n [\n f\"{'+' if sort['direction'] == 'asc' else '-'}{sort['field']}\"\n for sort in sortby\n ]\n )\n\n def _format_fields(self, value: FieldsLike | None) -> Fields | None:\n if value is None:\n return None\n\n if self.client and not self.client.conforms_to(ConformanceClasses.FIELDS):\n warnings.warn(DoesNotConformTo(\"FIELDS\"))\n\n if isinstance(value, str):\n return self._fields_to_dict(value.split(\",\"))\n if isinstance(value, list):\n if len(value) == 1:\n return self._fields_to_dict(value[0].split(\",\"))\n return self._fields_to_dict(value)\n if isinstance(value, dict):\n return value\n\n raise Exception(\n \"sortby must be of type None, str, List[str], or List[Dict[str, str]\"\n )\n\n @staticmethod\n def _fields_to_dict(fields: list[str]) -> Fields:\n includes: list[str] = []\n excludes: list[str] = []\n for field in fields:\n if field.startswith(\"-\"):\n excludes.append(field[1:])\n elif field.startswith(\"+\"):\n includes.append(field[1:])\n else:\n includes.append(field)\n return {\"include\": includes, \"exclude\": excludes}\n\n @staticmethod\n def _fields_dict_to_str(fields: Fields) -> str:\n includes = [f\"+{x}\" for x in fields.get(\"include\", [])]\n excludes = [f\"-{x}\" for x in fields.get(\"exclude\", [])]\n return \",\".join(chain(includes, excludes))\n\n @staticmethod\n def _format_intersects(value: IntersectsLike | None) -> Intersects | None:\n if value is None:\n return None\n if isinstance(value, dict):\n if value.get(\"type\") == \"Feature\":\n return deepcopy(value.get(\"geometry\"))\n else:\n return deepcopy(value)\n if isinstance(value, str):\n return dict(json.loads(value))\n if hasattr(value, \"__geo_interface__\"):\n return dict(deepcopy(getattr(value, \"__geo_interface__\")))\n raise Exception(\n \"intersects must be of type None, str, dict, or an object that \"\n \"implements __geo_interface__\"\n )\n\n\nif TYPE_CHECKING:\n from pystac_client import client as _client\n\n\ndef __getattr__(name: str) -> Any:\n if name in (\"DEFAUL_LIMIT\", \"DEFAULT_LIMIT_AND_MAX_ITEMS\"):\n warnings.warn(\n f\"{name} is deprecated and will be removed in v0.8\", DeprecationWarning\n )\n return 100\n raise AttributeError(f\"module {__name__} has no attribute {name}\")\n\n\nclass ItemSearch(BaseSearch):\n \"\"\"Represents a deferred query to a STAC search endpoint as described in the\n `STAC API - Item Search spec\n `__.\n\n No request is sent to the API until a method is called to iterate\n through the resulting STAC Items, either :meth:`ItemSearch.item_collections`,\n :meth:`ItemSearch.items`, or :meth:`ItemSearch.items_as_dicts`.\n\n All parameters except ``url``, ``method``, ``max_items``, and ``client``\n correspond to query parameters\n described in the `STAC API - Item Search: Query Parameters Table\n `__\n docs. Please refer\n to those docs for details on how these parameters filter search results.\n\n Args:\n url: The URL to the search page of the STAC API.\n method : The HTTP method to use when making a request to the service.\n This must be either ``\"GET\"``, ``\"POST\"``, or\n ``None``. If ``None``, this will default to ``\"POST\"``.\n If a ``\"POST\"`` request receives a ``405`` status for\n the response, it will automatically retry with\n ``\"GET\"`` for all subsequent requests.\n max_items : The maximum number of items to return from the search, even\n if there are more matching results. This allows the client to limit the\n total number of Items returned from the :meth:`items`,\n :meth:`item_collections`, and :meth:`items_as_dicts methods`. The client\n will continue to request pages of items until the number of max items is\n reached. By default (``max_items=None``) all items matching the query\n will be returned.\n stac_io: An instance of StacIO for retrieving results. Normally comes\n from the Client that returns this ItemSearch client: An instance of a\n root Client used to set the root on resulting Items.\n client: An instance of Client for retrieving results. This is normally populated\n by the client that returns this ItemSearch instance.\n limit: A recommendation to the service as to the number of items to return\n *per page* of results. Defaults to 100.\n ids: List of one or more Item ids to filter on.\n collections: List of one or more Collection IDs or :class:`pystac.Collection`\n instances.\n bbox: A list, tuple, or iterator representing a bounding box of 2D\n or 3D coordinates. Results will be filtered\n to only those intersecting the bounding box.\n intersects: A string or dictionary representing a GeoJSON geometry or feature,\n or an object that implements a ``__geo_interface__`` property, as supported\n by several libraries including Shapely, ArcPy, PySAL, and geojson. Results\n filtered to only those intersecting the geometry.\n datetime: Either a single datetime or datetime range used to filter results.\n You may express a single datetime using a :class:`datetime.datetime`\n instance, a `RFC 3339-compliant `__\n timestamp, or a simple date string (see below). Instances of\n :class:`datetime.datetime` may be either\n timezone aware or unaware. Timezone aware instances will be converted to\n a UTC timestamp before being passed\n to the endpoint. Timezone unaware instances are assumed to represent UTC\n timestamps. You may represent a\n datetime range using a ``\"/\"`` separated string as described in the spec,\n or a list, tuple, or iterator\n of 2 timestamps or datetime instances. For open-ended ranges, use either\n ``\"..\"`` (``'2020-01-01:00:00:00Z/..'``,\n ``['2020-01-01:00:00:00Z', '..']``) or a value of ``None``\n (``['2020-01-01:00:00:00Z', None]``).\n\n If using a simple date string, the datetime can be specified in\n ``YYYY-mm-dd`` format, optionally truncating\n to ``YYYY-mm`` or just ``YYYY``. Simple date strings will be expanded to\n include the entire time period, for example:\n\n - ``2017`` expands to ``2017-01-01T00:00:00Z/2017-12-31T23:59:59Z``\n - ``2017-06`` expands to ``2017-06-01T00:00:00Z/2017-06-30T23:59:59Z``\n - ``2017-06-10`` expands to ``2017-06-10T00:00:00Z/2017-06-10T23:59:59Z``\n\n If used in a range, the end of the range expands to the end of that\n day/month/year, for example:\n\n - ``2017/2018`` expands to\n ``2017-01-01T00:00:00Z/2018-12-31T23:59:59Z``\n - ``2017-06/2017-07`` expands to\n ``2017-06-01T00:00:00Z/2017-07-31T23:59:59Z``\n - ``2017-06-10/2017-06-11`` expands to\n ``2017-06-10T00:00:00Z/2017-06-11T23:59:59Z``\n\n query: List or JSON of query parameters as per the STAC API `query` extension\n filter: JSON of query parameters as per the STAC API `filter` extension\n filter_lang: Language variant used in the filter body. If `filter` is a\n dictionary or not provided, defaults\n to 'cql2-json'. If `filter` is a string, defaults to `cql2-text`.\n sortby: A single field or list of fields to sort the response by\n fields: A list of fields to include in the response. Note this may\n result in invalid STAC objects, as they may not have required fields.\n Use `items_as_dicts` to avoid object unmarshalling errors.\n modifier : A callable that modifies the children collection and items\n returned by this Client. This can be useful for injecting\n authentication parameters into child assets to access data\n from non-public sources.\n\n The callable should expect a single argument, which will be one\n of the following types:\n\n * :class:`pystac.Collection`\n * :class:`pystac.Item`\n * :class:`pystac.ItemCollection`\n * A STAC item-like :class:`dict`\n * A STAC collection-like :class:`dict`\n\n The callable should mutate the argument in place and return ``None``.\n\n ``modifier`` propagates recursively to children of this Client.\n After getting a child collection with, e.g.\n :meth:`Client.get_collection`, the child items of that collection\n will still be signed with ``modifier``.\n \"\"\"\n\n _stac_io: StacApiIO\n\n def __init__(\n self,\n url: str,\n *,\n method: str | None = \"POST\",\n max_items: int | None = None,\n stac_io: StacApiIO | None = None,\n client: Optional[\"_client.Client\"] = None,\n limit: int | None = None,\n ids: IDsLike | None = None,\n collections: CollectionsLike | None = None,\n bbox: BBoxLike | None = None,\n intersects: IntersectsLike | None = None,\n datetime: DatetimeLike | None = None,\n query: QueryLike | None = None,\n filter: FilterLike | None = None,\n filter_lang: FilterLangLike | None = None,\n sortby: SortbyLike | None = None,\n fields: FieldsLike | None = None,\n modifier: Callable[[Modifiable], None] | None = None,\n ):\n super().__init__(\n url=url,\n method=method,\n max_items=max_items,\n stac_io=stac_io,\n client=client,\n limit=limit,\n ids=ids,\n collections=collections,\n bbox=bbox,\n intersects=intersects,\n datetime=datetime,\n query=query,\n filter=filter,\n filter_lang=filter_lang,\n sortby=sortby,\n fields=fields,\n modifier=modifier,\n )\n\n if client and client._stac_io is not None and stac_io is None:\n self._stac_io = client._stac_io\n if not client.conforms_to(ConformanceClasses.ITEM_SEARCH):\n warnings.warn(DoesNotConformTo(\"ITEM_SEARCH\"))\n else:\n self._stac_io = stac_io or StacApiIO()\n\n @lru_cache(1)\n def matched(self) -> int | None:\n \"\"\"Return number matched for search\n\n Returns the value from the `numberMatched` or `context.matched` field.\n Not all APIs will support counts in which case a warning will be issued\n\n Returns:\n int: Total count of matched items. If counts are not supported `None`\n is returned.\n \"\"\"\n params = {**self.get_parameters(), \"limit\": 1}\n resp = self._stac_io.read_json(self.url, method=self.method, parameters=params)\n found = None\n if \"context\" in resp:\n found = resp[\"context\"].get(\"matched\", None)\n elif \"numberMatched\" in resp:\n found = resp[\"numberMatched\"]\n if found is None:\n warnings.warn(\"numberMatched or context.matched not in response\")\n return found\n\n # ------------------------------------------------------------------------\n # Result sets\n # ------------------------------------------------------------------------\n # By item\n def items(self) -> Iterator[Item]:\n \"\"\"Iterator that yields :class:`pystac.Item` instances for each item matching\n the given search parameters.\n\n Yields:\n Item : each Item matching the search criteria\n \"\"\"\n for item in self.items_as_dicts():\n # already signed in items_as_dicts\n yield Item.from_dict(item, root=self.client, preserve_dict=False)\n\n def items_as_dicts(self) -> Iterator[dict[str, Any]]:\n \"\"\"Iterator that yields :class:`dict` instances for each item matching\n the given search parameters.\n\n Yields:\n Item : each Item matching the search criteria\n \"\"\"\n for page in self.pages_as_dicts():\n yield from page.get(\"features\", [])\n\n # ------------------------------------------------------------------------\n # By Page\n def pages(self) -> Iterator[ItemCollection]:\n \"\"\"Iterator that yields ItemCollection objects. Each ItemCollection is\n a page of results from the search.\n\n Yields:\n ItemCollection : a group of Items matching the search criteria within an\n ItemCollection\n \"\"\"\n if isinstance(self._stac_io, StacApiIO):\n for page in self.pages_as_dicts():\n # already signed in pages_as_dicts\n yield ItemCollection.from_dict(\n page, preserve_dict=False, root=self.client\n )\n\n def pages_as_dicts(self) -> Iterator[dict[str, Any]]:\n \"\"\"Iterator that yields :class:`dict` instances for each page\n of results from the search.\n\n Yields:\n Dict : a group of items matching the search\n criteria as a feature-collection-like dictionary.\n \"\"\"\n if isinstance(self._stac_io, StacApiIO):\n num_items = 0\n for page in self._stac_io.get_pages(\n self.url, self.method, self.get_parameters()\n ):\n call_modifier(self.modifier, page)\n features = page.get(\"features\", [])\n if features:\n num_items += len(features)\n if self._max_items and num_items > self._max_items:\n # Slice the features down to make sure we hit max_items\n page[\"features\"] = features[0 : -(num_items - self._max_items)]\n yield page\n if self._max_items and num_items >= self._max_items:\n return\n else:\n return\n\n # ------------------------------------------------------------------------\n # Everything\n\n @lru_cache(1)\n def item_collection(self) -> ItemCollection:\n \"\"\"\n Get the matching items as a :py:class:`pystac.ItemCollection`.\n\n Return:\n ItemCollection: The item collection\n \"\"\"\n # Bypass the cache here, so that we can pass __preserve_dict__\n # without mutating what's in the cache.\n feature_collection = self.item_collection_as_dict.__wrapped__(self)\n # already signed in item_collection_as_dict\n return ItemCollection.from_dict(\n feature_collection, preserve_dict=False, root=self.client\n )\n\n @lru_cache(1)\n def item_collection_as_dict(self) -> dict[str, Any]:\n \"\"\"\n Get the matching items as an item-collection-like dict.\n\n The dictionary will have two keys:\n\n 1. ``'type'`` with the value ``'FeatureCollection'``\n 2. ``'features'`` with the value being a list of dictionaries\n for the matching items.\n\n Return:\n Dict : A GeoJSON FeatureCollection\n \"\"\"\n features = []\n for page in self.pages_as_dicts():\n for feature in page[\"features\"]:\n features.append(feature)\n feature_collection = {\"type\": \"FeatureCollection\", \"features\": features}\n return feature_collection\n\n # Deprecated methods\n # not caching these, since they're cached in the implementation\n\n def get_item_collections(self) -> Iterator[ItemCollection]:\n \"\"\"DEPRECATED\n\n .. deprecated:: 0.4.0\n Use :meth:`ItemSearch.pages` instead.\n\n Yields:\n ItemCollection : a group of Items matching the search criteria.\n \"\"\"\n warnings.warn(\n \"get_item_collections() is deprecated, use pages() instead\",\n FutureWarning,\n )\n return self.pages()\n\n def item_collections(self) -> Iterator[ItemCollection]:\n \"\"\"DEPRECATED\n\n .. deprecated:: 0.5.0\n Use :meth:`ItemSearch.pages` instead.\n\n Yields:\n ItemCollection : a group of Items matching the search criteria within an\n ItemCollection\n \"\"\"\n warnings.warn(\n \"item_collections() is deprecated, use pages() instead\",\n FutureWarning,\n )\n return self.pages()\n\n def get_items(self) -> Iterator[Item]:\n \"\"\"DEPRECATED.\n\n .. deprecated:: 0.4.0\n Use :meth:`ItemSearch.items` instead.\n\n Yields:\n Item : each Item matching the search criteria\n \"\"\"\n warnings.warn(\n \"get_items() is deprecated, use items() instead\",\n FutureWarning,\n )\n return self.items()\n\n def get_all_items(self) -> ItemCollection:\n \"\"\"DEPRECATED\n\n .. deprecated:: 0.4.0\n Use :meth:`ItemSearch.item_collection` instead.\n\n Return:\n item_collection : ItemCollection\n \"\"\"\n warnings.warn(\n \"get_all_items() is deprecated, use item_collection() instead.\",\n FutureWarning,\n )\n return self.item_collection()\n\n def get_all_items_as_dict(self) -> dict[str, Any]:\n \"\"\"DEPRECATED\n\n .. deprecated:: 0.4.0\n Use :meth:`ItemSearch.item_collection_as_dict` instead.\n\n Return:\n Dict : A GeoJSON FeatureCollection\n \"\"\"\n warnings.warn(\n \"get_all_items_as_dict() is deprecated, use item_collection_as_dict() \"\n \"instead.\",\n FutureWarning,\n )\n return self.item_collection_as_dict()\n" }, { "path": "pystac_client/mixins.py", "content": "import warnings\nfrom typing import Any\n\nimport pystac\n\nfrom pystac_client._utils import urljoin\nfrom pystac_client.conformance import ConformanceClasses\nfrom pystac_client.exceptions import APIError\nfrom pystac_client.stac_api_io import StacApiIO\nfrom pystac_client.warnings import DoesNotConformTo, MissingLink\n\nQUERYABLES_REL = \"http://www.opengis.net/def/rel/ogc/1.0/queryables\"\nQUERYABLES_ENDPOINT = \"queryables\"\n\n\nclass StacAPIObject(pystac.STACObject):\n _stac_io: StacApiIO | None\n\n def conforms_to(self, conformance_class: str | ConformanceClasses) -> bool:\n raise NotImplementedError\n\n\nclass BaseMixin(StacAPIObject):\n def _get_href(self, rel: str, link: pystac.Link | None, endpoint: str) -> str:\n if link and isinstance(link.href, str):\n href = link.absolute_href\n else:\n warnings.warn(MissingLink(rel, self.__class__.__name__), stacklevel=2)\n href = urljoin(self.self_href, endpoint)\n return href\n\n\nclass QueryablesMixin(BaseMixin):\n \"\"\"Mixin for adding support for /queryables endpoint\"\"\"\n\n def get_queryables_from(self, url: str) -> dict[str, Any]:\n \"\"\"Return all queryables.\n\n Output is a dictionary that can be used in ``jsonshema.validate``\n\n Args:\n url: a queryables url\n\n Return:\n Dict[str, Any]: Dictionary containing queryable fields\n \"\"\"\n\n if self._stac_io is None:\n raise APIError(\"API access is not properly configured\")\n\n result = self._stac_io.read_json(url)\n if \"properties\" not in result:\n raise APIError(\n f\"Invalid response from {QUERYABLES_ENDPOINT}: \"\n \"expected 'properties' attribute\"\n )\n\n return result\n\n def get_queryables(self) -> dict[str, Any]:\n url = self._get_queryables_href()\n return self.get_queryables_from(url)\n\n def _get_queryables_href(self) -> str:\n if not self.conforms_to(ConformanceClasses.FILTER):\n raise DoesNotConformTo(ConformanceClasses.FILTER.name)\n\n link = self.get_single_link(QUERYABLES_REL)\n href = self._get_href(QUERYABLES_REL, link, QUERYABLES_ENDPOINT)\n return href\n" }, { "path": "pystac_client/py.typed", "content": "" }, { "path": "pystac_client/stac_api_io.py", "content": "import json\nimport logging\nimport warnings\nfrom collections.abc import Callable, Iterator\nfrom copy import deepcopy\nfrom typing import (\n TYPE_CHECKING,\n Any,\n Optional,\n Union,\n)\nfrom urllib.parse import urlparse\n\nimport pystac\nfrom pystac.link import Link\nfrom pystac.serialization import (\n identify_stac_object,\n identify_stac_object_type,\n merge_common_properties,\n migrate_to_latest,\n)\nfrom pystac.stac_io import DefaultStacIO\nfrom requests import Request, Session\nfrom requests.adapters import HTTPAdapter\nfrom urllib3 import Retry\n\nimport pystac_client\n\nfrom .exceptions import APIError\n\nif TYPE_CHECKING:\n from pystac.catalog import Catalog as Catalog_Type\n from pystac.stac_object import STACObject as STACObject_Type\n\nlogger = logging.getLogger(__name__)\n\n\nTimeout = Union[float, tuple[float, float], tuple[float, None]]\n\n\nclass StacApiIO(DefaultStacIO):\n def __init__(\n self,\n headers: dict[str, str] | None = None,\n conformance: list[str] | None = None,\n parameters: dict[str, Any] | None = None,\n request_modifier: Callable[[Request], Request | None] | None = None,\n timeout: Timeout | None = None,\n max_retries: int | Retry | None = 5,\n ):\n \"\"\"Initialize class for API IO\n\n Args:\n headers : Optional dictionary of headers to include in all requests\n conformance (DEPRECATED) : Optional list of `Conformance Classes\n `__.\n\n .. deprecated:: 0.7.0\n Conformance can be altered on the client class directly\n\n parameters: Optional dictionary of query string parameters to\n include in all requests.\n request_modifier: Optional callable that can be used to modify Request\n objects before they are sent. If provided, the callable receives a\n `request.Request` and must either modify the object directly or return\n a new / modified request instance.\n timeout: Optional float or (float, float) tuple following the semantics\n defined by `Requests\n `__.\n max_retries: The number of times to retry requests. Set to ``None`` to\n disable retries.\n\n Return:\n StacApiIO : StacApiIO instance\n \"\"\"\n # TODO - this should super() to parent class\n\n if conformance is not None:\n warnings.warn(\n (\n \"The `conformance` option is deprecated and will be \"\n \"removed in the next major release. Instead use \"\n \"`Client.set_conforms_to` or `Client.add_conforms_to` to control \"\n \"behavior.\"\n ),\n category=FutureWarning,\n )\n\n self.session = Session()\n if max_retries:\n self.session.mount(\"http://\", HTTPAdapter(max_retries=max_retries))\n self.session.mount(\"https://\", HTTPAdapter(max_retries=max_retries))\n self.timeout = timeout\n self.update(\n headers=headers,\n parameters=parameters,\n request_modifier=request_modifier,\n timeout=timeout,\n )\n\n def update(\n self,\n headers: dict[str, str] | None = None,\n parameters: dict[str, Any] | None = None,\n request_modifier: Callable[[Request], Request | None] | None = None,\n timeout: Timeout | None = None,\n ) -> None:\n \"\"\"Updates this StacApi's headers, parameters, and/or request_modifer.\n\n Args:\n headers : Optional dictionary of headers to include in all requests\n parameters: Optional dictionary of query string parameters to\n include in all requests.\n request_modifier: Optional callable that can be used to modify Request\n objects before they are sent. If provided, the callable receives a\n `request.Request` and must either modify the object directly or return\n a new / modified request instance.\n timeout: Optional float or (float, float) tuple following the semantics\n defined by `Requests\n `__.\n \"\"\"\n self.session.headers.update(headers or {})\n self.session.params.update(parameters or {}) # type: ignore\n self._req_modifier = request_modifier\n self.timeout = timeout\n\n def read_text(self, source: pystac.link.HREF, *args: Any, **kwargs: Any) -> str:\n \"\"\"Read text from the given URI.\n\n Overwrites the default method for reading text from a URL or file to allow\n :class:`urllib.request.Request` instances as input. This method also raises\n any :exc:`urllib.error.HTTPError` exceptions rather than catching\n them to allow us to handle different response status codes as needed.\n \"\"\"\n if isinstance(source, Link):\n link = source.to_dict()\n href = link[\"href\"]\n # get headers and body from Link and add to request from simple STAC\n # resolver\n merge = bool(link.get(\"merge\", False))\n\n # If the link object includes a \"method\" property, use that. If not\n # fall back to 'GET'.\n method = link.get(\"method\", \"GET\")\n # If the link object includes a \"headers\" property, use that and\n # respect the \"merge\" property.\n headers = link.get(\"headers\", None)\n\n # If \"POST\" use the body object that and respect the \"merge\" property.\n link_body = link.get(\"body\", {})\n if method == \"POST\":\n parameters = (\n {**(kwargs.get(\"parameters\", {})), **link_body}\n if merge\n else link_body\n )\n else:\n # parameters are already in the link href\n parameters = {}\n\n return self.request(\n href, method=method, headers=headers, parameters=parameters\n )\n else: # str or something that can be str'ed\n href = str(source)\n if _is_url(href):\n return self.request(href, *args, **kwargs)\n else:\n with open(href) as f:\n href_contents = f.read()\n return href_contents\n\n def request(\n self,\n href: str,\n method: str | None = None,\n headers: dict[str, str] | None = None,\n parameters: dict[str, Any] | None = None,\n ) -> str:\n \"\"\"Makes a request to an http endpoint\n\n Args:\n href (str): The request URL\n method (Optional[str], optional): The http method to use, 'GET' or 'POST'.\n Defaults to None, which will result in 'GET' being used.\n headers (Optional[Dict[str, str]], optional): Additional headers to include\n in request. Defaults to None.\n parameters (Optional[Dict[str, Any]], optional): parameters to send with\n request. Defaults to None.\n\n Raises:\n APIError: raised if the server returns an error response\n\n Return:\n str: The decoded response from the endpoint\n \"\"\"\n if method == \"POST\":\n request = Request(method=method, url=href, headers=headers, json=parameters)\n else:\n params = deepcopy(parameters) or {}\n request = Request(method=\"GET\", url=href, headers=headers, params=params)\n try:\n modified = self._req_modifier(request) if self._req_modifier else None\n prepped = self.session.prepare_request(modified or request)\n msg = f\"{prepped.method} {prepped.url} Headers: {prepped.headers}\"\n if method == \"POST\":\n msg += f\" Payload: {json.dumps(request.json)}\"\n if self.timeout is not None:\n msg += f\" Timeout: {self.timeout}\"\n logger.debug(msg)\n send_kwargs = self.session.merge_environment_settings(\n prepped.url, proxies={}, stream=None, verify=True, cert=None\n )\n resp = self.session.send(prepped, timeout=self.timeout, **send_kwargs)\n except Exception as err:\n logger.debug(err)\n raise APIError(str(err))\n if resp.status_code != 200:\n raise APIError.from_response(resp)\n try:\n return resp.content.decode(\"utf-8\")\n except Exception as err:\n raise APIError(str(err))\n\n def write_text_to_href(self, href: str, *args: Any, **kwargs: Any) -> None:\n if _is_url(href):\n raise APIError(\"Transactions not supported\")\n else:\n return super().write_text_to_href(href, *args, **kwargs)\n\n def stac_object_from_dict(\n self,\n d: dict[str, Any],\n href: pystac.link.HREF | None = None,\n root: Optional[\"Catalog_Type\"] = None,\n preserve_dict: bool = True,\n ) -> \"STACObject_Type\":\n \"\"\"Deserializes a :class:`~pystac.STACObject` sub-class instance from a\n dictionary.\n\n Args:\n d : The dictionary to deserialize\n href : Optional href to associate with the STAC object\n root : Optional root :class:`~pystac.Catalog` to associate with the\n STAC object.\n preserve_dict: If ``False``, the dict parameter ``d`` may be modified\n during this method call. Otherwise the dict is not mutated.\n Defaults to ``True``, which results results in a deepcopy of the\n parameter. Set to ``False`` when possible to avoid the performance\n hit of a deepcopy.\n \"\"\"\n if identify_stac_object_type(d) == pystac.STACObjectType.ITEM:\n collection_cache = None\n if root is not None:\n collection_cache = root._resolved_objects.as_collection_cache()\n\n # Merge common properties in case this is an older STAC object.\n merge_common_properties(\n d, json_href=str(href), collection_cache=collection_cache\n )\n\n info = identify_stac_object(d)\n d = migrate_to_latest(d, info)\n\n if info.object_type == pystac.STACObjectType.CATALOG:\n result = pystac_client.client.Client.from_dict(\n d, href=str(href), root=root, migrate=False, preserve_dict=preserve_dict\n )\n result._stac_io = self\n return result\n\n if info.object_type == pystac.STACObjectType.COLLECTION:\n collection_client = (\n pystac_client.collection_client.CollectionClient.from_dict(\n d,\n href=str(href),\n root=root,\n migrate=False,\n preserve_dict=preserve_dict,\n )\n )\n collection_client._stac_io = self\n return collection_client\n\n if info.object_type == pystac.STACObjectType.ITEM:\n return pystac.Item.from_dict(\n d, href=str(href), root=root, migrate=False, preserve_dict=preserve_dict\n )\n\n raise ValueError(f\"Unknown STAC object type {info.object_type}\")\n\n def get_pages(\n self,\n url: str,\n method: str | None = None,\n parameters: dict[str, Any] | None = None,\n ) -> Iterator[dict[str, Any]]:\n \"\"\"Iterator that yields dictionaries for each page at a STAC paging\n endpoint, e.g., /collections, /search\n\n Return:\n Dict[str, Any] : JSON content from a single page\n \"\"\"\n page = self.read_json(url, method=method, parameters=parameters)\n if not (page.get(\"features\") or page.get(\"collections\")):\n return None\n yield page\n\n next_link = next(\n (link for link in page.get(\"links\", []) if link[\"rel\"] == \"next\"), None\n )\n while next_link:\n link = Link.from_dict(next_link)\n page = self.read_json(link, parameters=parameters)\n if not (page.get(\"features\") or page.get(\"collections\")):\n return None\n yield page\n\n # get the next link and make the next request\n next_link = next(\n (link for link in page.get(\"links\", []) if link[\"rel\"] == \"next\"), None\n )\n\n\ndef _is_url(href: str) -> bool:\n url = urlparse(href)\n return bool(url.scheme) and bool(url.netloc)\n" }, { "path": "pystac_client/version.py", "content": "__version__ = \"0.9.0\"\n" }, { "path": "pystac_client/warnings.py", "content": "import warnings\nfrom collections.abc import Iterator\nfrom contextlib import contextmanager\n\n\nclass PystacClientWarning(UserWarning):\n \"\"\"Base warning class\"\"\"\n\n ...\n\n\nclass NoConformsTo(PystacClientWarning):\n \"\"\"Inform user when client does not have \"conformsTo\" set\"\"\"\n\n def __str__(self) -> str:\n return \"Server does not advertise any conformance classes.\"\n\n\nclass DoesNotConformTo(PystacClientWarning):\n \"\"\"Inform user when client does not conform to extension\"\"\"\n\n def __str__(self) -> str:\n return \"Server does not conform to {}\".format(\", \".join(self.args))\n\n\nclass MissingLink(PystacClientWarning):\n \"\"\"Inform user when link is not found\"\"\"\n\n def __str__(self) -> str:\n return \"No link with rel='{}' could be found on this {}.\".format(*self.args)\n\n\nclass FallbackToPystac(PystacClientWarning):\n \"\"\"Inform user when falling back to pystac implementation\"\"\"\n\n def __str__(self) -> str:\n return \"Falling back to pystac. This might be slow.\"\n\n\n@contextmanager\ndef strict() -> Iterator[None]:\n \"\"\"Context manager for raising all pystac-client warnings as errors\n\n For more fine-grained control or to filter warnings in the whole\n python session, use the :py:mod:`warnings` module directly.\n\n Examples:\n\n >>> from pystac_client import Client\n >>> from pystac_client.warnings import strict\n >>> with strict():\n ... Client.open(\"https://perfect-api.test\")\n\n For finer-grained control:\n\n >>> import warnings\n >>> from pystac_client import Client\n >>> from pystac_client.warnings import MissingLink\n >>> warnings.filterwarnings(\"error\", category=FallbackToPystac)\n >>> Client.open(\"https://imperfect-api.test\")\n \"\"\"\n\n warnings.filterwarnings(\"error\", category=PystacClientWarning)\n try:\n yield\n finally:\n warnings.filterwarnings(\"default\", category=PystacClientWarning)\n\n\n@contextmanager\ndef ignore() -> Iterator[None]:\n \"\"\"Context manager for ignoring all pystac-client warnings\n\n For more fine-grained control or to set filter warnings in the whole\n python session, use the ``warnings`` module directly.\n\n Examples:\n\n >>> from pystac_client import Client\n >>> from pystac_client.warnings import ignore\n >>> with ignore():\n ... Client.open(\"https://imperfect-api.test\")\n\n For finer-grained control:\n\n >>> import warnings\n >>> from pystac_client import Client\n >>> from pystac_client.warnings import MissingLink\n >>> warnings.filterwarnings(\"ignore\", category=MissingLink)\n >>> Client.open(\"https://imperfect-api.test\")\n \"\"\"\n warnings.filterwarnings(\"ignore\", category=PystacClientWarning)\n try:\n yield\n finally:\n warnings.filterwarnings(\"default\", category=PystacClientWarning)\n" }, { "path": "scripts/build-docs", "content": "#!/bin/bash\n\nset -e\n\nif [[ -n \"${CI}\" ]]; then\n set -x\nfi\n\nfunction usage() {\n echo -n \\\n \"Usage: $(basename \"$0\")\nBuild documentation\n\"\n}\n\nif [ \"${BASH_SOURCE[0]}\" = \"${0}\" ]; then\n if [ \"${1:-}\" = \"--help\" ]; then\n usage\n else\n uv run -- sphinx-build -M html docs docs/build\n fi\nfi\n" }, { "path": "scripts/format", "content": "#!/bin/bash\n\nset -e\n\nif [[ -n \"${CI}\" ]]; then\n set -x\nfi\n\nfunction usage() {\n echo -n \\\n \"Usage: $(basename \"$0\")\nFormat code with ruff\n\"\n}\n\nif [ \"${BASH_SOURCE[0]}\" = \"${0}\" ]; then\n if [ \"${1:-}\" = \"--help\" ]; then\n usage\n else\n pre-commit run ruff-format --all-files\n fi\nfi\n" }, { "path": "scripts/lint", "content": "#!/bin/bash\n\nset -e\n\nif [[ -n \"${CI}\" ]]; then\n set -x\nfi\n\nfunction usage() {\n echo -n \\\n \"Usage: $(basename \"$0\")\nExecute project linters.\n\"\n}\n\nif [ \"${BASH_SOURCE[0]}\" = \"${0}\" ]; then\n if [ \"${1:-}\" = \"--help\" ]; then\n usage\n else\n pre-commit run codespell --all-files\n pre-commit run doc8 --all-files\n pre-commit run ruff --all-files\n pre-commit run mypy --all-files\n fi\nfi\n" }, { "path": "scripts/test", "content": "#!/bin/bash\n\nset -e\n\nif [[ -n \"${CI}\" ]]; then\n set -x\nfi\n\nfunction usage() {\n echo -n \\\n \"Usage: $(basename \"$0\")\nExecute project linters and test suites.\n\"\n}\n\nif [ \"${BASH_SOURCE[0]}\" = \"${0}\" ]; then\n if [ \"${1:-}\" = \"--help\" ]; then\n usage\n else\n ./scripts/lint\n ./scripts/format\n # Test suite with coverage enabled\n pytest --cov pystac_client --cov-report term-missing\n coverage xml\n fi\nfi\n" }, { "path": "tests/__init__.py", "content": "" }, { "path": "tests/cassettes/test_cli/TestCLICollections.test_collection_search[inprocess].yaml", "content": "interactions:\n- request:\n body: null\n headers:\n Accept:\n - '*/*'\n Accept-Encoding:\n - gzip, deflate\n Connection:\n - keep-alive\n User-Agent:\n - python-requests/2.32.4\n method: GET\n uri: https://earth-search.aws.element84.com/v1\n response:\n body:\n string: '{\"stac_version\":\"1.0.0\",\"type\":\"Catalog\",\"id\":\"earth-search-aws\",\"title\":\"Earth\n Search by Element 84\",\"description\":\"A STAC API of public datasets on AWS\",\"links\":[{\"rel\":\"self\",\"type\":\"application/json\",\"href\":\"https://earth-search.aws.element84.com/v1\"},{\"rel\":\"root\",\"type\":\"application/json\",\"href\":\"https://earth-search.aws.element84.com/v1\"},{\"rel\":\"conformance\",\"type\":\"application/json\",\"href\":\"https://earth-search.aws.element84.com/v1/conformance\"},{\"rel\":\"data\",\"type\":\"application/json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections\"},{\"rel\":\"search\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/search\",\"method\":\"GET\"},{\"rel\":\"search\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/search\",\"method\":\"POST\"},{\"rel\":\"aggregate\",\"type\":\"application/json\",\"href\":\"https://earth-search.aws.element84.com/v1/aggregate\",\"method\":\"GET\"},{\"rel\":\"aggregations\",\"type\":\"application/json\",\"href\":\"https://earth-search.aws.element84.com/v1/aggregations\"},{\"rel\":\"service-desc\",\"type\":\"application/vnd.oai.openapi\",\"href\":\"https://earth-search.aws.element84.com/v1/api\"},{\"rel\":\"service-doc\",\"type\":\"text/html\",\"href\":\"https://earth-search.aws.element84.com/v1/api.html\"},{\"rel\":\"http://www.opengis.net/def/rel/ogc/1.0/queryables\",\"type\":\"application/schema+json\",\"href\":\"https://earth-search.aws.element84.com/v1/queryables\"},{\"rel\":\"server\",\"type\":\"text/html\",\"href\":\"https://stac-utils.github.io/stac-server/\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/cop-dem-glo-30\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/naip\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/cop-dem-glo-90\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/landsat-c2-l2\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l2a\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-l1c\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-c1-l2a\"},{\"rel\":\"child\",\"type\":\"application/geo+json\",\"href\":\"https://earth-search.aws.element84.com/v1/collections/sentinel-1-grd\"}],\"conformsTo\":[\"https://api.stacspec.org/v1.0.0/core\",\"https://api.stacspec.org/v1.0.0/collections\",\"https://api.stacspec.org/v1.0.0/ogcapi-features\",\"https://api.stacspec.org/v1.0.0/item-search\",\"https://api.stacspec.org/v1.0.0/ogcapi-features#fields\",\"https://api.stacspec.org/v1.0.0/ogcapi-features#sort\",\"https://api.stacspec.org/v1.0.0/ogcapi-features#query\",\"https://api.stacspec.org/v1.0.0/item-search#fields\",\"https://api.stacspec.org/v1.0.0/item-search#sort\",\"https://api.stacspec.org/v1.0.0/item-search#query\",\"https://api.stacspec.org/v0.3.0/aggregation\",\"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core\",\"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30\",\"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson\"]}'\n headers:\n Connection:\n - keep-alive\n Content-Encoding:\n - gzip\n Content-Length:\n - '612'\n Content-Type:\n - application/json; charset=utf-8\n Date:\n - Thu, 31 Jul 2025 15:07:08 GMT\n Via:\n - 1.1 e466a87164c3f9591e3c8ac45a4b2074.cloudfront.net (CloudFront)\n X-Amz-Cf-Id:\n - BfohYGnnu9Fx0ZZqR51CvEqCOxuIQKkjOFMZvMYaOi0KK2RABmvlAQ==\n X-Amz-Cf-Pop:\n - DEN52-C1\n X-Amzn-Trace-Id:\n - Root=1-688b869b-1fa6fba64ccf038d38007705;Parent=673235f71f8f12d4;Sampled=0;Lineage=1:9e2884e9:0\n X-Cache:\n - Miss from cloudfront\n access-control-allow-origin:\n - '*'\n etag:\n - W/\"d29-QPs+H0wJLUX32EZKNgzG3k6wfQ8\"\n x-amz-apigw-id:\n - OlH4bHzVPHcERag=\n x-amzn-Remapped-content-length:\n - '3369'\n x-amzn-RequestId:\n - ac628085-2bc3-4d7a-8afc-98327982c116\n x-powered-by:\n - Express\n status:\n code: 200\n message: OK\n- request:\n body: null\n headers:\n Accept:\n - '*/*'\n Accept-Encoding:\n - gzip, deflate\n Connection:\n - keep-alive\n User-Agent:\n - python-requests/2.32.4\n method: GET\n uri: https://earth-search.aws.element84.com/v1/collections?q=sentinel\n response:\n body:\n string: \"{\\\"collections\\\":[{\\\"type\\\":\\\"Collection\\\",\\\"id\\\":\\\"sentinel-2-pre-c1-l2a\\\",\\\"title\\\":\\\"Sentinel-2\n Pre-Collection 1 Level-2A \\\",\\\"description\\\":\\\"Sentinel-2 Pre-Collection 1\n Level-2A (baseline < 05.00), with data and metadata matching collection sentinel-2-c1-l2a\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"links\\\":[{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5270/S2_-742ikth\\\",\\\"title\\\":\\\"Copernicus\n Sentinel-2 MSI Level-2A (L2A) Bottom-of-Atmosphere Radiance\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"title\\\":\\\"proprietary\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1\\\"},{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a/items\\\"},{\\\"rel\\\":\\\"http://www.opengis.net/def/rel/ogc/1.0/queryables\\\",\\\"type\\\":\\\"application/schema+json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a/queryables\\\"},{\\\"rel\\\":\\\"aggregate\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a/aggregate\\\",\\\"method\\\":\\\"GET\\\"},{\\\"rel\\\":\\\"aggregations\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a/aggregations\\\"}],\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/view/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.0.0/schema.json\\\"],\\\"item_assets\\\":{\\\"red\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Red - 10m\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B04\\\",\\\"common_name\\\":\\\"red\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.038}],\\\"gsd\\\":10,\\\"proj:shape\\\":[10980,10980],\\\"proj:transform\\\":[10,0,600000,0,-10,4000000],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":10,\\\"scale\\\":0.0001,\\\"offset\\\":-0.1}],\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"]},\\\"green\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Green - 10m\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B03\\\",\\\"common_name\\\":\\\"green\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.045}],\\\"gsd\\\":10,\\\"proj:shape\\\":[10980,10980],\\\"proj:transform\\\":[10,0,600000,0,-10,4000000],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":10,\\\"scale\\\":0.0001,\\\"offset\\\":-0.1}],\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"]},\\\"blue\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Blue - 10m\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B02\\\",\\\"common_name\\\":\\\"blue\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.098}],\\\"gsd\\\":10,\\\"proj:shape\\\":[10980,10980],\\\"proj:transform\\\":[10,0,600000,0,-10,4000000],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":10,\\\"scale\\\":0.0001,\\\"offset\\\":-0.1}],\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"]},\\\"visual\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"True color 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12)\\\",\\\"center_wavelength\\\":2.19,\\\"full_width_half_max\\\":0.242}]}},{\\\"id\\\":\\\"cop-dem-glo-30\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/cop-dem-glo-30\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\\\",\\\"title\\\":\\\"Copernicus\n DEM License\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1\\\"},{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/cop-dem-glo-30/items\\\"},{\\\"rel\\\":\\\"http://www.opengis.net/def/rel/ogc/1.0/queryables\\\",\\\"type\\\":\\\"application/schema+json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/cop-dem-glo-30/queryables\\\"},{\\\"rel\\\":\\\"aggregate\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/cop-dem-glo-30/aggregate\\\",\\\"method\\\":\\\"GET\\\"},{\\\"rel\\\":\\\"aggregations\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://earth-search.aws.element84.com/v1/collections/cop-dem-glo-30/aggregations\\\"}],\\\"title\\\":\\\"Copernicus\n DEM GLO-30\\\",\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2021-04-22T00:00:00Z\\\",\\\"2021-04-22T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Copernicus\\\",\\\"DEM\\\",\\\"DSM\\\",\\\"Elevation\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://spacedata.copernicus.eu/documents/20123/121286/CSCDA_ESA_Mission-specific+Annex_31_Oct_22.pdf\\\",\\\"name\\\":\\\"European\n Space Agency\\\",\\\"roles\\\":[\\\"licensor\\\"]},{\\\"url\\\":\\\"https://registry.opendata.aws/copernicus-dem/\\\",\\\"name\\\":\\\"Sinergise\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://doi.org/10.5069/G9028PQB\\\",\\\"name\\\":\\\"OpenTopography\\\",\\\"roles\\\":[\\\"host\\\"]},{\\\"name\\\":\\\"AWS\\\",\\\"roles\\\":[\\\"host\\\"],\\\"url\\\":\\\"https://registry.opendata.aws/sentinel-1\\\"},{\\\"name\\\":\\\"Element\n 84\\\",\\\"roles\\\":[\\\"processor\\\"],\\\"url\\\":\\\"https://element84.com\\\"}],\\\"summaries\\\":{\\\"gsd\\\":[30],\\\"platform\\\":[\\\"tandem-x\\\"],\\\"proj:epsg\\\":[4326],\\\"storage:platform\\\":[\\\"AWS\\\"],\\\"storage:region\\\":[\\\"eu-central-1\\\"],\\\"storage:requester_pays\\\":[false]},\\\"description\\\":\\\"The\n Copernicus DEM is a Digital Surface Model (DSM) which represents the surface\n of the Earth including buildings, infrastructure and vegetation. 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20m\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"swir16\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"SWIR\n 1 (band 11)\\\",\\\"center_wavelength\\\":1.61,\\\"full_width_half_max\\\":0.143}],\\\"gsd\\\":20,\\\"proj:shape\\\":[5490,5490],\\\"proj:transform\\\":[20,0,399960,0,-20,4900020],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"bits_per_sample\\\":15,\\\"spatial_resolution\\\":20,\\\"scale\\\":0.0001,\\\"offset\\\":0}],\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"]},\\\"swir22\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"SWIR 2 (band 12)\n - 20m\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"swir22\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"SWIR\n 2 (band 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application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Water Vapour (WVP)\\\",\\\"gsd\\\":20,\\\"proj:shape\\\":[5490,5490],\\\"proj:transform\\\":[20,0,300000,0,-20,9500020],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":20,\\\"unit\\\":\\\"cm\\\",\\\"scale\\\":0.001,\\\"offset\\\":0}],\\\"roles\\\":[\\\"data\\\"]},\\\"thumbnail\\\":{\\\"href\\\":\\\"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/24/M/UV/2025/7/S2B_24MUV_20250731_0_L2A/preview.jpg\\\",\\\"type\\\":\\\"image/jpeg\\\",\\\"title\\\":\\\"Thumbnail\n of preview image\\\",\\\"roles\\\":[\\\"thumbnail\\\"]},\\\"aot-jp2\\\":{\\\"href\\\":\\\"s3://sentinel-s2-l2a/tiles/24/M/UV/2025/7/31/0/R20m/AOT.jp2\\\",\\\"type\\\":\\\"image/jp2\\\",\\\"title\\\":\\\"Aerosol\n optical thickness 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application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Water Vapour (WVP)\\\",\\\"gsd\\\":20,\\\"proj:shape\\\":[5490,5490],\\\"proj:transform\\\":[20,0,399960,0,-20,9500020],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":20,\\\"unit\\\":\\\"cm\\\",\\\"scale\\\":0.001,\\\"offset\\\":0}],\\\"roles\\\":[\\\"data\\\"]},\\\"thumbnail\\\":{\\\"href\\\":\\\"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/24/M/VV/2025/7/S2B_24MVV_20250731_0_L2A/preview.jpg\\\",\\\"type\\\":\\\"image/jpeg\\\",\\\"title\\\":\\\"Thumbnail\n of preview image\\\",\\\"roles\\\":[\\\"thumbnail\\\"]},\\\"aot-jp2\\\":{\\\"href\\\":\\\"s3://sentinel-s2-l2a/tiles/24/M/VV/2025/7/31/0/R20m/AOT.jp2\\\",\\\"type\\\":\\\"image/jp2\\\",\\\"title\\\":\\\"Aerosol\n optical thickness 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application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Water Vapour (WVP)\\\",\\\"gsd\\\":20,\\\"proj:shape\\\":[5490,5490],\\\"proj:transform\\\":[20,0,499980,0,-20,9500020],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":20,\\\"unit\\\":\\\"cm\\\",\\\"scale\\\":0.001,\\\"offset\\\":0}],\\\"roles\\\":[\\\"data\\\"]},\\\"thumbnail\\\":{\\\"href\\\":\\\"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/24/M/WV/2025/7/S2B_24MWV_20250731_0_L2A/preview.jpg\\\",\\\"type\\\":\\\"image/jpeg\\\",\\\"title\\\":\\\"Thumbnail\n of preview image\\\",\\\"roles\\\":[\\\"thumbnail\\\"]},\\\"aot-jp2\\\":{\\\"href\\\":\\\"s3://sentinel-s2-l2a/tiles/24/M/WV/2025/7/31/0/R20m/AOT.jp2\\\",\\\"type\\\":\\\"image/jp2\\\",\\\"title\\\":\\\"Aerosol\n optical thickness 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gzip, deflate\n Connection:\n - keep-alive\n User-Agent:\n - python-requests/2.25.1\n method: GET\n uri: https://planetarycomputer.microsoft.com/api/stac/v1\n response:\n body:\n string: !!binary |\n H4sIAAl/A2EC/82YUW/bNhDHv8pBe1xk2clL4Tc58ZwAcWZEHvYwFMOZOktsKZElaWde0e++oyzb\n aucVQ4pWfjLA4x1/vP+RPOtjJPNoHFVSWO302sdGRFeRl14RD88Pw7BQWJNHu4NbXZmNJwvZMr2F\n dPHA83Nywkrjpa7ZKyO0osSVInAGedBTZbRFBRWHyNEj7B1Wsi5gitaX4ISkWhAEqyPvoNTOUw6r\n HfiS4GsgvL7Q9Vrbyi11NP4jKr03bpwkaOTAeRTOkBhoWyTb0WA4GMYr9h9cJ0JbYt//OVvyJmLX\n 7OxVTj992JDdta7s+fLyMtCG6kK6AW8oCRESXQiOGK8J/caSi0cJh0vC7rq0r/DW6G6Gr3cvSL9z\n rO3bqyjs9s8tWbfXutkuB1ayfu84+R8jS4rHHal1KKSdCXWExigpQinUSROISSyt2XJIpDnIKlpV\n B8eSHPBQSHQSluYEJ9Gnq8MyuRbutIynv3xS+kp1SvhX3iYXKbQzv2HZJsJp6bbmkKv2qxs9gITj\n kvz+S3YDHU8QCp0jB7IyiiqqjzUvHTiy26a8vwG6S3liP9bxGWzW+ucz6HCq/dfTtDE6+vFx/15l\n IrRSJEKwz2Qrpcp/wJJJWaw6KbyfTcZwj7bStfybNZ4pveILcSJ1xQUArBFcD0fDPkB5pkWhZIWe\n OsTLMHzbDveRP0vYTeDzNOUMyqKEZ3JabcIsmIbpVgrpd5AKQa4XqR2fW1mTiq9jdd2Fzo4GeKRt\n 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Hawaii\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"3dep-seamless\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-seamless/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-seamless\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map?qt-news_science_products=0#qt-news_science_products\\\",\\\"title\\\":\\\"Public\n 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format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-174.001666666983,-15.00166666667,164.0016666666,84.00166666666]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1925-01-01T00:00:00Z\\\",\\\"2020-05-06T00:00:00Z\\\"]]}},\\\"license\\\":\\\"PDDL-1.0\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"3DEP\\\",\\\"NED\\\",\\\"Elevation\\\",\\\"DEM\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/ngp/3dep\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[10,30]},\\\"description\\\":\\\"U.S.-wide\n digital elevation data at horizontal resolutions ranging from one to sixty\n meters.\\\\n\\\\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep)\n from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map)\n are the primary elevation data product produced and distributed by the USGS.\n The 3DEP program provides raster elevation data for the conterminous United\n States, Alaska, Hawaii, and the island territories, at a variety of spatial\n resolutions. The seamless DEM layers produced by the 3DEP program are updated\n frequently to integrate newly available, improved elevation source data. \\\\n\\\\nDEM\n layers are available nationally at grid spacings of 1 arc-second (approximately\n 30 meters) for the conterminous United States, and at approximately 1, 3,\n and 9 meters for parts of the United States. Most seamless DEM data for Alaska\n is available at a resolution of approximately 60 meters, where only lower\n resolution source data exist.\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"]},\\\"gpkg\\\":{\\\"type\\\":\\\"application/geopackage+sqlite3\\\",\\\"roles\\\":[\\\"metadata\\\"]},\\\"metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"]},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/jpeg\\\",\\\"roles\\\":[\\\"thumbnail\\\"]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"3dep\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"ai4edataeuwest\\\",\\\"msft:short_description\\\":\\\"U.S.-wide\n digital elevation data at horizontal resolutions ranging from one to sixty\n meters\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"3dep-lidar-dsm\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dsm/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dsm\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\\\",\\\"title\\\":\\\"About\n 3DEP Products & Services\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dsm\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n 3DEP Lidar Digital Surface Model\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/dsm-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/3dep-lidar-dsm.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2012-01-01T00:00:00Z\\\",\\\"2022-01-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"3DEP\\\",\\\"COG\\\",\\\"DSM\\\"],\\\"providers\\\":[{\\\"name\\\":\\\"Landrush\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/ngp/3dep/\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[2.0]},\\\"description\\\":\\\"This\n collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).\n It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)\n to output a collection of Cloud Optimized GeoTIFFs, removing all points that\n have been classified as noise.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"COG data\\\",\\\"raster:bands\\\":[{\\\"type\\\":\\\"Z\\\",\\\"unit\\\":\\\"metre\\\",\\\"sampling\\\":\\\"point\\\",\\\"data_type\\\":\\\"float32\\\",\\\"description\\\":\\\"Z\n PDAL Dimension\\\"}]},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"3dep-lidar\\\",\\\"msft:container\\\":\\\"usgs-3dep-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"usgslidareuwest\\\",\\\"msft:short_description\\\":\\\"3DEP\n Lidar collection for the Digital Surface Model (DSM) Cloud Optimized Geotiffs\n (COGs).\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"fia\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.fs.usda.gov/rds/archive/datauseinfo/open\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"USDA\n Open Access Data Use Agreement\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/fia\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Forest\n Inventory and Analysis\\\",\\\"assets\\\":{\\\"guide\\\":{\\\"href\\\":\\\"https://www.fia.fs.fed.us/library/database-documentation/current/ver80/FIADB%20User%20Guide%20P2_8-0.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Database\n Description and User Guide\\\"},\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/fia.png\\\",\\\"type\\\":\\\"image/gif\\\",\\\"title\\\":\\\"Forest\n Inventory and Analysis\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/fia.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[138.06,0.92,163.05,9.78],[165.28,4.57,172.03,14.61],[131.13,2.95,134.73,8.1],[-124.763068,24.523096,-66.949895,49.384358],[-179.148909,51.214183,-129.974167,71.365162],[172.461667,51.357688,179.77847,53.01075],[-178.334698,18.910361,-154.806773,28.402123],[144.618068,13.234189,144.956712,13.654383],[-67.945404,17.88328,-65.220703,18.515683],[144.886331,14.110472,146.064818,20.553802],[-65.085452,17.673976,-64.564907,18.412655],[-171.089874,-14.548699,-168.1433,-11.046934],[-178.334698,18.910361,-154.806773,28.402123]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2020-06-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"CC0-1.0\\\",\\\"keywords\\\":[\\\"Forest\\\",\\\"Species\\\",\\\"Carbon\\\",\\\"Biomass\\\",\\\"USDA\\\",\\\"Forest\n Service\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.fia.fs.fed.us/\\\",\\\"name\\\":\\\"Forest\n Inventory & Analysis\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://carbonplan.org/\\\",\\\"name\\\":\\\"CarbonPlan\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"description\\\":\\\"Status\n and trends on U.S. forest location, health, growth, mortality, and production,\n from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/)\n (FIA) program.\\\\n\\\\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide\n survey of the forest assets of the United States. The FIA research program\n has been in existence since 1928. FIA's primary objective is to determine\n the extent, condition, volume, growth, and use of trees on the nation's forest\n land.\\\\n\\\\nDomain: continental U.S., 1928-2018\\\\n\\\\nResolution: plot-level\n (irregular polygon)\\\\n\\\\nThis dataset was curated and brought to Azure by\n [CarbonPlan](https://carbonplan.org/).\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Dataset\n root\\\",\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"table:tables\\\":[{\\\"name\\\":\\\"Survey\n Table\\\",\\\"description\\\":\\\"Survey table. This table contains one record for\n each year an inventory is conducted in a State for annual inventory or one\n record for each periodic inventory.

* SURVEY.CN = PLOT.SRV_CN links\n the unique inventory record for a State and year to the plot records.\\\",\\\"msft:item_name\\\":\\\"survey\\\"},{\\\"name\\\":\\\"County\n Table\\\",\\\"description\\\":\\\"County table. This table contains survey unit codes\n and is also a reference table for the county codes and names.

* COUNTY.CN\n = PLOT.CTY_CN links the unique county record to the plot record.\\\",\\\"msft:item_name\\\":\\\"county\\\"},{\\\"name\\\":\\\"Plot\n Table\\\",\\\"description\\\":\\\"Plot table. This table provides information relevant\n to the entire 1-acre field plot. This table links to most other tables, and\n the linkage is made using PLOT.CN = TABLE_NAME.PLT_CN (TABLE_NAME is the name\n of any table containing the column name PLT_CN). Below are some examples of\n linking PLOT to other tables.

* PLOT.CN = COND.PLT_CN links the unique\n plot record to the condition class record(s).

* PLOT.CN = SUBPLOT.PLT_CN\n links the unique plot record to the subplot records.

* PLOT.CN = TREE.PLT_CN\n links the unique plot record to the tree records.

* PLOT.CN = SEEDLING.PLT_CN\n links the unique plot record to the seedling records.\\\",\\\"msft:item_name\\\":\\\"plot\\\"},{\\\"name\\\":\\\"Condition\n Table\\\",\\\"description\\\":\\\"Condition table. This table provides information\n on the discrete combination of landscape attributes that define the condition\n (a condition will have the same land class, reserved status, owner group,\n forest type, stand-size class, regeneration status, and stand density).

*\n PLOT.CN = COND.PLT_CN links the condition class record(s) to the plot table.\n

* COND.PLT_CN = SITETREE.PLT_CN and COND.CONDID = SITETREE.CONDID\n links the condition class record to the site tree data.

* COND.PLT_CN\n = TREE.PLT_CN and COND.CONDID = TREE.CONDID links the condition class record\n to the tree data.\\\",\\\"msft:item_name\\\":\\\"cond\\\"},{\\\"name\\\":\\\"Subplot Table\\\",\\\"description\\\":\\\"Subplot\n table. This table describes the features of a single subplot. There are multiple\n subplots per 1-acre field plot and there can be multiple conditions sampled\n on each subplot.

* PLOT.CN = SUBPLOT.PLT_CN links the unique plot\n record to the subplot records.

* SUBPLOT.PLT_CN = COND.PLT_CN and\n SUBPLOT.MACRCOND = COND.CONDID links the macroplot conditions to the condition\n class record.

* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.SUBPCOND\n = COND.CONDID links the subplot conditions to the condition class record.\n

* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.MICRCOND = COND.CONDID\n links the microplot conditions to the condition class record.\\\",\\\"msft:item_name\\\":\\\"subplot\\\"},{\\\"name\\\":\\\"Subplot\n Condition Table\\\",\\\"description\\\":\\\"Subplot condition table. This table contains\n information about the proportion of a subplot in a condition.

* PLOT.CN\n = SUBP_COND.PLT_CN links the subplot condition class record to the plot table.\n

* SUBP_COND.PLT_CN = COND.PLT_CN and SUBP_COND.CONDID = COND.CONDID\n links the condition class records found on the four subplots to the subplot\n description.\\\",\\\"msft:item_name\\\":\\\"subp_cond\\\"},{\\\"name\\\":\\\"boundary\\\",\\\"description\\\":\\\"Boundary\n table. This table provides a description of the demarcation line between two\n conditions that occur on a single subplot\\\",\\\"msft:item_name\\\":\\\"boundary\\\"},{\\\"name\\\":\\\"Subplot\n Condition Change Matrix\\\",\\\"description\\\":\\\"Subplot condition change matrix\n table. This table contains information about the mix of current and previous\n conditions that occupy the same area on the subplot.

* PLOT.CN = SUBP_COND_CHNG_MTRX.PLT_CN\n links the subplot condition change matrix records to the unique plot record.\n

* PLOT.PREV_PLT_CN = SUBP_COND_CHNG_MTRX.PREV_PLT_CN links the subplot\n condition change matrix records to the unique previous plot record.\\\",\\\"msft:item_name\\\":\\\"subp_cond_chng_mtrx\\\"},{\\\"name\\\":\\\"Tree\n Table\\\",\\\"description\\\":\\\"Tree table. This table provides information for\n each tree 1 inch in diameter and larger found on a microplot, subplot, or\n core optional macroplot.

* PLOT.CN = TREE.PLT_CN links the tree records\n to the unique plot record.

* COND.PLT_CN = TREE.PLT_CN and COND.CONDID\n = TREE.CONDID links the tree records to the unique condition record.\\\",\\\"msft:item_name\\\":\\\"tree\\\"},{\\\"name\\\":\\\"Tree\n Woodland Stems Table\\\",\\\"description\\\":\\\"Tree woodland stems table. This table\n stores data for the individual stems of a woodland species tree. Individual\n woodland stem diameter measurements contribute to the calculation of the diameter\n stored on the parent TREE table record.

* TREE.CN = TREE_WOODLAND_STEMS.TRE_CN\n links a woodland stems record to the corresponding unique tree record.\\\",\\\"msft:item_name\\\":\\\"tree_woodland_stems\\\"},{\\\"name\\\":\\\"Tree\n Regional Biomass Table\\\",\\\"description\\\":\\\"Tree regional biomass table. This\n table contains biomass estimates computed using equations and methodology\n that varies by FIA work unit. This table retains valuable information for\n generating biomass estimates that match earlier published reports.

*\n TREE.CN = TREE_REGIONAL_BIOMASS.TRE_CN links a tree regional biomass record\n to the corresponding unique tree.\\\",\\\"msft:item_name\\\":\\\"tree_regional_biomass\\\"},{\\\"name\\\":\\\"Tree\n Net Growth, Removal, and Mortality Component Table\\\",\\\"description\\\":\\\"Tree\n net growth, removal, and mortality component table. This table stores information\n used to compute net growth, removals, and mortality estimates for remeasurement\n trees. Each remeasurement tree has a single record in this table.

*\n TREE_GRM_COMPONENT.TRE_CN = TREE.TRE_CN links the records in this table to\n the corresponding tree record in the TREE table.\\\",\\\"msft:item_name\\\":\\\"tree_grm_component\\\"},{\\\"name\\\":\\\"Tree\n Net Growth, Removal, and Mortality Midpoint Table\\\",\\\"description\\\":\\\"Tree\n net growth, removal, and mortality midpoint table. This table contains information\n about a remeasured tree at the midpoint of the remeasurement period. It does\n not contain a record for every tree. Midpoint estimates are computed for trees\n that experience mortality, removal, or land use diversion or reversion. The\n information in this table is used to compute net growth, removal, and mortality\n estimates on remeasurement trees.

* TREE_GRM_MIDPT.TRE_CN = TREE.TRE_CN\n links the records in this table to the corresponding tree record in the TREE\n table.\\\",\\\"msft:item_name\\\":\\\"tree_grm_midpt\\\"},{\\\"name\\\":\\\"Tree Net Growth,\n Removal, and Mortality Begin Table\\\",\\\"description\\\":\\\"Tree net growth, removal,\n and mortality begin table. This table contains information for remeasured\n trees where values have been calculated for the beginning of the remeasurement\n period. Only those trees where information was recalculated for time 1 (T1)\n are included. The information in this table is used to produce net growth,\n removal and mortality estimates on remeasured trees.

* TREE_GRM_BEGIN.TRE_CN\n = TREE.TRE_CN links the records in this table to the corresponding tree record\n in the TREE table.\\\",\\\"msft:item_name\\\":\\\"tree_grm_begin\\\"},{\\\"name\\\":\\\"Tree\n Net Growth, Removal, and Mortality Estimation Table\\\",\\\"description\\\":\\\"Tree\n net growth, removal, and mortality estimation table. This table contains information\n used to produce estimates of growth, removals and mortality.

* PLOT.CN\n = TREE_GRM_ESTN.PLT_CN links the tree GRM estimation records to the unique\n plot record.

* TREE.CN = TREE_GRM_ESTN.TRE_CN links the tree GRM estimation\n records to the unique tree record.\\\",\\\"msft:item_name\\\":\\\"tree_grm_estn\\\"},{\\\"name\\\":\\\"Seedling\n Table\\\",\\\"description\\\":\\\"Seedling table. This table provides a count of the\n number of live trees of a species found on a microplot that are less than\n 1 inch in diameter but at least 6 inches in length for conifer species or\n at least 12 inches in length for hardwood species.

* PLOT.CN = SEEDLING.PLT_CN\n links the seedling records to the unique plot record.

* COND.PLT_CN\n = SEEDLING.PLT_CN and COND.CONDID = SEEDLING.CONDID links the condition record\n to the seedling record.\\\",\\\"msft:item_name\\\":\\\"seedling\\\"},{\\\"name\\\":\\\"Site\n Tree Table\\\",\\\"description\\\":\\\"Site tree table. This table provides information\n on the site tree(s) collected in order to calculate site index and/or site\n productivity information for a condition.

* PLOT.CN = SITETREE.PLT_CN\n links the site tree records to the unique plot record.

* SITETREE.PLT_CN\n = COND.PLT_CN and SITETREE.CONDID = COND.CONDID links the site tree record(s)\n to the unique condition class record.\\\",\\\"msft:item_name\\\":\\\"sitetree\\\"},{\\\"name\\\":\\\"Invasive\n Subplot Species Table\\\",\\\"description\\\":\\\"Invasive subplot species table.\n This table provides percent cover data of invasive species identified on the\n subplot.

* PLOT.CN = INVASIVE_SUBPLOT_SPP.PLT_CN links the invasive\n subplot species record(s) to the unique plot record.

* SUBP_COND.PLT_CN\n = INVASIVE_SUBPLOT_SPP.PLT_CN and SUBP_COND.CONDID = INVASIVE_SUBPLOT_SPP.CONDID\n and SUBP_COND.SUBP = INVASIVE_SUBPLOT_SPP.SUBP links the invasive subplot\n species record(s) to the unique subplot condition record.

* INVASIVE_SUBPLOT_SPP.VEG_SPCD\n = REF_PLANT_DICTIONARY.SYMBOL links the invasive vegetation subplot NRCS species\n code to the plant dictionary reference species code.\\\",\\\"msft:item_name\\\":\\\"invasive_subplot_spp\\\"},{\\\"name\\\":\\\"Phase\n 2 Vegetation Subplot Species Table\\\",\\\"description\\\":\\\"Phase 2 Vegetation\n subplot species table. This table provides percent cover data of vegetation\n species identified on the subplot.

* PLOT.CN = P2VEG_SUBPLOT_SPP.PLT_CN\n links the vegetation subplot species record(s) to the unique plot record.\n

* SUBP_COND.PLT_CN = P2VEG_SUBPLOT_SPP.PLT_CN and SUBP_COND.CONDID\n = P2VEG_SUBPLOT_SPP.CONDID and SUBP_COND.SUBP = P2VEG_SUBPLOT_SPP.SUBP links\n the vegetation subplot species record(s) to the unique subplot condition record.\n

* P2VEG_SUBPLOT_SPP.VEG_SPCD = REF_PLANT_DICTIONARY.SYMBOL links the\n P2 vegetation subplot NRCS species code to the plant dictionary reference\n species code.\\\",\\\"msft:item_name\\\":\\\"p2veg_subplot_spp\\\"},{\\\"name\\\":\\\"Phase\n 2 Vegetation Subplot Structure Table\\\",\\\"description\\\":\\\"Phase 2 Vegetation\n subplot structure table. This table provides percent cover by layer by growth\n habit.

* PLOT.CN = P2VEG_SUBP_STRUCTURE. PLT_CN links the subplot\n structure record(s) to the unique plot record.

* SUBP_COND.PLT_CN\n = P2VEG_SUBP_STRUCTURE.PLT_CN and SUBP_COND.CONDID = P2VEG_SUBP_STRUCTURE.CONDID\n and SUBP_COND.SUBP = P2VEG_SUBP_STRUCTURE.SUBP links the vegetation subplot\n structure record(s) to the unique subplot condition record.\\\",\\\"msft:item_name\\\":\\\"p2veg_subp_structure\\\"},{\\\"name\\\":\\\"Down\n Woody Material Visit Table\\\",\\\"description\\\":\\\"Down woody material visit table.\n This table provides general information on down woody material indicator visit,\n such as the date of the DWM survey.

* PLOT.CN = DWM_VISIT.PLT_CN links\n the down woody material indicator visit record to the unique plot record.\\\",\\\"msft:item_name\\\":\\\"dwm_visit\\\"},{\\\"name\\\":\\\"Down\n Woody Material Coarse Woody Debris Table\\\",\\\"description\\\":\\\"Down woody material\n coarse woody debris table. This table provides information for each piece\n of coarse woody debris measured along the transects.

* PLOT.CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN\n links the down woody material coarse woody debris records to the unique plot\n record.

* COND.PLT_CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN and COND.CONDID=\n DWM_COARSE_WOODY_DEBRIS.CONDID links the coarse woody debris records to the\n unique condition record.\\\",\\\"msft:item_name\\\":\\\"dwm_coarse_woody_debris\\\"},{\\\"name\\\":\\\"Down\n Woody Material Duff, Litter, Fuel Table\\\",\\\"description\\\":\\\"Down woody material\n duff, litter, fuel table. This table provides information on the duff, litter,\n fuelbed depths measured at a point on the transects.

* PLOT.CN = DWM_DUFF_LITTER_FUEL.PLT_CN\n links the duff, litter, fuelbed records to the unique plot record.

*\n COND.PLT_CN = DWM_DUFF_LITTER_FUEL.PLT_CN and COND.CONDID= DWM_DUFF_LITTER_FUEL.CONDID\n links the duff, litter, fuel records to the unique condition record.\\\",\\\"msft:item_name\\\":\\\"dwm_duff_litter_fuel\\\"},{\\\"name\\\":\\\"Down\n Woody Material Fine Woody Debris Table\\\",\\\"description\\\":\\\"Down woody material\n fine woody debris table. This table provides information on the fine woody\n debris measured along a segment of the transects.

* PLOT.CN = DWM_FINE_WOODY_DEBRIS.PLT_CN\n links the fine woody debris records to the unique plot record.

* COND.PLT_CN\n = DWM_FINE_WOODY_DEBRIS.PLT_CN and COND.CONDID= DWM_FINE_WOODY_DEBRIS.CONDID\n links the fine woody debris records to the unique condition record.\\\",\\\"msft:item_name\\\":\\\"dwm_fine_woody_debris\\\"},{\\\"name\\\":\\\"Down\n Woody Material Microplot Fuel Table\\\",\\\"description\\\":\\\"Down woody material\n microplot fuel table. This table provides information on the fuel loads (shrubs\n and herbs) measured on the microplot.

* PLOT.CN = DWM_MICROPLOT_FUEL.PLT_CN\n links the microplot fuel records to the unique plot record.\\\",\\\"msft:item_name\\\":\\\"dwm_microplot_fuel\\\"},{\\\"name\\\":\\\"Down\n Woody Material Residual Pile Table\\\",\\\"description\\\":\\\"Down woody material\n residual pile table. This table provides information on the wood piles measured\n on the subplot.

* PLOT.CN = DWM_RESIDUAL_PILE.PLT_CN links the wood\n piles records to the unique plot record.

* COND.PLT_CN = DWM_RESIDUAL_PILE.PLT_CN\n and COND.CONDID= DWM_RESIDUAL_PILE.CONDID links the wood piles records to\n the unique condition record.\\\",\\\"msft:item_name\\\":\\\"dwm_residual_pile\\\"},{\\\"name\\\":\\\"Down\n Woody Material Transect Segment Table\\\",\\\"description\\\":\\\"Down woody material\n transect segment table. This table describes the down woody material transect\n segment lengths by condition class.

* PLOT.CN = DWM_TRANSECT_SEGMENT.PLT_CN\n links the down woody material transect length records to the unique plot record.\n

* COND.PLT_CN = DWM_TRANSECT_SEGMENT.PLT_CN and COND.CONDID= DWM_TRANSECT_SEGMENT.CONDID\n links the down woody material transect segment records to the unique condition\n record.\\\",\\\"msft:item_name\\\":\\\"dwm_transect_segment\\\"},{\\\"name\\\":\\\"Condition\n Down Woody Material Calculation Table\\\",\\\"description\\\":\\\"Condition down woody\n material calculation table. This table contains calculated values and condition-level\n estimates for down woody attributes by plot number (PLOT), condition class\n number (CONDID), and evaluation identifier (EVALID).

* PLOT.CN = COND_DWM_CALC.PLT_CN\n links the down woody material calculation records to the unique plot record.\n

* COND.CN = COND_DWM_CALC.CND_CN links the down woody material calculation\n records to the unique condition record.

* POP_STRATUM. CN = COND_DWM_CALC.STRATUM_CN\n links the down woody material calculation records to the unique population\n stratum record.\\\",\\\"msft:item_name\\\":\\\"cond_dwm_calc\\\"},{\\\"name\\\":\\\"Plot Regeneration\n Table\\\",\\\"description\\\":\\\"Plot regeneration table. This table contains the\n information for the four subplots describing the amount of animal browse pressure\n exerted on the regeneration of trees.

* PLOT.CN = PLOT_REGEN.PLT_CN\n links the unique plot record to the unique plot regeneration record.\\\",\\\"msft:item_name\\\":\\\"plot_regen\\\"},{\\\"name\\\":\\\"Subplot\n Regeneration Table\\\",\\\"description\\\":\\\"Subplot regeneration table. This table\n provides information on the subplot survey status and the site survey limitations,\n if any, for the tree regeneration study.

* PLOT.CN = SUBPLOT_REGEN.PLT_CN\n links the unique plot record to the subplot regeneration records.

*\n SUBPLOT.PLT_CN = SUBPLOT_REGEN.PLT_CN and SUBPLOT.SUBP = SUBPLOT_REGEN.SUBP\n links the subplot record to the subplot regeneration record.\\\",\\\"msft:item_name\\\":\\\"subplot_regen\\\"},{\\\"name\\\":\\\"Seedling\n Regeneration Table\\\",\\\"description\\\":\\\"Seedling regeneration. This table contains\n provides information on the seedling count by condition, species, source,\n and length class for the tree regeneration study.

* PLOT.CN = SEEDLING_REGEN.PLT_CN\n links the unique plot record to the seedling regeneration records.

*\n COND.PLT_CN = SEEDLING_REGEN.PLT_CN and COND.CONDID = SEEDLING_REGEN.CONDID\n links the regeneration seedling records to the unique condition record.\\\",\\\"msft:item_name\\\":\\\"seedling_regen\\\"},{\\\"name\\\":\\\"Population\n Estimation Unit Table\\\",\\\"description\\\":\\\"Population estimation unit table.\n This table contains information about estimation units. An estimation unit\n is a geographic area that can be drawn on a map. It has a known area, and\n the sampling intensity must be the same within a stratum within an estimation\n unit. Generally, estimation units are contiguous areas, but exceptions are\n made when certain ownerships, usually National Forests, are sampled at different\n intensities. One record in the POP_ESTN_UNIT table corresponds to a single\n estimation unit. POP_ESTN_UNIT.CN = POP_STRATUM.ESTN_UNIT_CN links the unique\n stratified geographical area (ESTN_UNIT) to the strata (STRATUMCD) that are\n assigned to each ESTN_UNIT.\\\",\\\"msft:item_name\\\":\\\"pop_estn_unit\\\"},{\\\"name\\\":\\\"Population\n Evaluation Table\\\",\\\"description\\\":\\\"Population evaluation table. This table\n provides information about evaluations. An evaluation is the combination of\n a set of plots (the sample) and a set of Phase 1 data (obtained through remote\n sensing, called a stratification) that can be used to produce population estimates\n for a State (an evaluation may be created to produce population estimates\n for a region other than a State, such as the Black Hills National Forest).\n A record in the POP_EVAL table identifies one evaluation and provides some\n descriptive information about how the evaluation may be used.

* POP_ESTN_UNIT.EVAL_CN\n = POP_EVAL.CN links the unique evaluation identifier (EVALID) in the POP_EVAL\n table to the unique geographical areas (ESTN_UNIT) that are stratified. Within\n a population evaluation (EVALID) there can be multiple population estimation\n units, or geographic areas across which there are a number of values being\n estimated (e.g., estimation of volume across counties for a given State).\\\",\\\"msft:item_name\\\":\\\"pop_eval\\\"},{\\\"name\\\":\\\"Population\n Evaluation Attribute Table\\\",\\\"description\\\":\\\"Population evaluation attribute\n table. This table provides information as to which population estimates can\n be provided by an evaluation. If an evaluation can produce only 22 of all\n the population estimates in the REF_POP_ATTRIBUTE table, there will be 22\n records in the POP_EVAL_ATTRIBUTE table (one per population estimate) for\n that evaluation.

* POP_EVAL.CN = POP_EVAL_ATTRIBUTE.EVAL_CN links\n the unique evaluation identifier to the list of population estimates that\n can be derived for that evaluation.\\\",\\\"msft:item_name\\\":\\\"pop_eval_attribute\\\"},{\\\"name\\\":\\\"Population\n Evaluation Group Table\\\",\\\"description\\\":\\\"Population evaluation group table.\n This table lists and describes the evaluation groups. One record in the POP_EVAL_GRP\n table can be linked to all the evaluations that were used in generating estimates\n for a State inventory report.

* POP_EVAL_GRP.CN = POP_EVAL_TYP.EVAL_GRP_CN\n links the evaluation group record to the evaluation type record.\\\",\\\"msft:item_name\\\":\\\"pop_eval_grp\\\"},{\\\"name\\\":\\\"Population\n Evaluation Type Table\\\",\\\"description\\\":\\\"Population evaluation type table.\n This table provides information on the type of evaluations that were used\n to generate a set of tables for an inventory report. In a typical State inventory\n report, one evaluation is used to generate an estimate of the total land area;\n a second evaluation is used to generate current estimates of volume, numbers\n of trees and biomass; and a third evaluation is used for estimating growth,\n removals and mortality.

* POP_EVAL_TYP.EVAL_CN = POP_EVAL.CN links\n the evaluation type record to the evaluation record.

* POP_EVAL_TYP.EVAL_GRP_CN\n = POP_EVAL_GRP.CN links the evaluation type record to the evaluation group\n record.

* POP_EVAL_TYP.EVAL_TYP = REF_POP_EVAL_TYP_DESCR.EVAL_TYP\n links an evaluation type record to an evaluation type description reference\n record.\\\",\\\"msft:item_name\\\":\\\"pop_eval_typ\\\"},{\\\"name\\\":\\\"Population Plot\n Stratum Assignment Table\\\",\\\"description\\\":\\\"Population plot stratum assignment\n table. This table provides a way to assign stratum information to a plot.\n Stratum information is assigned to a plot by overlaying the plot's location\n on the Phase 1 imagery. Plots are linked to their appropriate stratum for\n an evaluation via the POP_PLOT_STRATUM_ASSGN table.

* POP_PLOT_STRATUM_ASSGN.PLT_CN\n = PLOT.CN links the stratum assigned to the plot record.\\\",\\\"msft:item_name\\\":\\\"pop_plot_stratum_assgn\\\"},{\\\"name\\\":\\\"Population\n Stratum Table\\\",\\\"description\\\":\\\"Population stratum table. This table provides\n information about individual strata. The area within an estimation unit is\n divided into strata. The area for each stratum can be calculated by determining\n the proportion of Phase 1 pixels/plots in each stratum and multiplying that\n proportion by the total area in the estimation unit. Information for a single\n stratum is stored in a single record of the POP_STRATUM table.

* POP_STRATUM.CN\n = POP_PLOT_STRATUM_ASSGN.STRATUM_CN links the defined stratum to each plot.\\\",\\\"msft:item_name\\\":\\\"pop_stratum\\\"},{\\\"name\\\":\\\"Plot\n Geometry Table\\\",\\\"description\\\":\\\"Plot geometry table. This table contains\n geometric attributes associated with the plot location, such as the hydrological\n unit and roadless codes.

* PLOTGEOM.CN = PLOT.CN links the unique\n plot record between the two tables.\\\",\\\"msft:item_name\\\":\\\"plotgeom\\\"},{\\\"name\\\":\\\"Plot\n Snapshot Table\\\",\\\"description\\\":\\\"Plot snapshot table. This table combines\n the information in the PLOT table with information in the PLOT_EVAL_GRP and\n POP_STRATUM tables to provide a snapshot of the plot records with their associated\n expansion and adjustment factors.

* PLOTSNAP.CN = PLOT.CN links the\n unique plot record between the two tables.\\\",\\\"msft:item_name\\\":\\\"plotsnap\\\"}],\\\"msft:container\\\":\\\"cpdata\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"cpdataeuwest\\\",\\\"msft:short_description\\\":\\\"Status\n and trends on U.S. forest location, health, growth, mortality, and production,\n from the U.S. Forest Service's Forest Inventory and Analysis (FIA) program\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"gridmet\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gridmet/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gridmet\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://creativecommons.org/publicdomain/zero/1.0/legalcode\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"CC0\n 1.0\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/gridmet\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"gridMET\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gridmet.png\\\",\\\"role\\\":[\\\"thumbnail\\\"],\\\"type\\\":\\\"image/png\\\",\\\"title\\\":\\\"gridMET\n thumbnail\\\"},\\\"zarr-abfs\\\":{\\\"href\\\":\\\"abfs://gridmet/gridmet.zarr\\\",\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\",\\\"zarr\\\"],\\\"description\\\":\\\"Azure\n Blob File System URI of the gridMET Zarr Group on Azure Blob Storage for use\n with adlfs.\\\",\\\"xarray:open_kwargs\\\":{\\\"consolidated\\\":true},\\\"xarray:storage_options\\\":{\\\"account_name\\\":\\\"ai4edataeuwest\\\"}},\\\"zarr-https\\\":{\\\"href\\\":\\\"https://ai4edataeuwest.blob.core.windows.net/gridmet/gridmet.zarr\\\",\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\",\\\"zarr\\\",\\\"https\\\"],\\\"title\\\":\\\"gridMET\n HTTPS Zarr root\\\",\\\"description\\\":\\\"HTTPS URI of the gridMET Zarr Group on\n Azure Blob Storage.\\\",\\\"xarray:open_kwargs\\\":{\\\"consolidated\\\":true}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-124.76666663333334,25.066666666666666,-67.05833330000002,49.400000000000006]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1979-01-01T00:00:00Z\\\",\\\"2020-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CC0-1.0\\\",\\\"keywords\\\":[\\\"gridMET\\\",\\\"Water\\\",\\\"Precipitation\\\",\\\"Temperature\\\",\\\"Vapor\n Pressure\\\",\\\"Climate\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://developmentseed.org/\\\",\\\"name\\\":\\\"Development\n Seed\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"http://www.climatologylab.org/gridmet.html\\\",\\\"name\\\":\\\"Climatology\n Lab\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"http://onlinelibrary.wiley.com/doi/10.1002/joc.3413/full\\\",\\\"name\\\":\\\"Abatzoglou,\n J.T.\\\",\\\"roles\\\":[\\\"producer\\\"]}],\\\"description\\\":\\\"gridMET is a dataset of\n daily surface meteorological data at approximately four-kilometer resolution,\n covering the contiguous U.S. from 1979 to the present. These data can provide\n important inputs for ecological, agricultural, and hydrological models.\\\\n\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"cube:variables\\\":{\\\"wind_speed\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"m/s\\\",\\\"attrs\\\":{\\\"units\\\":\\\"m/s\\\",\\\"long_name\\\":\\\"vs\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"Daily Mean Wind Speed\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"vs\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n Mean Wind Speed\\\"},\\\"air_temperature\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"K\\\",\\\"attrs\\\":{\\\"units\\\":\\\"K\\\",\\\"long_name\\\":\\\"tmmn\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"Daily Minimum Temperature\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"tmmn\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n Minimum Temperature\\\"},\\\"burning_index_g\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"Unitless\\\",\\\"attrs\\\":{\\\"units\\\":\\\"Unitless\\\",\\\"long_name\\\":\\\"bi\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"BI-G\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"bi\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"BI-G\\\"},\\\"relative_humidity\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"%\\\",\\\"attrs\\\":{\\\"units\\\":\\\"%\\\",\\\"long_name\\\":\\\"rmin\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"Daily Minimum Relative Humidity\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"rmin\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n Minimum Relative Humidity\\\"},\\\"specific_humidity\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"kg/kg\\\",\\\"attrs\\\":{\\\"units\\\":\\\"kg/kg\\\",\\\"long_name\\\":\\\"sph\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"Daily mean specific humidity\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"sph\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n mean specific humidity\\\"},\\\"wind_from_direction\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"Degrees\n Clockwise from north\\\",\\\"attrs\\\":{\\\"units\\\":\\\"Degrees Clockwise from north\\\",\\\"long_name\\\":\\\"th\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"Daily mean wind direction\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"th\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n mean wind direction\\\"},\\\"precipitation_amount\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm\\\",\\\"long_name\\\":\\\"pr\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"Daily Accumulated Precipitation\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"pr\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n Accumulated Precipitation\\\"},\\\"dead_fuel_moisture_100hr\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"Percent\\\",\\\"attrs\\\":{\\\"units\\\":\\\"Percent\\\",\\\"long_name\\\":\\\"fm100\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"100 hour fuel moisture\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"fm100\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"100\n hour fuel moisture\\\"},\\\"dead_fuel_moisture_1000hr\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"Percent\\\",\\\"attrs\\\":{\\\"units\\\":\\\"Percent\\\",\\\"long_name\\\":\\\"fm1000\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"1000 hour fuel moisture\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"fm1000\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"1000\n hour fuel moisture\\\"},\\\"mean_vapor_pressure_deficit\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"kPa\\\",\\\"attrs\\\":{\\\"units\\\":\\\"kPa\\\",\\\"long_name\\\":\\\"vpd\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"mean vapor presure deficit\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"vpd\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"mean\n vapor presure deficit\\\"},\\\"potential_evapotranspiration\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm\\\",\\\"long_name\\\":\\\"pet\\\",\\\"dimensions\\\":\\\"lon\n lat time\\\",\\\"description\\\":\\\"Daily reference evapotranspiration (short grass)\\\",\\\"grid_mapping\\\":\\\"crs\\\",\\\"standard_name\\\":\\\"pet\\\",\\\"coordinate_system\\\":\\\"WGS84,EPSG:4326\\\"},\\\"shape\\\":[15341,585,1386],\\\"chunks\\\":[30,585,1386],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n reference evapotranspiration (short grass)\\\"},\\\"surface_downwelling_shortwave_flux_in_air\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"W\n 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Annual averages\n are provided for minimum and maximum temperature, vapor pressure, and snow\n water equivalent, and annual totals are provided for the precipitation variable.\\\\n\\\\n[Daymet](https://daymet.ornl.gov/)\n provides measurements of near-surface meteorological conditions; the main\n purpose is to provide data estimates where no instrumentation exists. The\n dataset covers the period from January 1, 1980 to the present. Each year is\n processed individually at the close of a calendar year. 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ORNL\n DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1855\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"daymet\\\",\\\"cube:variables\\\":{\\\"vp\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"Pa\\\",\\\"attrs\\\":{\\\"units\\\":\\\"Pa\\\",\\\"long_name\\\":\\\"monthly\n average of daily average vapor pressure\\\",\\\"cell_methods\\\":\\\"area: mean time:\n mean within days time: mean over days\\\",\\\"grid_mapping\\\":\\\"lambert_conformal_conic\\\"},\\\"shape\\\":[492,231,364],\\\"chunks\\\":[12,231,364],\\\"dimensions\\\":[\\\"time\\\",\\\"y\\\",\\\"x\\\"],\\\"description\\\":\\\"monthly\n average of daily average vapor pressure\\\"},\\\"lat\\\":{\\\"type\\\":\\\"auxiliary\\\",\\\"unit\\\":\\\"degrees_north\\\",\\\"attrs\\\":{\\\"units\\\":\\\"degrees_north\\\",\\\"long_name\\\":\\\"latitude\n coordinate\\\",\\\"standard_name\\\":\\\"latitude\\\"},\\\"shape\\\":[231,364],\\\"chunks\\\":[231,364],\\\"dimensions\\\":[\\\"y\\\",\\\"x\\\"],\\\"description\\\":\\\"latitude\n coordinate\\\"},\\\"lon\\\":{\\\"type\\\":\\\"auxiliary\\\",\\\"unit\\\":\\\"degrees_east\\\",\\\"attrs\\\":{\\\"units\\\":\\\"degrees_east\\\",\\\"long_name\\\":\\\"longitude\n coordinate\\\",\\\"standard_name\\\":\\\"longitude\\\"},\\\"shape\\\":[231,364],\\\"chunks\\\":[231,364],\\\"dimensions\\\":[\\\"y\\\",\\\"x\\\"],\\\"description\\\":\\\"longitude\n coordinate\\\"},\\\"swe\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm\\\",\\\"long_name\\\":\\\"monthly\n total snow water equivalent\\\",\\\"cell_methods\\\":\\\"area: mean time: sum within\n days time: sum over days\\\",\\\"grid_mapping\\\":\\\"lambert_conformal_conic\\\"},\\\"shape\\\":[492,231,364],\\\"chunks\\\":[12,231,364],\\\"dimensions\\\":[\\\"time\\\",\\\"y\\\",\\\"x\\\"],\\\"description\\\":\\\"monthly\n total snow water equivalent\\\"},\\\"prcp\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm\\\",\\\"long_name\\\":\\\"monthly\n total precipitation\\\",\\\"cell_methods\\\":\\\"area: mean time: sum within days\n time: sum over days\\\",\\\"grid_mapping\\\":\\\"lambert_conformal_conic\\\"},\\\"shape\\\":[492,231,364],\\\"chunks\\\":[12,231,364],\\\"dimensions\\\":[\\\"time\\\",\\\"y\\\",\\\"x\\\"],\\\"description\\\":\\\"monthly\n total precipitation\\\"},\\\"tmax\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"degrees C\\\",\\\"attrs\\\":{\\\"units\\\":\\\"degrees\n C\\\",\\\"long_name\\\":\\\"monthly average of daily maximum temperature\\\",\\\"cell_methods\\\":\\\"area:\n mean time: maximum within days time: mean over 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projection\\\",\\\"reference_system\\\":{\\\"name\\\":\\\"undefined\\\",\\\"type\\\":\\\"ProjectedCRS\\\",\\\"$schema\\\":\\\"https://proj.org/schemas/v0.4/projjson.schema.json\\\",\\\"base_crs\\\":{\\\"name\\\":\\\"undefined\\\",\\\"datum\\\":{\\\"name\\\":\\\"undefined\\\",\\\"type\\\":\\\"GeodeticReferenceFrame\\\",\\\"ellipsoid\\\":{\\\"name\\\":\\\"undefined\\\",\\\"semi_major_axis\\\":6378137,\\\"inverse_flattening\\\":298.257223563}},\\\"coordinate_system\\\":{\\\"axis\\\":[{\\\"name\\\":\\\"Longitude\\\",\\\"unit\\\":\\\"degree\\\",\\\"direction\\\":\\\"east\\\",\\\"abbreviation\\\":\\\"lon\\\"},{\\\"name\\\":\\\"Latitude\\\",\\\"unit\\\":\\\"degree\\\",\\\"direction\\\":\\\"north\\\",\\\"abbreviation\\\":\\\"lat\\\"}],\\\"subtype\\\":\\\"ellipsoidal\\\"}},\\\"conversion\\\":{\\\"name\\\":\\\"unknown\\\",\\\"method\\\":{\\\"id\\\":{\\\"code\\\":9802,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Lambert\n Conic Conformal 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projection\\\",\\\"reference_system\\\":{\\\"name\\\":\\\"undefined\\\",\\\"type\\\":\\\"ProjectedCRS\\\",\\\"$schema\\\":\\\"https://proj.org/schemas/v0.4/projjson.schema.json\\\",\\\"base_crs\\\":{\\\"name\\\":\\\"undefined\\\",\\\"datum\\\":{\\\"name\\\":\\\"undefined\\\",\\\"type\\\":\\\"GeodeticReferenceFrame\\\",\\\"ellipsoid\\\":{\\\"name\\\":\\\"undefined\\\",\\\"semi_major_axis\\\":6378137,\\\"inverse_flattening\\\":298.257223563}},\\\"coordinate_system\\\":{\\\"axis\\\":[{\\\"name\\\":\\\"Longitude\\\",\\\"unit\\\":\\\"degree\\\",\\\"direction\\\":\\\"east\\\",\\\"abbreviation\\\":\\\"lon\\\"},{\\\"name\\\":\\\"Latitude\\\",\\\"unit\\\":\\\"degree\\\",\\\"direction\\\":\\\"north\\\",\\\"abbreviation\\\":\\\"lat\\\"}],\\\"subtype\\\":\\\"ellipsoidal\\\"}},\\\"conversion\\\":{\\\"name\\\":\\\"unknown\\\",\\\"method\\\":{\\\"id\\\":{\\\"code\\\":9802,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Lambert\n Conic Conformal (2SP)\\\"},\\\"parameters\\\":[{\\\"id\\\":{\\\"code\\\":8823,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Latitude\n of 1st standard parallel\\\",\\\"unit\\\":\\\"degree\\\",\\\"value\\\":25},{\\\"id\\\":{\\\"code\\\":8824,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Latitude\n of 2nd standard parallel\\\",\\\"unit\\\":\\\"degree\\\",\\\"value\\\":60},{\\\"id\\\":{\\\"code\\\":8821,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Latitude\n of false origin\\\",\\\"unit\\\":\\\"degree\\\",\\\"value\\\":42.5},{\\\"id\\\":{\\\"code\\\":8822,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Longitude\n of false origin\\\",\\\"unit\\\":\\\"degree\\\",\\\"value\\\":-100},{\\\"id\\\":{\\\"code\\\":8826,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Easting\n at false origin\\\",\\\"unit\\\":\\\"metre\\\",\\\"value\\\":0},{\\\"id\\\":{\\\"code\\\":8827,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Northing\n at false origin\\\",\\\"unit\\\":\\\"metre\\\",\\\"value\\\":0}]},\\\"coordinate_system\\\":{\\\"axis\\\":[{\\\"name\\\":\\\"Easting\\\",\\\"unit\\\":\\\"metre\\\",\\\"direction\\\":\\\"east\\\",\\\"abbreviation\\\":\\\"E\\\"},{\\\"name\\\":\\\"Northing\\\",\\\"unit\\\":\\\"metre\\\",\\\"direction\\\":\\\"north\\\",\\\"abbreviation\\\":\\\"N\\\"}],\\\"subtype\\\":\\\"Cartesian\\\"}}},\\\"nv\\\":{\\\"type\\\":\\\"count\\\",\\\"values\\\":[0,1],\\\"description\\\":\\\"Size\n of the 'time_bnds' variable.\\\"},\\\"time\\\":{\\\"type\\\":\\\"temporal\\\",\\\"extent\\\":[\\\"1980-01-16T12:00:00Z\\\",\\\"2020-12-16T00:00:00Z\\\"],\\\"description\\\":\\\"24-hour\n day based on local time\\\"}},\\\"msft:group_keys\\\":[\\\"monthly\\\",\\\"puerto rico\\\"],\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"daymeteuwest\\\",\\\"msft:short_description\\\":\\\"Monthly\n climate summaries on a 1-km grid for Puerto Rico\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"gnatsgo-tables\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-tables/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-tables\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"gSSURGO\n User Guide\\\",\\\"description\\\":\\\"User guide for gSSURGO\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcseprd1464658&ext=pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"gNATSGO\n Overview Slides\\\",\\\"description\\\":\\\"Slides giving a high level overview of\n the gNATSGO dataset\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://creativecommons.org/publicdomain/zero/1.0/\\\",\\\"title\\\":\\\"CC0\n 1.0 Universal Public Domain Dedication\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"gNATSGO\n Soil Database - Tables\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gnatsgo-tables.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"gNATSGO\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/gnatsgo-tables.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-170.8513,-14.3799,-169.4152,-14.1432],[138.0315,5.116,163.1902,10.2773],[144.6126,13.2327,144.9658,13.6572],[-159.7909,18.8994,-154.7815,22.2464],[170.969,6.0723,171.9169,8.71933],[145.0127,14.1086,145.9242,18.8172],[130.8048,2.9268,134.9834,8.0947],[157.3678,49.0546,-117.2864,71.4567],[-67.9506,17.014,-64.3973,19.3206],[-127.8881,22.8782,-65.2748,51.6039]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2020-07-01T00:00:00Z\\\",\\\"2020-07-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CC0-1.0\\\",\\\"keywords\\\":[\\\"Soils\\\",\\\"NATSGO\\\",\\\"SSURGO\\\",\\\"STATSGO2\\\",\\\"RSS\\\",\\\"USDA\\\",\\\"United\n States\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.nrcs.usda.gov/\\\",\\\"name\\\":\\\"United\n States Department of Agriculture, Natural Resources Conservation Service\\\",\\\"roles\\\":[\\\"licensor\\\",\\\"producer\\\",\\\"processor\\\",\\\"host\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"description\\\":\\\"This\n collection contains the table data for gNATSGO. This table data can be used\n to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters)\n Collection.\\\\n\\\\nThe gridded National Soil Survey Geographic Database (gNATSGO)\n is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that\n provides complete coverage of the best available soils information for all\n areas of the United States and Island Territories. It was created by combining\n data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic\n Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single\n seamless ESRI file geodatabase.\\\\n\\\\nSSURGO is the SPSD flagship soils database\n that has over 100 years of field-validated detailed soil mapping data. SSURGO\n contains soils information for more than 90 percent of the United States and\n island territories, but unmapped land remains. STATSGO2 is a general soil\n map that has soils data for all of the United States and island territories,\n but the data is not as detailed as the SSURGO data. The Raster Soil Surveys\n (RSSs) are the next generation soil survey databases developed using advanced\n digital soil mapping methods.\\\\n\\\\nThe gNATSGO database is composed primarily\n of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are\n newer product with relatively limited spatial extent. These RSSs were merged\n into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent\n of RSS is expected to increase in the coming years.\\\\n\\\\nSee the [official\n documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"soils\\\"}}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"table:tables\\\":[{\\\"name\\\":\\\"chaashto\\\",\\\"description\\\":\\\"The\n Horizon AASHTO table contains the American Association of State Highway Transportation\n Officials classification(s) for the referenced horizon. One row in this table\n is marked as the representative AASHTO classification for the horizon.\\\"},{\\\"name\\\":\\\"chconsistence\\\",\\\"description\\\":\\\"The\n Horizon Consistence table contains descriptive terms of soil consistence --\n rupture resistance, plasticity, and stickiness -- for the referenced horizon.\n \\ One row in this table is marked as having the representative characteristics\n for the horizon.\\\"},{\\\"name\\\":\\\"chdesgnsuffix\\\",\\\"description\\\":\\\"The Horizon\n Designation Suffix table contains the designation suffix(es), one per row,\n \\ for the referenced horizon. For example, the \\\\\\\"h\\\\\\\" and \\\\\\\"s\\\\\\\" of\n a Bhs horizon appear as two rows in this table.\\\"},{\\\"name\\\":\\\"chfrags\\\",\\\"description\\\":\\\"The\n Horizon Fragments table lists the mineral and organic fragments that generally\n occur in the referenced horizon. If the Volume % is greater than zero (low=5,\n RV=10, high=15) in a row, the kind and size of fragment in that row exists\n everywhere this horizon and component occur in the map unit. If the Volume\n % includes zero (low=0, RV=5, high=10), the kind and size of fragment may\n exist in some places, but not in others.\\\"},{\\\"name\\\":\\\"chorizon\\\",\\\"description\\\":\\\"The\n Horizon table lists the horizon(s) and related data for the referenced map\n unit component. If the horizon thickness is greater than zero (low=5, RV=8,\n high=12), the horizon exists everywhere this component occurs. If the horizon\n thickness includes zero (low=0, RV=1, high=3), the horizon may exist in some\n places, but not in other places.\\\\r\\\\nHorizons that have two distinct parts,\n such as E/B or E&Bt horizons, are recorded twice. Once for the characteristics\n of the first part; and again on another row, using the same depths and thicknesses,\n for the characteristics of the other part.\\\"},{\\\"name\\\":\\\"chpores\\\",\\\"description\\\":\\\"The\n Horizon Pores table lists the voids for the referenced horizon. If the Quantity\n is greater than zero (low=2, RV=5, high=10) in a row, the voids in that row\n exist everywhere the horizon and component occur in the map unit. If the\n Quantity includes zero (low=0, RV=2, high=5), the voids may exist in some\n places, but not in others. More than one row can be marked as an RV row because\n a horizon may have more than one size or shape of void.\\\"},{\\\"name\\\":\\\"chstruct\\\",\\\"description\\\":\\\"The\n Horizon Structure table lists the individual soil structure size, grade, and\n shape terms for the referenced horizon. Terms in this table are assembled\n into a structure group string which is recorded in the Horizon Structure Group\n table.\\\"},{\\\"name\\\":\\\"chstructgrp\\\",\\\"description\\\":\\\"The Horizon Structure\n Group table lists the ranges of soil structure for the referenced horizon.\n \\ The row with the typically occurring structure is marked as being representative.\n \\ The entry in this table is based on grouping of entries in the Horizon Structure\n table.\\\"},{\\\"name\\\":\\\"chtext\\\",\\\"description\\\":\\\"The Horizon Text table contains\n notes and narrative descriptions related to the referenced horizon. Some\n notes may provide additional information about the horizon for which there\n is no explicit column for such data. In many cases, the table is empty for\n a particular horizon.\\\"},{\\\"name\\\":\\\"chtexture\\\",\\\"description\\\":\\\"The Horizon\n Texture table lists the individual texture(s), or term(s) used in lieu of\n texture, for the referenced horizon. Only the unmodified texture terms are\n listed in the Horizon Texture table; modifiers are listed in the Horizon Texture\n Modifier table. For example, a gravelly loamy sand is shown as \\\\\\\"GR-LS\\\\\\\"\n in the Horizon Texture Group table, \\\\\\\"ls\\\\\\\" in the Horizon Texture table,\n and \\\\\\\"gr\\\\\\\" in the Horizon Texture Modifier table.\\\"},{\\\"name\\\":\\\"chtexturegrp\\\",\\\"description\\\":\\\"The\n Horizon Texture Group table lists the range of textures for the referenced\n horizon as a concatenation of horizon texture and texture modifier(s). For\n example, a horizon that is gravelly loamy sand in some places and gravelly\n loamy coarse sand in other places is shown as GR-LS on one row and GR-LCOS\n on another row in this table. The row with the typically occurring texture\n is identified as the RV row. Stratified textures are shown in one row. For\n example, a horizon that is stratified gravelly loamy fine sand and cobbly\n coarse sand is shown as SR- GR-LFS CB-COS on one row and the Stratified? column\n for that row is marked \\\\\\\"yes\\\\\\\". If two or more textures always occur\n together but are not stratified, all of the textures are listed on one row\n and the Stratified? column for that row is marked \\\\\\\"no\\\\\\\".\\\"},{\\\"name\\\":\\\"chtexturemod\\\",\\\"description\\\":\\\"The\n Horizon Texture Modifier table lists the texture modifier(s) for the referenced\n texture. For example, a gravelly loamy sand is shown as \\\\\\\"GR-LS\\\\\\\" in\n the Horizon Texture Group table, \\\\\\\"ls\\\\\\\" in the Horizon Texture table,\n and \\\\\\\"gr\\\\\\\" in this table.\\\"},{\\\"name\\\":\\\"chunified\\\",\\\"description\\\":\\\"The\n Horizon Unified table contains the Unified Soil Classification(s) for the\n referenced horizon. One row in the Horizon Unified table is marked as the\n representative Unified classification for the horizon.\\\"},{\\\"name\\\":\\\"cocanopycover\\\",\\\"description\\\":\\\"The\n Component Canopy Cover table lists the overstory plants that typically occur\n on the referenced map unit component.\\\"},{\\\"name\\\":\\\"cocropyld\\\",\\\"description\\\":\\\"The\n Component Crop Yield table lists commonly grown crops and their expected range\n in yields when grown on the referenced map unit component. Yields for the\n map unit as a whole are given in the Mapunit Crop Yield table.\\\"},{\\\"name\\\":\\\"codiagfeatures\\\",\\\"description\\\":\\\"The\n Component Diagnostic Features table lists the typical soil features, such\n as ochric epipedon or cambic horizon, for the referenced map unit component.\\\"},{\\\"name\\\":\\\"coecoclass\\\",\\\"description\\\":\\\"The\n Component Ecological Classification table identifies the ecological sites\n typically associated with the referenced map unit component. These may include\n the official NRCS forestland and rangland ecological sites, as well as those\n of other classification systems, such as the USFS Habitat Types.\\\"},{\\\"name\\\":\\\"coeplants\\\",\\\"description\\\":\\\"The\n Component Existing Plants table lists the plants, either rangeland or forestland\n plants, that typically occur on the referenced map unit component.\\\"},{\\\"name\\\":\\\"coerosionacc\\\",\\\"description\\\":\\\"The\n Component Erosion Accelerated table lists the kinds of accelerated erosion\n that occur on the referenced map unit component. One row in this table is\n marked as the representative kind of accelerated erosion for that component.\\\"},{\\\"name\\\":\\\"coforprod\\\",\\\"description\\\":\\\"The\n Component Forest Productivity table lists the site index and the annual productivity\n in cubic feet per acre per year (CAMI) of forest overstory tree species that\n typically occur on the referenced map unit component.\\\"},{\\\"name\\\":\\\"coforprodo\\\",\\\"description\\\":\\\"The\n Component Forest Productivity - Other table lists the site index and annual\n productivity of forest overstory tree species in units other than cubic feet\n per acre per year for trees that typically occur on the referenced map unit\n component.\\\"},{\\\"name\\\":\\\"cogeomordesc\\\",\\\"description\\\":\\\"The Component Geomorphic\n Description table lists the geomorphic features on which the referenced map\n unit component typically occurs.\\\"},{\\\"name\\\":\\\"cohydriccriteria\\\",\\\"description\\\":\\\"The\n Component Hydric Criteria table lists the hydric soil criteria met for those\n referenced map unit components that are classified as a \\\\\\\"hydric soil.\\\\\\\"\\\"},{\\\"name\\\":\\\"cointerp\\\",\\\"description\\\":\\\"The\n Component Interpretation table lists the predictions of behavior and limiting\n features for specified uses made for the referenced map unit component.\\\"},{\\\"name\\\":\\\"comonth\\\",\\\"description\\\":\\\"The\n Component Month table lists the monthly flooding and ponding characteristics\n for the referenced map unit component. This table has one row for each month\n of the year.\\\"},{\\\"name\\\":\\\"component\\\",\\\"description\\\":\\\"The Component table\n lists the map unit components identified in the referenced map unit, and selected\n properties of each component. If the Component % is greater than zero (low=65,\n RV=75, high=90) for a component, that component exists in every delineation\n of that mapunit. If the Component % includes zero (low=0, RV=50, high=90),\n the component may exist in some delineations, but not in others.\\\"},{\\\"name\\\":\\\"copm\\\",\\\"description\\\":\\\"The\n Component Parent Material table lists the individual parent material(s) for\n the referenced map unit component. In some cases where soils developed\n in multiple materials in a vertical sequence, that sequence will be noted.\n \\ In other cases multiple entries with no vertical sequence noted indicates\n the soil may have formed in one of the materials listed.\\\"},{\\\"name\\\":\\\"copmgrp\\\",\\\"description\\\":\\\"The\n Component Parent Material Group table lists the concatenated string of parent\n material(s) in which the referenced map unit component formed based on entries\n in the Component Parent Material table. For example, a component formed in\n one parent material, such as loess, or one vertical sequence of parent materials,\n such as loamy glacial drift over silty residuum weathered from shale, has\n one row in this table. A component formed in one parent material in some\n locations, but another parent material (or sequence of parent materials) in\n other locations has two rows in this table, one for each parent material (or\n sequence of parent materials). One row is identified as the representative\n parent material.\\\"},{\\\"name\\\":\\\"copwindbreak\\\",\\\"description\\\":\\\"The Component\n Potential Windbreak table lists the windbreak plant species commonly recommended\n for the referenced map unit component. A windbreak plant listed in this table\n may be used alone or in combination with other plants.\\\"},{\\\"name\\\":\\\"corestrictions\\\",\\\"description\\\":\\\"The\n Component Restrictions table lists the root restrictive feature(s) or layer(s)\n for the referenced map unit component. If the thickness of the restrictive\n layer is greater than zero (low=5, RV=8, high=10), the restrictive layer exists\n in all delineations of the map unit where the component occurs. If the thickness\n of the restrictive layer includes zero (low=0, RV=2, high=5), the restrictive\n layer may exist in some delineations, but not in others. This table will\n be empty if the component does not have restrictive features, but could have\n several rows if several restrictive features occur in the soil.\\\"},{\\\"name\\\":\\\"cosoilmoist\\\",\\\"description\\\":\\\"The\n Component Soil Moisture table describes the typical soil moisture profile\n for the referenced map unit component during the month referenced in the Component\n Month table. The soil moisture profiles for each month, taken as a group\n of twelve months, describe the representative situation for the component\n throughout the year.\\\"},{\\\"name\\\":\\\"cosoiltemp\\\",\\\"description\\\":\\\"The Component\n Soil Temperature table describes the typical soil temperature profile for\n the referenced map unit component during the month referenced in the Component\n Month table. The soil temperature profiles for each month, taken as a group\n of twelve months, describe the representative situation for the component\n throughout the year.\\\"},{\\\"name\\\":\\\"cosurffrags\\\",\\\"description\\\":\\\"The Component\n Surface Fragments table lists the organic or mineral fragments that generally\n occur on the surface of the referenced map unit component. If the cover percent\n is greater than zero (low=0.1, RV=1, high=3) for a row in this table, the\n fragment is in every delineation of the map unit where the referenced component\n occurs. If the Cover % includes zero (low=0, RV=0.01, high=1) for a row in\n this table, the fragment may exist in some delineations and not in others.\\\"},{\\\"name\\\":\\\"cosurfmorphgc\\\",\\\"description\\\":\\\"The\n Component Three Dimensional Surface Morphometry table lists the typical geomorphic\n position (s) of the referenced map unit component, in three dimension terms.\n \\ The geomorphic position(s) listed in this table apply to the geomorphic\n feature referenced in the Component Geomorphic Description table.\\\"},{\\\"name\\\":\\\"cosurfmorphhpp\\\",\\\"description\\\":\\\"The\n Component Two Dimensional Surface Morphometry table lists the geomorphic position(s)\n of the referenced map unit component, in two dimensional hillslope profile\n terms. The geomorphic position(s) listed in this table apply to the geomorphic\n feature referenced in the Component Geomorphic Description table.\\\"},{\\\"name\\\":\\\"cosurfmorphmr\\\",\\\"description\\\":\\\"The\n Component Microrelief Surface Morphometry table lists microrelief features\n associated with the referenced geomorphic (microfeature) feature shown in\n the Component Geomorphic Description table.\\\"},{\\\"name\\\":\\\"cosurfmorphss\\\",\\\"description\\\":\\\"The\n Component Slope Shape Surface Morphometry table lists the geomorphic shape(s)\n of the referenced map unit component, in slope shape terms. The slope shape\n terms listed in this table apply to the referenced geomorphic feature shown\n in the Component Geomorphic Description table.\\\"},{\\\"name\\\":\\\"cotaxfmmin\\\",\\\"description\\\":\\\"The\n Component Taxonomic Family Mineralogy table lists the mineralogy characteristics,\n as defined in Soil Taxonomy, that apply to the referenced map unit component.\\\"},{\\\"name\\\":\\\"cotaxmoistcl\\\",\\\"description\\\":\\\"The\n Component Taxonomic Moisture Class table provides clear identification of\n the intended taxonomic moisture class, as defined in Soil Taxonomy, that apply\n to the referenced map unit component, even though moisture class is implied\n at a higher taxonomic level. The class or classes listed in this table describe\n the representative situation for the component.\\\"},{\\\"name\\\":\\\"cotext\\\",\\\"description\\\":\\\"The\n Component Text table contains notes and narrative descriptions for the referenced\n map unit component. In many cases, the table will be empty for a particular\n component.\\\"},{\\\"name\\\":\\\"cotreestomng\\\",\\\"description\\\":\\\"The Component Trees\n To Manage table lists the trees commonly recommended for managing on the referenced\n map unit component.\\\"},{\\\"name\\\":\\\"cotxfmother\\\",\\\"description\\\":\\\"The Component\n Taxonomic Family Other Criteria table lists the other taxonomic characteristics,\n such as classes of coatings or permanent cracks, as defined in Soil Taxonomy,\n that apply to the referenced map unit component. The characteristics listed\n in this table describe the representative situation for the component.\\\"},{\\\"name\\\":\\\"distinterpmd\\\",\\\"description\\\":\\\"The\n Distribution Interp Metadata table records the set of NASIS fuzzy logic interpretations\n which were generated for the map unit components included in a set of distribution\n data.\\\"},{\\\"name\\\":\\\"distlegendmd\\\",\\\"description\\\":\\\"The Distribution Legend\n Metadata table records information about the legends or soil survey areas\n selected for inclusion in a set of distributed data. The presence of a legend\n in this table does not imply that all of the available data for that legend\n was included in the set of data that was distributed. Only certain map units\n and components for that legend may have been selected. The record of the\n criteria used for selecting map units and components may be found in the Distribution\n Metadata table.\\\"},{\\\"name\\\":\\\"distmd\\\",\\\"description\\\":\\\"The Distribution\n Metadata table records information associated with the selection of a set\n of data for distribution to some entity or information system external to\n NASIS. A set of distribution data may include only selected map units from\n a legend or legends, and only selected components of those map units. This\n table records the criteria used for selecting map units and components for\n inclusion in the set of distributed data. Other recorded information includes\n the name of the NASIS user who initiated a distribution request, and the times\n when that request was made, and when that request was ultimately processed.\\\"},{\\\"name\\\":\\\"laoverlap\\\",\\\"description\\\":\\\"The\n Legend Area Overlap table lists the geographic areas that are coincident with\n the soil survey area identified in the Legend table. For example, a survey\n area that covers two counties would have two rows in this table, one for each\n county. Other types of geographic areas listed might include state, MLRA,\n rainfall (R) factor area, climate (C) factor area, etc.\\\"},{\\\"name\\\":\\\"legend\\\",\\\"description\\\":\\\"The\n Legend table identifies the soil survey area that the legend is related to,\n and related information about that legend.\\\"},{\\\"name\\\":\\\"legendtext\\\",\\\"description\\\":\\\"The\n Legend Text table contains notes and narrative descriptions related to the\n referenced legend. Legend text is optional. In many cases, this table is\n empty.\\\"},{\\\"name\\\":\\\"mapunit\\\",\\\"description\\\":\\\"The Mapunit table identifies\n the map units included in the referenced legend. Data related the map unit\n as a whole are also given.\\\"},{\\\"name\\\":\\\"muaggatt\\\",\\\"description\\\":\\\"The\n Mapunit Aggregated Attribute table records a variety of soil attributes and\n interpretations that have been aggregated from the component level to a single\n value at the map unit level. They have been aggregated by one or more appropriate\n means in order to express a consolidated value or interpretation for the map\n unit as a whole.\\\"},{\\\"name\\\":\\\"muaoverlap\\\",\\\"description\\\":\\\"The Mapunit\n Area Overlap table lists the map units that exist in the overlap between the\n entire soil survey area and the referenced geographic area in the Legend Area\n Overlap table.\\\"},{\\\"name\\\":\\\"mucropyld\\\",\\\"description\\\":\\\"The Mapunit Crop\n Yield table lists commonly grown crops and their expected yields for the referenced\n map unit as a whole. Yields for individual map unit components are given\n in the Component Crop Yield table.\\\"},{\\\"name\\\":\\\"mutext\\\",\\\"description\\\":\\\"The\n Mapunit Text table contains notes and narrative descriptions related to the\n referenced map unit.\\\"},{\\\"name\\\":\\\"sacatalog\\\",\\\"description\\\":\\\"This table\n records the primary dynamic metadata associated with a soil survey area. This\n includes such things as survey area version, tabular data version, etc. The\n remaining dynamic metadata, which soil interpretations were generated for\n the corresponding soil survey area, is recorded in the Survey Area Interpretation\n table.\\\"},{\\\"name\\\":\\\"sainterp\\\",\\\"description\\\":\\\"This table records information\n about the soil interpretations that were generated for a soil survey area.\\\"},{\\\"name\\\":\\\"valu1\\\",\\\"description\\\":\\\"Included\n with the gSSURGO database, but not a part of the standard SSURGO dataset is\n a table called Valu1. This table contains 57 pre-summarized or 'ready to map'\n attributes derived from the official SSURGO database. These attribute data\n are pre-summarized to the map unit level using best-practice generalization\n methods intended to meet the needs of most users. The generalization methods\n include map unit component weighted averages and percent of the map unit meeting\n a given criteria. These themes were prepared to better meet the mapping needs\n of users of soil survey information and can be used with both SSURGO and gridded\n SSURGO (gSSURGO) datasets.\\\"}],\\\"msft:group_id\\\":\\\"gnatsgo\\\",\\\"msft:container\\\":\\\"gnatsgo-stac\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"soils\\\",\\\"msft:short_description\\\":\\\"The\n gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS\n Soil & Plant Science Division (SPSD) composite database that provides complete\n coverage of the best available soils information for all areas of the United\n States and Island Territories.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"hgb\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hgb/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hgb\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://earthdata.nasa.gov/earth-observation-data/data-use-policy\\\",\\\"title\\\":\\\"EOSDIS\n Data Use Policy\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/hgb\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"HGB:\n Harmonized Global Biomass for 2010\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hgb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Harmonized\n Global Biomass\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/hgb.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-61.002778,180.0,84.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2010-12-31T00:00:00Z\\\",\\\"2010-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Biomass\\\",\\\"Carbon\\\",\\\"ORNL\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1763\\\",\\\"name\\\":\\\"Oak\n Ridge National Laboratory Distributed Active Archive Center\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://carbonplan.org\\\",\\\"name\\\":\\\"CarbonPlan\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[300]},\\\"description\\\":\\\"This\n dataset provides temporally consistent and harmonized global maps of aboveground\n and belowground biomass carbon density for the year 2010 at 300m resolution.\n The aboveground biomass map integrates land-cover-specific, remotely sensed\n maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed\n from the published literature and, where necessary, updated to cover the focal\n extent or time period. The belowground biomass map similarly integrates matching\n maps derived from each aboveground biomass map and land-cover-specific empirical\n models. Aboveground and belowground maps were then integrated separately using\n ancillary maps of percent tree/land cover and a rule-based decision tree.\n Maps reporting the accumulated uncertainty of pixel-level estimates are also\n provided.\\\\n\\\",\\\"item_assets\\\":{\\\"aboveground\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Global above-ground\n biomass\\\"},\\\"belowground\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Global\n below-ground biomass\\\"},\\\"aboveground_uncertainty\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Uncertainty\n associated with global above-ground biomass\\\"},\\\"belowground_uncertainty\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Uncertainty\n associated with global below-ground biomass\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"cpdata\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"cpdataeuwest\\\",\\\"msft:short_description\\\":\\\"Global\n maps of aboveground and belowground biomass carbon density for the year 2010\n at 300m resolution\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"cop-dem-glo-30\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-30/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-30\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\\\",\\\"title\\\":\\\"Copernicus\n DEM License\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-30\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Copernicus\n DEM GLO-30\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/cop-dem.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"COP\n DEM GLO-30\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/cop-dem-glo-30.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2021-04-22T00:00:00Z\\\",\\\"2021-04-22T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Copernicus\\\",\\\"DEM\\\",\\\"DSM\\\",\\\"Elevation\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\\\",\\\"name\\\":\\\"European\n Space Agency\\\",\\\"roles\\\":[\\\"licensor\\\"]},{\\\"url\\\":\\\"https://registry.opendata.aws/copernicus-dem/\\\",\\\"name\\\":\\\"Sinergise\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://doi.org/10.5069/G9028PQB\\\",\\\"name\\\":\\\"OpenTopography\\\",\\\"roles\\\":[\\\"host\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[30],\\\"platform\\\":[\\\"tandem-x\\\"]},\\\"description\\\":\\\"The\n Copernicus DEM is a digital surface model (DSM), which represents the surface\n of the Earth including buildings, infrastructure, and vegetation. This DSM\n is based on radar satellite data acquired during the TanDEM-X Mission, which\n was funded by a public-private partnership between the German Aerospace Centre\n (DLR) and Airbus Defence and Space.\\\\n\\\\nCopernicus DEM is available at both\n 30-meter and 90-meter resolution; this dataset has a horizontal resolution\n of approximately 30 meters.\\\\n\\\\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf)\n for more information.\\\\n\\\\nSee the dataset page on OpenTopography: \\\\n\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"copernicus-dem\\\",\\\"msft:container\\\":\\\"copernicus-dem-stac\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"elevationeuwest\\\",\\\"msft:short_description\\\":\\\"Copernicus\n DEM 30 is a near-global digital surface model (DSM) with a horizontal resolution\n of approximately 30 meters, derived from radar satellite data acquired during\n the TanDEM-X mission.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"cop-dem-glo-90\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-90/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cop-dem-glo-90\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\\\",\\\"title\\\":\\\"Copernicus\n DEM License\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-90\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Copernicus\n DEM GLO-90\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/cop-dem-glo-90.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"COP\n DEM GLO-90\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/cop-dem-glo-90.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2021-04-22T00:00:00Z\\\",\\\"2021-04-22T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Copernicus\\\",\\\"DEM\\\",\\\"Elevation\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf\\\",\\\"name\\\":\\\"European\n Space Agency\\\",\\\"roles\\\":[\\\"licensor\\\"]},{\\\"url\\\":\\\"https://registry.opendata.aws/copernicus-dem/\\\",\\\"name\\\":\\\"Sinergise\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://doi.org/10.5069/G9028PQB\\\",\\\"name\\\":\\\"OpenTopography\\\",\\\"roles\\\":[\\\"host\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[90],\\\"platform\\\":[\\\"tandem-x\\\"]},\\\"description\\\":\\\"The\n Copernicus DEM is a digital surface model (DSM), which represents the surface\n of the Earth including buildings, infrastructure, and vegetation. 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NEX\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"cmip6:model\\\":[\\\"ACCESS-CM2\\\",\\\"ACCESS-ESM1-5\\\",\\\"BCC-CSM2-MR\\\",\\\"CESM2\\\",\\\"CESM2-WACCM\\\",\\\"CMCC-CM2-SR5\\\",\\\"CMCC-ESM2\\\",\\\"CNRM-CM6-1\\\",\\\"CNRM-ESM2-1\\\",\\\"CanESM5\\\",\\\"EC-Earth3\\\",\\\"EC-Earth3-Veg-LR\\\",\\\"FGOALS-g3\\\",\\\"GFDL-CM4\\\",\\\"GFDL-CM4_gr2\\\",\\\"GFDL-ESM4\\\",\\\"GISS-E2-1-G\\\",\\\"HadGEM3-GC31-LL\\\",\\\"HadGEM3-GC31-MM\\\",\\\"IITM-ESM\\\",\\\"INM-CM4-8\\\",\\\"INM-CM5-0\\\",\\\"IPSL-CM6A-LR\\\",\\\"KACE-1-0-G\\\",\\\"KIOST-ESM\\\",\\\"MIROC-ES2L\\\",\\\"MIROC6\\\",\\\"MPI-ESM1-2-HR\\\",\\\"MPI-ESM1-2-LR\\\",\\\"MRI-ESM2-0\\\",\\\"NESM3\\\",\\\"NorESM2-LM\\\",\\\"NorESM2-MM\\\",\\\"TaiESM1\\\",\\\"UKESM1-0-LL\\\"],\\\"cmip6:scenario\\\":[\\\"historical\\\",\\\"ssp245\\\",\\\"ssp585\\\"],\\\"cmip6:variable\\\":[\\\"hurs\\\",\\\"huss\\\",\\\"pr\\\",\\\"rlds\\\",\\\"rsds\\\",\\\"sfcWind\\\",\\\"tas\\\",\\\"tasmax\\\",\\\"tasmin\\\"]},\\\"description\\\":\\\"The\n NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios\n derived from the General Circulation Model (GCM) runs conducted under the\n Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the\n four \\u201CTier 1\\u201D greenhouse gas emissions scenarios known as Shared\n Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support\n of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change\n (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP\n model runs for which daily scenarios were produced and distributed through\n the Earth System Grid Federation. The purpose of this dataset is to provide\n a set of global, high resolution, bias-corrected climate change projections\n that can be used to evaluate climate change impacts on processes that are\n sensitive to finer-scale climate gradients and the effects of local topography\n on climate conditions.\\\\n\\\\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/)\n maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6)\n where you can find more information about these datasets. Users are encouraged\n to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf),\n provided alongside the data set, where more detailed information, references\n and acknowledgements can be found.\\\\n\\\\nThis collection contains many NetCDF\n files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\\\\n\\\\n-\n **model** is the name of a modeling group (e.g. \\\\\\\"ACCESS-CM-2\\\\\\\"). See\n the `cmip6:model` summary in the STAC collection for a full list of models.\\\\n-\n **scenario** is one of \\\\\\\"historical\\\\\\\", \\\\\\\"ssp245\\\\\\\" or \\\\\\\"ssp585\\\\\\\".\\\\n-\n **variable** is one of \\\\\\\"hurs\\\\\\\", \\\\\\\"huss\\\\\\\", \\\\\\\"pr\\\\\\\", \\\\\\\"rlds\\\\\\\",\n \\\\\\\"rsds\\\\\\\", \\\\\\\"sfcWind\\\\\\\", \\\\\\\"tas\\\\\\\", \\\\\\\"tasmax\\\\\\\", \\\\\\\"tasmin\\\\\\\".\\\\n-\n **year** depends on the value of *scenario*. For \\\\\\\"historical\\\\\\\", the values\n range from 1950 to 2014 (inclusive). For \\\\\\\"ssp245\\\\\\\" and \\\\\\\"ssp585\\\\\\\",\n the years range from 2015 to 2100 (inclusive).\\\\n\\\\nIn addition to the NetCDF\n files, we provide some *experimental* **reference files** as collection-level\n dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\\\\nThese\n files include the positions of data variables within the binary NetCDF files,\n which can speed up reading the metadata. 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format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-170.8513,-14.3799,-169.4152,-14.1432],[138.0315,5.116,163.1902,10.2773],[144.6126,13.2327,144.9658,13.6572],[-159.7909,18.8994,-154.7815,22.2464],[170.969,6.0723,171.9169,8.71933],[145.0127,14.1086,145.9242,18.8172],[130.8048,2.9268,134.9834,8.0947],[157.3678,49.0546,-117.2864,71.4567],[-67.9506,17.014,-64.3973,19.3206],[-127.8881,22.8782,-65.2748,51.6039]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2020-07-01T00:00:00Z\\\",\\\"2020-07-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CC0-1.0\\\",\\\"keywords\\\":[\\\"Soils\\\",\\\"NATSGO\\\",\\\"SSURGO\\\",\\\"STATSGO2\\\",\\\"RSS\\\",\\\"USDA\\\",\\\"United\n States\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.nrcs.usda.gov/\\\",\\\"name\\\":\\\"United\n States Department of Agriculture, Natural Resources Conservation Service\\\",\\\"roles\\\":[\\\"licensor\\\",\\\"producer\\\",\\\"processor\\\",\\\"host\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"description\\\":\\\"This\n collection contains the raster data for gNATSGO. In order to use the map unit\n values contained in the `mukey` raster asset, you'll need to join to tables\n represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables)\n Collection. Many items have commonly used values encoded in additional raster\n assets.\\\\n\\\\nThe gridded National Soil Survey Geographic Database (gNATSGO)\n is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that\n provides complete coverage of the best available soils information for all\n areas of the United States and Island Territories. It was created by combining\n data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic\n Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single\n seamless ESRI file geodatabase.\\\\n\\\\nSSURGO is the SPSD flagship soils database\n that has over 100 years of field-validated detailed soil mapping data. SSURGO\n contains soils information for more than 90 percent of the United States and\n island territories, but unmapped land remains. STATSGO2 is a general soil\n map that has soils data for all of the United States and island territories,\n but the data is not as detailed as the SSURGO data. The Raster Soil Surveys\n (RSSs) are the next generation soil survey databases developed using advanced\n digital soil mapping methods.\\\\n\\\\nThe gNATSGO database is composed primarily\n of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are\n newer product with relatively limited spatial extent. These RSSs were merged\n into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent\n of RSS is expected to increase in the coming years.\\\\n\\\\nSee the [official\n documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)\\\",\\\"item_assets\\\":{\\\"mukey\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"mukey\\\",\\\"description\\\":\\\"Map\n unit key is the unique identifier of a record in the Mapunit table. Use this\n column to join the Component table to the Map Unit table and the Valu1 table\n to the MapUnitRaster_10m raster map layer to map valu1 themes.\\\"},\\\"aws0_5\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws0_5\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in a standard zone 1 (0-5 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n layer based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"soc0_5\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc0_5\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard layer 1 or standard zone 1\n (0-5 cm depth). The concentration of organic carbon present in the soil expressed\n in grams C per square meter to a depth of 5 cm. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk0_5a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_5a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 1 or standard zone 1 (0-5 cm) expressed\n in cm (weighted average) for the available water storage calculation. NULL\n values are presented where data are incomplete or not available.\\\"},\\\"tk0_5s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_5s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 1 or standard zone 1 (0-5 cm) expressed\n in cm (weighted average) for the Soil Organic Carbon calculation. NULL values\n are presented where data are incomplete or not available.\\\"},\\\"aws0_20\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws0_20\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard zone 2 (0-20 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n zone based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"aws0_30\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws0_30\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard zone 3 (0-30 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n zone based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"aws5_20\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws5_20\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard layer 2 (5-20 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n layer based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"soc0_20\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc0_20\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard zone 2 (0-20 cm depth). The\n concentration of organic carbon present in the soil expressed in grams C per\n square meter to a depth of 20 cm. NULL values are presented where data are\n incomplete or not available.\\\"},\\\"soc0_30\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc0_30\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard zone 3 (0-30 cm depth). The\n concentration of organic carbon present in the soil expressed in grams C per\n square meter to a depth of 30 cm. NULL values are presented where data are\n incomplete or not available.\\\"},\\\"soc5_20\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc5_20\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard layer 2 (5-20 cm depth). The\n concentration of organic carbon present in the soil expressed in grams C per\n square meter for the 5-20 cm layer. NULL values are presented where data are\n incomplete or not available.\\\"},\\\"tk0_20a\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_20a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard zone 2 (0-20 cm) expressed in cm (weighted\n average) for the available water storage calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk0_20s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_20s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard zone 2 (0-20 cm) expressed in cm (weighted\n average) for the Soil Organic Carbon calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk0_30a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_30a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard zone 3 (0-30 cm) expressed in cm (weighted\n average) for the available water storage calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk0_30s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_30s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard zone 3 (0-30 cm) expressed in cm (weighted\n average) for the Soil Organic Carbon calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk5_20a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk5_20a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 2 (5-20 cm) expressed in cm (weighted\n average) for the available water storage calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk5_20s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk5_20s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 2 (5-20 cm) expressed in cm (weighted\n average) for the Soil Organic Carbon calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"aws0_100\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws0_100\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard zone 4 (0-100 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n zone based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"aws0_150\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws0_150\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard zone 5 (0-150 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n zone based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"aws0_999\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws0_999\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in total soil profile (0 cm to the reported depth\n of the soil profile), expressed in mm. The volume of plant available water\n that the soil can store in this layer based on all map unit components (weighted\n average). NULL values are presented where data are incomplete or not available.\\\"},\\\"aws20_50\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws20_50\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard layer 3 (20-50 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n layer based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"droughty\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"droughty\\\",\\\"description\\\":\\\"zone\n for commodity crops that is less than or equal to 6 inches (152 mm) expressed\n as \\\\\\\"1\\\\\\\" for a drought vulnerable soil landscape map unit or \\\\\\\"0\\\\\\\"\n for a non-droughty soil landscape map unit or NULL for miscellaneous areas\n (includes water bodies) or where data were not available. It is computed as\n a weighted average for major earthy components. Earthy components are those\n soil series or higher level taxa components that can support crop growth (Dobos\n et al., 2012). Major components are those soil components where the majorcompflag\n = 'Yes'\\\"},\\\"nccpi3sg\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"nccpi3sg\\\",\\\"description\\\":\\\"National\n Commodity Crop Productivity Index for Small Grains (weighted average) for\n major earthy components. Values range from .01 (low productivity) to .99 (high\n productivity). Earthy components are those soil series or higher level taxa\n components that can support crop growth (Dobos et al., 2012). Major components\n are those soil components where the majorcompflag = 'Yes' (SSURGO component\n table). NULL values are presented where data are incomplete or not available.\\\"},\\\"soc0_100\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc0_100\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard zone 4 (0-100 cm depth). The\n concentration of organic carbon present in the soil expressed in grams C per\n square meter to a depth of 100 cm. NULL values are presented where data are\n incomplete or not available.\\\"},\\\"soc0_150\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc0_150\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard zone 5 (0-150 cm depth). The\n concentration of organic carbon present in the soil expressed in grams C per\n square meter to a depth of 150 cm. NULL values are presented where data are\n incomplete or not available.\\\"},\\\"soc0_999\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc0_999\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in total soil profile (0 cm to the reported\n depth of the soil profile). The concentration of organic carbon present in\n the soil expressed in grams C per square meter for the total reported soil\n profile depth. NULL values are presented where data are incomplete or not\n available.\\\"},\\\"soc20_50\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc20_50\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard layer 3 (20-50 cm depth).\n The concentration of organic carbon present in the soil expressed in grams\n C per square meter for the 20-50 cm layer. NULL values are presented where\n data are incomplete or not available.\\\"},\\\"tk0_100a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_100a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted\n average) for the available water storage calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk0_100s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_100s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted\n average) for the Soil Organic Carbon calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk0_150a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_150a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted\n average) for the available water storage calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk0_150s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_150s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted\n average) for the Soil Organic Carbon calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk0_999a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_999a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in total soil profile (0 cm to the reported depth\n of the soil profile) expressed in cm (weighted average) for the available\n water storage calculation. NULL values are presented where data are incomplete\n or not available.\\\"},\\\"tk0_999s\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk0_999s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in total soil profile (0 cm to the reported depth\n of the soil profile) expressed in cm (weighted average) for the Soil Organic\n Carbon calculation. NULL values are presented where data are incomplete or\n not available.\\\"},\\\"tk20_50a\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk20_50a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted\n average) for the available water storage calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk20_50s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk20_50s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted\n average) for the Soil Organic Carbon calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"aws50_100\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws50_100\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard layer 3 (50-100 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n layer based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"musumcpct\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"musumcpct\\\",\\\"description\\\":\\\"The\n sum of the comppct_r (SSURGO component table) values for all listed components\n in the map unit. Useful metadata information. NULL values are presented where\n data are incomplete or not available.\\\"},\\\"nccpi3all\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"nccpi3all\\\",\\\"description\\\":\\\"National\n Commodity Crop Productivity Index that has the highest value among Corn and\n Soybeans, Small Grains, or Cotton (weighted average) for major earthy components.\n Values range from .01 (low productivity) to .99 (high productivity). Earthy\n components are those soil series or higher level taxa components that can\n support crop growth (Dobos et al., 2012). Major components are those soil\n components where the majorcompflag = 'Yes' (SSURGO component table). NULL\n values are presented where data are incomplete or not available.\\\"},\\\"nccpi3cot\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"nccpi3cot\\\",\\\"description\\\":\\\"National\n Commodity Crop Productivity Index for Cotton (weighted average) for major\n earthy components. Values range from .01 (low productivity) to .99 (high productivity).\n Earthy components are those soil series or higher level taxa components that\n can support crop growth (Dobos et al., 2012). Major components are those soil\n components where the majorcompflag = 'Yes' (SSURGO component table). NULL\n values are presented where data are incomplete or not available.\\\"},\\\"nccpi3soy\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"nccpi3soy\\\",\\\"description\\\":\\\"National\n Commodity Crop Productivity Index for Soybeans (weighted average) for major\n earthy components. Values range from .01 (low productivity) to .99 (high productivity).\n Earthy components are those soil series or higher level taxa components that\n can support crop growth (Dobos et al., 2012). Major components are those soil\n components where the majorcompflag = 'Yes' (SSURGO component table). NULL\n values are presented where data are incomplete or not available.\\\"},\\\"pwsl1pomu\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"pwsl1pomu\\\",\\\"description\\\":\\\"Potential\n Wetland Soil Landscapes (PWSL) is expressed as the percentage of the map unit\n that meets the PWSL criteria. The hydric rating (soil component variable \\u201Chydricrating\\u201D)\n is an indicator of wet soils. For version 1 (pwsl1), those soil components\n that meet the following criteria are tagged as PWSL and their comppct_r values\n are summed for each map unit. Soil components with hydricrating = 'YES' are\n considered PWSL. Soil components with hydricrating = \\u201CNO\\u201D are not\n PWSL. Soil components with hydricrating = 'UNRANKED' are tested using other\n attributes, and will be considered PWSL if any of the following conditions\n are met: drainagecl = 'Poorly drained' or 'Very poorly drained' or the localphase\n or the otherph data fields contain any of the phrases \\\\\\\"drained\\\\\\\" or \\\\\\\"undrained\\\\\\\"\n or \\\\\\\"channeled\\\\\\\" or \\\\\\\"protected\\\\\\\" or \\\\\\\"ponded\\\\\\\" or \\\\\\\"flooded\\\\\\\".\n If these criteria do not determine the PWSL for a component and hydricrating\n = 'UNRANKED', then the map unit will be classified as PWSL if the map unit\n name contains any of the phrases \\\\\\\"drained\\\\\\\" or \\\\\\\"undrained\\\\\\\" or \\\\\\\"channeled\\\\\\\"\n or \\\\\\\"protected\\\\\\\" or \\\\\\\"ponded\\\\\\\" or \\\\\\\"flooded\\\\\\\". For version 1 (pwsl1),\n waterbodies are identified as \\\\\\\"999\\\\\\\" when map unit names match a list\n of terms that identify water or intermittent water or map units have a sum\n of the comppct_r for \\\\\\\"Water\\\\\\\" that is 80% or greater. NULL values are\n presented where data are incomplete or not available.\\\"},\\\"rootznaws\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"rootznaws\\\",\\\"description\\\":\\\"Root\n zone (commodity crop) available water storage estimate (RZAWS) , expressed\n in mm, is the volume of plant available water that the soil can store within\n the root zone based on all map unit earthy major components (weighted average).\n Earthy components are those soil series or higher level taxa components that\n can support crop growth (Dobos et al., 2012). Major components are those soil\n components where the majorcompflag = 'Yes' (SSURGO component table). NULL\n values are presented where data are incomplete or not available.\\\"},\\\"rootznemc\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"rootznemc\\\",\\\"description\\\":\\\"Root\n zone depth is the depth within the soil profile that commodity crop (cc) roots\n can effectively extract water and nutrients for growth. Root zone depth influences\n soil productivity significantly. Soil component horizon criteria for root-limiting\n depth include: presence of hard bedrock, soft bedrock, a fragipan, a duripan,\n sulfuric material, a dense layer, a layer having a pH of less than 3.5, or\n a layer having an electrical conductivity of more than 12 within the component\n soil profile. If no root-restricting zone is identified, a depth of 150 cm\n is used to approximate the root zone depth (Dobos et al., 2012). Root zone\n depth is computed for all map unit major earthy components (weighted average).\n Earthy components are those soil series or higher level taxa components that\n can support crop growth (Dobos et al., 2012). Major components are those soil\n components where the majorcompflag = 'Yes' (SSURGO component table). NULL\n values are presented where data are incomplete or not available.\\\"},\\\"soc50_100\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc50_100\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard layer 4 (50-100 cm depth).\n The concentration of organic carbon present in the soil expressed in grams\n C per square meter for the 50-100 cm layer. NULL values are presented where\n data are incomplete or not available.\\\"},\\\"tk50_100a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk50_100a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted\n average) for the available water storage calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk50_100s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk50_100s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted\n average) for the Soil Organic Carbon calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"aws100_150\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws100_150\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard layer 5 (100-150 cm depth), expressed\n in mm. The volume of plant available water that the soil can store in this\n layer based on all map unit components (weighted average). NULL values are\n presented where data are incomplete or not available.\\\"},\\\"aws150_999\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"aws150_999\\\",\\\"description\\\":\\\"Available\n water storage estimate (AWS) in standard layer 6 (150 cm to the reported depth\n of the soil profile), expressed in mm. The volume of plant available water\n that the soil can store in this layer based on all map unit components (weighted\n average). NULL values are presented where data are incomplete or not available.\\\"},\\\"musumcpcta\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"musumcpcta\\\",\\\"description\\\":\\\"The\n sum of the comppct_r (SSURGO component table) values used in the available\n water storage calculation for the map unit. Useful metadata information. NULL\n values are presented where data are incomplete or not available.\\\"},\\\"musumcpcts\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"musumcpcts\\\",\\\"description\\\":\\\"The\n sum of the comppct_r (SSURGO component table) values used in the soil organic\n carbon calculation for the map unit. Useful metadata information. NULL values\n are presented where data are incomplete or not available.\\\"},\\\"nccpi3corn\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"nccpi3corn\\\",\\\"description\\\":\\\"National\n Commodity Crop Productivity Index for Corn (weighted average) for major earthy\n components. Values range from .01 (low productivity) to .99 (high productivity).\n Earthy components are those soil series or higher level taxa components that\n can support crop growth (Dobos et al., 2012). Major components are those soil\n components where the majorcompflag = 'Yes' (SSURGO component table). NULL\n values are presented where data are incomplete or not available.\\\"},\\\"pctearthmc\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"pctearthmc\\\",\\\"description\\\":\\\"The\n National Commodity Crop Productivity Index map unit percent earthy is the\n map unit summed comppct_r for major earthy components. Earthy components are\n those soil series or higher level taxa components that can support crop growth\n (Dobos et al., 2012). Major components are those soil components where the\n majorcompflag = 'Yes' (SSURGO component table). Useful metadata information.\n NULL values are presented where data are incomplete or not available.\\\"},\\\"soc100_150\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc100_150\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard layer 5 (100-150 cm depth).\n The concentration of organic carbon present in the soil expressed in grams\n C per square meter for the 100-150 cm layer. NULL values are presented where\n data are incomplete or not available.\\\"},\\\"soc150_999\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"soc150_999\\\",\\\"description\\\":\\\"Soil\n organic carbon stock estimate (SOC) in standard layer 6 (150 cm to the reported\n depth of the soil profile). The concentration of organic carbon present in\n the soil expressed in grams C per square meter for the 150 cm and greater\n depth layer. NULL values are presented where data are incomplete or not available.\\\"},\\\"tk100_150a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk100_150a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted\n average) for the available water storage calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk100_150s\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk100_150s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted\n average) for the Soil Organic Carbon calculation. NULL values are presented\n where data are incomplete or not available.\\\"},\\\"tk150_999a\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk150_999a\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 6 (150 cm to the reported depth\n of the soil profile) expressed in cm (weighted average) for the available\n water storage calculation. NULL values are presented where data are incomplete\n or not available.\\\"},\\\"tk150_999s\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"tk150_999s\\\",\\\"description\\\":\\\"Thickness\n of soil components used in standard layer 6 (150 cm to the reported depth\n of the soil profile) expressed in cm (weighted average) for the Soil Organic\n Carbon calculation. NULL values are presented where data are incomplete or\n not available.\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"gnatsgo\\\",\\\"msft:container\\\":\\\"gnatsgo-stac\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"soils\\\",\\\"msft:short_description\\\":\\\"Raster\n data for the gridded National Soil Survey Geographic Database (gNATSGO), a\n USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides\n complete coverage of the best available soils information for all areas of\n the United States and Island Territories.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"3dep-lidar-hag\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-hag/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-hag\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\\\",\\\"title\\\":\\\"About\n 3DEP Products & Services\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-hag\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n 3DEP Lidar Height above Ground\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hag-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/3dep-lidar-hag.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2012-01-01T00:00:00Z\\\",\\\"2022-01-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"3DEP\\\",\\\"COG\\\",\\\"Elevation\\\"],\\\"providers\\\":[{\\\"name\\\":\\\"Landrush\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/ngp/3dep/\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[2.0]},\\\"description\\\":\\\"This\n COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc)\n data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf)\n followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\\\\n\\\\nThe\n Height Above Ground Nearest Neighbor filter takes as input a point cloud with\n Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround,\n that contains the normalized height values.\\\\n\\\\nGround points may be generated\n with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf)\n or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf),\n but you can use any method you choose, as long as the ground returns are marked.\\\\n\\\\nNormalized\n heights are a commonly used attribute of point cloud data. This can also be\n referred to as height above ground (HAG) or above ground level (AGL) heights.\n In the end, it is simply a measure of a point's relative height as opposed\n to its raw elevation value.\\\\n\\\\nThe filter finds the number of ground points\n nearest to the non-ground point under consideration. It calculates an average\n ground height weighted by the distance of each ground point from the non-ground\n point. The HeightAboveGround is the difference between the Z value of the\n non-ground point and the interpolated ground height.\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"COG\n data\\\",\\\"raster:bands\\\":[{\\\"name\\\":\\\"HeightAboveGround\\\",\\\"unit\\\":\\\"metre\\\",\\\"sampling\\\":\\\"point\\\",\\\"data_type\\\":\\\"float32\\\",\\\"description\\\":\\\"Raster\n for Height Above Ground (HAG)\\\"}]},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"3dep-lidar\\\",\\\"msft:container\\\":\\\"usgs-3dep-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"usgslidareuwest\\\",\\\"msft:short_description\\\":\\\"3DEP\n Lidar collection for the Height Above Ground (HAG or Elevation) Cloud Optimized\n Geotiffs (COGs). All USGS HAG COG STAC items will be associated with this\n STAC collection.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"io-lulc-annual-v02\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-annual-v02/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-annual-v02\\\"},{\\\"rel\\\":\\\"related\\\",\\\"href\\\":\\\"https://livingatlas.arcgis.com/landcover/\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://creativecommons.org/licenses/by/4.0/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"CC\n BY 4.0\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"10m\n Annual Land Use Land Cover (9-class) V2\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-lulc-annual-v02.png\\\",\\\"title\\\":\\\"10m\n Annual Land Use Land Cover (9-class)\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/io-lulc-annual-v02.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2017-01-01T00:00:00Z\\\",\\\"2024-01-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CC-BY-4.0\\\",\\\"keywords\\\":[\\\"Global\\\",\\\"Land\n Cover\\\",\\\"Land Use\\\",\\\"Sentinel\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.esri.com/\\\",\\\"name\\\":\\\"Esri\\\",\\\"roles\\\":[\\\"licensor\\\"]},{\\\"url\\\":\\\"https://www.impactobservatory.com/\\\",\\\"name\\\":\\\"Impact\n Observatory\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"spatial_resolution\\\":10}]},\\\"description\\\":\\\"Time\n series of annual global maps of land use and land cover (LULC). It currently\n has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery\n at 10m resolution. Each map is a composite of LULC predictions for 9 classes\n throughout the year in order to generate a representative snapshot of each\n year.\\\\n\\\\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/),\n Microsoft, and Esri, displays a global map of land use and land cover (LULC)\n derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017\n - 2023. Each map is a composite of LULC predictions for 9 classes throughout\n the year in order to generate a representative snapshot of each year. This\n dataset was generated by Impact Observatory, which used billions of human-labeled\n pixels (curated by the National Geographic Society) to train a deep learning\n model for land classification. Each global map was produced by applying this\n model to the Sentinel-2 annual scene collections from the Mircosoft Planetary\n Computer. Each of the maps has an assessed average accuracy of over 75%.\\\\n\\\\nThese\n maps have been improved from Impact Observatory\\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class)\n and provide a relative reduction in the amount of anomalous change between\n classes, particularly between \\u201CBare\\u201D and any of the vegetative classes\n \\u201CTrees,\\u201D \\u201CCrops,\\u201D \\u201CFlooded Vegetation,\\u201D and\n \\u201CRangeland\\u201D. This updated time series of annual global maps is also\n re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\\\\n\\\\nAll\n years are available under a Creative Commons BY-4.0.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Global\n land cover data\\\",\\\"file:values\\\":[{\\\"values\\\":[0],\\\"summary\\\":\\\"No Data\\\"},{\\\"values\\\":[1],\\\"summary\\\":\\\"Water\\\"},{\\\"values\\\":[2],\\\"summary\\\":\\\"Trees\\\"},{\\\"values\\\":[4],\\\"summary\\\":\\\"Flooded\n vegetation\\\"},{\\\"values\\\":[5],\\\"summary\\\":\\\"Crops\\\"},{\\\"values\\\":[7],\\\"summary\\\":\\\"Built\n area\\\"},{\\\"values\\\":[8],\\\"summary\\\":\\\"Bare ground\\\"},{\\\"values\\\":[9],\\\"summary\\\":\\\"Snow/ice\\\"},{\\\"values\\\":[10],\\\"summary\\\":\\\"Clouds\\\"},{\\\"values\\\":[11],\\\"summary\\\":\\\"Rangeland\\\"}]}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"io-land-cover\\\",\\\"msft:container\\\":\\\"io-lulc\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/label/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/file/v2.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"ai4edataeuwest\\\",\\\"msft:short_description\\\":\\\"Global\n land cover information with 9 classes for 2017-2023 at 10m resolution\\\"},{\\\"id\\\":\\\"goes-cmi\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-cmi/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-cmi\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.nesdisia.noaa.gov/policy.html\\\",\\\"title\\\":\\\"Public\n Domain\\\"},{\\\"rel\\\":\\\"preview\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/data/v1/collection/map?collection=goes-cmi\\\",\\\"title\\\":\\\"Map\n of collection mosaic\\\",\\\"type\\\":\\\"text/html\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/goes-cmi\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"GOES-R\n Cloud & Moisture Imagery\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/goes-cmi-thumb.png\\\",\\\"title\\\":\\\"GOES\n CMIP\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/goes-cmi.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"W-MON\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}},\\\"tilejson\\\":{\\\"title\\\":\\\"Mosaic\n TileJSON with default rendering\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/data/v1/collection/tilejson.json?collection=goes-cmi&expression=C02_2km_wm%3B0.45%2AC02_2km_wm%2B0.1%2AC03_2km_wm%2B0.45%2AC01_2km_wm%3BC01_2km_wm&nodata=-1&rescale=1%2C1000&color_formula=Gamma+RGB+2.5+Saturation+1.4+Sigmoidal+RGB+2+0.7&asset_as_band=True&resampling=bilinear&format=png\\\",\\\"type\\\":\\\"application/json\\\",\\\"roles\\\":[\\\"tiles\\\"]}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-81.33,6.3,81.33],[141.7,-81.33,180.0,81.33]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2017-02-28T00:16:52Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"GOES\\\",\\\"NOAA\\\",\\\"NASA\\\",\\\"Satellite\\\",\\\"Cloud\\\",\\\"Moisture\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.nasa.gov/content/goes\\\",\\\"name\\\":\\\"NASA\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.goes-r.gov/\\\",\\\"name\\\":\\\"NOAA\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"GOES-16\\\",\\\"GOES-17\\\",\\\"GOES-18\\\",\\\"GOES-19\\\"],\\\"goes:mode\\\":[\\\"3\\\",\\\"4\\\",\\\"6\\\"],\\\"instruments\\\":[\\\"ABI\\\"],\\\"goes:image-type\\\":[\\\"FULL\n DISK\\\",\\\"CONUS\\\",\\\"MESOSCALE\\\"],\\\"goes:processing-level\\\":[\\\"L2\\\"]},\\\"description\\\":\\\"The\n GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product\n provides 16 reflective and emissive bands at high temporal cadence over the\n Western Hemisphere.\\\\n\\\\nThe GOES-R series is the latest in the Geostationary\n Operational Environmental Satellites (GOES) program, which has been operated\n in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R\n Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary\n orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer.\n The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions\n between 0.5km and 2km.\\\\n\\\\n### Geographic coverage\\\\n\\\\nThe ABI captures\n three levels of coverage, each at a different temporal cadence depending on\n the modes described below. The geographic coverage for each image is described\n by the `goes:image-type` STAC Item property.\\\\n\\\\n- _FULL DISK_: a circular\n image depicting nearly full coverage of the Western Hemisphere.\\\\n- _CONUS_:\n a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental\n U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\\\\n- _MESOSCALE_:\n a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between\n two different mesoscale geographic regions.\\\\n\\\\n### Modes\\\\n\\\\nThere are\n three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and\n Mode 6.\\\\n\\\\n- Mode _3_ consists of one observation of the full disk scene\n of the Earth, three observations of the continental United States (CONUS),\n and thirty observations for each of two distinct mesoscale views every fifteen\n minutes.\\\\n- Mode _4_ consists of the observation of the full disk scene every\n five minutes.\\\\n- Mode _6_ consists of one observation of the full disk scene\n of the Earth, two observations of the continental United States (CONUS), and\n twenty observations for each of two distinct mesoscale views every ten minutes.\\\\n\\\\nThe\n mode that each image was captured with is described by the `goes:mode` STAC\n Item property.\\\\n\\\\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8)\n video for an idea of how the ABI scans multiple geographic regions over time.\\\\n\\\\n###\n Cloud and Moisture Imagery\\\\n\\\\nThe Cloud and Moisture Imagery product contains\n one or more images with pixel values identifying \\\\\\\"brightness values\\\\\\\"\n that are scaled to support visual analysis. Cloud and Moisture Imagery product\n (CMIP) files are generated for each of the sixteen ABI reflective and emissive\n bands. In addition, there is a multi-band product file that includes the imagery\n at all bands (MCMIP).\\\\n\\\\nThe Planetary Computer STAC Collection `goes-cmi`\n captures both the CMIP and MCMIP product files into individual STAC Items\n for each observation from a GOES-R satellite. It contains the original CMIP\n and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of\n the data from each MCMIP band (2km); the full-resolution CMIP band for bands\n 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful\n for rendering.\\\\n\\\\nThis product is not in a standard coordinate reference\n system (CRS), which can cause issues with some tooling that does not handle\n non-standard large geographic regions.\\\\n\\\\n### For more information\\\\n- [Beginner\\u2019s\n Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\\\\n-\n [GOES-R Series Product Definition and Users\\u2019 Guide: Volume 5 (Level 2A+\n Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish\n verison](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/raw/main/GOES/QuickGuides/Spanish/Guia%20introductoria%20para%20datos%20de%20la%20serie%20GOES-R%20V1.1%20FINAL2%20-%20Copy.pdf))\\\\n\\\\n\\\",\\\"item_assets\\\":{\\\"C01_1km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 01 (full resolution)\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 1\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"Daytime aerosol over land,\n coastal water mapping\\\",\\\"center_wavelength\\\":0.47}]},\\\"C01_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 01\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI Band\n 1\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"Daytime aerosol over land,\n coastal water mapping\\\",\\\"center_wavelength\\\":0.47}]},\\\"C02_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 02\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI Band\n 2\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Daytime clouds, fog, insolation,\n winds\\\",\\\"center_wavelength\\\":0.64}]},\\\"C03_1km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 03 (full resolution)\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 3\\\",\\\"common_name\\\":\\\"nir09\\\",\\\"description\\\":\\\"Daytime vegetation, burn\n scar, aerosol over water, winds\\\",\\\"center_wavelength\\\":0.87}]},\\\"C03_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 03\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI Band\n 3\\\",\\\"common_name\\\":\\\"nir09\\\",\\\"description\\\":\\\"Daytime vegetation, burn scar,\n aerosol over water, winds\\\",\\\"center_wavelength\\\":0.87}]},\\\"C04_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 04\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI Band\n 4\\\",\\\"common_name\\\":\\\"cirrus\\\",\\\"description\\\":\\\"Daytime cirrus cloud\\\",\\\"center_wavelength\\\":1.38}]},\\\"C05_1km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 05 (full resolution)\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 5\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"Daytime cloud-top phase\n and particle size, snow\\\",\\\"center_wavelength\\\":1.61}]},\\\"C05_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 05\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI Band\n 5\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"Daytime cloud-top phase and\n particle size, snow\\\",\\\"center_wavelength\\\":1.61}]},\\\"C06_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 06\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI Band\n 6\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"Daytime land, cloud properties,\n particle size, vegetation, snow\\\",\\\"center_wavelength\\\":2.25}]},\\\"C07_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 07\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 7\\\",\\\"description\\\":\\\"Surface and cloud, fog at night, fire, winds\\\",\\\"center_wavelength\\\":3.89}]},\\\"C08_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 08\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 8\\\",\\\"description\\\":\\\"High-level atmospheric water vapor, winds, rainfall\\\",\\\"center_wavelength\\\":6.17}]},\\\"C09_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 09\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 9\\\",\\\"description\\\":\\\"Midlevel atmospheric water vapor, winds, rainfall\\\",\\\"center_wavelength\\\":6.93}]},\\\"C10_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 10\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 10\\\",\\\"description\\\":\\\"Lower-level water vapor, winds, and silicon dioxide\\\",\\\"center_wavelength\\\":7.34}]},\\\"C11_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 11\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 11\\\",\\\"description\\\":\\\"Total water for stability, cloud phase, dust,\n silicon dioxide, rainfall\\\",\\\"center_wavelength\\\":8.44}]},\\\"C12_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 12\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 12\\\",\\\"description\\\":\\\"Total ozone, turbulence, winds\\\",\\\"center_wavelength\\\":9.61}]},\\\"C13_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 13\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 13\\\",\\\"description\\\":\\\"Surface and clouds\\\",\\\"center_wavelength\\\":10.33}]},\\\"C14_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 14\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 14\\\",\\\"description\\\":\\\"Imagery, sea surface temperature, clouds, rainfall\\\",\\\"center_wavelength\\\":11.19}]},\\\"C15_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 15\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 15\\\",\\\"description\\\":\\\"Total water, volcanic ash, sea surface temperature\\\",\\\"center_wavelength\\\":12.27}]},\\\"C16_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery brightness temperature at top of atmosphere - Band 16\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 16\\\",\\\"description\\\":\\\"Air temperature, cloud heights\\\",\\\"center_wavelength\\\":13.27}]},\\\"MCMIP-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery - Multiband\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI Band 1\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"Daytime\n aerosol over land, coastal water mapping\\\",\\\"center_wavelength\\\":0.47},{\\\"name\\\":\\\"ABI\n Band 2\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Daytime clouds, fog, insolation,\n winds\\\",\\\"center_wavelength\\\":0.64},{\\\"name\\\":\\\"ABI Band 3\\\",\\\"common_name\\\":\\\"nir09\\\",\\\"description\\\":\\\"Daytime\n vegetation, burn scar, aerosol over water, winds\\\",\\\"center_wavelength\\\":0.87},{\\\"name\\\":\\\"ABI\n Band 4\\\",\\\"common_name\\\":\\\"cirrus\\\",\\\"description\\\":\\\"Daytime cirrus cloud\\\",\\\"center_wavelength\\\":1.38},{\\\"name\\\":\\\"ABI\n Band 5\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"Daytime cloud-top phase\n and particle size, snow\\\",\\\"center_wavelength\\\":1.61},{\\\"name\\\":\\\"ABI Band\n 6\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"Daytime land, cloud properties,\n particle size, vegetation, snow\\\",\\\"center_wavelength\\\":2.25},{\\\"name\\\":\\\"ABI\n Band 7\\\",\\\"description\\\":\\\"Surface and cloud, fog at night, fire, winds\\\",\\\"center_wavelength\\\":3.89},{\\\"name\\\":\\\"ABI\n Band 8\\\",\\\"description\\\":\\\"High-level atmospheric water vapor, winds, rainfall\\\",\\\"center_wavelength\\\":6.17},{\\\"name\\\":\\\"ABI\n Band 9\\\",\\\"description\\\":\\\"Midlevel atmospheric water vapor, winds, rainfall\\\",\\\"center_wavelength\\\":6.93},{\\\"name\\\":\\\"ABI\n Band 10\\\",\\\"description\\\":\\\"Lower-level water vapor, winds, and silicon dioxide\\\",\\\"center_wavelength\\\":7.34},{\\\"name\\\":\\\"ABI\n Band 11\\\",\\\"description\\\":\\\"Total water for stability, cloud phase, dust,\n silicon dioxide, rainfall\\\",\\\"center_wavelength\\\":8.44},{\\\"name\\\":\\\"ABI Band\n 12\\\",\\\"description\\\":\\\"Total ozone, turbulence, winds\\\",\\\"center_wavelength\\\":9.61},{\\\"name\\\":\\\"ABI\n Band 13\\\",\\\"description\\\":\\\"Surface and clouds\\\",\\\"center_wavelength\\\":10.33},{\\\"name\\\":\\\"ABI\n Band 14\\\",\\\"description\\\":\\\"Imagery, sea surface temperature, clouds, rainfall\\\",\\\"center_wavelength\\\":11.19},{\\\"name\\\":\\\"ABI\n Band 15\\\",\\\"description\\\":\\\"Total water, volcanic ash, sea surface temperature\\\",\\\"center_wavelength\\\":12.27},{\\\"name\\\":\\\"ABI\n Band 16\\\",\\\"description\\\":\\\"Air temperature, cloud heights\\\",\\\"center_wavelength\\\":13.27}],\\\"description\\\":\\\"The\n Cloud and Moisture Imagery product contains one or more Earth-view images\n with pixel values identifying 'brightness values' that are scaled to support\n visual analysis.\\\"},\\\"C02_0.5km\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 02 (full resolution)\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 2\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Daytime clouds, fog, insolation,\n winds\\\",\\\"center_wavelength\\\":0.64}]},\\\"C01_2km_wm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 01, Web Mercator\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 1\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"Daytime aerosol over land,\n coastal water mapping\\\",\\\"center_wavelength\\\":0.47}]},\\\"C02_2km_wm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 02, Web Mercator\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 2\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Daytime clouds, fog, insolation,\n winds\\\",\\\"center_wavelength\\\":0.64}]},\\\"C03_2km_wm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery reflectance factor - Band 03, Web Mercator\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 3\\\",\\\"common_name\\\":\\\"nir09\\\",\\\"description\\\":\\\"Daytime vegetation, burn\n scar, aerosol over water, winds\\\",\\\"center_wavelength\\\":0.87}]},\\\"C01_DQF_1km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"quality-mask\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery data quality flags - Band 01 (full resolution)\\\"},\\\"C01_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 01\\\"},\\\"C02_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 02\\\"},\\\"C03_DQF_1km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"quality-mask\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery data quality flags - Band 03 (full resolution)\\\"},\\\"C03_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 03\\\"},\\\"C04_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 04\\\"},\\\"C05_DQF_1km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"quality-mask\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery data quality flags - Band 05 (full resolution)\\\"},\\\"C05_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 05\\\"},\\\"C06_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 06\\\"},\\\"C07_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 07\\\"},\\\"C08_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 08\\\"},\\\"C09_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 09\\\"},\\\"C10_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 10\\\"},\\\"C11_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 11\\\"},\\\"C12_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 12\\\"},\\\"C13_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 13\\\"},\\\"C14_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 14\\\"},\\\"C15_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 15\\\"},\\\"C16_DQF_2km\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"Cloud and Moisture\n Imagery data quality flags - Band 16\\\"},\\\"CMIP_C01-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 01\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 1\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"Daytime aerosol over land,\n coastal water mapping\\\",\\\"center_wavelength\\\":0.47}]},\\\"CMIP_C02-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 02\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 2\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Daytime clouds, fog, insolation,\n winds\\\",\\\"center_wavelength\\\":0.64}]},\\\"CMIP_C03-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 03\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 3\\\",\\\"common_name\\\":\\\"nir09\\\",\\\"description\\\":\\\"Daytime vegetation, burn\n scar, aerosol over water, winds\\\",\\\"center_wavelength\\\":0.87}]},\\\"CMIP_C04-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 04\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 4\\\",\\\"common_name\\\":\\\"cirrus\\\",\\\"description\\\":\\\"Daytime cirrus cloud\\\",\\\"center_wavelength\\\":1.38}]},\\\"CMIP_C05-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 05\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 5\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"Daytime cloud-top phase\n and particle size, snow\\\",\\\"center_wavelength\\\":1.61}]},\\\"CMIP_C06-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery reflectance factor - Band 06\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 6\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"Daytime land, cloud\n properties, particle size, vegetation, snow\\\",\\\"center_wavelength\\\":2.25}]},\\\"CMIP_C07-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery brightness temperature at top of atmosphere - Band 07\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 7\\\",\\\"description\\\":\\\"Surface and cloud, fog at night, fire, winds\\\",\\\"center_wavelength\\\":3.89}]},\\\"CMIP_C08-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery brightness temperature at top of atmosphere - Band 08\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 8\\\",\\\"description\\\":\\\"High-level atmospheric water vapor, winds, rainfall\\\",\\\"center_wavelength\\\":6.17}]},\\\"CMIP_C09-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery brightness temperature at top of atmosphere - Band 09\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 9\\\",\\\"description\\\":\\\"Midlevel atmospheric water vapor, winds, rainfall\\\",\\\"center_wavelength\\\":6.93}]},\\\"CMIP_C10-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery brightness temperature at top of atmosphere - Band 10\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"ABI\n Band 10\\\",\\\"description\\\":\\\"Lower-level water vapor, winds, and silicon dioxide\\\",\\\"center_wavelength\\\":7.34}]},\\\"CMIP_C11-nc\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n and Moisture Imagery brightness temperature at top of atmosphere - 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CONUS404, so named because it covers the CONterminous United\n States for over 40 years at 4 km resolution, was produced by the Weather Research\n and Forecasting (WRF) model simulations run by NCAR as part of a collaboration\n with the USGS Water Mission Area. The CONUS404 includes 42 years of data (water\n years 1980-2021) and the spatial domain extends beyond the CONUS into Canada\n and Mexico, thereby capturing transboundary river basins and covering all\n contributing areas for CONUS surface waters.\\\\n\\\\nThe CONUS404 dataset, produced\n using WRF version 3.9.1.1, is the successor to the CONUS1 dataset in [ds612.0](https://rda.ucar.edu/datasets/ds612.0/)\n (Liu, et al., 2017) with improved representation of weather and climate conditions\n in the central United States due to the addition of a shallow groundwater\n module and several other improvements in the NOAH-Multiparameterization land\n surface model. It also uses a more up-to-date and higher-resolution reanalysis\n dataset (ERA5) as input and covers a longer period than CONUS1.\\\",\\\"msft:region\\\":\\\"eastus\\\",\\\"sci:citation\\\":\\\"Rasmussen,\n R. M., Chen, F., Liu, C.H., Ikeda, K., Prein, A., Kim, J., Schneider, T.,\n Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C.,\n Harrold, M., Xue, L., Chen, S., Newman, A., Dougherty, E., Abolafia-Rosenzweig,\n R., Lybarger, N. D., Viger, R., Lesmes, D., Skalak, K., Brakebill, J., Cline,\n D., Dunne, K., Rasmussen, K., & Miguez-Macho, G. (2023). CONUS404: The NCAR\\u2013USGS\n 4-km Long-Term Regional Hydroclimate Reanalysis over the CONUS. 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States.\\\"},{\\\"id\\\":\\\"sentinel-1-rtc\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-rtc/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-rtc\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://creativecommons.org/licenses/by/4.0/\\\",\\\"title\\\":\\\"CC\n BY 4.0\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel\n 1 Radiometrically Terrain Corrected (RTC)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-1-rtc.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Sentinel\n 1 RTC\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-1-rtc.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"MS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2014-10-10T00:28:21Z\\\",null]]}},\\\"license\\\":\\\"CC-BY-4.0\\\",\\\"keywords\\\":[\\\"ESA\\\",\\\"Copernicus\\\",\\\"Sentinel\\\",\\\"C-Band\\\",\\\"SAR\\\",\\\"RTC\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://catalyst.earth\\\",\\\"name\\\":\\\"Catalyst\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"licensor\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"SENTINEL-1A\\\",\\\"SENTINEL-1B\\\",\\\"SENTINEL-1C\\\"],\\\"constellation\\\":[\\\"Sentinel-1\\\"],\\\"s1:resolution\\\":[\\\"high\\\"],\\\"s1:orbit_source\\\":[\\\"DOWNLINK\\\",\\\"POEORB\\\",\\\"PREORB\\\",\\\"RESORB\\\"],\\\"sar:looks_range\\\":[5],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"sar:product_type\\\":[\\\"GRD\\\"],\\\"sar:looks_azimuth\\\":[1],\\\"sar:polarizations\\\":[[\\\"VV\\\",\\\"VH\\\"],[\\\"HH\\\",\\\"HV\\\"],[\\\"VV\\\"],[\\\"VH\\\"],[\\\"HH\\\"]],\\\"sar:frequency_band\\\":[\\\"C\\\"],\\\"s1:processing_level\\\":[\\\"1\\\"],\\\"sar:instrument_mode\\\":[\\\"IW\\\"],\\\"sar:center_frequency\\\":[5.405],\\\"sar:resolution_range\\\":[20],\\\"s1:product_timeliness\\\":[\\\"NRT-10m\\\",\\\"NRT-1h\\\",\\\"NRT-3h\\\",\\\"Fast-24h\\\",\\\"Off-line\\\",\\\"Reprocessing\\\"],\\\"sar:resolution_azimuth\\\":[22],\\\"sar:pixel_spacing_range\\\":[10],\\\"sar:observation_direction\\\":[\\\"right\\\"],\\\"sar:pixel_spacing_azimuth\\\":[10],\\\"sar:looks_equivalent_number\\\":[4.4],\\\"sat:platform_international_designator\\\":[\\\"2014-016A\\\",\\\"2016-025A\\\",\\\"0000-000A\\\"]},\\\"description\\\":\\\"The\n [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission\n is a constellation of two polar-orbiting satellites, operating day and night\n performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically\n Terrain Corrected (RTC) data in this collection is a radiometrically terrain\n corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd)\n products produced by the European Space Agency. The RTC processing is performed\n by [Catalyst](https://catalyst.earth/).\\\\n\\\\nRadiometric Terrain Correction\n accounts for terrain variations that affect both the position of a given point\n on the Earth's surface and the brightness of the radar return, as expressed\n in radar geometry. Without treatment, the hill-slope modulations of the radiometry\n threaten to overwhelm weaker thematic land cover-induced backscatter differences.\n Additionally, comparison of backscatter from multiple satellites, modes, or\n tracks loses meaning.\\\\n\\\\nA Planetary Computer account is required to retrieve\n SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed)\n for more information.\\\\n\\\\n### Methodology\\\\n\\\\nThe Sentinel-1 GRD product\n is converted to calibrated intensity using the conversion algorithm described\n in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration\n of S-1 Level-1 Products Generated by the S-1 IPF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/S1-Radiometric-Calibration-V1.0.pdf).\n The flat earth calibration values for gamma correction (i.e. perpendicular\n to the radar line of sight) are extracted from the GRD metadata. The calibration\n coefficients are applied as a two-dimensional correction in range (by sample\n number) and azimuth (by time). All available polarizations are calibrated\n and written as separate layers of a single file. The calibrated SAR output\n is reprojected to nominal map orientation with north at the top and west to\n the left.\\\\n\\\\nThe data is then radiometrically terrain corrected using PlanetDEM\n as the elevation source. The correction algorithm is nominally based upon\n D. Small, [\\u201CFlattening Gamma: Radiometric Terrain Correction for SAR\n Imagery\\u201D](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf),\n IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August\n 2011, pp 3081-3093. For each image scan line, the digital elevation model\n is interpolated to determine the elevation corresponding to the position associated\n with the known near slant range distance and arc length for each input pixel.\n The elevations at the four corners of each pixel are estimated using bilinear\n resampling. The four elevations are divided into two triangular facets and\n reprojected onto the plane perpendicular to the radar line of sight to provide\n an estimate of the area illuminated by the radar for each earth flattened\n pixel. The uncalibrated sum at each earth flattened pixel is normalized by\n dividing by the flat earth surface area. The adjustment for gamma intensity\n is given by dividing the normalized result by the cosine of the incident angle.\n Pixels which are not illuminated by the radar due to the viewing geometry\n are flagged as shadow.\\\\n\\\\nCalibrated data is then orthorectified to the\n appropriate UTM projection. The orthorectified output maintains the original\n sample sizes (in range and azimuth) and was not shifted to any specific grid.\\\\n\\\\nRTC\n data is processed only for the Interferometric Wide Swath (IW) mode, which\n is the main acquisition mode over land and satisfies the majority of service\n requirements.\\\\n\\\",\\\"item_assets\\\":{\\\"hh\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"HH: horizontal\n transmit, horizontal receive\\\",\\\"description\\\":\\\"Terrain-corrected gamma naught\n values of signal transmitted with horizontal polarization and received with\n horizontal polarization with radiometric terrain correction applied.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":-32768,\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":10}]},\\\"hv\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"HV:\n horizontal transmit, vertical receive\\\",\\\"description\\\":\\\"Terrain-corrected\n gamma naught values of signal transmitted with horizontal polarization and\n received with vertical polarization with radiometric terrain correction applied.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":-32768,\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":10}]},\\\"vh\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VH:\n vertical transmit, horizontal receive\\\",\\\"description\\\":\\\"Terrain-corrected\n gamma naught values of signal transmitted with vertical polarization and received\n with horizontal polarization with radiometric terrain correction applied.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":-32768,\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":10}]},\\\"vv\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VV:\n vertical transmit, vertical receive\\\",\\\"description\\\":\\\"Terrain-corrected\n gamma naught values of signal transmitted with vertical polarization and received\n with vertical polarization with radiometric terrain correction applied.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":-32768,\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":10}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-1\\\",\\\"msft:container\\\":\\\"sentinel1-grd-rtc\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sar/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel1euwestrtc\\\",\\\"msft:requires_account\\\":true,\\\"msft:short_description\\\":\\\"Radiometrically\n terrain corrected SAR imagery derived from the Sentinel 1 Level 1 GRD product.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"3dep-lidar-intensity\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-intensity/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-intensity\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\\\",\\\"title\\\":\\\"About\n 3DEP Products & Services\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-intensity\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n 3DEP Lidar Intensity\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/intensity-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2012-01-01T00:00:00Z\\\",\\\"2022-01-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"3DEP\\\",\\\"COG\\\",\\\"Intensity\\\"],\\\"providers\\\":[{\\\"name\\\":\\\"Landrush\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/ngp/3dep/\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[2.0]},\\\"description\\\":\\\"This\n collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).\n It is a collection of Cloud Optimized GeoTIFFs representing the pulse return\n magnitude.\\\\n\\\\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html)\n and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier)\n and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)\n to remove outliers and noise.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"COG\n data\\\",\\\"raster:bands\\\":[{\\\"name\\\":\\\"Intensity\\\",\\\"unit\\\":\\\"metre\\\",\\\"sampling\\\":\\\"point\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"description\\\":\\\"Intensity\n PDAL dimension\\\"}]},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"3dep-lidar\\\",\\\"msft:container\\\":\\\"usgs-3dep-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\\\"],\\\"msft:storage_account\\\":\\\"usgslidareuwest\\\",\\\"msft:short_description\\\":\\\"3DEP\n Lidar collection for the Cloud Optimized Geotiffs (COGs) based on the Intensity\n dimension.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"3dep-lidar-pointsourceid\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-pointsourceid/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-pointsourceid\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\\\",\\\"title\\\":\\\"About\n 3DEP Products & Services\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-pointsourceid\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n 3DEP Lidar Point Source\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/pointsourceid-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/3dep-lidar-pointsourceid.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2012-01-01T00:00:00Z\\\",\\\"2022-01-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"3DEP\\\",\\\"COG\\\",\\\"PointSourceId\\\"],\\\"providers\\\":[{\\\"name\\\":\\\"Landrush\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/ngp/3dep/\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[5.0]},\\\"description\\\":\\\"This\n collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).\n It is a collection of Cloud Optimized GeoTIFFs representing the file source\n ID from which the point originated. Zero indicates that the point originated\n in the current file.\\\\n\\\\nThis values are based on the PointSourceId [PDAL\n dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier)\n and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)\n to remove outliers and noise.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"COG\n data\\\",\\\"raster:bands\\\":[{\\\"name\\\":\\\"PointSourceId\\\",\\\"unit\\\":\\\"metre\\\",\\\"sampling\\\":\\\"point\\\",\\\"data_type\\\":\\\"int32\\\",\\\"description\\\":\\\"PointSourceId\n PDAL dimension\\\"}]},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"3dep-lidar\\\",\\\"msft:container\\\":\\\"usgs-3dep-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"usgslidareuwest\\\",\\\"msft:short_description\\\":\\\"3DEP\n Lidar collection for Cloud Optimized Geotiffs (COGs) using PDAL's PointSourceId\n dimension.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"mtbs\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mtbs/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mtbs\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://catalog.data.gov/sl/dataset/mtbs-wildfire-burn-severity-mosaics-40c75\\\",\\\"title\\\":\\\"CC\n BY\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/mtbs\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"MTBS:\n Monitoring Trends in Burn Severity\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/mtbs.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"MTBS\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/mtbs.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-166.705027,56.331164,-137.664767,69.980073],[-127.579429,23.680916,-65.871833,50.870718]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1984-12-31T00:00:00Z\\\",\\\"2018-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"MTBS\\\",\\\"USGS\\\",\\\"USFS\\\",\\\"USDA\\\",\\\"Forest\\\",\\\"Fire\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.mtbs.gov/\\\",\\\"name\\\":\\\"MTBS\n program\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://carbonplan.org\\\",\\\"name\\\":\\\"CarbonPlan\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[30]},\\\"description\\\":\\\"[Monitoring\n Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency\n program whose goal is to consistently map the burn severity and extent of\n large fires across the United States from 1984 to the present. This includes\n all fires 1000 acres or greater in the Western United States and 500 acres\n or greater in the Eastern United States. The burn severity mosaics in this\n dataset consist of thematic raster images of MTBS burn severity classes for\n all currently completed MTBS fires for the continental United States and Alaska.\\\\n\\\",\\\"item_assets\\\":{\\\"metadata\\\":{\\\"type\\\":\\\"text/html\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Metadata\\\"},\\\"burn-severity\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Burn\n severity\\\",\\\"file:values\\\":[{\\\"values\\\":[0],\\\"summary\\\":\\\"No Data\\\"},{\\\"values\\\":[1],\\\"summary\\\":\\\"Unburned\n to low\\\"},{\\\"values\\\":[2],\\\"summary\\\":\\\"Low\\\"},{\\\"values\\\":[3],\\\"summary\\\":\\\"Moderate\\\"},{\\\"values\\\":[4],\\\"summary\\\":\\\"High\\\"},{\\\"values\\\":[5],\\\"summary\\\":\\\"Increased\n Greenness\\\"}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"cpdata\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/file/v2.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"cpdataeuwest\\\",\\\"msft:short_description\\\":\\\"Annual\n burn severity mosaics for the continental United States and Alaska\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"noaa-c-cap\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-c-cap/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-c-cap\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://coast.noaa.gov/data/digitalcoast/pdf/ccap-product-page.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Handout\n - C-CAP Products\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://coast.noaa.gov/data/digitalcoast/pdf/ccap-faq-regional.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Frequently\n Asked Questions\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://coast.noaa.gov/data/digitalcoast/pdf/ccap-class-scheme-regional.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"C-CAP\n Classification Scheme and Class Definitions\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://coast.noaa.gov/digitalcoast/data/ccapregional.html\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"C-CAP\n Regional Land Cover and Change\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-c-cap\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"C-CAP\n Regional Land Cover and Change\\\",\\\"assets\\\":{\\\"boundary\\\":{\\\"href\\\":\\\"https://coast.noaa.gov/data/digitalcoast/zip/ccap-mapping-bndry-wgs84.zip\\\",\\\"type\\\":\\\"application/zip\\\",\\\"title\\\":\\\"C-CAP\n Mapping Boundary (shp file)\\\"},\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-c-cap.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"NOAA\n C-CAP\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-c-cap.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-160.315986,17.892786,-64.966857,49.471148],[-127.984554,22.718719,-64.966857,49.471148],[-160.315986,18.879215,-154.706564,22.271167],[-67.290869,17.892786,-65.230246,18.538629]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1975-01-01T00:00:00Z\\\",\\\"2016-12-31T00:00:00Z\\\"],[\\\"1975-01-01T00:00:00Z\\\",\\\"2016-12-31T00:00:00Z\\\"],[\\\"1992-01-01T00:00:00Z\\\",\\\"2005-12-31T00:00:00Z\\\"],[\\\"2010-01-01T00:00:00Z\\\",\\\"2010-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Land\n Cover\\\",\\\"Land Use\\\",\\\"NOAA\\\",\\\"Coastal\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://coast.noaa.gov/digitalcoast/data/ccapregional.html\\\",\\\"name\\\":\\\"National\n Oceanic and Atmospheric Administration, Office for Coastal Management\\\",\\\"roles\\\":[\\\"licensor\\\",\\\"producer\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https//planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"description\\\":\\\"Nationally\n standardized, raster-based inventories of land cover for the coastal areas\n of the U.S. Data are derived, through the Coastal Change Analysis Program,\n from the analysis of multiple dates of remotely sensed imagery. Two file\n types are available: individual dates that supply a wall-to-wall map, and\n change files that compare one date to another. The use of standardized data\n and procedures assures consistency through time and across geographies. 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Name\n of Data Set: Coastal Change Analysis Program (C-CAP) Regional Land Cover.\n Charleston, SC: NOAA Office for Coastal Management. 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A COPC file is a LAZ\n 1.4 file that stores point data organized in a clustered octree. It contains\n a VLR that describes the octree organization of data that are stored in LAZ\n 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected\n COPC files.\\\\n\\\\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud)\n (LPC) content stored in the compressed [LASzip](https://laszip.org?) format.\n Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io)\n organization for use in Planetary Computer, which supports incremental spatial\n access and cloud streaming.\\\\n\\\\nLPC can be summarized for construction of\n digital terrain models (DTM), filtered for extraction of features like vegetation\n and buildings, and visualized to provide a point cloud map of the physical\n spaces the laser scanner interacted with. LPC content from 3DEP is used to\n compute and extract a variety of landscape characterization products, and\n some of them are provided by Planetary Computer, including Height Above Ground,\n Relative Intensity Image, and DTM and Digital Surface Models.\\\\n\\\\nThe LAZ\n tiles represent a one-to-one mapping of original tiled content as provided\n by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with\n the exception that the data were reprojected and normalized into appropriate\n UTM zones for their location without adjustment to the vertical datum. In\n some cases, vertical datum description may not match actual data values, especially\n for pre-2010 USGS 3DEP point cloud data.\\\\n\\\\nIn addition to these COPC files,\n various higher-level derived products are available as Cloud Optimized GeoTIFFs\n in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/vnd.laszip+copc\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"COPC\n data\\\",\\\"pc:type\\\":\\\"lidar\\\",\\\"pc:encoding\\\":\\\"application/vnd.laszip+copc\\\"},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COPC\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"3dep-lidar\\\",\\\"msft:container\\\":\\\"usgs-3dep-copc\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/pointcloud/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"usgslidareuwest\\\",\\\"msft:short_description\\\":\\\"Nationwide\n Lidar point cloud data in COPC format.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"modis-64A1-061\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-64A1-061/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-64A1-061\\\"},{\\\"rel\\\":\\\"help\\\",\\\"href\\\":\\\"https://lpdaac.usgs.gov/documents/1006/MCD64_User_Guide_V61.pdf\\\",\\\"title\\\":\\\"MCD64\n User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD64A1\\\",\\\"title\\\":\\\"MCD64A1\n file specification\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5067/MODIS/MCD64A1.061\\\",\\\"title\\\":\\\"LP\n DAAC - MCD64A1\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\\\",\\\"title\\\":\\\"LP\n DAAC - Data Citation and Policies\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/modis-64A1-061\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"MODIS\n Burned Area Monthly\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-64A1-061.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"MODIS\n Burned Area Monthly thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/modis-64A1-061.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"MS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2000-11-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"NASA\\\",\\\"MODIS\\\",\\\"Satellite\\\",\\\"Imagery\\\",\\\"Global\\\",\\\"Fire\\\",\\\"MCD64A1\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://lpdaac.usgs.gov/\\\",\\\"name\\\":\\\"NASA\n LP DAAC at the USGS EROS Center\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"aqua\\\",\\\"terra\\\"],\\\"instruments\\\":[\\\"modis\\\"]},\\\"description\\\":\\\"The\n Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a\n monthly, global gridded 500 meter (m) product containing per-pixel burned-area\n and quality information. 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The data layers\n provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty,\n Quality Assurance, along with First Day and Last Day of reliable change detection\n of the year.\\\",\\\"item_assets\\\":{\\\"QA\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Quality Assurance\n Indicators\\\",\\\"raster:bands\\\":[{\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":500}]},\\\"hdf\\\":{\\\"type\\\":\\\"application/x-hdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Source\n data containing all bands\\\"},\\\"Last_Day\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Last day of the\n year of reliable change detection\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"Day\\\",\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Federal\n Geographic Data Committee (FGDC) Metadata\\\"},\\\"Burn_Date\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Burn\n day of year\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"Day\\\",\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"First_Day\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"First\n day of the year of reliable change detection\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"Day\\\",\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"Burn_Date_Uncertainty\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Estimated\n uncertainty in burn day\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"Day\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":500}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"modis\\\",\\\"msft:container\\\":\\\"modis-061\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.5067/MODIS/MCD64A1.061\\\",\\\"citation\\\":\\\"Giglio,\n L., Justice, C., Boschetti, L., & Roy, D. (2021). MODIS/Terra+Aqua\n Burned Area Monthly L3 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS\n Land Processes DAAC. https://doi.org/10.5067/MODIS/MCD64A1.061\\\"}],\\\"msft:storage_account\\\":\\\"modiseuwest\\\",\\\"msft:short_description\\\":\\\"MODIS\n Burned Area Monthly\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"alos-fnf-mosaic\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-fnf-mosaic/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-fnf-mosaic\\\"},{\\\"rel\\\":\\\"handbook\\\",\\\"href\\\":\\\"https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Global\n 25 m Resolution PALSAR-2/PALSAR Forest/Non-Forest Map (FNF)\\\",\\\"description\\\":\\\"Also\n includes data usage information\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://earth.jaxa.jp/policy/en.html\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"JAXA\n Terms of Use of Research Data\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/alos-fnf-mosaic\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"ALOS\n Forest/Non-Forest Annual Mosaic\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/alos-fnf-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"ALOS\n PALSAR MOSAIC\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/alos-fnf-mosaic.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,85.0,180.0,-56.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2015-01-01T00:00:00Z\\\",\\\"2020-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"version\\\":\\\"2.0.0\\\",\\\"keywords\\\":[\\\"ALOS\\\",\\\"JAXA\\\",\\\"Forest\\\",\\\"Land\n Cover\\\",\\\"Global\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm\\\",\\\"name\\\":\\\"Japan\n Aerospace Exploration Agency\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://developmentseed.org\\\",\\\"name\\\":\\\"Development\n Seed\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\n Planetary Computer\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"ALOS\\\",\\\"ALOS-2\\\"],\\\"instruments\\\":[\\\"PALSAR\\\",\\\"PALSAR-2\\\"]},\\\"description\\\":\\\"The\n global 25m resolution SAR mosaics and forest/non-forest maps are free and\n open annual datasets generated by [JAXA](https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm)\n using the L-band Synthetic Aperture Radar sensors on the Advanced Land Observing\n Satellite-2 (ALOS-2 PALSAR-2), the Advanced Land Observing Satellite (ALOS\n PALSAR) and the Japanese Earth Resources Satellite-1 (JERS-1 SAR).\\\\n\\\\nThe\n global forest/non-forest maps (FNF) were generated by a Random Forest machine\n learning-based classification method, with the re-processed global 25m resolution\n [PALSAR-2 mosaic dataset](https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic)\n (Ver. 2.0.0) as input. Here, the \\\\\\\"forest\\\\\\\" is defined as the tree covered\n land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance\n with the FAO definition of forest. 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collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).\n It is a collection of Cloud Optimized GeoTIFFs representing the number of\n returns for a given pulse.\\\\n\\\\nThis values are based on the PointSourceId\n [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier)\n and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)\n to remove outliers and noise.\\\\n\\\\nThe values are based on the NumberOfReturns\n [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier)\n and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)\n to remove outliers and noise.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; 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dimension.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"mobi\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mobi/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/mobi\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://aka.ms/mobi-license\\\",\\\"title\\\":\\\"NatureServe\n MoBI license\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/mobi\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"MoBI:\n Map of Biodiversity Importance\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/mobi.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"MoBI\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/mobi.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet 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Species included in the project are those which,\n as of September 2018, had a global conservation status of G1 (critical imperiled)\n or G2 (imperiled) or which are listed as threatened or endangered at the full\n species level under the United States Endangered Species Act. Taxonomic groups\n included in the project are vertebrates (birds, mammals, amphibians, reptiles,\n turtles, crocodilians, and freshwater and anadromous fishes), vascular plants,\n selected aquatic invertebrates (freshwater mussels and crayfish) and selected\n pollinators (bumblebees, butterflies, and skippers).\\\\n\\\\nThere are three\n types of spatial data provided, described in more detail below: species richness,\n range-size rarity, and protection-weighted range-size rarity. 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For site-specific decision-making,\n these data should be used in conjunction with field surveys and/or documented\n occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\\\\n\\\",\\\"item_assets\\\":{\\\"RSR_All\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Range-size\n rarity for all species\\\"},\\\"RSR_Plants\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Range-size rarity\n for vascular plants\\\"},\\\"RSR_Vertebrates\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Range-size rarity\n for vertebrates\\\"},\\\"RSR_AquaticInverts\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n 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States\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"landsat-c2-l2\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5066/P9IAXOVV\\\",\\\"title\\\":\\\"Landsat\n 4-5 TM Collection 2 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blue (Thematic Mapper)\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.07},{\\\"name\\\":\\\"TM_B2\\\",\\\"common_name\\\":\\\"green\\\",\\\"description\\\":\\\"Visible\n green (Thematic Mapper)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.08},{\\\"name\\\":\\\"TM_B3\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Visible\n red (Thematic Mapper)\\\",\\\"center_wavelength\\\":0.66,\\\"full_width_half_max\\\":0.06},{\\\"name\\\":\\\"TM_B4\\\",\\\"common_name\\\":\\\"nir08\\\",\\\"description\\\":\\\"Near\n infrared (Thematic Mapper)\\\",\\\"center_wavelength\\\":0.83,\\\"full_width_half_max\\\":0.14},{\\\"name\\\":\\\"TM_B5\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"Short-wave\n infrared (Thematic Mapper)\\\",\\\"center_wavelength\\\":1.65,\\\"full_width_half_max\\\":0.2},{\\\"name\\\":\\\"TM_B6\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"Long-wave\n infrared (Thematic Mapper)\\\",\\\"center_wavelength\\\":11.45,\\\"full_width_half_max\\\":2.1},{\\\"name\\\":\\\"TM_B7\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"Short-wave\n infrared (Thematic Mapper)\\\",\\\"center_wavelength\\\":2.22,\\\"full_width_half_max\\\":0.27},{\\\"name\\\":\\\"ETM_B1\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"Visible\n blue (Enhanced Thematic Mapper Plus)\\\",\\\"center_wavelength\\\":0.48,\\\"full_width_half_max\\\":0.07},{\\\"name\\\":\\\"ETM_B2\\\",\\\"common_name\\\":\\\"green\\\",\\\"description\\\":\\\"Visible\n green (Enhanced Thematic Mapper Plus)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.08},{\\\"name\\\":\\\"ETM_B3\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Visible\n red (Enhanced Thematic Mapper Plus)\\\",\\\"center_wavelength\\\":0.66,\\\"full_width_half_max\\\":0.06},{\\\"name\\\":\\\"ETM_B4\\\",\\\"common_name\\\":\\\"nir08\\\",\\\"description\\\":\\\"Near\n infrared (Enhanced Thematic Mapper Plus)\\\",\\\"center_wavelength\\\":0.84,\\\"full_width_half_max\\\":0.13},{\\\"name\\\":\\\"ETM_B5\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"Short-wave\n infrared (Enhanced Thematic Mapper Plus)\\\",\\\"center_wavelength\\\":1.65,\\\"full_width_half_max\\\":0.2},{\\\"name\\\":\\\"ETM_B6\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"Long-wave\n infrared (Enhanced Thematic Mapper Plus)\\\",\\\"center_wavelength\\\":11.34,\\\"full_width_half_max\\\":2.05},{\\\"name\\\":\\\"ETM_B7\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"Short-wave\n infrared (Enhanced Thematic Mapper Plus)\\\",\\\"center_wavelength\\\":2.2,\\\"full_width_half_max\\\":0.28},{\\\"name\\\":\\\"OLI_B1\\\",\\\"common_name\\\":\\\"coastal\\\",\\\"description\\\":\\\"Coastal/Aerosol\n (Operational Land Imager)\\\",\\\"center_wavelength\\\":0.44,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"OLI_B2\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"Visible\n blue (Operational Land Imager)\\\",\\\"center_wavelength\\\":0.48,\\\"full_width_half_max\\\":0.06},{\\\"name\\\":\\\"OLI_B3\\\",\\\"common_name\\\":\\\"green\\\",\\\"description\\\":\\\"Visible\n green (Operational Land Imager)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.06},{\\\"name\\\":\\\"OLI_B4\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Visible\n red (Operational Land Imager)\\\",\\\"center_wavelength\\\":0.65,\\\"full_width_half_max\\\":0.04},{\\\"name\\\":\\\"OLI_B5\\\",\\\"common_name\\\":\\\"nir08\\\",\\\"description\\\":\\\"Near\n infrared (Operational Land Imager)\\\",\\\"center_wavelength\\\":0.87,\\\"full_width_half_max\\\":0.03},{\\\"name\\\":\\\"OLI_B6\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"Short-wave\n infrared (Operational Land Imager)\\\",\\\"center_wavelength\\\":1.61,\\\"full_width_half_max\\\":0.09},{\\\"name\\\":\\\"OLI_B7\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"Short-wave\n infrared (Operational Land Imager)\\\",\\\"center_wavelength\\\":2.2,\\\"full_width_half_max\\\":0.19},{\\\"name\\\":\\\"TIRS_B10\\\",\\\"common_name\\\":\\\"lwir11\\\",\\\"description\\\":\\\"Long-wave\n infrared (Thermal Infrared Sensor)\\\",\\\"center_wavelength\\\":10.9,\\\"full_width_half_max\\\":0.59}],\\\"platform\\\":[\\\"landsat-4\\\",\\\"landsat-5\\\",\\\"landsat-7\\\",\\\"landsat-8\\\",\\\"landsat-9\\\"],\\\"instruments\\\":[\\\"tm\\\",\\\"etm+\\\",\\\"oli\\\",\\\"tirs\\\"],\\\"view:off_nadir\\\":{\\\"maximum\\\":15,\\\"minimum\\\":0}},\\\"description\\\":\\\"Landsat\n Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products),\n consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance)\n and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature)\n image data. Collection 2 Level-2 Science Products are available from August\n 22, 1982 to present.\\\\n\\\\nThis dataset represents the global archive of Level-2\n data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2)\n acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/)\n onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/)\n onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/)\n and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/)\n onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/)\n format.\\\\n\\\",\\\"item_assets\\\":{\\\"qa\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Surface Temperature\n Quality Assessment Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Quality Assessment\n Band (ST_QA) Surface Temperature Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"kelvin\\\",\\\"scale\\\":0.01,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]},\\\"ang\\\":{\\\"type\\\":\\\"text/plain\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Angle\n Coefficients File\\\",\\\"description\\\":\\\"Collection 2 Level-1 Angle Coefficients\n File\\\"},\\\"red\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Red\n Band\\\",\\\"eo:bands\\\":[{\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"Visible red\\\"}],\\\"raster:bands\\\":[{\\\"scale\\\":0.0000275,\\\"nodata\\\":0,\\\"offset\\\":-0.2,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"blue\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Blue\n Band\\\",\\\"eo:bands\\\":[{\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"Visible blue\\\"}],\\\"raster:bands\\\":[{\\\"scale\\\":0.0000275,\\\"nodata\\\":0,\\\"offset\\\":-0.2,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"drad\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Downwelled\n Radiance Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Downwelled Radiance\n Band (ST_DRAD) Surface Temperature Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"watt/steradian/square_meter/micrometer\\\",\\\"scale\\\":0.001,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]},\\\"emis\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Emissivity\n Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Emissivity Band (ST_EMIS) Surface\n Temperature Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"emissivity coefficient\\\",\\\"scale\\\":0.0001,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]},\\\"emsd\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Emissivity\n Standard Deviation Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Emissivity\n Standard Deviation Band (ST_EMSD) Surface Temperature Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"emissivity\n coefficient\\\",\\\"scale\\\":0.0001,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]},\\\"lwir\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"temperature\\\"],\\\"title\\\":\\\"Surface\n Temperature Band\\\",\\\"eo:bands\\\":[{\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"Long-wave\n infrared\\\"}],\\\"description\\\":\\\"Collection 2 Level-2 Thermal Infrared Band\n (ST_B6) Surface Temperature\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"kelvin\\\",\\\"scale\\\":0.00341802,\\\"nodata\\\":0,\\\"offset\\\":149.0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"trad\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Thermal\n Radiance Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Thermal Radiance Band\n (ST_TRAD) Surface Temperature Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"watt/steradian/square_meter/micrometer\\\",\\\"scale\\\":0.001,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]},\\\"urad\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Upwelled\n Radiance Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Upwelled Radiance Band\n (ST_URAD) Surface Temperature Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"watt/steradian/square_meter/micrometer\\\",\\\"scale\\\":0.001,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]},\\\"atran\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Atmospheric\n Transmittance Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Atmospheric Transmittance\n Band (ST_ATRAN) Surface Temperature Product\\\",\\\"raster:bands\\\":[{\\\"scale\\\":0.0001,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]},\\\"cdist\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n Distance Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Cloud Distance Band\n (ST_CDIST) Surface Temperature Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"kilometer\\\",\\\"scale\\\":0.01,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]},\\\"green\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Green\n Band\\\",\\\"eo:bands\\\":[{\\\"common_name\\\":\\\"green\\\",\\\"description\\\":\\\"Visible\n green\\\",\\\"center_wavelength\\\":0.56}],\\\"raster:bands\\\":[{\\\"scale\\\":0.0000275,\\\"nodata\\\":0,\\\"offset\\\":-0.2,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"nir08\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Near\n Infrared Band 0.8\\\",\\\"eo:bands\\\":[{\\\"common_name\\\":\\\"nir08\\\",\\\"description\\\":\\\"Near\n infrared\\\"}],\\\"raster:bands\\\":[{\\\"scale\\\":0.0000275,\\\"nodata\\\":0,\\\"offset\\\":-0.2,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"lwir11\\\":{\\\"gsd\\\":100,\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"temperature\\\"],\\\"title\\\":\\\"Surface\n Temperature Band\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"TIRS_B10\\\",\\\"common_name\\\":\\\"lwir11\\\",\\\"description\\\":\\\"Long-wave\n infrared\\\",\\\"center_wavelength\\\":10.9,\\\"full_width_half_max\\\":0.59}],\\\"description\\\":\\\"Collection\n 2 Level-2 Thermal Infrared Band (ST_B10) Surface Temperature\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"kelvin\\\",\\\"scale\\\":0.00341802,\\\"nodata\\\":0,\\\"offset\\\":149.0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"swir16\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Short-wave\n Infrared Band 1.6\\\",\\\"eo:bands\\\":[{\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"Short-wave\n infrared\\\"}],\\\"raster:bands\\\":[{\\\"scale\\\":0.0000275,\\\"nodata\\\":0,\\\"offset\\\":-0.2,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"swir22\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Short-wave\n Infrared Band 2.2\\\",\\\"eo:bands\\\":[{\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"Short-wave\n infrared\\\"}],\\\"description\\\":\\\"Collection 2 Level-2 Short-wave Infrared Band\n 2.2 (SR_B7) Surface Reflectance\\\",\\\"raster:bands\\\":[{\\\"scale\\\":0.0000275,\\\"nodata\\\":0,\\\"offset\\\":-0.2,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"coastal\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Coastal/Aerosol\n Band\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"OLI_B1\\\",\\\"common_name\\\":\\\"coastal\\\",\\\"description\\\":\\\"Coastal/Aerosol\\\",\\\"center_wavelength\\\":0.44,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Collection\n 2 Level-2 Coastal/Aerosol Band (SR_B1) Surface Reflectance\\\",\\\"raster:bands\\\":[{\\\"scale\\\":0.0000275,\\\"nodata\\\":0,\\\"offset\\\":-0.2,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"mtl.txt\\\":{\\\"type\\\":\\\"text/plain\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Product\n Metadata File (txt)\\\",\\\"description\\\":\\\"Collection 2 Level-2 Product Metadata\n File (txt)\\\"},\\\"mtl.xml\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Product\n Metadata File (xml)\\\",\\\"description\\\":\\\"Collection 2 Level-2 Product Metadata\n File (xml)\\\"},\\\"cloud_qa\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"cloud\\\",\\\"cloud-shadow\\\",\\\"snow-ice\\\",\\\"water-mask\\\"],\\\"title\\\":\\\"Cloud\n Quality Assessment Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Cloud Quality\n Assessment Band (SR_CLOUD_QA) Surface Reflectance Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"bit\n index\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:bitfields\\\":[{\\\"name\\\":\\\"ddv\\\",\\\"length\\\":1,\\\"offset\\\":0,\\\"classes\\\":[{\\\"name\\\":\\\"not_ddv\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n has no DDV\\\"},{\\\"name\\\":\\\"ddv\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel has DDV\\\"}],\\\"description\\\":\\\"Dense\n Dark Vegetation (DDV)\\\"},{\\\"name\\\":\\\"cloud\\\",\\\"length\\\":1,\\\"offset\\\":1,\\\"classes\\\":[{\\\"name\\\":\\\"not_cloud\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n has no cloud\\\"},{\\\"name\\\":\\\"cloud\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel has\n cloud\\\"}],\\\"description\\\":\\\"Cloud mask\\\"},{\\\"name\\\":\\\"cloud_shadow\\\",\\\"length\\\":1,\\\"offset\\\":2,\\\"classes\\\":[{\\\"name\\\":\\\"not_shadow\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n has no cloud shadow\\\"},{\\\"name\\\":\\\"shadow\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel\n has cloud shadow\\\"}],\\\"description\\\":\\\"Cloud shadow mask\\\"},{\\\"name\\\":\\\"cloud_adjacent\\\",\\\"length\\\":1,\\\"offset\\\":3,\\\"classes\\\":[{\\\"name\\\":\\\"not_adjacent\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n is not adjacent to cloud\\\"},{\\\"name\\\":\\\"adjacent\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel\n is adjacent to cloud\\\"}],\\\"description\\\":\\\"Cloud adjacency\\\"},{\\\"name\\\":\\\"snow\\\",\\\"length\\\":1,\\\"offset\\\":4,\\\"classes\\\":[{\\\"name\\\":\\\"not_snow\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n is not snow\\\"},{\\\"name\\\":\\\"shadow\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel is\n snow\\\"}],\\\"description\\\":\\\"Snow mask\\\"},{\\\"name\\\":\\\"water\\\",\\\"length\\\":1,\\\"offset\\\":5,\\\"classes\\\":[{\\\"name\\\":\\\"not_water\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n is not water\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel is\n water\\\"}],\\\"description\\\":\\\"Water mask\\\"}]},\\\"mtl.json\\\":{\\\"type\\\":\\\"application/json\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Product\n Metadata File (json)\\\",\\\"description\\\":\\\"Collection 2 Level-2 Product Metadata\n File (json)\\\"},\\\"qa_pixel\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"cloud\\\",\\\"cloud-shadow\\\",\\\"snow-ice\\\",\\\"water-mask\\\"],\\\"title\\\":\\\"Pixel\n Quality Assessment Band\\\",\\\"description\\\":\\\"Collection 2 Level-1 Pixel Quality\n Assessment Band (QA_PIXEL)\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"bit index\\\",\\\"nodata\\\":1,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"qa_radsat\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"saturation\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"bit\n index\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"qa_aerosol\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data-mask\\\",\\\"water-mask\\\"],\\\"title\\\":\\\"Aerosol\n Quality Assessment Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Aerosol Quality\n Assessment Band (SR_QA_AEROSOL) Surface Reflectance Product\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"bit\n index\\\",\\\"nodata\\\":1,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:bitfields\\\":[{\\\"name\\\":\\\"fill\\\",\\\"length\\\":1,\\\"offset\\\":0,\\\"classes\\\":[{\\\"name\\\":\\\"not_fill\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n is not fill\\\"},{\\\"name\\\":\\\"fill\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel is fill\\\"}],\\\"description\\\":\\\"Image\n or fill data\\\"},{\\\"name\\\":\\\"retrieval\\\",\\\"length\\\":1,\\\"offset\\\":1,\\\"classes\\\":[{\\\"name\\\":\\\"not_valid\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n retrieval is not valid\\\"},{\\\"name\\\":\\\"valid\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel\n retrieval is valid\\\"}],\\\"description\\\":\\\"Valid aerosol retrieval\\\"},{\\\"name\\\":\\\"water\\\",\\\"length\\\":1,\\\"offset\\\":2,\\\"classes\\\":[{\\\"name\\\":\\\"not_water\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n is not water\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel is\n water\\\"}],\\\"description\\\":\\\"Water mask\\\"},{\\\"name\\\":\\\"interpolated\\\",\\\"length\\\":1,\\\"offset\\\":5,\\\"classes\\\":[{\\\"name\\\":\\\"not_interpolated\\\",\\\"value\\\":0,\\\"description\\\":\\\"Pixel\n is not interpolated aerosol\\\"},{\\\"name\\\":\\\"interpolated\\\",\\\"value\\\":1,\\\"description\\\":\\\"Pixel\n is interpolated aerosol\\\"}],\\\"description\\\":\\\"Aerosol interpolation\\\"},{\\\"name\\\":\\\"level\\\",\\\"length\\\":2,\\\"offset\\\":6,\\\"classes\\\":[{\\\"name\\\":\\\"climatology\\\",\\\"value\\\":0,\\\"description\\\":\\\"No\n aerosol correction applied\\\"},{\\\"name\\\":\\\"low\\\",\\\"value\\\":1,\\\"description\\\":\\\"Low\n aerosol level\\\"},{\\\"name\\\":\\\"medium\\\",\\\"value\\\":2,\\\"description\\\":\\\"Medium\n aerosol level\\\"},{\\\"name\\\":\\\"high\\\",\\\"value\\\":3,\\\"description\\\":\\\"High aerosol\n level\\\"}],\\\"description\\\":\\\"Aerosol level\\\"}]},\\\"atmos_opacity\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Atmospheric\n Opacity Band\\\",\\\"description\\\":\\\"Collection 2 Level-2 Atmospheric Opacity\n Band (SR_ATMOS_OPACITY) Surface Reflectance Product\\\",\\\"raster:bands\\\":[{\\\"scale\\\":0.001,\\\"nodata\\\":-9999,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":30}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"landsat\\\",\\\"msft:container\\\":\\\"landsat-c2\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/view/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"landsateuwest\\\",\\\"msft:short_description\\\":\\\"Landsat\n Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4\n and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the\n Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat\n 8 and 9.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"era5-pds\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/era5-pds/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/era5-pds\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://apps.ecmwf.int/datasets/licences/copernicus/\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"License\n to Use Copernicus Products\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://confluence.ecmwf.int/display/CKB/ERA5\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Project\n homepage\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"How\n to cite\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/era5-pds\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"ERA5\n - PDS\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/era5-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"title\\\":\\\"Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/era5-pds.parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"media_type\\\":\\\"application/x-parquet\\\",\\\"description\\\":\\\"Snapshot\n of the collection's STAC items exported to GeoParquet format.\\\",\\\"extra_fields\\\":{\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1979-01-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"ERA5\\\",\\\"ECMWF\\\",\\\"Precipitation\\\",\\\"Temperature\\\",\\\"Reanalysis\\\",\\\"Weather\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ecmwf.int/\\\",\\\"name\\\":\\\"ECMWF\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetos.com/\\\",\\\"name\\\":\\\"Planet\n OS\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"era5:kind\\\":[\\\"fc\\\",\\\"an\\\"]},\\\"description\\\":\\\"ERA5\n is the fifth generation ECMWF atmospheric reanalysis of the global climate\\\\ncovering\n the period from January 1950 to present. ERA5 is produced by the\\\\nCopernicus\n Climate Change Service (C3S) at ECMWF.\\\\n\\\\nReanalysis combines model data\n with observations from across the world into a\\\\nglobally complete and consistent\n dataset using the laws of physics. This\\\\nprinciple, called data assimilation,\n is based on the method used by numerical\\\\nweather prediction centres, where\n every so many hours (12 hours at ECMWF) a\\\\nprevious forecast is combined\n with newly available observations in an optimal\\\\nway to produce a new best\n estimate of the state of the atmosphere, called\\\\nanalysis, from which an\n updated, improved forecast is issued. Reanalysis works\\\\nin the same way,\n but at reduced resolution to allow for the provision of a\\\\ndataset spanning\n back several decades. Reanalysis does not have the constraint\\\\nof issuing\n timely forecasts, so there is more time to collect observations, and\\\\nwhen\n going further back in time, to allow for the ingestion of improved versions\\\\nof\n the original observations, which all benefit the quality of the reanalysis\\\\nproduct.\\\\n\\\\nThis\n dataset was converted to Zarr by [Planet OS](https://planetos.com/).\\\\nSee\n [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\\\\nfor\n more.\\\\n\\\\n## STAC Metadata\\\\n\\\\nTwo types of data variables are provided:\n \\\\\\\"forecast\\\\\\\" (`fc`) and \\\\\\\"analysis\\\\\\\" (`an`).\\\\n\\\\n* An **analysis**,\n of the atmospheric conditions, is a blend of observations\\\\n with a previous\n forecast. An analysis can only provide\\\\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\\\\n\n \\ parameters (parameters valid at a specific time, e.g temperature at 12:00),\\\\n\n \\ but not accumulated parameters, mean rates or min/max parameters.\\\\n* A\n **forecast** starts with an analysis at a specific time (the 'initialization\\\\n\n \\ time'), and a model computes the atmospheric conditions for a number of\\\\n\n \\ 'forecast steps', at increasing 'validity times', into the future. A forecast\\\\n\n \\ can provide\\\\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\\\\n\n \\ parameters, accumulated parameters, mean rates, and min/max parameters.\\\\n\\\\nEach\n [STAC](https://stacspec.org/) item in this collection covers a single month\\\\nand\n the entire globe. There are two STAC items per month, one for each type of\n data\\\\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties\n to\\\\nindicate which kind of variables that STAC item catalogs.\\\\n\\\\n## How\n to acknowledge, cite and refer to ERA5\\\\n\\\\nAll users of data on the Climate\n Data Store (CDS) disks (using either the web interface or the CDS API) must\n provide clear and visible attribution to the Copernicus programme and are\n asked to cite and reference the dataset provider:\\\\n\\\\nAcknowledge according\n to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\\\\n\\\\nCite\n each dataset used as indicated on the relevant CDS entries (see link to \\\\\\\"Citation\\\\\\\"\n under References on the Overview page of the dataset entry).\\\\n\\\\nThroughout\n the content of your publication, the dataset used is referred to as Author\n (YYYY).\\\\n\\\\nThe 3-steps procedure above is illustrated with this example:\n [Use Case 2: ERA5 hourly data on single levels from 1979 to present](https://confluence.ecmwf.int/display/CKB/Use+Case+2%3A+ERA5+hourly+data+on+single+levels+from+1979+to+present).\\\\n\\\\nFor\n complete details, please refer to [How to acknowledge and cite a Climate Data\n Store (CDS) catalogue entry and the data published as part of it](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it).\\\",\\\"item_assets\\\":{\\\"surface_air_pressure\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Surface\n pressure\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"sea_surface_temperature\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Sea\n surface temperature\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"eastward_wind_at_10_metres\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"10\n metre U wind component\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"air_temperature_at_2_metres\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"2\n metre temperature\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"eastward_wind_at_100_metres\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"100\n metre U wind component\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"northward_wind_at_10_metres\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"10\n metre V wind component\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"northward_wind_at_100_metres\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"100\n metre V wind component\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"air_pressure_at_mean_sea_level\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Mean\n sea level pressure\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"dew_point_temperature_at_2_metres\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"2\n metre dewpoint temperature\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"precipitation_amount_1hour_Accumulation\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Total\n precipitation\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"air_temperature_at_2_metres_1hour_Maximum\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Maximum\n temperature at 2 metres since previous post-processing\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"air_temperature_at_2_metres_1hour_Minimum\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Minimum\n temperature at 2 metres since previous post-processing\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}},\\\"integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Surface\n solar radiation downwards\\\",\\\"xarray:open_kwargs\\\":{\\\"chunks\\\":{},\\\"engine\\\":\\\"zarr\\\",\\\"consolidated\\\":true,\\\"storage_options\\\":{\\\"account_name\\\":\\\"cpdataeuwest\\\"}}}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"cube:variables\\\":{\\\"time1_bounds\\\":{\\\"type\\\":\\\"data\\\",\\\"attrs\\\":{},\\\"shape\\\":[null,2],\\\"dimensions\\\":[\\\"time\\\",\\\"nv\\\"]},\\\"surface_air_pressure\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"Pa\\\",\\\"attrs\\\":{\\\"units\\\":\\\"Pa\\\",\\\"nameCDM\\\":\\\"Surface_pressure_surface\\\",\\\"long_name\\\":\\\"Surface\n pressure\\\",\\\"nameECMWF\\\":\\\"Surface pressure\\\",\\\"product_type\\\":\\\"analysis\\\",\\\"standard_name\\\":\\\"surface_air_pressure\\\",\\\"shortNameECMWF\\\":\\\"sp\\\"},\\\"shape\\\":[null,721,1440],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Surface\n pressure\\\"},\\\"sea_surface_temperature\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"K\\\",\\\"attrs\\\":{\\\"units\\\":\\\"K\\\",\\\"nameCDM\\\":\\\"Sea_surface_temperature_surface\\\",\\\"long_name\\\":\\\"Sea\n surface temperature\\\",\\\"nameECMWF\\\":\\\"Sea surface temperature\\\",\\\"product_type\\\":\\\"analysis\\\",\\\"standard_name\\\":\\\"sea_surface_temperature\\\",\\\"shortNameECMWF\\\":\\\"sst\\\"},\\\"shape\\\":[null,721,1440],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Sea\n surface temperature\\\"},\\\"eastward_wind_at_10_metres\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"m\n s**-1\\\",\\\"attrs\\\":{\\\"units\\\":\\\"m s**-1\\\",\\\"nameCDM\\\":\\\"10_metre_U_wind_component_surface\\\",\\\"long_name\\\":\\\"10\n metre U wind component\\\",\\\"nameECMWF\\\":\\\"10 metre U wind component\\\",\\\"product_type\\\":\\\"analysis\\\",\\\"standard_name\\\":\\\"eastward_wind\\\",\\\"shortNameECMWF\\\":\\\"10u\\\"},\\\"shape\\\":[null,721,1440],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"10\n metre U wind component\\\"},\\\"air_temperature_at_2_metres\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"K\\\",\\\"attrs\\\":{\\\"units\\\":\\\"K\\\",\\\"nameCDM\\\":\\\"2_metre_temperature_surface\\\",\\\"long_name\\\":\\\"2\n metre temperature\\\",\\\"nameECMWF\\\":\\\"2 metre temperature\\\",\\\"product_type\\\":\\\"analysis\\\",\\\"standard_name\\\":\\\"air_temperature\\\",\\\"shortNameECMWF\\\":\\\"2t\\\"},\\\"shape\\\":[null,721,1440],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"2\n metre 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post-processing\\\"},\\\"air_temperature_at_2_metres_1hour_Minimum\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"K\\\",\\\"attrs\\\":{\\\"units\\\":\\\"K\\\",\\\"nameCDM\\\":\\\"Minimum_temperature_at_2_metres_since_previous_post-processing_surface_1_Hour_2\\\",\\\"long_name\\\":\\\"Minimum\n temperature at 2 metres since previous post-processing\\\",\\\"nameECMWF\\\":\\\"Minimum\n temperature at 2 metres since previous post-processing\\\",\\\"product_type\\\":\\\"forecast\\\",\\\"standard_name\\\":\\\"air_temperature\\\",\\\"shortNameECMWF\\\":\\\"mn2t\\\"},\\\"shape\\\":[null,721,1440],\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Minimum\n temperature at 2 metres since previous post-processing\\\"},\\\"integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air_1hour_Accumulation\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"J\n m**-2\\\",\\\"attrs\\\":{\\\"units\\\":\\\"J 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downwards\\\"}},\\\"msft:container\\\":\\\"era5\\\",\\\"cube:dimensions\\\":{\\\"lat\\\":{\\\"axis\\\":\\\"y\\\",\\\"step\\\":-0.25,\\\"type\\\":\\\"spatial\\\",\\\"extent\\\":[-90.0,90.0],\\\"description\\\":\\\"latitude\\\",\\\"reference_system\\\":\\\"epsg:4326\\\"},\\\"lon\\\":{\\\"axis\\\":\\\"x\\\",\\\"step\\\":0.25,\\\"type\\\":\\\"spatial\\\",\\\"extent\\\":[0.0,359.75],\\\"description\\\":\\\"longitude\\\",\\\"reference_system\\\":\\\"epsg:4326\\\"},\\\"time\\\":{\\\"step\\\":\\\"P0DT1H0M0S\\\",\\\"type\\\":\\\"temporal\\\",\\\"extent\\\":[\\\"1970-01-01T00:00:00Z\\\",null]}},\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"cpdataeuwest\\\",\\\"msft:short_description\\\":\\\"A\n comprehensive reanalysis, which assimilates as many observations as possible\n in the upper air and near surface.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"chloris-biomass\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chloris-biomass/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/chloris-biomass\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://spdx.org/licenses/CC-BY-NC-SA-4.0.html\\\",\\\"title\\\":\\\"Creative\n Commons Attribution Non Commercial Share Alike 4.0 International\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/chloris-biomass\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Chloris\n Biomass\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/chloris-biomass.jpg\\\",\\\"type\\\":\\\"image/jpg\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Chloris\n Biomass\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/chloris-biomass.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-179.95,-60,179.95,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2003-07-31T00:00:00Z\\\",\\\"2019-07-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CC-BY-NC-SA-4.0\\\",\\\"keywords\\\":[\\\"Chloris\\\",\\\"Biomass\\\",\\\"MODIS\\\",\\\"Carbon\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"http://chloris.earth/\\\",\\\"name\\\":\\\"Chloris\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[4633]},\\\"description\\\":\\\"The\n Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and\n change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems.\n It covers the period 2003 - 2019, at annual time steps. The global dataset\n has a circa 4.6 km spatial resolution.\\\\n\\\\nThe maps and data sets were generated\n by combining multiple remote sensing measurements from space borne satellites,\n processed using state-of-the-art machine learning and statistical methods,\n validated with field data from multiple countries. The dataset provides direct\n estimates of aboveground stock and change, and are not based on land use or\n land cover area change, and as such they include gains and losses of carbon\n stock in all types of woody vegetation - whether natural or plantations.\\\\n\\\\nAnnual\n stocks are expressed in units of tons of biomass. Annual changes in stocks\n are expressed in units of CO2 equivalent, i.e., the amount of CO2 released\n from or taken up by terrestrial ecosystems for that specific pixel.\\\\n\\\\nThe\n spatial data sets are available on [Microsoft\\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass)\n under a Creative Common license of the type Attribution-Non Commercial-Share\n Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\\\\n\\\\n[Chloris\n Geospatial](https://chloris.earth/) is a mission-driven technology company\n that develops software and data products on the state of natural capital for\n use by business, governments, and the social sector.\\\\n\\\",\\\"item_assets\\\":{\\\"biomass\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Annual\n estimates of aboveground woody biomass.\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"tonnes\\\",\\\"nodata\\\":2147483647,\\\"data_type\\\":\\\"uint32\\\"}]},\\\"biomass_wm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Annual\n estimates of aboveground woody biomass (Web Mercator).\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"tonnes\\\",\\\"nodata\\\":2147483647,\\\"data_type\\\":\\\"uint32\\\"}]},\\\"biomass_change\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Annual\n estimates of changes (gains and losses) in aboveground woody biomass from\n the previous year.\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"tonnes\\\",\\\"nodata\\\":-32768,\\\"data_type\\\":\\\"int16\\\"}]},\\\"biomass_change_wm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Annual\n estimates of changes (gains and losses) in aboveground woody biomass from\n the previous year (Web Mercator).\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"tonnes\\\",\\\"nodata\\\":-32768,\\\"data_type\\\":\\\"int16\\\"}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"chloris-biomass\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"ai4edataeuwest\\\",\\\"msft:short_description\\\":\\\"The\n Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and\n change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems\n during the period 2003 - 2019, at annual time steps. The global dataset has\n a circa 4.6 km spatial resolution.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"kaza-hydroforecast\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/kaza-hydroforecast\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://cdla.dev/sharing-1-0/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Community\n Data License Agreement \\u2013 Sharing, Version 1.0\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/kaza-hydroforecast\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"HydroForecast\n - Kwando & Upper Zambezi Rivers\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/kaza-hydroforecast-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"title\\\":\\\"Thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[21.04494,-17.792517,23.343421,-13.08062],[23.343421,-17.792517,23.343421,-17.792517],[23.343421,-17.792517,23.343421,-17.792517],[21.04494,-15.13158,21.04494,-15.13158],[21.80157,-16.01209,21.80157,-16.01209],[22.91715,-17.38856,22.91715,-17.38856],[23.00610530305009,-17.347398048598034,23.00610530305009,-17.347398048598034],[22.669092,-14.971843,22.669092,-14.971843],[22.675487,-13.08062,22.675487,-13.08062]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2022-01-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"CDLA-Sharing-1.0\\\",\\\"version\\\":\\\"1.0.0\\\",\\\"keywords\\\":[\\\"Water\\\",\\\"HydroForecast\\\",\\\"Streamflow\\\",\\\"Hydrology\\\",\\\"Upstream\n Tech\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.upstream.tech\\\",\\\"name\\\":\\\"Upstream\n Tech\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"description\\\":\\\"This\n dataset is a daily updated set of HydroForecast seasonal river flow forecasts\n at six locations in the Kwando and Upper Zambezi river basins. More details\n about the locations, project context, and to interactively view current and\n previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\\\\n\\\\n##\n Flow forecast dataset and model description\\\\n\\\\n[HydroForecast](https://www.upstream.tech/hydroforecast)\n is a theory-guided machine learning hydrologic model that predicts streamflow\n in basins across the world. For the Kwando and Upper Zambezi, HydroForecast\n makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview).\n The model's output is probabilistic and the mean, median and a range of quantiles\n are available at each forecast step.\\\\n\\\\nThe underlying model has the following\n attributes: \\\\n\\\\n* Timestep: 10 days\\\\n* Horizon: 10 to 180 days \\\\n* Update\n frequency: daily\\\\n* Units: cubic meters per second (m\\xB3/s)\\\\n \\\\n##\n Site details\\\\n\\\\nThe model produces output for six locations in the Kwando\n and Upper Zambezi river basins.\\\\n\\\\n* Upper Zambezi sites\\\\n * Zambezi\n at Chavuma\\\\n * Luanginga at Kalabo\\\\n* Kwando basin sites\\\\n * Kwando\n at Kongola -- total basin flows\\\\n * Kwando Sub-basin 1\\\\n * Kwando\n Sub-basin 2 \\\\n * Kwando Sub-basin 3\\\\n * Kwando Sub-basin 4\\\\n *\n Kwando Kongola Sub-basin\\\\n\\\\n## STAC metadata\\\\n\\\\nThere is one STAC item\n per location. Each STAC item has a single asset linking to a Parquet file\n in Azure Blob Storage.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Forecast\n Data\\\",\\\"description\\\":\\\"All seasonal river flow forecasts for a specific\n forecast site, produced by Upstream Tech's HydroForecast model\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"table:columns\\\":[{\\\"name\\\":\\\"initialization_time\\\",\\\"type\\\":\\\"timestamp\\\",\\\"description\\\":\\\"The\n initial timestamp in the forecast model which created this forecast step.\n All rows with the same initialization time are part of the same \\\\\\\"forecast\\\\\\\"\n and are produced at the same time.\\\"},{\\\"name\\\":\\\"valid_time\\\",\\\"type\\\":\\\"timestamp\\\",\\\"description\\\":\\\"The\n point in time this forecast step is predicting. valid_time = initialization_time\n + lead_time_hours. The discharge values in this row are the average rate from\n this time (inclusive) until the next valid time (exclusive).\\\"},{\\\"name\\\":\\\"lead_time_hours\\\",\\\"type\\\":\\\"int64\\\",\\\"description\\\":\\\"The\n number of hours ahead of the initialization time this forecast step is predicting.\\\"},{\\\"name\\\":\\\"discharge_mean\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n mean of the probabilistic streamflow prediction, as an average rate across\n the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_median\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n median of the probabilistic streamflow prediction, as an average rate across\n the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.01\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.01 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.025\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.025 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.05\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.05 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.1\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.1 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.25\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.25 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.5\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.5 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.75\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.75 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.9\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.9 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.95\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.95 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.975\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.975 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"},{\\\"name\\\":\\\"discharge_q0.99\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"The\n quantile 0.99 of the probabilistic streamflow prediction, as an average rate\n across the forecast step in m\\xB3/s.\\\"}],\\\"msft:container\\\":\\\"hydroforecast\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"ai4edataeuwest\\\",\\\"msft:short_description\\\":\\\"Seasonal\n river flow forecasts for the Kwando and Upper Zambezi rivers in the KAZA region\n of Africa, produced by Upstream Tech's HydroForecast model.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"planet-nicfi-analytic\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-analytic/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-analytic\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Participant\n License Agreement.\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Participant\n License Agreement.\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://www.planet.com/nicfi/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NICFI\n Program - Satellite Imagery and Monitoring | Planet\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://developers.planet.com/nicfi/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NICFI\n Program Resource Center\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/planet-nicfi-analytic\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Planet-NICFI\n Basemaps (Analytic)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/planet-nicfi-analytic-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/planet-nicfi-analytic.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-34.161818157002,180.0,30.145127179625]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2015-12-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Planet\\\",\\\"NICFI\\\",\\\"Satellite\\\",\\\"Tropics\\\",\\\"Imagery\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"http://planet.com\\\",\\\"name\\\":\\\"Planet\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"],\\\"description\\\":\\\"Contact\n Planet at [planet.com/contact-sales](https://www.planet.com/contact-sales/)\\\"},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[4.77],\\\"eo:bands\\\":[{\\\"name\\\":\\\"Blue\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"visible\n blue\\\"},{\\\"name\\\":\\\"Green\\\",\\\"common_name\\\":\\\"green\\\",\\\"description\\\":\\\"visible\n green\\\"},{\\\"name\\\":\\\"Red\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"visible\n red\\\"},{\\\"name\\\":\\\"NIR\\\",\\\"common_name\\\":\\\"nir\\\",\\\"description\\\":\\\"near-infrared\\\"}],\\\"planet-nicfi:cadence\\\":[\\\"biannual\\\",\\\"monthly\\\"],\\\"planet-nicfi:percent_covered\\\":{\\\"maximum\\\":100,\\\"minimum\\\":0}},\\\"description\\\":\\\"*Note:\n Assets in this collection are only available to winners of the [GEO-Microsoft\n Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528).\n Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/)\n and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\\\\n\\\\nThrough\n Norway\\u2019s International Climate & Forests Initiative (NICFI), users can\n access Planet\\u2019s high-resolution, analysis-ready mosaics of the world\\u2019s\n tropics in order to help reduce and reverse the loss of tropical forests,\n combat climate change, conserve biodiversity, and facilitate sustainable development.\\\\n\\\\nIn\n support of NICFI\\u2019s mission, you can use this data for a number of projects\n including, but not limited to:\\\\n\\\\n* Advance scientific research about the\n world\\u2019s tropical forests and the critical services they provide.\\\\n*\n Implement and improve policies for sustainable forest management and land\n use in developing tropical forest countries and jurisdictions.\\\\n* Increase\n transparency and accountability in the tropics.\\\\n* Protect and improve the\n rights of indigenous peoples and local communities in tropical forest countries.\\\\n*\n Innovate solutions towards reducing pressure on forests from global commodities\n and financial markets.\\\\n* In short, the primary purpose of the NICFI Program\n is to support reducing and reversing the loss of tropical forests, contributing\n to combating climate change, conserving biodiversity, contributing to forest\n regrowth, restoration, and enhancement, and facilitating sustainable development,\n all of which must be Non-Commercial Use.\\\\n\\\\nTo learn how more about the\n NICFI program, streaming and downloading basemaps please read the [NICFI Data\n Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\\\\n\\\\nThis\n collection contains both monthly and biannual mosaics. Biannual mosaics are\n available from December 2015 - August 2020. Monthly mosaics are available\n from September 2020. The STAC items include a `planet-nicfi:cadence` field\n indicating the type of mosaic.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Data\\\",\\\"description\\\":\\\"a\n 'ground truth' representation of spatially accurate data with minimized effects\n of atmosphere and sensor characteristics\\\"},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Thumbnail\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"planet-nicfi\\\",\\\"msft:container\\\":\\\"nicfi\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"planet\\\",\\\"msft:short_description\\\":\\\"Planet's\n high-resolution, analysis-ready mosaics of the world's tropics\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"modis-17A2H-061\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A2H-061/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A2H-061\\\"},{\\\"rel\\\":\\\"help\\\",\\\"href\\\":\\\"https://lpdaac.usgs.gov/documents/972/MOD17_User_Guide_V61.pdf\\\",\\\"title\\\":\\\"MOD17\n User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD17A2H\\\",\\\"title\\\":\\\"MOD17A2H\n file specification\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD17A2H\\\",\\\"title\\\":\\\"MYD17A2H\n file specification\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5067/MODIS/MOD17A2H.061\\\",\\\"title\\\":\\\"LP\n DAAC - MOD17A2H\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5067/MODIS/MYD17A2H.061\\\",\\\"title\\\":\\\"LP\n DAAC - MYD17A2H\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\\\",\\\"title\\\":\\\"LP\n DAAC - Data Citation and Policies\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/modis-17A2H-061\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"MODIS\n Gross Primary Productivity 8-Day\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-17A2H-061.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"MODIS\n Gross Primary Productivity 8-Day thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/modis-17A2H-061.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"MS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2000-02-18T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"NASA\\\",\\\"MODIS\\\",\\\"Satellite\\\",\\\"Vegetation\\\",\\\"Global\\\",\\\"MOD17A2H\\\",\\\"MYD17A2H\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://lpdaac.usgs.gov/\\\",\\\"name\\\":\\\"NASA\n LP DAAC at the USGS EROS Center\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"aqua\\\",\\\"terra\\\"],\\\"instruments\\\":[\\\"modis\\\"]},\\\"description\\\":\\\"The\n Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day\n composite of values with 500 meter (m) pixel size based on the radiation use\n efficiency concept that can be potentially used as inputs to data models to\n calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry\n of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data\n product includes information about GPP and Net Photosynthesis (PSN). The PSN\n band values are the GPP less the Maintenance Respiration (MR). The data product\n also contains a PSN Quality Control (QC) layer. The quality layer contains\n quality information for both the GPP and the PSN.\\\",\\\"item_assets\\\":{\\\"hdf\\\":{\\\"type\\\":\\\"application/x-hdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Source\n data containing all bands\\\"},\\\"Gpp_500m\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Gross Primary Productivity\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"kg\n C/m^2\\\",\\\"scale\\\":0.0001,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Federal\n Geographic Data Committee (FGDC) Metadata\\\"},\\\"PsnNet_500m\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Net\n Photosynthesis\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"kg C/m^2\\\",\\\"scale\\\":0.0001,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"Psn_QC_500m\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Quality\n control indicators\\\",\\\"raster:bands\\\":[{\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":500}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"modis\\\",\\\"msft:container\\\":\\\"modis-061\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.5067/MODIS/MOD17A2H.061\\\",\\\"citation\\\":\\\"Running,\n S., Mu, Q., & Zhao, M. (2021). MODIS/Terra Gross Primary Productivity\n 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes\n DAAC. https://doi.org/10.5067/MODIS/MOD17A2H.061\\\"},{\\\"doi\\\":\\\"10.5067/MODIS/MYD17A2H.061\\\",\\\"citation\\\":\\\"Running,\n S., Mu, Q., & Zhao, M. (2021). MODIS/Aqua Gross Primary Productivity\n 8-Day L4 Global 500m SIN Grid V061 [Data set]. 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projection\\\",\\\"reference_system\\\":{\\\"name\\\":\\\"undefined\\\",\\\"type\\\":\\\"ProjectedCRS\\\",\\\"$schema\\\":\\\"https://proj.org/schemas/v0.4/projjson.schema.json\\\",\\\"base_crs\\\":{\\\"name\\\":\\\"undefined\\\",\\\"datum\\\":{\\\"name\\\":\\\"undefined\\\",\\\"type\\\":\\\"GeodeticReferenceFrame\\\",\\\"ellipsoid\\\":{\\\"name\\\":\\\"undefined\\\",\\\"semi_major_axis\\\":6378137,\\\"inverse_flattening\\\":298.257223563}},\\\"coordinate_system\\\":{\\\"axis\\\":[{\\\"name\\\":\\\"Longitude\\\",\\\"unit\\\":\\\"degree\\\",\\\"direction\\\":\\\"east\\\",\\\"abbreviation\\\":\\\"lon\\\"},{\\\"name\\\":\\\"Latitude\\\",\\\"unit\\\":\\\"degree\\\",\\\"direction\\\":\\\"north\\\",\\\"abbreviation\\\":\\\"lat\\\"}],\\\"subtype\\\":\\\"ellipsoidal\\\"}},\\\"conversion\\\":{\\\"name\\\":\\\"unknown\\\",\\\"method\\\":{\\\"id\\\":{\\\"code\\\":9802,\\\"authority\\\":\\\"EPSG\\\"},\\\"name\\\":\\\"Lambert\n Conic Conformal 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origin\\\",\\\"unit\\\":\\\"metre\\\",\\\"value\\\":0}]},\\\"coordinate_system\\\":{\\\"axis\\\":[{\\\"name\\\":\\\"Easting\\\",\\\"unit\\\":\\\"metre\\\",\\\"direction\\\":\\\"east\\\",\\\"abbreviation\\\":\\\"E\\\"},{\\\"name\\\":\\\"Northing\\\",\\\"unit\\\":\\\"metre\\\",\\\"direction\\\":\\\"north\\\",\\\"abbreviation\\\":\\\"N\\\"}],\\\"subtype\\\":\\\"Cartesian\\\"}}},\\\"nv\\\":{\\\"type\\\":\\\"count\\\",\\\"values\\\":[0,1],\\\"description\\\":\\\"Size\n of the 'time_bnds' variable.\\\"},\\\"time\\\":{\\\"type\\\":\\\"temporal\\\",\\\"extent\\\":[\\\"1980-01-01T12:00:00Z\\\",\\\"2020-12-30T12:00:00Z\\\"],\\\"description\\\":\\\"24-hour\n day based on local time\\\"}},\\\"msft:group_keys\\\":[\\\"daily\\\",\\\"puerto rico\\\"],\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"daymeteuwest\\\",\\\"msft:short_description\\\":\\\"Daily\n surface weather data on a 1-km grid for Puerto Rico\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"3dep-lidar-dtm-native\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm-native/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm-native\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\\\",\\\"title\\\":\\\"About\n 3DEP Products & 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COG\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2012-01-01T00:00:00Z\\\",\\\"2022-01-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"3DEP\\\",\\\"COG\\\",\\\"DTM\\\"],\\\"providers\\\":[{\\\"name\\\":\\\"Landrush\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/ngp/3dep/\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[2.0]},\\\"description\\\":\\\"This\n collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).\n It creates a Digital Terrain Model (DTM) using the vendor provided (native)\n ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range)\n to output a collection of Cloud Optimized GeoTIFFs, removing all points that\n have been classified as noise.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"COG\n data\\\",\\\"raster:bands\\\":[{\\\"name\\\":\\\"Z\\\",\\\"unit\\\":\\\"metre\\\",\\\"sampling\\\":\\\"point\\\",\\\"data_type\\\":\\\"float32\\\",\\\"description\\\":\\\"Raster\n for Z PDAL Dimension\\\"}]},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"3dep-lidar\\\",\\\"msft:container\\\":\\\"usgs-3dep-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\\\"],\\\"msft:storage_account\\\":\\\"usgslidareuwest\\\",\\\"msft:short_description\\\":\\\"3DEP\n Lidar collection for the Digital Terrain Model (DTM) Cloud Optimized Geotiffs\n (COGs) using vendor provided ground classification.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"3dep-lidar-classification\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-classification/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-classification\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\\\",\\\"title\\\":\\\"About\n 3DEP Products & Services\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-classification\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n 3DEP Lidar Classification\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/3dep-lidar-classification-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2012-01-01T00:00:00Z\\\",\\\"2022-01-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"3DEP\\\",\\\"COG\\\",\\\"Classification\\\"],\\\"providers\\\":[{\\\"name\\\":\\\"Landrush\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/ngp/3dep/\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[5.0]},\\\"description\\\":\\\"This\n collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).\n It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry\n and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm).\n See [LAS specification](https://www.ogc.org/standards/LAS) for details.\\\\n\\\\nThis\n COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html)\n and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html)\n to select a subset of interesting classifications. Do note that not all LiDAR\n collections contain a full compliment of classification labels.\\\\nTo remove\n outliers, the PDAL pipeline uses a noise filter and then outputs the Classification\n dimension.\\\\n\\\\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets)\n and [`classification`](https://github.com/stac-extensions/classification)\n extensions. These classes are displayed in the \\\\\\\"Item assets\\\\\\\" below.\n You can programmatically access the full list of class values and descriptions\n using the `classification:classes` field form the `data` asset on the STAC\n collection.\\\\n\\\\nClassification rasters were produced as a subset of LiDAR\n classification categories:\\\\n\\\\n```\\\\n0, Never Classified\\\\n1, Unclassified\\\\n2,\n Ground\\\\n3, Low Vegetation\\\\n4, Medium Vegetation\\\\n5, High Vegetation\\\\n6,\n Building\\\\n9, Water\\\\n10, Rail\\\\n11, Road\\\\n17, Bridge Deck\\\\n```\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"COG\n data\\\",\\\"raster:bands\\\":[{\\\"name\\\":\\\"Classification\\\",\\\"unit\\\":\\\"metre\\\",\\\"sampling\\\":\\\"point\\\",\\\"data_type\\\":\\\"int16\\\",\\\"description\\\":\\\"Raster\n for Classification PDAL dimension, ASPRS Lidar Point Classification Standard\\\"}],\\\"classification:classes\\\":[{\\\"value\\\":0,\\\"description\\\":\\\"Never\n Classified\\\"},{\\\"value\\\":1,\\\"description\\\":\\\"Unclassified\\\"},{\\\"value\\\":2,\\\"description\\\":\\\"Ground\\\"},{\\\"value\\\":3,\\\"description\\\":\\\"Low\n Vegetation\\\"},{\\\"value\\\":4,\\\"description\\\":\\\"Medium Vegetation\\\"},{\\\"value\\\":5,\\\"description\\\":\\\"High\n Vegetation\\\"},{\\\"value\\\":6,\\\"description\\\":\\\"Building\\\"},{\\\"value\\\":9,\\\"description\\\":\\\"Water\\\"},{\\\"value\\\":10,\\\"description\\\":\\\"Rail\\\"},{\\\"value\\\":11,\\\"description\\\":\\\"Road\\\"},{\\\"value\\\":17,\\\"description\\\":\\\"Bridge\n Deck\\\"}]},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"3dep-lidar\\\",\\\"msft:container\\\":\\\"usgs-3dep-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/classification/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"usgslidareuwest\\\",\\\"msft:short_description\\\":\\\"3DEP\n Lidar collection for Cloud Optimized Geotiffs (COGs) created according to\n PDAL's Intensity dimension\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"3dep-lidar-dtm\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-dtm\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\\\",\\\"title\\\":\\\"About\n 3DEP Products & Services\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dtm\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n 3DEP Lidar Digital Terrain Model\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/dtm-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/3dep-lidar-dtm.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-166.8546920006028,17.655357747708283,-64.56116757979399,71.39330810146807],[144.60180842809473,13.21774453924126,146.08202179248926,18.18369664008955]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2012-01-01T00:00:00Z\\\",\\\"2022-01-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"3DEP\\\",\\\"COG\\\",\\\"DTM\\\"],\\\"providers\\\":[{\\\"name\\\":\\\"Landrush\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/ngp/3dep/\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[2.0]},\\\"description\\\":\\\"This\n collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc).\n It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf)\n to output a collection of Cloud Optimized GeoTIFFs.\\\\n\\\\nThe Simple Morphological\n Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"COG\n data\\\",\\\"raster:bands\\\":[{\\\"type\\\":\\\"Z\\\",\\\"unit\\\":\\\"metre\\\",\\\"sampling\\\":\\\"point\\\",\\\"data_type\\\":\\\"float32\\\",\\\"description\\\":\\\"Z\n PDAL Dimension\\\"}]},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"3DEP\n Lidar COG\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"3dep-lidar\\\",\\\"msft:container\\\":\\\"usgs-3dep-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json#\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"usgslidareuwest\\\",\\\"msft:short_description\\\":\\\"3DEP\n Lidar collection for the Digital Terrain Model (DTM) Cloud Optimized Geotiffs\n (COGs) using PDAL's filters.smrf.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"gap\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits\\\",\\\"title\\\":\\\"Public\n Domain\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/gap\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n Gap Land Cover\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gap.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"USGS\n GAP\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/gap.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}},\\\"original-data-ak\\\":{\\\"href\\\":\\\"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_ak.zip\\\",\\\"type\\\":\\\"application/zip\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Alaska\n source data\\\",\\\"description\\\":\\\"Original source data for Alaska\\\"},\\\"original-data-hi\\\":{\\\"href\\\":\\\"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_hi.zip\\\",\\\"type\\\":\\\"application/zip\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Hawaii\n source data\\\",\\\"description\\\":\\\"Original source data for Hawaii\\\"},\\\"original-data-pr\\\":{\\\"href\\\":\\\"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/pr_landcover.zip\\\",\\\"type\\\":\\\"application/zip\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Puerto\n Rico source data\\\",\\\"description\\\":\\\"Original source data for Puerto Rico\\\"},\\\"original-data-conus\\\":{\\\"href\\\":\\\"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gap_landfire_nationalterrestrialecosystems2011.zip\\\",\\\"type\\\":\\\"application/zip\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"CONUS\n source data\\\",\\\"description\\\":\\\"Original source data for the continental United\n States (CONUS)\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-127.9710481801793,22.797789263564383,-65.26634281147894,51.64692620669362],[-178.13166387448902,49.09079265233118,179.87849702345594,71.43382483774205],[-160.26640694607218,18.851824447510786,-154.66974350173518,22.295114188194738],[-67.9573345827195,17.874066536543,-65.21836408976736,18.5296513469496]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1999-01-01T00:00:00Z\\\",\\\"2011-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"GAP\\\",\\\"LANDFIRE\\\",\\\"Land\n Cover\\\",\\\"United States\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[30],\\\"label:classes\\\":[{\\\"classes\\\":[\\\"0\\\",\\\"South\n Florida Bayhead Swamp\\\",\\\"South Florida Cypress Dome\\\",\\\"South Florida Dwarf\n Cypress Savanna\\\",\\\"South Florida Mangrove Swamp\\\",\\\"South Florida Hardwood\n Hammock\\\",\\\"Southeast Florida Coastal Strand and Maritime Hammock\\\",\\\"Southwest\n Florida Coastal Strand and Maritime Hammock\\\",\\\"South Florida Pine Rockland\\\",\\\"Atlantic\n Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Open Understory\\\",\\\"Atlantic\n Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory\\\",\\\"Atlantic\n Coastal Plain Upland Longleaf Pine Woodland\\\",\\\"Atlantic Coastal Plain Xeric\n River Dune\\\",\\\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland\n - Open Understory Modifier\\\",\\\"East Gulf Coastal Plain Interior Upland Longleaf\n Pine Woodland - Scrub/Shrub Modifier\\\",\\\"Florida Longleaf Pine Sandhill -\n Scrub/Shrub Understory Modifier\\\",\\\"Florida Longleaf Pine Sandhill- Open Understory\n Modifier\\\",\\\"West Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland\\\",\\\"Atlantic\n Coastal Plain Central Maritime Forest\\\",\\\"Atlantic Coastal Plain Southern\n Maritime Forest\\\",\\\"Central and South Texas Coastal Fringe Forest and Woodland\\\",\\\"East\n Gulf Coastal Plain Limestone Forest\\\",\\\"East Gulf Coastal Plain Maritime Forest\\\",\\\"East\n Gulf Coastal Plain Southern Loess Bluff Forest\\\",\\\"East Gulf Coastal Plain\n Southern Mesic Slope Forest\\\",\\\"Mississippi Delta Maritime Forest\\\",\\\"Southern\n Coastal Plain Dry Upland Hardwood Forest\\\",\\\"Southern Coastal Plain Oak Dome\n and Hammock\\\",\\\"West Gulf Coastal Plain Chenier and Upper Texas Coastal Fringe\n Forest and Woodland\\\",\\\"West Gulf Coastal Plain Mesic Hardwood Forest\\\",\\\"East-Central\n Texas Plains Pine Forest and Woodland\\\",\\\"West Gulf Coastal Plain Pine-Hardwood\n Forest\\\",\\\"West Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest\n and Woodland\\\",\\\"Atlantic Coastal Plain Fall-Line Sandhills Longleaf Pine\n Woodland - Loblolly Modifier\\\",\\\"Deciduous Plantations\\\",\\\"East Gulf Coastal\n Plain Interior Upland Longleaf Pine Woodland - Loblolly Modifier\\\",\\\"East\n Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood\n Modifier\\\",\\\"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Offsite Hardwood\n Modifier\\\",\\\"Evergreen Plantation or Managed Pine\\\",\\\"California Central Valley\n Mixed Oak Savanna\\\",\\\"California Coastal Closed-Cone Conifer Forest and Woodland\\\",\\\"California\n Coastal Live Oak Woodland and Savanna\\\",\\\"California Lower Montane Blue Oak-Foothill\n Pine Woodland and Savanna\\\",\\\"Central and Southern California Mixed Evergreen\n Woodland\\\",\\\"Mediterranean California Lower Montane Black Oak-Conifer Forest\n and Woodland\\\",\\\"Southern California Oak Woodland and Savanna\\\",\\\"Madrean\n Encinal\\\",\\\"Madrean Pinyon-Juniper Woodland\\\",\\\"Madrean Pine-Oak Forest and\n Woodland\\\",\\\"Madrean Upper Montane Conifer-Oak Forest and Woodland\\\",\\\"Edwards\n Plateau Dry-Mesic Slope Forest and Woodland\\\",\\\"Edwards Plateau Limestone\n Savanna and Woodland\\\",\\\"Edwards Plateau Mesic Canyon\\\",\\\"Llano Uplift Acidic\n Forest, Woodland and Glade\\\",\\\"East Cascades Oak-Ponderosa Pine Forest and\n Woodland\\\",\\\"Mediterranean California Mixed Evergreen Forest\\\",\\\"Mediterranean\n California Mixed Oak Woodland\\\",\\\"North Pacific Dry Douglas-fir-(Madrone)\n Forest and Woodland\\\",\\\"North Pacific Oak Woodland\\\",\\\"Edwards Plateau Limestone\n Shrubland\\\",\\\"Allegheny-Cumberland Dry Oak Forest and Woodland - Hardwood\\\",\\\"Allegheny-Cumberland\n Dry Oak Forest and Woodland - Pine Modifier\\\",\\\"Central and Southern Appalachian\n Montane Oak Forest\\\",\\\"Central and Southern Appalachian Northern Hardwood\n Forest\\\",\\\"Central Appalachian Oak and Pine Forest\\\",\\\"Crosstimbers Oak Forest\n and Woodland\\\",\\\"East Gulf Coastal Plain Black Belt Calcareous Prairie and\n Woodland - Woodland Modifier\\\",\\\"East Gulf Coastal Plain Northern Dry Upland\n Hardwood Forest\\\",\\\"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory\n Upland - Hardwood Modifier\\\",\\\"East Gulf Coastal Plain Northern Loess Plain\n Oak-Hickory Upland - Juniper Modifier\\\",\\\"East-Central Texas Plains Post Oak\n Savanna and Woodland\\\",\\\"Lower Mississippi River Dune Woodland and Forest\\\",\\\"Mississippi\n River Alluvial Plain Dry-Mesic Loess Slope Forest\\\",\\\"North-Central Interior\n Dry Oak Forest and Woodland\\\",\\\"North-Central Interior Dry-Mesic Oak Forest\n and Woodland\\\",\\\"Northeastern Interior Dry Oak Forest - Mixed Modifier\\\",\\\"Northeastern\n Interior Dry Oak Forest - Virginia/Pitch Pine Modifier\\\",\\\"Northeastern Interior\n Dry Oak Forest-Hardwood Modifier\\\",\\\"Northeastern Interior Dry-Mesic Oak Forest\\\",\\\"Northern\n Atlantic Coastal Plain Dry Hardwood Forest\\\",\\\"Crowley's Ridge Sand Forest\\\",\\\"Ouachita\n Montane Oak Forest\\\",\\\"Ozark-Ouachita Dry Oak Woodland\\\",\\\"Ozark-Ouachita\n Dry-Mesic Oak Forest\\\",\\\"Southern and Central Appalachian Oak Forest\\\",\\\"Southern\n and Central Appalachian Oak Forest - Xeric\\\",\\\"Southern Interior Low Plateau\n Dry-Mesic Oak Forest\\\",\\\"Southern Ridge and Valley Dry Calcareous Forest\\\",\\\"Southern\n Ridge and Valley Dry Calcareous Forest - Pine modifier\\\",\\\"East Gulf Coastal\n Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier\\\",\\\"Managed\n Tree Plantation\\\",\\\"Ruderal forest\\\",\\\"Southern Piedmont Dry Oak-(Pine) Forest\n - Loblolly Pine Modifier\\\",\\\"Acadian Low-Elevation Spruce-Fir-Hardwood Forest\\\",\\\"Acadian-Appalachian\n Montane Spruce-Fir Forest\\\",\\\"Appalachian Hemlock-Hardwood Forest\\\",\\\"Central\n and Southern Appalachian Spruce-Fir Forest\\\",\\\"0\\\",\\\"Laurentian-Acadian Northern\n Hardwoods Forest\\\",\\\"Laurentian-Acadian Northern Pine-(Oak) Forest\\\",\\\"Laurentian-Acadian\n Pine-Hemlock-Hardwood Forest\\\",\\\"Paleozoic Plateau Bluff and Talus\\\",\\\"Southern\n Appalachian Northern Hardwood Forest\\\",\\\"Atlantic Coastal Plain Dry and Dry-Mesic\n Oak Forest\\\",\\\"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland\n - Offsite Hardwood\\\",\\\"East Gulf Coastal Plain Interior Shortleaf Pine-Oak\n Forest - Hardwood Modifier\\\",\\\"East Gulf Coastal Plain Interior Shortleaf\n Pine-Oak Forest - Mixed Modifier\\\",\\\"Ozark-Ouachita Shortleaf Pine-Bluestem\n Woodland\\\",\\\"Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland\\\",\\\"Southeastern\n Interior Longleaf Pine Woodland\\\",\\\"Southern Appalachian Low Mountain Pine\n Forest\\\",\\\"Southern Piedmont Dry Oak-(Pine) Forest\\\",\\\"Southern Piedmont Dry\n Oak-(Pine) Forest - Hardwood Modifier\\\",\\\"Southern Piedmont Dry Oak-(Pine)\n Forest - Mixed Modifier\\\",\\\"Southern Piedmont Dry Oak-Heath Forest - Mixed\n Modifier\\\",\\\"Eastern Great Plains Tallgrass Aspen Parkland\\\",\\\"Northwestern\n Great Plains Aspen Forest and Parkland\\\",\\\"Northwestern Great Plains Shrubland\\\",\\\"Western\n Great Plains Dry Bur Oak Forest and Woodland\\\",\\\"Western Great Plains Wooded\n Draw and Ravine\\\",\\\"Southern Atlantic Coastal Plain Mesic Hardwood Forest\\\",\\\"East\n Gulf Coastal Plain Northern Loess Bluff Forest\\\",\\\"East Gulf Coastal Plain\n Northern Mesic Hardwood Forest\\\",\\\"North-Central Interior Beech-Maple Forest\\\",\\\"North-Central\n Interior Maple-Basswood Forest\\\",\\\"Ozark-Ouachita Mesic Hardwood Forest\\\",\\\"South-Central\n Interior Mesophytic Forest\\\",\\\"Southern and Central Appalachian Cove Forest\\\",\\\"Crowley's\n Ridge Mesic Loess Slope Forest\\\",\\\"Southern Piedmont Mesic Forest\\\",\\\"Appalachian\n Shale Barrens\\\",\\\"Atlantic Coastal Plain Northern Maritime Forest\\\",\\\"Laurentian\n Pine-Oak Barrens\\\",\\\"Northeastern Interior Pine Barrens\\\",\\\"Northern Atlantic\n Coastal Plain Pitch Pine Barrens\\\",\\\"Southern Appalachian Montane Pine Forest\n and Woodland\\\",\\\"East Cascades Mesic Montane Mixed-Conifer Forest and Woodland\\\",\\\"Middle\n Rocky Mountain Montane Douglas-fir Forest and Woodland\\\",\\\"Northern Rocky\n Mountain Dry-Mesic Montane Mixed Conifer Forest\\\",\\\"Northern Rocky Mountain\n Foothill Conifer Wooded Steppe\\\",\\\"Northern Rocky Mountain Mesic Montane Mixed\n Conifer Forest\\\",\\\"Northern Rocky Mountain Ponderosa Pine Woodland and Savanna\\\",\\\"Northern\n Rocky Mountain Western Larch Savanna\\\",\\\"Northwestern Great Plains - Black\n Hills Ponderosa Pine Woodland and Savanna\\\",\\\"Rocky Mountain Foothill Limber\n Pine-Juniper Woodland\\\",\\\"Inter-Mountain Basins Aspen-Mixed Conifer Forest\n and Woodland\\\",\\\"Inter-Mountain Basins Subalpine Limber-Bristlecone Pine Woodland\\\",\\\"Northern\n Rocky Mountain Subalpine Woodland and Parkland\\\",\\\"Rocky Mountain Aspen Forest\n and Woodland\\\",\\\"Rocky Mountain Lodgepole Pine Forest\\\",\\\"Rocky Mountain Poor-Site\n Lodgepole Pine Forest\\\",\\\"Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest\n and Woodland\\\",\\\"Rocky Mountain Subalpine Mesic Spruce-Fir Forest and Woodland\\\",\\\"Rocky\n Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland\\\",\\\"Rocky Mountain\n Bigtooth Maple Ravine Woodland\\\",\\\"Southern Rocky Mountain Dry-Mesic Montane\n Mixed Conifer Forest and Woodland\\\",\\\"Southern Rocky Mountain Mesic Montane\n Mixed Conifer Forest and Woodland\\\",\\\"Southern Rocky Mountain Ponderosa Pine\n Savanna\\\",\\\"Southern Rocky Mountain Ponderosa Pine Woodland\\\",\\\"California\n Montane Jeffrey Pine-(Ponderosa Pine) Woodland\\\",\\\"Klamath-Siskiyou Lower\n Montane Serpentine Mixed Conifer Woodland\\\",\\\"Klamath-Siskiyou Upper Montane\n Serpentine Mixed Conifer Woodland\\\",\\\"Mediterranean California Dry-Mesic Mixed\n Conifer Forest and Woodland\\\",\\\"Mediterranean California Mesic Mixed Conifer\n Forest and Woodland\\\",\\\"Sierran-Intermontane Desert Western White Pine-White\n Fir Woodland\\\",\\\"California Coastal Redwood Forest\\\",\\\"North Pacific Broadleaf\n Landslide Forest and Shrubland\\\",\\\"North Pacific Dry-Mesic Silver Fir-Western\n Hemlock-Douglas-fir Forest\\\",\\\"North Pacific Hypermaritime Sitka Spruce Forest\\\",\\\"North\n Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest\\\",\\\"North Pacific\n Lowland Mixed Hardwood-Conifer Forest and Woodland\\\",\\\"North Pacific Maritime\n Dry-Mesic Douglas-fir-Western Hemlock Forest\\\",\\\"North Pacific Maritime Mesic-Wet\n Douglas-fir-Western Hemlock Forest\\\",\\\"North Pacific Mesic Western Hemlock-Silver\n Fir Forest\\\",\\\"North Pacific Wooded Volcanic Flowage\\\",\\\"Mediterranean California\n Red Fir Forest\\\",\\\"Mediterranean California Subalpine Woodland\\\",\\\"North Pacific\n Maritime Mesic Subalpine Parkland\\\",\\\"North Pacific Mountain Hemlock Forest\\\",\\\"Northern\n California Mesic Subalpine Woodland\\\",\\\"Northern Pacific Mesic Subalpine Woodland\\\",\\\"Sierra\n Nevada Subalpine Lodgepole Pine Forest and Woodland\\\",\\\"Columbia Plateau Western\n Juniper Woodland and Savanna\\\",\\\"Great Basin Pinyon-Juniper Woodland\\\",\\\"Inter-Mountain\n Basins Curl-leaf Mountain Mahogany Woodland and Shrubland\\\",\\\"Inter-Mountain\n Basins Juniper Savanna\\\",\\\"Colorado Plateau Pinyon-Juniper Shrubland\\\",\\\"Colorado\n Plateau Pinyon-Juniper Woodland\\\",\\\"Southern Rocky Mountain Juniper Woodland\n and Savanna\\\",\\\"Southern Rocky Mountain Pinyon-Juniper Woodland\\\",\\\"Northwestern\n Great Plains Floodplain\\\",\\\"Northwestern Great Plains Riparian\\\",\\\"Western\n Great Plains Floodplain\\\",\\\"Western Great Plains Floodplain Systems\\\",\\\"Western\n Great Plains Riparian Woodland and Shrubland\\\",\\\"Central Appalachian Floodplain\n - Forest Modifier\\\",\\\"Central Appalachian Riparian - Forest Modifier\\\",\\\"Central\n Interior and Appalachian Floodplain Systems\\\",\\\"Central Interior and Appalachian\n Riparian Systems\\\",\\\"Laurentian-Acadian Floodplain Systems\\\",\\\"Ozark-Ouachita\n Riparian\\\",\\\"South-Central Interior Large Floodplain\\\",\\\"South-Central Interior\n Large Floodplain - Forest Modifier\\\",\\\"South-Central Interior Small Stream\n and Riparian\\\",\\\"North-Central Interior and Appalachian Rich Swamp\\\",\\\"0\\\",\\\"0\\\",\\\"Laurentian-Acadian\n Swamp Systems\\\",\\\"North-Central Interior Wet Flatwoods\\\",\\\"0\\\",\\\"South-Central\n Interior / Upper Coastal Plain Wet Flatwoods\\\",\\\"0\\\",\\\"Southern Piedmont/Ridge\n and Valley Upland Depression Swamp\\\",\\\"Atlantic Coastal Plain Blackwater Stream\n Floodplain Forest - Forest Modifier\\\",\\\"Atlantic Coastal Plain Brownwater\n Stream Floodplain Forest\\\",\\\"Atlantic Coastal Plain Northern Tidal Wooded\n Swamp\\\",\\\"Atlantic Coastal Plain Small Blackwater River Floodplain Forest\\\",\\\"Atlantic\n Coastal Plain Small Brownwater River Floodplain Forest\\\",\\\"Atlantic Coastal\n Plain Southern Tidal Wooded Swamp\\\",\\\"East Gulf Coastal Plain Large River\n Floodplain Forest - Forest Modifier\\\",\\\"East Gulf Coastal Plain Small Stream\n and River Floodplain Forest\\\",\\\"East Gulf Coastal Plain Tidal Wooded Swamp\\\",\\\"0\\\",\\\"Southeastern\n Great Plains Riparian Forest\\\",\\\"Southeastern Great Plains Floodplain Forest\\\",\\\"Mississippi\n River Bottomland Depression\\\",\\\"Mississippi River Floodplain and Riparian\n Forest\\\",\\\"Mississippi River Low Floodplain (Bottomland) Forest\\\",\\\"Mississippi\n River Riparian Forest\\\",\\\"Red River Large Floodplain Forest\\\",\\\"Southern Coastal\n Plain Blackwater River Floodplain Forest\\\",\\\"Southern Piedmont Large Floodplain\n Forest - Forest Modifier\\\",\\\"Southern Piedmont Small Floodplain and Riparian\n Forest\\\",\\\"West Gulf Coastal Plain Large River Floodplain Forest\\\",\\\"West\n Gulf Coastal Plain Near-Coast Large River Swamp\\\",\\\"West Gulf Coastal Plain\n Small Stream and River Forest\\\",\\\"Atlantic Coastal Plain Streamhead Seepage\n Swamp - Pocosin - and Baygall\\\",\\\"Gulf and Atlantic Coastal Plain Swamp\n Systems\\\",\\\"Southern Coastal Plain Hydric Hammock\\\",\\\"Southern Coastal Plain\n Seepage Swamp and Baygall\\\",\\\"West Gulf Coastal Plain Seepage Swamp and Baygall\\\",\\\"Atlantic\n Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Taxodium/Nyssa\n Modifier\\\",\\\"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest\n - Oak Dominated Modifier\\\",\\\"East Gulf Coastal Plain Southern Loblolly-Hardwood\n Flatwoods\\\",\\\"Lower Mississippi River Bottomland Depressions - Forest Modifier\\\",\\\"Lower\n Mississippi River Flatwoods\\\",\\\"Northern Atlantic Coastal Plain Basin Swamp\n and Wet Hardwood Forest\\\",\\\"Southern Coastal Plain Nonriverine Basin Swamp\\\",\\\"Southern\n Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier\\\",\\\"Southern\n Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier\\\",\\\"Southern\n Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier\\\",\\\"West\n Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods\\\",\\\"West Gulf Coastal\n Plain Pine-Hardwood Flatwoods\\\",\\\"Edwards Plateau Riparian\\\",\\\"Atlantic Coastal\n Plain Clay-Based Carolina Bay Forested Wetland\\\",\\\"Atlantic Coastal Plain\n Clay-Based Carolina Bay Herbaceous Wetland\\\",\\\"Atlantic Coastal Plain Southern\n Wet Pine Savanna and Flatwoods\\\",\\\"Central Atlantic Coastal Plain Wet Longleaf\n Pine Savanna and Flatwoods\\\",\\\"Central Florida Pine Flatwoods\\\",\\\"East Gulf\n Coastal Plain Near-Coast Pine Flatwoods\\\",\\\"East Gulf Coastal Plain Near-Coast\n Pine Flatwoods - Open Understory Modifier\\\",\\\"East Gulf Coastal Plain Near-Coast\n Pine Flatwoods - Scrub/Shrub Understory Modifier\\\",\\\"South Florida Pine Flatwoods\\\",\\\"Southern\n Coastal Plain Nonriverine Cypress Dome\\\",\\\"West Gulf Coastal Plain Wet Longleaf\n Pine Savanna and Flatwoods\\\",\\\"Columbia Basin Foothill Riparian Woodland and\n Shrubland\\\",\\\"Great Basin Foothill and Lower Montane Riparian Woodland and\n Shrubland\\\",\\\"0\\\",\\\"Northern Rocky Mountain Conifer Swamp\\\",\\\"Northern Rocky\n Mountain Lower Montane Riparian Woodland and Shrubland\\\",\\\"Rocky Mountain\n Lower Montane Riparian Woodland and Shrubland\\\",\\\"Rocky Mountain Montane Riparian\n Systems\\\",\\\"Rocky Mountain Subalpine-Montane Riparian Woodland\\\",\\\"North Pacific\n Hardwood-Conifer Swamp\\\",\\\"North Pacific Lowland Riparian Forest and Shrubland\\\",\\\"North\n Pacific Montane Riparian Woodland and Shrubland\\\",\\\"North Pacific Shrub Swamp\\\",\\\"California\n Central Valley Riparian Woodland and Shrubland\\\",\\\"Mediterranean California\n Foothill and Lower Montane Riparian Woodland\\\",\\\"Mediterranean California\n Serpentine Foothill and Lower Montane Riparian Woodland and Seep\\\",\\\"North\n American Warm Desert Lower Montane Riparian Woodland and Shrubland\\\",\\\"North\n American Warm Desert Riparian Systems\\\",\\\"North American Warm Desert Riparian\n Woodland and Shrubland\\\",\\\"Tamaulipan Floodplain\\\",\\\"Tamaulipan Riparian Systems\\\",\\\"Boreal\n Aspen-Birch Forest\\\",\\\"Boreal Jack Pine-Black Spruce Forest\\\",\\\"Boreal White\n Spruce-Fir-Hardwood Forest\\\",\\\"Boreal-Laurentian Conifer Acidic Swamp and\n Treed Poor Fen\\\",\\\"Eastern Boreal Floodplain\\\",\\\"South Florida Shell Hash\n Beach\\\",\\\"Southeast Florida Beach\\\",\\\"Southwest Florida Beach\\\",\\\"South Florida\n Everglades Sawgrass Marsh\\\",\\\"South Florida Freshwater Slough and Gator Hole\\\",\\\"South\n Florida Wet Marl Prairie\\\",\\\"California Maritime Chaparral\\\",\\\"California\n Mesic Chaparral\\\",\\\"California Xeric Serpentine Chaparral\\\",\\\"Klamath-Siskiyou\n Xeromorphic Serpentine Savanna and Chaparral\\\",\\\"Mediterranean California\n Mesic Serpentine Woodland and Chaparral\\\",\\\"Northern and Central California\n Dry-Mesic Chaparral\\\",\\\"Southern California Dry-Mesic Chaparral\\\",\\\"Southern\n California Coastal Scrub\\\",\\\"California Central Valley and Southern Coastal\n Grassland\\\",\\\"California Mesic Serpentine Grassland\\\",\\\"Columbia Basin Foothill\n and Canyon Dry Grassland\\\",\\\"Columbia Basin Palouse Prairie\\\",\\\"North Pacific\n Alpine and Subalpine Dry Grassland\\\",\\\"North Pacific Montane Grassland\\\",\\\"North\n Pacific Montane Shrubland\\\",\\\"Northern Rocky Mountain Lower Montane, Foothill\n and Valley Grassland\\\",\\\"Northern Rocky Mountain Montane-Foothill Deciduous\n Shrubland\\\",\\\"Northern Rocky Mountain Subalpine Deciduous Shrubland\\\",\\\"Northern\n Rocky Mountain Subalpine-Upper Montane Grassland\\\",\\\"Southern Rocky Mountain\n Montane-Subalpine Grassland\\\",\\\"Rocky Mountain Gambel Oak-Mixed Montane Shrubland\\\",\\\"Rocky\n Mountain Lower Montane-Foothill Shrubland\\\",\\\"California Northern Coastal\n Grassland\\\",\\\"North Pacific Herbaceous Bald and Bluff\\\",\\\"North Pacific Hypermaritime\n Shrub and Herbaceous Headland\\\",\\\"Willamette Valley Upland Prairie and Savanna\\\",\\\"Mediterranean\n California Subalpine Meadow\\\",\\\"Rocky Mountain Subalpine-Montane Mesic Meadow\\\",\\\"Central\n Mixedgrass Prairie\\\",\\\"Northwestern Great Plains Mixedgrass Prairie\\\",\\\"Western\n Great Plains Foothill and Piedmont Grassland\\\",\\\"Western Great Plains Tallgrass\n Prairie\\\",\\\"Western Great Plains Sand Prairie\\\",\\\"Western Great Plains Sandhill\n Steppe\\\",\\\"Western Great Plains Mesquite Woodland and Shrubland\\\",\\\"Western\n Great Plains Shortgrass Prairie\\\",\\\"Arkansas Valley Prairie and Woodland\\\",\\\"Central\n Tallgrass Prairie\\\",\\\"North-Central Interior Oak Savanna\\\",\\\"North-Central\n Interior Sand and Gravel Tallgrass Prairie\\\",\\\"North-Central Oak Barrens\\\",\\\"Northern\n Tallgrass Prairie\\\",\\\"Southeastern Great Plains Tallgrass Prairie\\\",\\\"Texas\n Blackland Tallgrass Prairie\\\",\\\"Texas-Louisiana Coastal Prairie\\\",\\\"Central\n Appalachian Pine-Oak Rocky Woodland\\\",\\\"Southern Appalachian Grass and Shrub\n Bald\\\",\\\"Southern Appalachian Grass and Shrub Bald - Herbaceous Modifier\\\",\\\"Southern\n Appalachian Grass and Shrub Bald - Shrub Modifier\\\",\\\"Central Appalachian\n Alkaline Glade and Woodland\\\",\\\"Central Interior Highlands Calcareous Glade\n and Barrens\\\",\\\"Central Interior Highlands Dry Acidic Glade and Barrens\\\",\\\"Cumberland\n Sandstone Glade and Barrens\\\",\\\"Great Lakes Alvar\\\",\\\"Nashville Basin Limestone\n Glade\\\",\\\"Southern Ridge and Valley / Cumberland Dry Calcareous Forest\\\",\\\"Southern\n Piedmont Glade and Barrens\\\",\\\"East Gulf Coastal Plain Black Belt Calcareous\n Prairie and Woodland - Herbaceous Modifier\\\",\\\"East Gulf Coastal Plain Jackson\n Prairie and Woodland\\\",\\\"Eastern Highland Rim Prairie and Barrens - Dry Modifier\\\",\\\"Coahuilan\n Chaparral\\\",\\\"Madrean Oriental Chaparral\\\",\\\"Mogollon Chaparral\\\",\\\"Sonora-Mojave\n Semi-Desert Chaparral\\\",\\\"California Montane Woodland and Chaparral\\\",\\\"Great\n Basin Semi-Desert Chaparral\\\",\\\"Florida Dry Prairie\\\",\\\"Florida Peninsula\n Inland Scrub\\\",\\\"West Gulf Coastal Plain Catahoula Barrens\\\",\\\"West Gulf Coastal\n Plain Nepheline Syenite Glade\\\",\\\"East Gulf Coastal Plain Jackson Plain Dry\n Flatwoods - Open Understory Modifier\\\",\\\"West Gulf Coastal Plain Northern\n Calcareous Prairie\\\",\\\"West Gulf Coastal Plain Southern Calcareous Prairie\\\",\\\"Acadian-Appalachian\n Subalpine Woodland and Heath-Krummholz\\\",\\\"Atlantic and Gulf Coastal Plain\n Interdunal Wetland\\\",\\\"Atlantic Coastal Plain Southern Dune and Maritime Grassland\\\",\\\"Central\n and Upper Texas Coast Dune and Coastal Grassland\\\",\\\"East Gulf Coastal Plain\n Dune and Coastal Grassland\\\",\\\"Great Lakes Dune\\\",\\\"Northern Atlantic Coastal\n Plain Dune and Swale\\\",\\\"Northern Atlantic Coastal Plain Heathland and Grassland\\\",\\\"South\n Texas Dune and Coastal Grassland\\\",\\\"South Texas Sand Sheet Grassland\\\",\\\"Southwest\n Florida Dune and Coastal Grassland\\\",\\\"North Pacific Coastal Cliff and Bluff\\\",\\\"North\n Pacific Maritime Coastal Sand Dune and Strand\\\",\\\"Northern California Coastal\n Scrub\\\",\\\"Mediterranean California Coastal Bluff\\\",\\\"Mediterranean California\n Northern Coastal Dune\\\",\\\"Mediterranean California Southern Coastal Dune\\\",\\\"Atlantic\n Coastal Plain Northern Sandy Beach\\\",\\\"Atlantic Coastal Plain Sea Island Beach\\\",\\\"Atlantic\n Coastal Plain Southern Beach\\\",\\\"Florida Panhandle Beach Vegetation\\\",\\\"Louisiana\n Beach\\\",\\\"Northern Atlantic Coastal Plain Sandy Beach\\\",\\\"Texas Coastal Bend\n Beach\\\",\\\"Upper Texas Coast Beach\\\",\\\"0\\\",\\\"Mediterranean California Serpentine\n Fen\\\",\\\"Mediterranean California Subalpine-Montane Fen\\\",\\\"North Pacific Bog\n and Fen\\\",\\\"Rocky Mountain Subalpine-Montane Fen\\\",\\\"Atlantic Coastal Plain\n Peatland Pocosin\\\",\\\"Southern and Central Appalachian Bog and Fen\\\",\\\"Atlantic\n Coastal Plain Central Fresh-Oligohaline Tidal Marsh\\\",\\\"Atlantic Coastal Plain\n Embayed Region Tidal Freshwater Marsh\\\",\\\"Atlantic Coastal Plain Northern\n Fresh and Oligohaline Tidal Marsh\\\",\\\"Florida Big Bend Fresh-Oligohaline Tidal\n Marsh\\\",\\\"Atlantic Coastal Plain Depression Pondshore\\\",\\\"Atlantic Coastal\n Plain Large Natural Lakeshore\\\",\\\"Central Florida Herbaceous Pondshore\\\",\\\"Central\n Florida Herbaceous Seep\\\",\\\"East Gulf Coastal Plain Savanna and Wet Prairie\\\",\\\"East\n Gulf Coastal Plain Depression Pondshore\\\",\\\"Floridian Highlands Freshwater\n Marsh\\\",\\\"Southern Coastal Plain Herbaceous Seepage Bog\\\",\\\"Southern Coastal\n Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier\\\",\\\"Southern Coastal\n Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier\\\",\\\"Texas-Louisiana\n Coastal Prairie Slough\\\",\\\"Central Interior and Appalachian Shrub-Herbaceous\n Wetland Systems\\\",\\\"Great Lakes Coastal Marsh Systems\\\",\\\"0\\\",\\\"0\\\",\\\"Laurentian-Acadian\n Shrub-Herbaceous Wetland Systems\\\",\\\"0\\\",\\\"Eastern Great Plains Wet Meadow,\n Prairie and Marsh\\\",\\\"Great Lakes Wet-Mesic Lakeplain Prairie\\\",\\\"Great Plains\n Prairie Pothole\\\",\\\"Western Great Plains Closed Depression Wetland\\\",\\\"Western\n Great Plains Depressional Wetland Systems\\\",\\\"Western Great Plains Open Freshwater\n Depression Wetland\\\",\\\"Cumberland Riverscour\\\",\\\"Inter-Mountain Basins Interdunal\n Swale Wetland\\\",\\\"North Pacific Avalanche Chute Shrubland\\\",\\\"North Pacific\n Intertidal Freshwater Wetland\\\",\\\"Temperate Pacific Freshwater Emergent Marsh\\\",\\\"Temperate\n Pacific Freshwater Mudflat\\\",\\\"Columbia Plateau Vernal Pool\\\",\\\"Northern California\n Claypan Vernal Pool\\\",\\\"Northern Rocky Mountain Wooded Vernal Pool\\\",\\\"Columbia\n Plateau Silver Sagebrush Seasonally Flooded Shrub-Steppe\\\",\\\"Rocky Mountain\n Alpine-Montane Wet Meadow\\\",\\\"Rocky Mountain Subalpine-Montane Riparian Shrubland\\\",\\\"Temperate\n Pacific Montane Wet Meadow\\\",\\\"Willamette Valley Wet Prairie\\\",\\\"Chihuahuan-Sonoran\n Desert Bottomland and Swale Grassland\\\",\\\"North American Arid West Emergent\n Marsh\\\",\\\"North American Warm Desert Riparian Mesquite Bosque\\\",\\\"Western\n Great Plains Saline Depression Wetland\\\",\\\"Acadian Salt Marsh and Estuary\n Systems\\\",\\\"Atlantic Coastal Plain Central Salt and Brackish Tidal Marsh\\\",\\\"Atlantic\n Coastal Plain Embayed Region Tidal Salt and Brackish Marsh\\\",\\\"Atlantic Coastal\n Plain Indian River Lagoon Tidal Marsh\\\",\\\"Atlantic Coastal Plain Northern\n Tidal Salt Marsh\\\",\\\"Florida Big Bend Salt-Brackish Tidal Marsh\\\",\\\"Gulf and\n Atlantic Coastal Plain Tidal Marsh Systems\\\",\\\"Mississippi Sound Salt and\n Brackish Tidal Marsh\\\",\\\"Texas Saline Coastal Prairie\\\",\\\"Temperate Pacific\n Tidal Salt and Brackish Marsh\\\",\\\"Inter-Mountain Basins Alkaline Closed Depression\\\",\\\"Inter-Mountain\n Basins Greasewood Flat\\\",\\\"Inter-Mountain Basins Playa\\\",\\\"North American\n Warm Desert Playa\\\",\\\"Apacherian-Chihuahuan Mesquite Upland Scrub\\\",\\\"Apacherian-Chihuahuan\n Semi-Desert Grassland and Steppe\\\",\\\"Chihuahuan Creosotebush, Mixed Desert\n and Thorn Scrub\\\",\\\"Chihuahuan Gypsophilous Grassland and Steppe\\\",\\\"Chihuahuan\n Loamy Plains Desert Grassland\\\",\\\"Chihuahuan Mixed Desert and Thorn Scrub\\\",\\\"Chihuahuan\n Sandy Plains Semi-Desert Grassland\\\",\\\"Chihuahuan Stabilized Coppice Dune\n and Sand Flat Scrub\\\",\\\"Chihuahuan Succulent Desert Scrub\\\",\\\"Madrean Juniper\n Savanna\\\",\\\"Mojave Mid-Elevation Mixed Desert Scrub\\\",\\\"North American Warm\n Desert Active and Stabilized Dune\\\",\\\"Sonora-Mojave Creosotebush-White Bursage\n Desert Scrub\\\",\\\"Sonoran Mid-Elevation Desert Scrub\\\",\\\"Sonoran Paloverde-Mixed\n Cacti Desert Scrub\\\",\\\"Chihuahuan Mixed Salt Desert Scrub\\\",\\\"Sonora-Mojave\n Mixed Salt Desert Scrub\\\",\\\"North American Warm Desert Wash\\\",\\\"South Texas\n Lomas\\\",\\\"Tamaulipan Calcareous Thornscrub\\\",\\\"Tamaulipan Clay Grassland\\\",\\\"Tamaulipan\n Mesquite Upland Scrub\\\",\\\"Tamaulipan Mixed Deciduous Thornscrub\\\",\\\"Tamaulipan\n Savanna Grassland\\\",\\\"Inter-Mountain Basins Mat Saltbush Shrubland\\\",\\\"Inter-Mountain\n Basins Mixed Salt Desert Scrub\\\",\\\"Inter-Mountain Basins Wash\\\",\\\"Columbia\n Plateau Steppe and Grassland\\\",\\\"Great Basin Xeric Mixed Sagebrush Shrubland\\\",\\\"Inter-Mountain\n Basins Big Sagebrush Shrubland\\\",\\\"Inter-Mountain Basins Big Sagebrush Steppe\\\",\\\"Inter-Mountain\n Basins Montane Sagebrush Steppe\\\",\\\"Colorado Plateau Mixed Low Sagebrush Shrubland\\\",\\\"Columbia\n Plateau Low Sagebrush Steppe\\\",\\\"Columbia Plateau Scabland Shrubland\\\",\\\"Wyoming\n Basins Dwarf Sagebrush Shrubland and Steppe\\\",\\\"Colorado Plateau Blackbrush-Mormon-tea\n Shrubland\\\",\\\"Inter-Mountain Basins Semi-Desert Grassland\\\",\\\"Inter-Mountain\n Basins Semi-Desert Shrub Steppe\\\",\\\"Southern Colorado Plateau Sand Shrubland\\\",\\\"Acadian-Appalachian\n Alpine Tundra\\\",\\\"Rocky Mountain Alpine Dwarf-Shrubland\\\",\\\"Rocky Mountain\n Alpine Fell-Field\\\",\\\"Rocky Mountain Alpine Turf\\\",\\\"Mediterranean California\n Alpine Dry Tundra\\\",\\\"Mediterranean California Alpine Fell-Field\\\",\\\"North\n Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow\\\",\\\"Rocky\n Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit\\\",\\\"Temperate Pacific\n Intertidal Mudflat\\\",\\\"Mediterranean California Eelgrass Bed\\\",\\\"North Pacific\n Maritime Eelgrass Bed\\\",\\\"South-Central Interior Large Floodplain - Herbaceous\n Modifier\\\",\\\"East Gulf Coastal Plain Large River Floodplain Forest - Herbaceous\n Modifier\\\",\\\"Temperate Pacific Freshwater Aquatic Bed\\\",\\\"Central California\n Coast Ranges Cliff and Canyon\\\",\\\"Mediterranean California Serpentine Barrens\\\",\\\"Southern\n California Coast Ranges Cliff and Canyon\\\",\\\"Central Interior Acidic Cliff\n and Talus\\\",\\\"Central Interior Calcareous Cliff and Talus\\\",\\\"East Gulf Coastal\n Plain Dry Chalk Bluff\\\",\\\"North-Central Appalachian Acidic Cliff and Talus\\\",\\\"North-Central\n Appalachian Circumneutral Cliff and Talus\\\",\\\"Southern Appalachian Montane\n Cliff\\\",\\\"Southern Interior Acid Cliff\\\",\\\"Southern Interior Calcareous Cliff\\\",\\\"Southern\n Piedmont Cliff\\\",\\\"Southern Appalachian Granitic Dome\\\",\\\"Southern Appalachian\n Rocky Summit\\\",\\\"Southern Piedmont Granite Flatrock\\\",\\\"Rocky Mountain Cliff,\n Canyon and Massive Bedrock\\\",\\\"Klamath-Siskiyou Cliff and Outcrop\\\",\\\"North\n Pacific Montane Massive Bedrock, Cliff and Talus\\\",\\\"North Pacific Serpentine\n Barren\\\",\\\"North Pacific Active Volcanic Rock and Cinder Land\\\",\\\"Sierra Nevada\n Cliff and Canyon\\\",\\\"Western Great Plains Badland\\\",\\\"Southwestern Great Plains\n Canyon\\\",\\\"Western Great Plains Cliff and Outcrop\\\",\\\"North American Warm\n Desert Badland\\\",\\\"North American Warm Desert Bedrock Cliff and Outcrop\\\",\\\"North\n American Warm Desert Pavement\\\",\\\"North American Warm Desert Volcanic Rockland\\\",\\\"Colorado\n Plateau Mixed Bedrock Canyon and Tableland\\\",\\\"Columbia Plateau Ash and Tuff\n Badland\\\",\\\"Geysers and Hot Springs\\\",\\\"Inter-Mountain Basins Active and Stabilized\n Dune\\\",\\\"Inter-Mountain Basins Cliff and Canyon\\\",\\\"Inter-Mountain Basins\n Shale Badland\\\",\\\"Inter-Mountain Basins Volcanic Rock and Cinder Land\\\",\\\"Rocky\n Mountain Alpine Bedrock and Scree\\\",\\\"Mediterranean California Alpine Bedrock\n and Scree\\\",\\\"North Pacific Alpine and Subalpine Bedrock and Scree\\\",\\\"Unconsolidated\n Shore\\\",\\\"Undifferentiated Barren Land\\\",\\\"North American Alpine Ice Field\\\",\\\"Orchards\n Vineyards and Other High Structure Agriculture\\\",\\\"Cultivated Cropland\\\",\\\"Pasture/Hay\\\",\\\"Introduced\n Upland Vegetation - Annual Grassland\\\",\\\"Introduced Upland Vegetation - Perennial\n Grassland and Forbland\\\",\\\"Modified/Managed Southern Tall Grassland\\\",\\\"Introduced\n Upland Vegetation - Shrub\\\",\\\"Introduced Riparian and Wetland Vegetation\\\",\\\"Introduced\n Upland Vegetation - Treed\\\",\\\"0\\\",\\\"Disturbed, Non-specific\\\",\\\"Recently Logged\n Areas\\\",\\\"Harvested Forest - Grass/Forb Regeneration\\\",\\\"Harvested Forest-Shrub\n Regeneration\\\",\\\"Harvested Forest - Northwestern Conifer Regeneration\\\",\\\"Recently\n Burned\\\",\\\"Recently burned grassland\\\",\\\"Recently burned shrubland\\\",\\\"Recently\n burned forest\\\",\\\"Disturbed/Successional - Grass/Forb Regeneration\\\",\\\"Disturbed/Successional\n - Shrub Regeneration\\\",\\\"Disturbed/Successional - Recently Chained Pinyon-Juniper\\\",\\\"Open\n Water (Aquaculture)\\\",\\\"Open Water (Brackish/Salt)\\\",\\\"Open Water (Fresh)\\\",\\\"Quarries,\n Mines, Gravel Pits and Oil Wells\\\",\\\"Developed, Open Space\\\",\\\"Developed,\n Low Intensity\\\",\\\"Developed, Medium Intensity\\\",\\\"Developed, High Intensity\\\"]}]},\\\"description\\\":\\\"The\n [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5),\n based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states),\n are the foundation of the most detailed, consistent map of vegetation available\n for the United States. These data facilitate planning and management for\n biological diversity on a regional and national scale.\\\\n\\\\nThis dataset includes\n the [land cover](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover)\n component of the GAP/LANDFIRE project.\\\\n\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"GeoTIFF\n data\\\",\\\"file:values\\\":[{\\\"values\\\":[0],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[1],\\\"summary\\\":\\\"South\n Florida Bayhead Swamp\\\"},{\\\"values\\\":[2],\\\"summary\\\":\\\"South Florida Cypress\n Dome\\\"},{\\\"values\\\":[3],\\\"summary\\\":\\\"South Florida Dwarf Cypress Savanna\\\"},{\\\"values\\\":[4],\\\"summary\\\":\\\"South\n Florida Mangrove Swamp\\\"},{\\\"values\\\":[5],\\\"summary\\\":\\\"South Florida Hardwood\n Hammock\\\"},{\\\"values\\\":[6],\\\"summary\\\":\\\"Southeast Florida Coastal Strand\n and Maritime Hammock\\\"},{\\\"values\\\":[7],\\\"summary\\\":\\\"Southwest Florida Coastal\n Strand and Maritime Hammock\\\"},{\\\"values\\\":[8],\\\"summary\\\":\\\"South Florida\n Pine Rockland\\\"},{\\\"values\\\":[9],\\\"summary\\\":\\\"Atlantic Coastal Plain Fall-line\n Sandhills Longleaf Pine Woodland - Open Understory\\\"},{\\\"values\\\":[10],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory\\\"},{\\\"values\\\":[11],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Upland Longleaf Pine Woodland\\\"},{\\\"values\\\":[12],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Xeric River Dune\\\"},{\\\"values\\\":[13],\\\"summary\\\":\\\"East Gulf\n Coastal Plain Interior Upland Longleaf Pine Woodland - Open Understory Modifier\\\"},{\\\"values\\\":[14],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Scrub/Shrub Modifier\\\"},{\\\"values\\\":[15],\\\"summary\\\":\\\"Florida\n Longleaf Pine Sandhill - Scrub/Shrub Understory Modifier\\\"},{\\\"values\\\":[16],\\\"summary\\\":\\\"Florida\n Longleaf Pine Sandhill- Open Understory Modifier\\\"},{\\\"values\\\":[17],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland\\\"},{\\\"values\\\":[18],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Central Maritime Forest\\\"},{\\\"values\\\":[19],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Southern Maritime Forest\\\"},{\\\"values\\\":[20],\\\"summary\\\":\\\"Central\n and South Texas Coastal Fringe Forest and Woodland\\\"},{\\\"values\\\":[21],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Limestone Forest\\\"},{\\\"values\\\":[22],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Maritime Forest\\\"},{\\\"values\\\":[23],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Southern Loess Bluff Forest\\\"},{\\\"values\\\":[24],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Southern Mesic Slope Forest\\\"},{\\\"values\\\":[25],\\\"summary\\\":\\\"Mississippi\n Delta Maritime Forest\\\"},{\\\"values\\\":[26],\\\"summary\\\":\\\"Southern Coastal Plain\n Dry Upland Hardwood Forest\\\"},{\\\"values\\\":[27],\\\"summary\\\":\\\"Southern Coastal\n Plain Oak Dome and Hammock\\\"},{\\\"values\\\":[28],\\\"summary\\\":\\\"West Gulf Coastal\n Plain Chenier and Upper Texas Coastal Fringe Forest and Woodland\\\"},{\\\"values\\\":[29],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Mesic Hardwood Forest\\\"},{\\\"values\\\":[30],\\\"summary\\\":\\\"East-Central\n Texas Plains Pine Forest and Woodland\\\"},{\\\"values\\\":[31],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Pine-Hardwood Forest\\\"},{\\\"values\\\":[32],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest and Woodland\\\"},{\\\"values\\\":[33],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Fall-Line Sandhills Longleaf Pine Woodland - Loblolly Modifier\\\"},{\\\"values\\\":[34],\\\"summary\\\":\\\"Deciduous\n Plantations\\\"},{\\\"values\\\":[35],\\\"summary\\\":\\\"East Gulf Coastal Plain Interior\n Upland Longleaf Pine Woodland - Loblolly Modifier\\\"},{\\\"values\\\":[36],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood\n Modifier\\\"},{\\\"values\\\":[37],\\\"summary\\\":\\\"East Gulf Coastal Plain Near-Coast\n Pine Flatwoods - Offsite Hardwood Modifier\\\"},{\\\"values\\\":[38],\\\"summary\\\":\\\"Evergreen\n Plantation or Managed Pine\\\"},{\\\"values\\\":[39],\\\"summary\\\":\\\"California Central\n Valley Mixed Oak Savanna\\\"},{\\\"values\\\":[40],\\\"summary\\\":\\\"California Coastal\n Closed-Cone Conifer Forest and Woodland\\\"},{\\\"values\\\":[41],\\\"summary\\\":\\\"California\n Coastal Live Oak Woodland and Savanna\\\"},{\\\"values\\\":[42],\\\"summary\\\":\\\"California\n Lower Montane Blue Oak-Foothill Pine Woodland and Savanna\\\"},{\\\"values\\\":[43],\\\"summary\\\":\\\"Central\n and Southern California Mixed Evergreen Woodland\\\"},{\\\"values\\\":[44],\\\"summary\\\":\\\"Mediterranean\n California Lower Montane Black Oak-Conifer Forest and Woodland\\\"},{\\\"values\\\":[45],\\\"summary\\\":\\\"Southern\n California Oak Woodland and Savanna\\\"},{\\\"values\\\":[46],\\\"summary\\\":\\\"Madrean\n Encinal\\\"},{\\\"values\\\":[47],\\\"summary\\\":\\\"Madrean Pinyon-Juniper Woodland\\\"},{\\\"values\\\":[48],\\\"summary\\\":\\\"Madrean\n Pine-Oak Forest and Woodland\\\"},{\\\"values\\\":[49],\\\"summary\\\":\\\"Madrean Upper\n Montane Conifer-Oak Forest and Woodland\\\"},{\\\"values\\\":[50],\\\"summary\\\":\\\"Edwards\n Plateau Dry-Mesic Slope Forest and Woodland\\\"},{\\\"values\\\":[51],\\\"summary\\\":\\\"Edwards\n Plateau Limestone Savanna and Woodland\\\"},{\\\"values\\\":[52],\\\"summary\\\":\\\"Edwards\n Plateau Mesic Canyon\\\"},{\\\"values\\\":[53],\\\"summary\\\":\\\"Llano Uplift Acidic\n Forest, Woodland and Glade\\\"},{\\\"values\\\":[54],\\\"summary\\\":\\\"East Cascades\n Oak-Ponderosa Pine Forest and Woodland\\\"},{\\\"values\\\":[55],\\\"summary\\\":\\\"Mediterranean\n California Mixed Evergreen Forest\\\"},{\\\"values\\\":[56],\\\"summary\\\":\\\"Mediterranean\n California Mixed Oak Woodland\\\"},{\\\"values\\\":[57],\\\"summary\\\":\\\"North Pacific\n Dry Douglas-fir-(Madrone) Forest and Woodland\\\"},{\\\"values\\\":[58],\\\"summary\\\":\\\"North\n Pacific Oak Woodland\\\"},{\\\"values\\\":[59],\\\"summary\\\":\\\"Edwards Plateau Limestone\n Shrubland\\\"},{\\\"values\\\":[60],\\\"summary\\\":\\\"Allegheny-Cumberland Dry Oak Forest\n and Woodland - Hardwood\\\"},{\\\"values\\\":[61],\\\"summary\\\":\\\"Allegheny-Cumberland\n Dry Oak Forest and Woodland - Pine Modifier\\\"},{\\\"values\\\":[62],\\\"summary\\\":\\\"Central\n and Southern Appalachian Montane Oak Forest\\\"},{\\\"values\\\":[63],\\\"summary\\\":\\\"Central\n and Southern Appalachian Northern Hardwood Forest\\\"},{\\\"values\\\":[64],\\\"summary\\\":\\\"Central\n Appalachian Oak and Pine Forest\\\"},{\\\"values\\\":[65],\\\"summary\\\":\\\"Crosstimbers\n Oak Forest and Woodland\\\"},{\\\"values\\\":[66],\\\"summary\\\":\\\"East Gulf Coastal\n Plain Black Belt Calcareous Prairie and Woodland - Woodland Modifier\\\"},{\\\"values\\\":[67],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Northern Dry Upland Hardwood Forest\\\"},{\\\"values\\\":[68],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Hardwood Modifier\\\"},{\\\"values\\\":[69],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Juniper Modifier\\\"},{\\\"values\\\":[70],\\\"summary\\\":\\\"East-Central\n Texas Plains Post Oak Savanna and Woodland\\\"},{\\\"values\\\":[71],\\\"summary\\\":\\\"Lower\n Mississippi River Dune Woodland and Forest\\\"},{\\\"values\\\":[72],\\\"summary\\\":\\\"Mississippi\n River Alluvial Plain Dry-Mesic Loess Slope Forest\\\"},{\\\"values\\\":[73],\\\"summary\\\":\\\"North-Central\n Interior Dry Oak Forest and Woodland\\\"},{\\\"values\\\":[74],\\\"summary\\\":\\\"North-Central\n Interior Dry-Mesic Oak Forest and Woodland\\\"},{\\\"values\\\":[75],\\\"summary\\\":\\\"Northeastern\n Interior Dry Oak Forest - Mixed Modifier\\\"},{\\\"values\\\":[76],\\\"summary\\\":\\\"Northeastern\n Interior Dry Oak Forest - Virginia/Pitch Pine Modifier\\\"},{\\\"values\\\":[77],\\\"summary\\\":\\\"Northeastern\n Interior Dry Oak Forest-Hardwood Modifier\\\"},{\\\"values\\\":[78],\\\"summary\\\":\\\"Northeastern\n Interior Dry-Mesic Oak Forest\\\"},{\\\"values\\\":[79],\\\"summary\\\":\\\"Northern Atlantic\n Coastal Plain Dry Hardwood Forest\\\"},{\\\"values\\\":[80],\\\"summary\\\":\\\"Crowley's\n Ridge Sand Forest\\\"},{\\\"values\\\":[81],\\\"summary\\\":\\\"Ouachita Montane Oak Forest\\\"},{\\\"values\\\":[82],\\\"summary\\\":\\\"Ozark-Ouachita\n Dry Oak Woodland\\\"},{\\\"values\\\":[83],\\\"summary\\\":\\\"Ozark-Ouachita Dry-Mesic\n Oak Forest\\\"},{\\\"values\\\":[84],\\\"summary\\\":\\\"Southern and Central Appalachian\n Oak Forest\\\"},{\\\"values\\\":[85],\\\"summary\\\":\\\"Southern and Central Appalachian\n Oak Forest - Xeric\\\"},{\\\"values\\\":[86],\\\"summary\\\":\\\"Southern Interior Low\n Plateau Dry-Mesic Oak Forest\\\"},{\\\"values\\\":[87],\\\"summary\\\":\\\"Southern Ridge\n and Valley Dry Calcareous Forest\\\"},{\\\"values\\\":[88],\\\"summary\\\":\\\"Southern\n Ridge and Valley Dry Calcareous Forest - Pine modifier\\\"},{\\\"values\\\":[89],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier\\\"},{\\\"values\\\":[90],\\\"summary\\\":\\\"Managed\n Tree Plantation\\\"},{\\\"values\\\":[91],\\\"summary\\\":\\\"Ruderal forest\\\"},{\\\"values\\\":[92],\\\"summary\\\":\\\"Southern\n Piedmont Dry Oak-(Pine) Forest - Loblolly Pine Modifier\\\"},{\\\"values\\\":[93],\\\"summary\\\":\\\"Acadian\n Low-Elevation Spruce-Fir-Hardwood Forest\\\"},{\\\"values\\\":[94],\\\"summary\\\":\\\"Acadian-Appalachian\n Montane Spruce-Fir Forest\\\"},{\\\"values\\\":[95],\\\"summary\\\":\\\"Appalachian Hemlock-Hardwood\n Forest\\\"},{\\\"values\\\":[96],\\\"summary\\\":\\\"Central and Southern Appalachian\n Spruce-Fir Forest\\\"},{\\\"values\\\":[97],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[98],\\\"summary\\\":\\\"Laurentian-Acadian\n Northern Hardwoods Forest\\\"},{\\\"values\\\":[99],\\\"summary\\\":\\\"Laurentian-Acadian\n Northern Pine-(Oak) Forest\\\"},{\\\"values\\\":[100],\\\"summary\\\":\\\"Laurentian-Acadian\n Pine-Hemlock-Hardwood Forest\\\"},{\\\"values\\\":[101],\\\"summary\\\":\\\"Paleozoic\n Plateau Bluff and Talus\\\"},{\\\"values\\\":[102],\\\"summary\\\":\\\"Southern Appalachian\n Northern Hardwood Forest\\\"},{\\\"values\\\":[103],\\\"summary\\\":\\\"Atlantic Coastal\n Plain Dry and Dry-Mesic Oak Forest\\\"},{\\\"values\\\":[104],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Offsite Hardwood\\\"},{\\\"values\\\":[105],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Hardwood Modifier\\\"},{\\\"values\\\":[106],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Mixed Modifier\\\"},{\\\"values\\\":[107],\\\"summary\\\":\\\"Ozark-Ouachita\n Shortleaf Pine-Bluestem Woodland\\\"},{\\\"values\\\":[108],\\\"summary\\\":\\\"Ozark-Ouachita\n Shortleaf Pine-Oak Forest and Woodland\\\"},{\\\"values\\\":[109],\\\"summary\\\":\\\"Southeastern\n Interior Longleaf Pine Woodland\\\"},{\\\"values\\\":[110],\\\"summary\\\":\\\"Southern\n Appalachian Low Mountain Pine Forest\\\"},{\\\"values\\\":[111],\\\"summary\\\":\\\"Southern\n Piedmont Dry Oak-(Pine) Forest\\\"},{\\\"values\\\":[112],\\\"summary\\\":\\\"Southern\n Piedmont Dry Oak-(Pine) Forest - Hardwood Modifier\\\"},{\\\"values\\\":[113],\\\"summary\\\":\\\"Southern\n Piedmont Dry Oak-(Pine) Forest - Mixed Modifier\\\"},{\\\"values\\\":[114],\\\"summary\\\":\\\"Southern\n Piedmont Dry Oak-Heath Forest - Mixed Modifier\\\"},{\\\"values\\\":[115],\\\"summary\\\":\\\"Eastern\n Great Plains Tallgrass Aspen Parkland\\\"},{\\\"values\\\":[116],\\\"summary\\\":\\\"Northwestern\n Great Plains Aspen Forest and Parkland\\\"},{\\\"values\\\":[117],\\\"summary\\\":\\\"Northwestern\n Great Plains Shrubland\\\"},{\\\"values\\\":[118],\\\"summary\\\":\\\"Western Great Plains\n Dry Bur Oak Forest and Woodland\\\"},{\\\"values\\\":[119],\\\"summary\\\":\\\"Western\n Great Plains Wooded Draw and Ravine\\\"},{\\\"values\\\":[120],\\\"summary\\\":\\\"Southern\n Atlantic Coastal Plain Mesic Hardwood Forest\\\"},{\\\"values\\\":[121],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Northern Loess Bluff Forest\\\"},{\\\"values\\\":[122],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Northern Mesic Hardwood Forest\\\"},{\\\"values\\\":[123],\\\"summary\\\":\\\"North-Central\n Interior Beech-Maple Forest\\\"},{\\\"values\\\":[124],\\\"summary\\\":\\\"North-Central\n Interior Maple-Basswood Forest\\\"},{\\\"values\\\":[125],\\\"summary\\\":\\\"Ozark-Ouachita\n Mesic Hardwood Forest\\\"},{\\\"values\\\":[126],\\\"summary\\\":\\\"South-Central Interior\n Mesophytic Forest\\\"},{\\\"values\\\":[127],\\\"summary\\\":\\\"Southern and Central\n Appalachian Cove Forest\\\"},{\\\"values\\\":[128],\\\"summary\\\":\\\"Crowley's Ridge\n Mesic Loess Slope Forest\\\"},{\\\"values\\\":[129],\\\"summary\\\":\\\"Southern Piedmont\n Mesic Forest\\\"},{\\\"values\\\":[130],\\\"summary\\\":\\\"Appalachian Shale Barrens\\\"},{\\\"values\\\":[131],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Northern Maritime Forest\\\"},{\\\"values\\\":[132],\\\"summary\\\":\\\"Laurentian\n Pine-Oak Barrens\\\"},{\\\"values\\\":[133],\\\"summary\\\":\\\"Northeastern Interior\n Pine Barrens\\\"},{\\\"values\\\":[134],\\\"summary\\\":\\\"Northern Atlantic Coastal\n Plain Pitch Pine Barrens\\\"},{\\\"values\\\":[135],\\\"summary\\\":\\\"Southern Appalachian\n Montane Pine Forest and Woodland\\\"},{\\\"values\\\":[136],\\\"summary\\\":\\\"East Cascades\n Mesic Montane Mixed-Conifer Forest and Woodland\\\"},{\\\"values\\\":[137],\\\"summary\\\":\\\"Middle\n Rocky Mountain Montane Douglas-fir Forest and Woodland\\\"},{\\\"values\\\":[138],\\\"summary\\\":\\\"Northern\n Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest\\\"},{\\\"values\\\":[139],\\\"summary\\\":\\\"Northern\n Rocky Mountain Foothill Conifer Wooded Steppe\\\"},{\\\"values\\\":[140],\\\"summary\\\":\\\"Northern\n Rocky Mountain Mesic Montane Mixed Conifer Forest\\\"},{\\\"values\\\":[141],\\\"summary\\\":\\\"Northern\n Rocky Mountain Ponderosa Pine Woodland and Savanna\\\"},{\\\"values\\\":[142],\\\"summary\\\":\\\"Northern\n Rocky Mountain Western Larch Savanna\\\"},{\\\"values\\\":[143],\\\"summary\\\":\\\"Northwestern\n Great Plains - Black Hills Ponderosa Pine Woodland and Savanna\\\"},{\\\"values\\\":[144],\\\"summary\\\":\\\"Rocky\n Mountain Foothill Limber Pine-Juniper Woodland\\\"},{\\\"values\\\":[145],\\\"summary\\\":\\\"Inter-Mountain\n Basins Aspen-Mixed Conifer Forest and Woodland\\\"},{\\\"values\\\":[146],\\\"summary\\\":\\\"Inter-Mountain\n Basins Subalpine Limber-Bristlecone Pine Woodland\\\"},{\\\"values\\\":[147],\\\"summary\\\":\\\"Northern\n Rocky Mountain Subalpine Woodland and Parkland\\\"},{\\\"values\\\":[148],\\\"summary\\\":\\\"Rocky\n Mountain Aspen Forest and Woodland\\\"},{\\\"values\\\":[149],\\\"summary\\\":\\\"Rocky\n Mountain Lodgepole Pine Forest\\\"},{\\\"values\\\":[150],\\\"summary\\\":\\\"Rocky Mountain\n Poor-Site Lodgepole Pine Forest\\\"},{\\\"values\\\":[151],\\\"summary\\\":\\\"Rocky Mountain\n Subalpine Dry-Mesic Spruce-Fir Forest and Woodland\\\"},{\\\"values\\\":[152],\\\"summary\\\":\\\"Rocky\n Mountain Subalpine Mesic Spruce-Fir Forest and Woodland\\\"},{\\\"values\\\":[153],\\\"summary\\\":\\\"Rocky\n Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland\\\"},{\\\"values\\\":[154],\\\"summary\\\":\\\"Rocky\n Mountain Bigtooth Maple Ravine Woodland\\\"},{\\\"values\\\":[155],\\\"summary\\\":\\\"Southern\n Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland\\\"},{\\\"values\\\":[156],\\\"summary\\\":\\\"Southern\n Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland\\\"},{\\\"values\\\":[157],\\\"summary\\\":\\\"Southern\n Rocky Mountain Ponderosa Pine Savanna\\\"},{\\\"values\\\":[158],\\\"summary\\\":\\\"Southern\n Rocky Mountain Ponderosa Pine Woodland\\\"},{\\\"values\\\":[159],\\\"summary\\\":\\\"California\n Montane Jeffrey Pine-(Ponderosa Pine) Woodland\\\"},{\\\"values\\\":[160],\\\"summary\\\":\\\"Klamath-Siskiyou\n Lower Montane Serpentine Mixed Conifer Woodland\\\"},{\\\"values\\\":[161],\\\"summary\\\":\\\"Klamath-Siskiyou\n Upper Montane Serpentine Mixed Conifer Woodland\\\"},{\\\"values\\\":[162],\\\"summary\\\":\\\"Mediterranean\n California Dry-Mesic Mixed Conifer Forest and Woodland\\\"},{\\\"values\\\":[163],\\\"summary\\\":\\\"Mediterranean\n California Mesic Mixed Conifer Forest and Woodland\\\"},{\\\"values\\\":[164],\\\"summary\\\":\\\"Sierran-Intermontane\n Desert Western White Pine-White Fir Woodland\\\"},{\\\"values\\\":[165],\\\"summary\\\":\\\"California\n Coastal Redwood Forest\\\"},{\\\"values\\\":[166],\\\"summary\\\":\\\"North Pacific Broadleaf\n Landslide Forest and Shrubland\\\"},{\\\"values\\\":[167],\\\"summary\\\":\\\"North Pacific\n Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest\\\"},{\\\"values\\\":[168],\\\"summary\\\":\\\"North\n Pacific Hypermaritime Sitka Spruce Forest\\\"},{\\\"values\\\":[169],\\\"summary\\\":\\\"North\n Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest\\\"},{\\\"values\\\":[170],\\\"summary\\\":\\\"North\n Pacific Lowland Mixed Hardwood-Conifer Forest and Woodland\\\"},{\\\"values\\\":[171],\\\"summary\\\":\\\"North\n Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest\\\"},{\\\"values\\\":[172],\\\"summary\\\":\\\"North\n Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest\\\"},{\\\"values\\\":[173],\\\"summary\\\":\\\"North\n Pacific Mesic Western Hemlock-Silver Fir Forest\\\"},{\\\"values\\\":[174],\\\"summary\\\":\\\"North\n Pacific Wooded Volcanic Flowage\\\"},{\\\"values\\\":[175],\\\"summary\\\":\\\"Mediterranean\n California Red Fir Forest\\\"},{\\\"values\\\":[176],\\\"summary\\\":\\\"Mediterranean\n California Subalpine Woodland\\\"},{\\\"values\\\":[177],\\\"summary\\\":\\\"North Pacific\n Maritime Mesic Subalpine Parkland\\\"},{\\\"values\\\":[178],\\\"summary\\\":\\\"North\n Pacific Mountain Hemlock Forest\\\"},{\\\"values\\\":[179],\\\"summary\\\":\\\"Northern\n California Mesic Subalpine Woodland\\\"},{\\\"values\\\":[180],\\\"summary\\\":\\\"Northern\n Pacific Mesic Subalpine Woodland\\\"},{\\\"values\\\":[181],\\\"summary\\\":\\\"Sierra\n Nevada Subalpine Lodgepole Pine Forest and Woodland\\\"},{\\\"values\\\":[182],\\\"summary\\\":\\\"Columbia\n Plateau Western Juniper Woodland and Savanna\\\"},{\\\"values\\\":[183],\\\"summary\\\":\\\"Great\n Basin Pinyon-Juniper Woodland\\\"},{\\\"values\\\":[184],\\\"summary\\\":\\\"Inter-Mountain\n Basins Curl-leaf Mountain Mahogany Woodland and Shrubland\\\"},{\\\"values\\\":[185],\\\"summary\\\":\\\"Inter-Mountain\n Basins Juniper Savanna\\\"},{\\\"values\\\":[186],\\\"summary\\\":\\\"Colorado Plateau\n Pinyon-Juniper Shrubland\\\"},{\\\"values\\\":[187],\\\"summary\\\":\\\"Colorado Plateau\n Pinyon-Juniper Woodland\\\"},{\\\"values\\\":[188],\\\"summary\\\":\\\"Southern Rocky\n Mountain Juniper Woodland and Savanna\\\"},{\\\"values\\\":[189],\\\"summary\\\":\\\"Southern\n Rocky Mountain Pinyon-Juniper Woodland\\\"},{\\\"values\\\":[190],\\\"summary\\\":\\\"Northwestern\n Great Plains Floodplain\\\"},{\\\"values\\\":[191],\\\"summary\\\":\\\"Northwestern Great\n Plains Riparian\\\"},{\\\"values\\\":[192],\\\"summary\\\":\\\"Western Great Plains Floodplain\\\"},{\\\"values\\\":[193],\\\"summary\\\":\\\"Western\n Great Plains Floodplain Systems\\\"},{\\\"values\\\":[194],\\\"summary\\\":\\\"Western\n Great Plains Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[195],\\\"summary\\\":\\\"Central\n Appalachian Floodplain - Forest Modifier\\\"},{\\\"values\\\":[196],\\\"summary\\\":\\\"Central\n Appalachian Riparian - Forest Modifier\\\"},{\\\"values\\\":[197],\\\"summary\\\":\\\"Central\n Interior and Appalachian Floodplain Systems\\\"},{\\\"values\\\":[198],\\\"summary\\\":\\\"Central\n Interior and Appalachian Riparian Systems\\\"},{\\\"values\\\":[199],\\\"summary\\\":\\\"Laurentian-Acadian\n Floodplain Systems\\\"},{\\\"values\\\":[200],\\\"summary\\\":\\\"Ozark-Ouachita Riparian\\\"},{\\\"values\\\":[201],\\\"summary\\\":\\\"South-Central\n Interior Large Floodplain\\\"},{\\\"values\\\":[202],\\\"summary\\\":\\\"South-Central\n Interior Large Floodplain - Forest Modifier\\\"},{\\\"values\\\":[203],\\\"summary\\\":\\\"South-Central\n Interior Small Stream and Riparian\\\"},{\\\"values\\\":[204],\\\"summary\\\":\\\"North-Central\n Interior and Appalachian Rich Swamp\\\"},{\\\"values\\\":[205],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[206],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[207],\\\"summary\\\":\\\"Laurentian-Acadian\n Swamp Systems\\\"},{\\\"values\\\":[208],\\\"summary\\\":\\\"North-Central Interior Wet\n Flatwoods\\\"},{\\\"values\\\":[209],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[210],\\\"summary\\\":\\\"South-Central\n Interior / Upper Coastal Plain Wet Flatwoods\\\"},{\\\"values\\\":[211],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[212],\\\"summary\\\":\\\"Southern\n Piedmont/Ridge and Valley Upland Depression Swamp\\\"},{\\\"values\\\":[213],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Blackwater Stream Floodplain Forest - Forest Modifier\\\"},{\\\"values\\\":[214],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Brownwater Stream Floodplain Forest\\\"},{\\\"values\\\":[215],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Northern Tidal Wooded Swamp\\\"},{\\\"values\\\":[216],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Small Blackwater River Floodplain Forest\\\"},{\\\"values\\\":[217],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Small Brownwater River Floodplain Forest\\\"},{\\\"values\\\":[218],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Southern Tidal Wooded Swamp\\\"},{\\\"values\\\":[219],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Large River Floodplain Forest - Forest Modifier\\\"},{\\\"values\\\":[220],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Small Stream and River Floodplain Forest\\\"},{\\\"values\\\":[221],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Tidal Wooded Swamp\\\"},{\\\"values\\\":[222],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[223],\\\"summary\\\":\\\"Southeastern\n Great Plains Riparian Forest\\\"},{\\\"values\\\":[224],\\\"summary\\\":\\\"Southeastern\n Great Plains Floodplain Forest\\\"},{\\\"values\\\":[225],\\\"summary\\\":\\\"Mississippi\n River Bottomland Depression\\\"},{\\\"values\\\":[226],\\\"summary\\\":\\\"Mississippi\n River Floodplain and Riparian Forest\\\"},{\\\"values\\\":[227],\\\"summary\\\":\\\"Mississippi\n River Low Floodplain (Bottomland) Forest\\\"},{\\\"values\\\":[228],\\\"summary\\\":\\\"Mississippi\n River Riparian Forest\\\"},{\\\"values\\\":[229],\\\"summary\\\":\\\"Red River Large Floodplain\n Forest\\\"},{\\\"values\\\":[230],\\\"summary\\\":\\\"Southern Coastal Plain Blackwater\n River Floodplain Forest\\\"},{\\\"values\\\":[231],\\\"summary\\\":\\\"Southern Piedmont\n Large Floodplain Forest - Forest Modifier\\\"},{\\\"values\\\":[232],\\\"summary\\\":\\\"Southern\n Piedmont Small Floodplain and Riparian Forest\\\"},{\\\"values\\\":[233],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Large River Floodplain Forest\\\"},{\\\"values\\\":[234],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Near-Coast Large River Swamp\\\"},{\\\"values\\\":[235],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Small Stream and River Forest\\\"},{\\\"values\\\":[236],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Streamhead Seepage Swamp - Pocosin - and Baygall\\\"},{\\\"values\\\":[237],\\\"summary\\\":\\\"Gulf\n and Atlantic Coastal Plain Swamp Systems\\\"},{\\\"values\\\":[238],\\\"summary\\\":\\\"Southern\n Coastal Plain Hydric Hammock\\\"},{\\\"values\\\":[239],\\\"summary\\\":\\\"Southern Coastal\n Plain Seepage Swamp and Baygall\\\"},{\\\"values\\\":[240],\\\"summary\\\":\\\"West Gulf\n Coastal Plain Seepage Swamp and Baygall\\\"},{\\\"values\\\":[241],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Taxodium/Nyssa\n Modifier\\\"},{\\\"values\\\":[242],\\\"summary\\\":\\\"Atlantic Coastal Plain Nonriverine\n Swamp and Wet Hardwood Forest - Oak Dominated Modifier\\\"},{\\\"values\\\":[243],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Southern Loblolly-Hardwood Flatwoods\\\"},{\\\"values\\\":[244],\\\"summary\\\":\\\"Lower\n Mississippi River Bottomland Depressions - Forest Modifier\\\"},{\\\"values\\\":[245],\\\"summary\\\":\\\"Lower\n Mississippi River Flatwoods\\\"},{\\\"values\\\":[246],\\\"summary\\\":\\\"Northern Atlantic\n Coastal Plain Basin Swamp and Wet Hardwood Forest\\\"},{\\\"values\\\":[247],\\\"summary\\\":\\\"Southern\n Coastal Plain Nonriverine Basin Swamp\\\"},{\\\"values\\\":[248],\\\"summary\\\":\\\"Southern\n Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier\\\"},{\\\"values\\\":[249],\\\"summary\\\":\\\"Southern\n Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier\\\"},{\\\"values\\\":[250],\\\"summary\\\":\\\"Southern\n Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier\\\"},{\\\"values\\\":[251],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods\\\"},{\\\"values\\\":[252],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Pine-Hardwood Flatwoods\\\"},{\\\"values\\\":[253],\\\"summary\\\":\\\"Edwards\n Plateau Riparian\\\"},{\\\"values\\\":[254],\\\"summary\\\":\\\"Atlantic Coastal Plain\n Clay-Based Carolina Bay Forested Wetland\\\"},{\\\"values\\\":[255],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Clay-Based Carolina Bay Herbaceous Wetland\\\"},{\\\"values\\\":[256],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Southern Wet Pine Savanna and Flatwoods\\\"},{\\\"values\\\":[257],\\\"summary\\\":\\\"Central\n Atlantic Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\\\"},{\\\"values\\\":[258],\\\"summary\\\":\\\"Central\n Florida Pine Flatwoods\\\"},{\\\"values\\\":[259],\\\"summary\\\":\\\"East Gulf Coastal\n Plain Near-Coast Pine Flatwoods\\\"},{\\\"values\\\":[260],\\\"summary\\\":\\\"East Gulf\n Coastal Plain Near-Coast Pine Flatwoods - Open Understory Modifier\\\"},{\\\"values\\\":[261],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Near-Coast Pine Flatwoods - Scrub/Shrub Understory Modifier\\\"},{\\\"values\\\":[262],\\\"summary\\\":\\\"South\n Florida Pine Flatwoods\\\"},{\\\"values\\\":[263],\\\"summary\\\":\\\"Southern Coastal\n Plain Nonriverine Cypress Dome\\\"},{\\\"values\\\":[264],\\\"summary\\\":\\\"West Gulf\n Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\\\"},{\\\"values\\\":[265],\\\"summary\\\":\\\"Columbia\n Basin Foothill Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[266],\\\"summary\\\":\\\"Great\n Basin Foothill and Lower Montane Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[267],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[268],\\\"summary\\\":\\\"Northern\n Rocky Mountain Conifer Swamp\\\"},{\\\"values\\\":[269],\\\"summary\\\":\\\"Northern Rocky\n Mountain Lower Montane Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[270],\\\"summary\\\":\\\"Rocky\n Mountain Lower Montane Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[271],\\\"summary\\\":\\\"Rocky\n Mountain Montane Riparian Systems\\\"},{\\\"values\\\":[272],\\\"summary\\\":\\\"Rocky\n Mountain Subalpine-Montane Riparian Woodland\\\"},{\\\"values\\\":[273],\\\"summary\\\":\\\"North\n Pacific Hardwood-Conifer Swamp\\\"},{\\\"values\\\":[274],\\\"summary\\\":\\\"North Pacific\n Lowland Riparian Forest and Shrubland\\\"},{\\\"values\\\":[275],\\\"summary\\\":\\\"North\n Pacific Montane Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[276],\\\"summary\\\":\\\"North\n Pacific Shrub Swamp\\\"},{\\\"values\\\":[277],\\\"summary\\\":\\\"California Central\n Valley Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[278],\\\"summary\\\":\\\"Mediterranean\n California Foothill and Lower Montane Riparian Woodland\\\"},{\\\"values\\\":[279],\\\"summary\\\":\\\"Mediterranean\n California Serpentine Foothill and Lower Montane Riparian Woodland and Seep\\\"},{\\\"values\\\":[280],\\\"summary\\\":\\\"North\n American Warm Desert Lower Montane Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[281],\\\"summary\\\":\\\"North\n American Warm Desert Riparian Systems\\\"},{\\\"values\\\":[282],\\\"summary\\\":\\\"North\n American Warm Desert Riparian Woodland and Shrubland\\\"},{\\\"values\\\":[283],\\\"summary\\\":\\\"Tamaulipan\n Floodplain\\\"},{\\\"values\\\":[284],\\\"summary\\\":\\\"Tamaulipan Riparian Systems\\\"},{\\\"values\\\":[285],\\\"summary\\\":\\\"Boreal\n Aspen-Birch Forest\\\"},{\\\"values\\\":[286],\\\"summary\\\":\\\"Boreal Jack Pine-Black\n Spruce Forest\\\"},{\\\"values\\\":[287],\\\"summary\\\":\\\"Boreal White Spruce-Fir-Hardwood\n Forest\\\"},{\\\"values\\\":[288],\\\"summary\\\":\\\"Boreal-Laurentian Conifer Acidic\n Swamp and Treed Poor Fen\\\"},{\\\"values\\\":[289],\\\"summary\\\":\\\"Eastern Boreal\n Floodplain\\\"},{\\\"values\\\":[290],\\\"summary\\\":\\\"South Florida Shell Hash Beach\\\"},{\\\"values\\\":[291],\\\"summary\\\":\\\"Southeast\n Florida Beach\\\"},{\\\"values\\\":[292],\\\"summary\\\":\\\"Southwest Florida Beach\\\"},{\\\"values\\\":[293],\\\"summary\\\":\\\"South\n Florida Everglades Sawgrass Marsh\\\"},{\\\"values\\\":[294],\\\"summary\\\":\\\"South\n Florida Freshwater Slough and Gator Hole\\\"},{\\\"values\\\":[295],\\\"summary\\\":\\\"South\n Florida Wet Marl Prairie\\\"},{\\\"values\\\":[296],\\\"summary\\\":\\\"California Maritime\n Chaparral\\\"},{\\\"values\\\":[297],\\\"summary\\\":\\\"California Mesic Chaparral\\\"},{\\\"values\\\":[298],\\\"summary\\\":\\\"California\n Xeric Serpentine Chaparral\\\"},{\\\"values\\\":[299],\\\"summary\\\":\\\"Klamath-Siskiyou\n Xeromorphic Serpentine Savanna and Chaparral\\\"},{\\\"values\\\":[300],\\\"summary\\\":\\\"Mediterranean\n California Mesic Serpentine Woodland and Chaparral\\\"},{\\\"values\\\":[301],\\\"summary\\\":\\\"Northern\n and Central California Dry-Mesic Chaparral\\\"},{\\\"values\\\":[302],\\\"summary\\\":\\\"Southern\n California Dry-Mesic Chaparral\\\"},{\\\"values\\\":[303],\\\"summary\\\":\\\"Southern\n California Coastal Scrub\\\"},{\\\"values\\\":[304],\\\"summary\\\":\\\"California Central\n Valley and Southern Coastal Grassland\\\"},{\\\"values\\\":[305],\\\"summary\\\":\\\"California\n Mesic Serpentine Grassland\\\"},{\\\"values\\\":[306],\\\"summary\\\":\\\"Columbia Basin\n Foothill and Canyon Dry Grassland\\\"},{\\\"values\\\":[307],\\\"summary\\\":\\\"Columbia\n Basin Palouse Prairie\\\"},{\\\"values\\\":[308],\\\"summary\\\":\\\"North Pacific Alpine\n and Subalpine Dry Grassland\\\"},{\\\"values\\\":[309],\\\"summary\\\":\\\"North Pacific\n Montane Grassland\\\"},{\\\"values\\\":[310],\\\"summary\\\":\\\"North Pacific Montane\n Shrubland\\\"},{\\\"values\\\":[311],\\\"summary\\\":\\\"Northern Rocky Mountain Lower\n Montane, Foothill and Valley Grassland\\\"},{\\\"values\\\":[312],\\\"summary\\\":\\\"Northern\n Rocky Mountain Montane-Foothill Deciduous Shrubland\\\"},{\\\"values\\\":[313],\\\"summary\\\":\\\"Northern\n Rocky Mountain Subalpine Deciduous Shrubland\\\"},{\\\"values\\\":[314],\\\"summary\\\":\\\"Northern\n Rocky Mountain Subalpine-Upper Montane Grassland\\\"},{\\\"values\\\":[315],\\\"summary\\\":\\\"Southern\n Rocky Mountain Montane-Subalpine Grassland\\\"},{\\\"values\\\":[316],\\\"summary\\\":\\\"Rocky\n Mountain Gambel Oak-Mixed Montane Shrubland\\\"},{\\\"values\\\":[317],\\\"summary\\\":\\\"Rocky\n Mountain Lower Montane-Foothill Shrubland\\\"},{\\\"values\\\":[318],\\\"summary\\\":\\\"California\n Northern Coastal Grassland\\\"},{\\\"values\\\":[319],\\\"summary\\\":\\\"North Pacific\n Herbaceous Bald and Bluff\\\"},{\\\"values\\\":[320],\\\"summary\\\":\\\"North Pacific\n Hypermaritime Shrub and Herbaceous Headland\\\"},{\\\"values\\\":[321],\\\"summary\\\":\\\"Willamette\n Valley Upland Prairie and Savanna\\\"},{\\\"values\\\":[322],\\\"summary\\\":\\\"Mediterranean\n California Subalpine Meadow\\\"},{\\\"values\\\":[323],\\\"summary\\\":\\\"Rocky Mountain\n Subalpine-Montane Mesic Meadow\\\"},{\\\"values\\\":[324],\\\"summary\\\":\\\"Central\n Mixedgrass Prairie\\\"},{\\\"values\\\":[325],\\\"summary\\\":\\\"Northwestern Great Plains\n Mixedgrass Prairie\\\"},{\\\"values\\\":[326],\\\"summary\\\":\\\"Western Great Plains\n Foothill and Piedmont Grassland\\\"},{\\\"values\\\":[327],\\\"summary\\\":\\\"Western\n Great Plains Tallgrass Prairie\\\"},{\\\"values\\\":[328],\\\"summary\\\":\\\"Western\n Great Plains Sand Prairie\\\"},{\\\"values\\\":[329],\\\"summary\\\":\\\"Western Great\n Plains Sandhill Steppe\\\"},{\\\"values\\\":[330],\\\"summary\\\":\\\"Western Great Plains\n Mesquite Woodland and Shrubland\\\"},{\\\"values\\\":[331],\\\"summary\\\":\\\"Western\n Great Plains Shortgrass Prairie\\\"},{\\\"values\\\":[332],\\\"summary\\\":\\\"Arkansas\n Valley Prairie and Woodland\\\"},{\\\"values\\\":[333],\\\"summary\\\":\\\"Central Tallgrass\n Prairie\\\"},{\\\"values\\\":[334],\\\"summary\\\":\\\"North-Central Interior Oak Savanna\\\"},{\\\"values\\\":[335],\\\"summary\\\":\\\"North-Central\n Interior Sand and Gravel Tallgrass Prairie\\\"},{\\\"values\\\":[336],\\\"summary\\\":\\\"North-Central\n Oak Barrens\\\"},{\\\"values\\\":[337],\\\"summary\\\":\\\"Northern Tallgrass Prairie\\\"},{\\\"values\\\":[338],\\\"summary\\\":\\\"Southeastern\n Great Plains Tallgrass Prairie\\\"},{\\\"values\\\":[339],\\\"summary\\\":\\\"Texas Blackland\n Tallgrass Prairie\\\"},{\\\"values\\\":[340],\\\"summary\\\":\\\"Texas-Louisiana Coastal\n Prairie\\\"},{\\\"values\\\":[341],\\\"summary\\\":\\\"Central Appalachian Pine-Oak Rocky\n Woodland\\\"},{\\\"values\\\":[342],\\\"summary\\\":\\\"Southern Appalachian Grass and\n Shrub Bald\\\"},{\\\"values\\\":[343],\\\"summary\\\":\\\"Southern Appalachian Grass and\n Shrub Bald - Herbaceous Modifier\\\"},{\\\"values\\\":[344],\\\"summary\\\":\\\"Southern\n Appalachian Grass and Shrub Bald - Shrub Modifier\\\"},{\\\"values\\\":[345],\\\"summary\\\":\\\"Central\n Appalachian Alkaline Glade and Woodland\\\"},{\\\"values\\\":[346],\\\"summary\\\":\\\"Central\n Interior Highlands Calcareous Glade and Barrens\\\"},{\\\"values\\\":[347],\\\"summary\\\":\\\"Central\n Interior Highlands Dry Acidic Glade and Barrens\\\"},{\\\"values\\\":[348],\\\"summary\\\":\\\"Cumberland\n Sandstone Glade and Barrens\\\"},{\\\"values\\\":[349],\\\"summary\\\":\\\"Great Lakes\n Alvar\\\"},{\\\"values\\\":[350],\\\"summary\\\":\\\"Nashville Basin Limestone Glade\\\"},{\\\"values\\\":[351],\\\"summary\\\":\\\"Southern\n Ridge and Valley / Cumberland Dry Calcareous Forest\\\"},{\\\"values\\\":[352],\\\"summary\\\":\\\"Southern\n Piedmont Glade and Barrens\\\"},{\\\"values\\\":[353],\\\"summary\\\":\\\"East Gulf Coastal\n Plain Black Belt Calcareous Prairie and Woodland - Herbaceous Modifier\\\"},{\\\"values\\\":[354],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Jackson Prairie and Woodland\\\"},{\\\"values\\\":[355],\\\"summary\\\":\\\"Eastern\n Highland Rim Prairie and Barrens - Dry Modifier\\\"},{\\\"values\\\":[356],\\\"summary\\\":\\\"Coahuilan\n Chaparral\\\"},{\\\"values\\\":[357],\\\"summary\\\":\\\"Madrean Oriental Chaparral\\\"},{\\\"values\\\":[358],\\\"summary\\\":\\\"Mogollon\n Chaparral\\\"},{\\\"values\\\":[359],\\\"summary\\\":\\\"Sonora-Mojave Semi-Desert Chaparral\\\"},{\\\"values\\\":[360],\\\"summary\\\":\\\"California\n Montane Woodland and Chaparral\\\"},{\\\"values\\\":[361],\\\"summary\\\":\\\"Great Basin\n Semi-Desert Chaparral\\\"},{\\\"values\\\":[362],\\\"summary\\\":\\\"Florida Dry Prairie\\\"},{\\\"values\\\":[363],\\\"summary\\\":\\\"Florida\n Peninsula Inland Scrub\\\"},{\\\"values\\\":[364],\\\"summary\\\":\\\"West Gulf Coastal\n Plain Catahoula Barrens\\\"},{\\\"values\\\":[365],\\\"summary\\\":\\\"West Gulf Coastal\n Plain Nepheline Syenite Glade\\\"},{\\\"values\\\":[366],\\\"summary\\\":\\\"East Gulf\n Coastal Plain Jackson Plain Dry Flatwoods - Open Understory Modifier\\\"},{\\\"values\\\":[367],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Northern Calcareous Prairie\\\"},{\\\"values\\\":[368],\\\"summary\\\":\\\"West\n Gulf Coastal Plain Southern Calcareous Prairie\\\"},{\\\"values\\\":[369],\\\"summary\\\":\\\"Acadian-Appalachian\n Subalpine Woodland and Heath-Krummholz\\\"},{\\\"values\\\":[370],\\\"summary\\\":\\\"Atlantic\n and Gulf Coastal Plain Interdunal Wetland\\\"},{\\\"values\\\":[371],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Southern Dune and Maritime Grassland\\\"},{\\\"values\\\":[372],\\\"summary\\\":\\\"Central\n and Upper Texas Coast Dune and Coastal Grassland\\\"},{\\\"values\\\":[373],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Dune and Coastal Grassland\\\"},{\\\"values\\\":[374],\\\"summary\\\":\\\"Great\n Lakes Dune\\\"},{\\\"values\\\":[375],\\\"summary\\\":\\\"Northern Atlantic Coastal Plain\n Dune and Swale\\\"},{\\\"values\\\":[376],\\\"summary\\\":\\\"Northern Atlantic Coastal\n Plain Heathland and Grassland\\\"},{\\\"values\\\":[377],\\\"summary\\\":\\\"South Texas\n Dune and Coastal Grassland\\\"},{\\\"values\\\":[378],\\\"summary\\\":\\\"South Texas\n Sand Sheet Grassland\\\"},{\\\"values\\\":[379],\\\"summary\\\":\\\"Southwest Florida\n Dune and Coastal Grassland\\\"},{\\\"values\\\":[380],\\\"summary\\\":\\\"North Pacific\n Coastal Cliff and Bluff\\\"},{\\\"values\\\":[381],\\\"summary\\\":\\\"North Pacific Maritime\n Coastal Sand Dune and Strand\\\"},{\\\"values\\\":[382],\\\"summary\\\":\\\"Northern California\n Coastal Scrub\\\"},{\\\"values\\\":[383],\\\"summary\\\":\\\"Mediterranean California\n Coastal Bluff\\\"},{\\\"values\\\":[384],\\\"summary\\\":\\\"Mediterranean California\n Northern Coastal Dune\\\"},{\\\"values\\\":[385],\\\"summary\\\":\\\"Mediterranean California\n Southern Coastal Dune\\\"},{\\\"values\\\":[386],\\\"summary\\\":\\\"Atlantic Coastal\n Plain Northern Sandy Beach\\\"},{\\\"values\\\":[387],\\\"summary\\\":\\\"Atlantic Coastal\n Plain Sea Island Beach\\\"},{\\\"values\\\":[388],\\\"summary\\\":\\\"Atlantic Coastal\n Plain Southern Beach\\\"},{\\\"values\\\":[389],\\\"summary\\\":\\\"Florida Panhandle\n Beach Vegetation\\\"},{\\\"values\\\":[390],\\\"summary\\\":\\\"Louisiana Beach\\\"},{\\\"values\\\":[391],\\\"summary\\\":\\\"Northern\n Atlantic Coastal Plain Sandy Beach\\\"},{\\\"values\\\":[392],\\\"summary\\\":\\\"Texas\n Coastal Bend Beach\\\"},{\\\"values\\\":[393],\\\"summary\\\":\\\"Upper Texas Coast Beach\\\"},{\\\"values\\\":[394],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[395],\\\"summary\\\":\\\"Mediterranean\n California Serpentine Fen\\\"},{\\\"values\\\":[396],\\\"summary\\\":\\\"Mediterranean\n California Subalpine-Montane Fen\\\"},{\\\"values\\\":[397],\\\"summary\\\":\\\"North\n Pacific Bog and Fen\\\"},{\\\"values\\\":[398],\\\"summary\\\":\\\"Rocky Mountain Subalpine-Montane\n Fen\\\"},{\\\"values\\\":[399],\\\"summary\\\":\\\"Atlantic Coastal Plain Peatland Pocosin\\\"},{\\\"values\\\":[400],\\\"summary\\\":\\\"Southern\n and Central Appalachian Bog and Fen\\\"},{\\\"values\\\":[401],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Central Fresh-Oligohaline Tidal Marsh\\\"},{\\\"values\\\":[402],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Embayed Region Tidal Freshwater Marsh\\\"},{\\\"values\\\":[403],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Northern Fresh and Oligohaline Tidal Marsh\\\"},{\\\"values\\\":[404],\\\"summary\\\":\\\"Florida\n Big Bend Fresh-Oligohaline Tidal Marsh\\\"},{\\\"values\\\":[405],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Depression Pondshore\\\"},{\\\"values\\\":[406],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Large Natural Lakeshore\\\"},{\\\"values\\\":[407],\\\"summary\\\":\\\"Central\n Florida Herbaceous Pondshore\\\"},{\\\"values\\\":[408],\\\"summary\\\":\\\"Central Florida\n Herbaceous Seep\\\"},{\\\"values\\\":[409],\\\"summary\\\":\\\"East Gulf Coastal Plain\n Savanna and Wet Prairie\\\"},{\\\"values\\\":[410],\\\"summary\\\":\\\"East Gulf Coastal\n Plain Depression Pondshore\\\"},{\\\"values\\\":[411],\\\"summary\\\":\\\"Floridian Highlands\n Freshwater Marsh\\\"},{\\\"values\\\":[412],\\\"summary\\\":\\\"Southern Coastal Plain\n Herbaceous Seepage Bog\\\"},{\\\"values\\\":[413],\\\"summary\\\":\\\"Southern Coastal\n Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier\\\"},{\\\"values\\\":[414],\\\"summary\\\":\\\"Southern\n Coastal Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier\\\"},{\\\"values\\\":[415],\\\"summary\\\":\\\"Texas-Louisiana\n Coastal Prairie Slough\\\"},{\\\"values\\\":[416],\\\"summary\\\":\\\"Central Interior\n and Appalachian Shrub-Herbaceous Wetland Systems\\\"},{\\\"values\\\":[417],\\\"summary\\\":\\\"Great\n Lakes Coastal Marsh Systems\\\"},{\\\"values\\\":[418],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[419],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[420],\\\"summary\\\":\\\"Laurentian-Acadian\n Shrub-Herbaceous Wetland Systems\\\"},{\\\"values\\\":[421],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[422],\\\"summary\\\":\\\"Eastern\n Great Plains Wet Meadow, Prairie and Marsh\\\"},{\\\"values\\\":[423],\\\"summary\\\":\\\"Great\n Lakes Wet-Mesic Lakeplain Prairie\\\"},{\\\"values\\\":[424],\\\"summary\\\":\\\"Great\n Plains Prairie Pothole\\\"},{\\\"values\\\":[425],\\\"summary\\\":\\\"Western Great Plains\n Closed Depression Wetland\\\"},{\\\"values\\\":[426],\\\"summary\\\":\\\"Western Great\n Plains Depressional Wetland Systems\\\"},{\\\"values\\\":[427],\\\"summary\\\":\\\"Western\n Great Plains Open Freshwater Depression Wetland\\\"},{\\\"values\\\":[428],\\\"summary\\\":\\\"Cumberland\n Riverscour\\\"},{\\\"values\\\":[429],\\\"summary\\\":\\\"Inter-Mountain Basins Interdunal\n Swale Wetland\\\"},{\\\"values\\\":[430],\\\"summary\\\":\\\"North Pacific Avalanche Chute\n Shrubland\\\"},{\\\"values\\\":[431],\\\"summary\\\":\\\"North Pacific Intertidal Freshwater\n Wetland\\\"},{\\\"values\\\":[432],\\\"summary\\\":\\\"Temperate Pacific Freshwater Emergent\n Marsh\\\"},{\\\"values\\\":[433],\\\"summary\\\":\\\"Temperate Pacific Freshwater Mudflat\\\"},{\\\"values\\\":[434],\\\"summary\\\":\\\"Columbia\n Plateau Vernal Pool\\\"},{\\\"values\\\":[435],\\\"summary\\\":\\\"Northern California\n Claypan Vernal Pool\\\"},{\\\"values\\\":[436],\\\"summary\\\":\\\"Northern Rocky Mountain\n Wooded Vernal Pool\\\"},{\\\"values\\\":[437],\\\"summary\\\":\\\"Columbia Plateau Silver\n Sagebrush Seasonally Flooded Shrub-Steppe\\\"},{\\\"values\\\":[438],\\\"summary\\\":\\\"Rocky\n Mountain Alpine-Montane Wet Meadow\\\"},{\\\"values\\\":[439],\\\"summary\\\":\\\"Rocky\n Mountain Subalpine-Montane Riparian Shrubland\\\"},{\\\"values\\\":[440],\\\"summary\\\":\\\"Temperate\n Pacific Montane Wet Meadow\\\"},{\\\"values\\\":[441],\\\"summary\\\":\\\"Willamette Valley\n Wet Prairie\\\"},{\\\"values\\\":[442],\\\"summary\\\":\\\"Chihuahuan-Sonoran Desert Bottomland\n and Swale Grassland\\\"},{\\\"values\\\":[443],\\\"summary\\\":\\\"North American Arid\n West Emergent Marsh\\\"},{\\\"values\\\":[444],\\\"summary\\\":\\\"North American Warm\n Desert Riparian Mesquite Bosque\\\"},{\\\"values\\\":[445],\\\"summary\\\":\\\"Western\n Great Plains Saline Depression Wetland\\\"},{\\\"values\\\":[446],\\\"summary\\\":\\\"Acadian\n Salt Marsh and Estuary Systems\\\"},{\\\"values\\\":[447],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Central Salt and Brackish Tidal Marsh\\\"},{\\\"values\\\":[448],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Embayed Region Tidal Salt and Brackish Marsh\\\"},{\\\"values\\\":[449],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Indian River Lagoon Tidal Marsh\\\"},{\\\"values\\\":[450],\\\"summary\\\":\\\"Atlantic\n Coastal Plain Northern Tidal Salt Marsh\\\"},{\\\"values\\\":[451],\\\"summary\\\":\\\"Florida\n Big Bend Salt-Brackish Tidal Marsh\\\"},{\\\"values\\\":[452],\\\"summary\\\":\\\"Gulf\n and Atlantic Coastal Plain Tidal Marsh Systems\\\"},{\\\"values\\\":[453],\\\"summary\\\":\\\"Mississippi\n Sound Salt and Brackish Tidal Marsh\\\"},{\\\"values\\\":[454],\\\"summary\\\":\\\"Texas\n Saline Coastal Prairie\\\"},{\\\"values\\\":[455],\\\"summary\\\":\\\"Temperate Pacific\n Tidal Salt and Brackish Marsh\\\"},{\\\"values\\\":[456],\\\"summary\\\":\\\"Inter-Mountain\n Basins Alkaline Closed Depression\\\"},{\\\"values\\\":[457],\\\"summary\\\":\\\"Inter-Mountain\n Basins Greasewood Flat\\\"},{\\\"values\\\":[458],\\\"summary\\\":\\\"Inter-Mountain Basins\n Playa\\\"},{\\\"values\\\":[459],\\\"summary\\\":\\\"North American Warm Desert Playa\\\"},{\\\"values\\\":[460],\\\"summary\\\":\\\"Apacherian-Chihuahuan\n Mesquite Upland Scrub\\\"},{\\\"values\\\":[461],\\\"summary\\\":\\\"Apacherian-Chihuahuan\n Semi-Desert Grassland and Steppe\\\"},{\\\"values\\\":[462],\\\"summary\\\":\\\"Chihuahuan\n Creosotebush, Mixed Desert and Thorn Scrub\\\"},{\\\"values\\\":[463],\\\"summary\\\":\\\"Chihuahuan\n Gypsophilous Grassland and Steppe\\\"},{\\\"values\\\":[464],\\\"summary\\\":\\\"Chihuahuan\n Loamy Plains Desert Grassland\\\"},{\\\"values\\\":[465],\\\"summary\\\":\\\"Chihuahuan\n Mixed Desert and Thorn Scrub\\\"},{\\\"values\\\":[466],\\\"summary\\\":\\\"Chihuahuan\n Sandy Plains Semi-Desert Grassland\\\"},{\\\"values\\\":[467],\\\"summary\\\":\\\"Chihuahuan\n Stabilized Coppice Dune and Sand Flat Scrub\\\"},{\\\"values\\\":[468],\\\"summary\\\":\\\"Chihuahuan\n Succulent Desert Scrub\\\"},{\\\"values\\\":[469],\\\"summary\\\":\\\"Madrean Juniper\n Savanna\\\"},{\\\"values\\\":[470],\\\"summary\\\":\\\"Mojave Mid-Elevation Mixed Desert\n Scrub\\\"},{\\\"values\\\":[471],\\\"summary\\\":\\\"North American Warm Desert Active\n and Stabilized Dune\\\"},{\\\"values\\\":[472],\\\"summary\\\":\\\"Sonora-Mojave Creosotebush-White\n Bursage Desert Scrub\\\"},{\\\"values\\\":[473],\\\"summary\\\":\\\"Sonoran Mid-Elevation\n Desert Scrub\\\"},{\\\"values\\\":[474],\\\"summary\\\":\\\"Sonoran Paloverde-Mixed Cacti\n Desert Scrub\\\"},{\\\"values\\\":[475],\\\"summary\\\":\\\"Chihuahuan Mixed Salt Desert\n Scrub\\\"},{\\\"values\\\":[476],\\\"summary\\\":\\\"Sonora-Mojave Mixed Salt Desert Scrub\\\"},{\\\"values\\\":[477],\\\"summary\\\":\\\"North\n American Warm Desert Wash\\\"},{\\\"values\\\":[478],\\\"summary\\\":\\\"South Texas Lomas\\\"},{\\\"values\\\":[479],\\\"summary\\\":\\\"Tamaulipan\n Calcareous Thornscrub\\\"},{\\\"values\\\":[480],\\\"summary\\\":\\\"Tamaulipan Clay Grassland\\\"},{\\\"values\\\":[481],\\\"summary\\\":\\\"Tamaulipan\n Mesquite Upland Scrub\\\"},{\\\"values\\\":[482],\\\"summary\\\":\\\"Tamaulipan Mixed\n Deciduous Thornscrub\\\"},{\\\"values\\\":[483],\\\"summary\\\":\\\"Tamaulipan Savanna\n Grassland\\\"},{\\\"values\\\":[484],\\\"summary\\\":\\\"Inter-Mountain Basins Mat Saltbush\n Shrubland\\\"},{\\\"values\\\":[485],\\\"summary\\\":\\\"Inter-Mountain Basins Mixed Salt\n Desert Scrub\\\"},{\\\"values\\\":[486],\\\"summary\\\":\\\"Inter-Mountain Basins Wash\\\"},{\\\"values\\\":[487],\\\"summary\\\":\\\"Columbia\n Plateau Steppe and Grassland\\\"},{\\\"values\\\":[488],\\\"summary\\\":\\\"Great Basin\n Xeric Mixed Sagebrush Shrubland\\\"},{\\\"values\\\":[489],\\\"summary\\\":\\\"Inter-Mountain\n Basins Big Sagebrush Shrubland\\\"},{\\\"values\\\":[490],\\\"summary\\\":\\\"Inter-Mountain\n Basins Big Sagebrush Steppe\\\"},{\\\"values\\\":[491],\\\"summary\\\":\\\"Inter-Mountain\n Basins Montane Sagebrush Steppe\\\"},{\\\"values\\\":[492],\\\"summary\\\":\\\"Colorado\n Plateau Mixed Low Sagebrush Shrubland\\\"},{\\\"values\\\":[493],\\\"summary\\\":\\\"Columbia\n Plateau Low Sagebrush Steppe\\\"},{\\\"values\\\":[494],\\\"summary\\\":\\\"Columbia Plateau\n Scabland Shrubland\\\"},{\\\"values\\\":[495],\\\"summary\\\":\\\"Wyoming Basins Dwarf\n Sagebrush Shrubland and Steppe\\\"},{\\\"values\\\":[496],\\\"summary\\\":\\\"Colorado\n Plateau Blackbrush-Mormon-tea Shrubland\\\"},{\\\"values\\\":[497],\\\"summary\\\":\\\"Inter-Mountain\n Basins Semi-Desert Grassland\\\"},{\\\"values\\\":[498],\\\"summary\\\":\\\"Inter-Mountain\n Basins Semi-Desert Shrub Steppe\\\"},{\\\"values\\\":[499],\\\"summary\\\":\\\"Southern\n Colorado Plateau Sand Shrubland\\\"},{\\\"values\\\":[500],\\\"summary\\\":\\\"Acadian-Appalachian\n Alpine Tundra\\\"},{\\\"values\\\":[501],\\\"summary\\\":\\\"Rocky Mountain Alpine Dwarf-Shrubland\\\"},{\\\"values\\\":[502],\\\"summary\\\":\\\"Rocky\n Mountain Alpine Fell-Field\\\"},{\\\"values\\\":[503],\\\"summary\\\":\\\"Rocky Mountain\n Alpine Turf\\\"},{\\\"values\\\":[504],\\\"summary\\\":\\\"Mediterranean California Alpine\n Dry Tundra\\\"},{\\\"values\\\":[505],\\\"summary\\\":\\\"Mediterranean California Alpine\n Fell-Field\\\"},{\\\"values\\\":[506],\\\"summary\\\":\\\"North Pacific Dry and Mesic\n Alpine Dwarf-Shrubland, Fell-field and Meadow\\\"},{\\\"values\\\":[507],\\\"summary\\\":\\\"Rocky\n Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit\\\"},{\\\"values\\\":[508],\\\"summary\\\":\\\"Temperate\n Pacific Intertidal Mudflat\\\"},{\\\"values\\\":[509],\\\"summary\\\":\\\"Mediterranean\n California Eelgrass Bed\\\"},{\\\"values\\\":[510],\\\"summary\\\":\\\"North Pacific Maritime\n Eelgrass Bed\\\"},{\\\"values\\\":[511],\\\"summary\\\":\\\"South-Central Interior Large\n Floodplain - Herbaceous Modifier\\\"},{\\\"values\\\":[512],\\\"summary\\\":\\\"East Gulf\n Coastal Plain Large River Floodplain Forest - Herbaceous Modifier\\\"},{\\\"values\\\":[513],\\\"summary\\\":\\\"Temperate\n Pacific Freshwater Aquatic Bed\\\"},{\\\"values\\\":[514],\\\"summary\\\":\\\"Central\n California Coast Ranges Cliff and Canyon\\\"},{\\\"values\\\":[515],\\\"summary\\\":\\\"Mediterranean\n California Serpentine Barrens\\\"},{\\\"values\\\":[516],\\\"summary\\\":\\\"Southern\n California Coast Ranges Cliff and Canyon\\\"},{\\\"values\\\":[517],\\\"summary\\\":\\\"Central\n Interior Acidic Cliff and Talus\\\"},{\\\"values\\\":[518],\\\"summary\\\":\\\"Central\n Interior Calcareous Cliff and Talus\\\"},{\\\"values\\\":[519],\\\"summary\\\":\\\"East\n Gulf Coastal Plain Dry Chalk Bluff\\\"},{\\\"values\\\":[520],\\\"summary\\\":\\\"North-Central\n Appalachian Acidic Cliff and Talus\\\"},{\\\"values\\\":[521],\\\"summary\\\":\\\"North-Central\n Appalachian Circumneutral Cliff and Talus\\\"},{\\\"values\\\":[522],\\\"summary\\\":\\\"Southern\n Appalachian Montane Cliff\\\"},{\\\"values\\\":[523],\\\"summary\\\":\\\"Southern Interior\n Acid Cliff\\\"},{\\\"values\\\":[524],\\\"summary\\\":\\\"Southern Interior Calcareous\n Cliff\\\"},{\\\"values\\\":[525],\\\"summary\\\":\\\"Southern Piedmont Cliff\\\"},{\\\"values\\\":[526],\\\"summary\\\":\\\"Southern\n Appalachian Granitic Dome\\\"},{\\\"values\\\":[527],\\\"summary\\\":\\\"Southern Appalachian\n Rocky Summit\\\"},{\\\"values\\\":[528],\\\"summary\\\":\\\"Southern Piedmont Granite\n Flatrock\\\"},{\\\"values\\\":[529],\\\"summary\\\":\\\"Rocky Mountain Cliff, Canyon and\n Massive Bedrock\\\"},{\\\"values\\\":[530],\\\"summary\\\":\\\"Klamath-Siskiyou Cliff\n and Outcrop\\\"},{\\\"values\\\":[531],\\\"summary\\\":\\\"North Pacific Montane Massive\n Bedrock, Cliff and Talus\\\"},{\\\"values\\\":[532],\\\"summary\\\":\\\"North Pacific\n Serpentine Barren\\\"},{\\\"values\\\":[533],\\\"summary\\\":\\\"North Pacific Active\n Volcanic Rock and Cinder Land\\\"},{\\\"values\\\":[534],\\\"summary\\\":\\\"Sierra Nevada\n Cliff and Canyon\\\"},{\\\"values\\\":[535],\\\"summary\\\":\\\"Western Great Plains Badland\\\"},{\\\"values\\\":[536],\\\"summary\\\":\\\"Southwestern\n Great Plains Canyon\\\"},{\\\"values\\\":[537],\\\"summary\\\":\\\"Western Great Plains\n Cliff and Outcrop\\\"},{\\\"values\\\":[538],\\\"summary\\\":\\\"North American Warm Desert\n Badland\\\"},{\\\"values\\\":[539],\\\"summary\\\":\\\"North American Warm Desert Bedrock\n Cliff and Outcrop\\\"},{\\\"values\\\":[540],\\\"summary\\\":\\\"North American Warm Desert\n Pavement\\\"},{\\\"values\\\":[541],\\\"summary\\\":\\\"North American Warm Desert Volcanic\n Rockland\\\"},{\\\"values\\\":[542],\\\"summary\\\":\\\"Colorado Plateau Mixed Bedrock\n Canyon and Tableland\\\"},{\\\"values\\\":[543],\\\"summary\\\":\\\"Columbia Plateau Ash\n and Tuff Badland\\\"},{\\\"values\\\":[544],\\\"summary\\\":\\\"Geysers and Hot Springs\\\"},{\\\"values\\\":[545],\\\"summary\\\":\\\"Inter-Mountain\n Basins Active and Stabilized Dune\\\"},{\\\"values\\\":[546],\\\"summary\\\":\\\"Inter-Mountain\n Basins Cliff and Canyon\\\"},{\\\"values\\\":[547],\\\"summary\\\":\\\"Inter-Mountain\n Basins Shale Badland\\\"},{\\\"values\\\":[548],\\\"summary\\\":\\\"Inter-Mountain Basins\n Volcanic Rock and Cinder Land\\\"},{\\\"values\\\":[549],\\\"summary\\\":\\\"Rocky Mountain\n Alpine Bedrock and Scree\\\"},{\\\"values\\\":[550],\\\"summary\\\":\\\"Mediterranean\n California Alpine Bedrock and Scree\\\"},{\\\"values\\\":[551],\\\"summary\\\":\\\"North\n Pacific Alpine and Subalpine Bedrock and Scree\\\"},{\\\"values\\\":[552],\\\"summary\\\":\\\"Unconsolidated\n Shore\\\"},{\\\"values\\\":[553],\\\"summary\\\":\\\"Undifferentiated Barren Land\\\"},{\\\"values\\\":[554],\\\"summary\\\":\\\"North\n American Alpine Ice Field\\\"},{\\\"values\\\":[555],\\\"summary\\\":\\\"Orchards Vineyards\n and Other High Structure Agriculture\\\"},{\\\"values\\\":[556],\\\"summary\\\":\\\"Cultivated\n Cropland\\\"},{\\\"values\\\":[557],\\\"summary\\\":\\\"Pasture/Hay\\\"},{\\\"values\\\":[558],\\\"summary\\\":\\\"Introduced\n Upland Vegetation - Annual Grassland\\\"},{\\\"values\\\":[559],\\\"summary\\\":\\\"Introduced\n Upland Vegetation - Perennial Grassland and Forbland\\\"},{\\\"values\\\":[560],\\\"summary\\\":\\\"Modified/Managed\n Southern Tall Grassland\\\"},{\\\"values\\\":[561],\\\"summary\\\":\\\"Introduced Upland\n Vegetation - Shrub\\\"},{\\\"values\\\":[562],\\\"summary\\\":\\\"Introduced Riparian\n and Wetland Vegetation\\\"},{\\\"values\\\":[563],\\\"summary\\\":\\\"Introduced Upland\n Vegetation - Treed\\\"},{\\\"values\\\":[564],\\\"summary\\\":\\\"0\\\"},{\\\"values\\\":[565],\\\"summary\\\":\\\"Disturbed,\n Non-specific\\\"},{\\\"values\\\":[566],\\\"summary\\\":\\\"Recently Logged Areas\\\"},{\\\"values\\\":[567],\\\"summary\\\":\\\"Harvested\n Forest - Grass/Forb Regeneration\\\"},{\\\"values\\\":[568],\\\"summary\\\":\\\"Harvested\n Forest-Shrub Regeneration\\\"},{\\\"values\\\":[569],\\\"summary\\\":\\\"Harvested Forest\n - Northwestern Conifer Regeneration\\\"},{\\\"values\\\":[570],\\\"summary\\\":\\\"Recently\n Burned\\\"},{\\\"values\\\":[571],\\\"summary\\\":\\\"Recently burned grassland\\\"},{\\\"values\\\":[572],\\\"summary\\\":\\\"Recently\n burned shrubland\\\"},{\\\"values\\\":[573],\\\"summary\\\":\\\"Recently burned forest\\\"},{\\\"values\\\":[574],\\\"summary\\\":\\\"Disturbed/Successional\n - Grass/Forb Regeneration\\\"},{\\\"values\\\":[575],\\\"summary\\\":\\\"Disturbed/Successional\n - Shrub Regeneration\\\"},{\\\"values\\\":[576],\\\"summary\\\":\\\"Disturbed/Successional\n - Recently Chained Pinyon-Juniper\\\"},{\\\"values\\\":[577],\\\"summary\\\":\\\"Open\n Water (Aquaculture)\\\"},{\\\"values\\\":[578],\\\"summary\\\":\\\"Open Water (Brackish/Salt)\\\"},{\\\"values\\\":[579],\\\"summary\\\":\\\"Open\n Water (Fresh)\\\"},{\\\"values\\\":[580],\\\"summary\\\":\\\"Quarries, Mines, Gravel Pits\n and Oil Wells\\\"},{\\\"values\\\":[581],\\\"summary\\\":\\\"Developed, Open Space\\\"},{\\\"values\\\":[582],\\\"summary\\\":\\\"Developed,\n Low Intensity\\\"},{\\\"values\\\":[583],\\\"summary\\\":\\\"Developed, Medium Intensity\\\"},{\\\"values\\\":[584],\\\"summary\\\":\\\"Developed,\n High 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reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"MODIS\n Gross Primary Productivity 8-Day Gap-Filled\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-17A2HGF-061.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"MODIS\n Gross Primary Productivity 8-Day Gap-Filled thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/modis-17A2HGF-061.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet 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The Moderate Resolution Imaging Spectroradiometer (MODIS) data\n product includes information about GPP and Net Photosynthesis (PSN). The PSN\n band values are the GPP less the Maintenance Respiration (MR). The data product\n also contains a PSN Quality Control (QC) layer. The quality layer contains\n quality information for both the GPP and the PSN. This product will be generated\n at the end of each year when the entire yearly 8-day 15A2H is available. Hence,\n the gap-filled A2HGF is the improved 17, which has cleaned the poor-quality\n inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active\n Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel.\n If any LAI/FPAR pixel did not meet the quality screening criteria, its value\n is determined through linear interpolation. However, users cannot get this\n product in near-real time because it will be generated only at the end of\n a given year.\\\",\\\"item_assets\\\":{\\\"hdf\\\":{\\\"type\\\":\\\"application/x-hdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Source\n data containing all bands\\\"},\\\"Gpp_500m\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Gross Primary Productivity\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"kg\n C/m^2\\\",\\\"scale\\\":0.0001,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Federal\n Geographic Data Committee (FGDC) Metadata\\\"},\\\"PsnNet_500m\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Net\n Photosynthesis\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"kg C/m^2\\\",\\\"scale\\\":0.0001,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"Psn_QC_500m\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Quality\n control indicators\\\",\\\"raster:bands\\\":[{\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":500}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"modis\\\",\\\"msft:container\\\":\\\"modis-061\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.5067/MODIS/MOD17A2HGF.061\\\",\\\"citation\\\":\\\"Running,\n S., & Zhao, M. (2021). MODIS/Terra Gross Primary Productivity Gap-Filled\n 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes\n DAAC. https://doi.org/10.5067/MODIS/MOD17A2HGF.061\\\"},{\\\"doi\\\":\\\"10.5067/MODIS/MYD17A2HGF.061\\\",\\\"citation\\\":\\\"Running,\n S., & Zhao, M. (2021). MODIS/Aqua Gross Primary Productivity Gap-Filled\n 8-Day L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes\n DAAC. https://doi.org/10.5067/MODIS/MYD17A2HGF.061\\\"}],\\\"msft:storage_account\\\":\\\"modiseuwest\\\",\\\"msft:short_description\\\":\\\"MODIS\n Gross Primary Productivity 8-Day Gap-Filled\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"planet-nicfi-visual\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-visual/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/planet-nicfi-visual\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Participant\n License Agreement.\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Participant\n License Agreement.\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://www.planet.com/nicfi/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NICFI\n Program - Satellite Imagery and Monitoring | Planet\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://developers.planet.com/nicfi/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NICFI\n Program Resource Center\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/planet-nicfi-visual\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Planet-NICFI\n Basemaps (Visual)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/planet-nicfi-visual-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/planet-nicfi-visual.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-34.161818157002,180.0,30.145127179625]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2015-12-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Planet\\\",\\\"NICFI\\\",\\\"Satellite\\\",\\\"Tropics\\\",\\\"Imagery\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"http://planet.com\\\",\\\"name\\\":\\\"Planet\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"],\\\"description\\\":\\\"Contact\n Planet at [planet.com/contact-sales](https://www.planet.com/contact-sales/)\\\"},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[4.77],\\\"eo:bands\\\":[{\\\"name\\\":\\\"Red\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"visible\n red\\\"},{\\\"name\\\":\\\"Green\\\",\\\"common_name\\\":\\\"green\\\",\\\"description\\\":\\\"visible\n green\\\"},{\\\"name\\\":\\\"Blue\\\",\\\"common_name\\\":\\\"blue\\\",\\\"description\\\":\\\"visible\n blue\\\"}],\\\"planet-nicfi:cadence\\\":[\\\"biannual\\\",\\\"monthly\\\"],\\\"planet-nicfi:percent_covered\\\":{\\\"maximum\\\":100,\\\"minimum\\\":0}},\\\"description\\\":\\\"*Note:\n Assets in this collection are only available to winners of the [GEO-Microsoft\n Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528).\n Others wishing to use the data can sign up and access it from Planet at [https://www.planet.com/nicfi/](https://www.planet.com/nicfi/)\n and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\\\\n\\\\nThrough\n Norway\\u2019s International Climate & Forests Initiative (NICFI), users can\n access Planet\\u2019s high-resolution, analysis-ready mosaics of the world\\u2019s\n tropics in order to help reduce and reverse the loss of tropical forests,\n combat climate change, conserve biodiversity, and facilitate sustainable development.\\\\n\\\\nIn\n support of NICFI\\u2019s mission, you can use this data for a number of projects\n including, but not limited to:\\\\n\\\\n* Advance scientific research about the\n world\\u2019s tropical forests and the critical services they provide.\\\\n*\n Implement and improve policies for sustainable forest management and land\n use in developing tropical forest countries and jurisdictions.\\\\n* Increase\n transparency and accountability in the tropics.\\\\n* Protect and improve the\n rights of indigenous peoples and local communities in tropical forest countries.\\\\n*\n Innovate solutions towards reducing pressure on forests from global commodities\n and financial markets.\\\\n* In short, the primary purpose of the NICFI Program\n is to support reducing and reversing the loss of tropical forests, contributing\n to combating climate change, conserving biodiversity, contributing to forest\n regrowth, restoration, and enhancement, and facilitating sustainable development,\n all of which must be Non-Commercial Use.\\\\n\\\\nTo learn how more about the\n NICFI program, streaming and downloading basemaps please read the [NICFI Data\n Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\\\\n\\\\nThis\n collection contains both monthly and biannual mosaics. Biannual mosaics are\n available from December 2015 - August 2020. Monthly mosaics are available\n from September 2020. The STAC items include a `planet-nicfi:cadence` field\n indicating the type of mosaic.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Data\\\",\\\"description\\\":\\\"a\n 'true-colour' representation of spatially accurate data with minimized haze,\n illumination, and topographic effects\\\"},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Thumbnail\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"planet-nicfi\\\",\\\"msft:container\\\":\\\"nicfi\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"planet\\\",\\\"msft:short_description\\\":\\\"Planet's\n high-resolution, analysis-ready mosaics of the world's tropics\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"gbif\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gbif\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.gbif.org/terms\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"GBIF\n Terms of Use\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/gbif\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Global\n Biodiversity Information Facility (GBIF)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gbif.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"title\\\":\\\"Forest\n Inventory and Analysis\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/gbif.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2021-04-13T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"GBIF\\\",\\\"Biodiversity\\\",\\\"Species\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.gbif.org/\\\",\\\"name\\\":\\\"Global\n Biodiversity Information Facility\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"description\\\":\\\"The\n [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is\n an international network and data infrastructure funded by the world's governments,\n providing global data that document the occurrence of species. GBIF currently\n integrates datasets documenting over 1.6 billion species occurrences.\\\\n\\\\nThe\n GBIF occurrence dataset combines data from a wide array of sources, including\n specimen-related data from natural history museums, observations from citizen\n science networks, and automated environmental surveys. While these data are\n constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots\n are taken and made available here. \\\\n\\\\nData are stored in [Parquet](https://parquet.apache.org/)\n format; the Parquet file schema is described below. Most field names correspond\n to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and\n have been interpreted by GBIF's systems to align taxonomy, location, dates,\n etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\\\\n\\\\nPlease\n refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines)\n for information about how to cite GBIF data in publications.. For analyses\n using the whole dataset, please use the following citation:\\\\n\\\\n> GBIF.org\n ([Date]) GBIF Occurrence Data [DOI of dataset]\\\\n\\\\nFor analyses where data\n are significantly filtered, please track the datasetKeys used and use a \\\\\\\"[derived\n dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\\\\\\\" record\n for citing the data.\\\\n\\\\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/)\n contains a number of articles that can help you analyze GBIF data.\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Dataset\n root\\\",\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"ai4edataeuwest\\\"}}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"table:columns\\\":[{\\\"name\\\":\\\"gbifid\\\",\\\"type\\\":\\\"int64\\\",\\\"description\\\":\\\"GBIF's\n identifier for the occurrence\\\"},{\\\"name\\\":\\\"datasetkey\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"GBIF's\n UUID for the [dataset](https://www.gbif.org/developer/registry#datasets) containing\n this occurrence\\\"},{\\\"name\\\":\\\"occurrenceid\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:occurrenceID](https://dwc.tdwg.org/terms/#occurrenceID).\\\"},{\\\"name\\\":\\\"kingdom\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:kingdom](https://dwc.tdwg.org/terms/#kingdom). This field has been aligned\n with the [GBIF backbone taxonomy](https://doi.org/10.15468/39omei).\\\"},{\\\"name\\\":\\\"phylum\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:phylum](https://dwc.tdwg.org/terms/#phylum). This field has been aligned\n with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"class\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:class](https://dwc.tdwg.org/terms/#class). This field has been aligned\n with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"order\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:order](https://dwc.tdwg.org/terms/#order). This field has been aligned\n with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"family\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:family](https://dwc.tdwg.org/terms/#family). This field has been aligned\n with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"genus\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:genus](https://dwc.tdwg.org/terms/#genus). This field has been aligned\n with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"species\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:species](https://dwc.tdwg.org/terms/#species). This field has been aligned\n with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"infraspecificepithet\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:infraspecificEpithet](https://dwc.tdwg.org/terms/#infraspecificEpithet).\n \\ This field has been aligned with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"taxonrank\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:taxonRank](https://dwc.tdwg.org/terms/#taxonRank). This field has been\n aligned with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"scientificname\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:scientificName](https://dwc.tdwg.org/terms/#scientificName). This field\n has been aligned with the GBIF backbone taxonomy.\\\"},{\\\"name\\\":\\\"verbatimscientificname\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"The\n scientific name as provided by the data publisher\\\"},{\\\"name\\\":\\\"verbatimscientificnameauthorship\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"The\n scientific name authorship provided by the data publisher.\\\"},{\\\"name\\\":\\\"countrycode\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:countryCode](https://dwc.tdwg.org/terms/#countryCode). GBIF's interpretation\n has set this to an ISO 3166-2 code.\\\"},{\\\"name\\\":\\\"locality\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:locality](https://dwc.tdwg.org/terms/#locality).\\\"},{\\\"name\\\":\\\"stateprovince\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:stateProvince](https://dwc.tdwg.org/terms/#stateProvince).\\\"},{\\\"name\\\":\\\"occurrencestatus\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:occurrenceStatus](https://dwc.tdwg.org/terms/#occurrenceStatus). Either\n the value `PRESENT` or `ABSENT`. **Many users will wish to filter for `PRESENT`\n data.**\\\"},{\\\"name\\\":\\\"individualcount\\\",\\\"type\\\":\\\"int32\\\",\\\"description\\\":\\\"See\n [dwc:individualCount](https://dwc.tdwg.org/terms/#individualCount).\\\"},{\\\"name\\\":\\\"publishingorgkey\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"GBIF's\n UUID for the [organization](https://www.gbif.org/developer/registry#organizations)\n publishing this occurrence.\\\"},{\\\"name\\\":\\\"decimallatitude\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"See\n [dwc:decimalLatitude](https://dwc.tdwg.org/terms/#decimalLatitude). GBIF's\n interpretation has normalized this to a WGS84 coordinate.\\\"},{\\\"name\\\":\\\"decimallongitude\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"See\n [dwc:decimalLongitude](https://dwc.tdwg.org/terms/#decimalLongitude). GBIF's\n interpretation has normalized this to a WGS84 coordinate.\\\"},{\\\"name\\\":\\\"coordinateuncertaintyinmeters\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"See\n [dwc:coordinateUncertaintyInMeters](https://dwc.tdwg.org/terms/#coordinateUncertaintyInMeters).\\\"},{\\\"name\\\":\\\"coordinateprecision\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"See\n [dwc:coordinatePrecision](https://dwc.tdwg.org/terms/#coordinatePrecision).\\\"},{\\\"name\\\":\\\"elevation\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"See\n [dwc:elevation](https://dwc.tdwg.org/terms/#elevation). If provided by the\n data publisher, GBIF's interpretation has normalized this value to metres.\\\"},{\\\"name\\\":\\\"elevationaccuracy\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"See\n [dwc:elevationAccuracy](https://dwc.tdwg.org/terms/#elevationAccuracy). If\n provided by the data publisher, GBIF's interpretation has normalized this\n value to metres.\\\"},{\\\"name\\\":\\\"depth\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"See\n [dwc:depth](https://dwc.tdwg.org/terms/#depth). If provided by the data publisher,\n GBIF's interpretation has normalized this value to metres.\\\"},{\\\"name\\\":\\\"depthaccuracy\\\",\\\"type\\\":\\\"double\\\",\\\"description\\\":\\\"See\n [dwc:depthAccuracy](https://dwc.tdwg.org/terms/#depthAccuracy). If provided\n by the data publisher, GBIF's interpretation has normalized this value to\n metres.\\\"},{\\\"name\\\":\\\"eventdate\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:eventDate](https://dwc.tdwg.org/terms/#eventDate). GBIF's interpretation\n has normalized this value to an ISO 8601 date with a local time.\\\"},{\\\"name\\\":\\\"day\\\",\\\"type\\\":\\\"int32\\\",\\\"description\\\":\\\"See\n [dwc:day](https://dwc.tdwg.org/terms/#day).\\\"},{\\\"name\\\":\\\"month\\\",\\\"type\\\":\\\"int32\\\",\\\"description\\\":\\\"See\n [dwc:month](https://dwc.tdwg.org/terms/#month).\\\"},{\\\"name\\\":\\\"year\\\",\\\"type\\\":\\\"int32\\\",\\\"description\\\":\\\"See\n [dwc:year](https://dwc.tdwg.org/terms/#year).\\\"},{\\\"name\\\":\\\"taxonkey\\\",\\\"type\\\":\\\"int32\\\",\\\"description\\\":\\\"The\n numeric identifier for the [taxon](https://www.gbif.org/developer/species#nameUsages)\n in GBIF's backbone taxonomy corresponding to `scientificname`.\\\"},{\\\"name\\\":\\\"specieskey\\\",\\\"type\\\":\\\"int32\\\",\\\"description\\\":\\\"The\n numeric identifier for the taxon in GBIF's backbone taxonomy corresponding\n to `species`.\\\"},{\\\"name\\\":\\\"basisofrecord\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:basisOfRecord](https://dwc.tdwg.org/terms/#basisOfRecord). One of `PRESERVED_SPECIMEN`,\n `FOSSIL_SPECIMEN`, `LIVING_SPECIMEN`, `OBSERVATION`, `HUMAN_OBSERVATION`,\n `MACHINE_OBSERVATION`, `MATERIAL_SAMPLE`, `LITERATURE`, `UNKNOWN`.\\\"},{\\\"name\\\":\\\"institutioncode\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:institutionCode](https://dwc.tdwg.org/terms/#institutionCode).\\\"},{\\\"name\\\":\\\"collectioncode\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:collectionCode](https://dwc.tdwg.org/terms/#collectionCode).\\\"},{\\\"name\\\":\\\"catalognumber\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:catalogNumber](https://dwc.tdwg.org/terms/#catalogNumber).\\\"},{\\\"name\\\":\\\"recordnumber\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:recordNumber](https://dwc.tdwg.org/terms/#recordNumber).\\\"},{\\\"name\\\":\\\"identifiedby\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:identifiedBy](https://dwc.tdwg.org/terms/#identifiedBy).\\\"},{\\\"name\\\":\\\"dateidentified\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:dateIdentified](https://dwc.tdwg.org/terms/#dateIdentified). An ISO 8601\n date.\\\"},{\\\"name\\\":\\\"license\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:license](https://dwc.tdwg.org/terms/#license). Either [`CC0_1_0`](https://creativecommons.org/publicdomain/zero/1.0/)\n or [`CC_BY_4_0`](https://creativecommons.org/licenses/by/4.0/). `CC_BY_NC_4_0`\n records are not present in this snapshot.\\\"},{\\\"name\\\":\\\"rightsholder\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:rightsHolder](https://dwc.tdwg.org/terms/#rightsHolder).\\\"},{\\\"name\\\":\\\"recordedby\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:recordedBy](https://dwc.tdwg.org/terms/#recordedBy).\\\"},{\\\"name\\\":\\\"typestatus\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:typeStatus](https://dwc.tdwg.org/terms/#typeStatus).\\\"},{\\\"name\\\":\\\"establishmentmeans\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:establishmentMeans](https://dwc.tdwg.org/terms/#establishmentMeans).\\\"},{\\\"name\\\":\\\"lastinterpreted\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"The\n ISO 8601 date when the record was last processed by GBIF. Data are reprocessed\n for several reasons, including changes to the backbone taxonomy, so this date\n is not necessarily the date the occurrence record last changed.\\\"},{\\\"name\\\":\\\"mediatype\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"See\n [dwc:mediaType](https://dwc.tdwg.org/terms/#mediaType). May contain `StillImage`,\n `MovingImage` or `Sound` (from [enumeration](http://api.gbif.org/v1/enumeration/basic/MediaType),\n detailing whether the occurrence has this media available.\\\"},{\\\"name\\\":\\\"issue\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"A\n list of [issues](https://gbif.github.io/gbif-api/apidocs/org/gbif/api/vocabulary/OccurrenceIssue.html)\n encountered by GBIF in processing this record. 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Along with the seven 500 meter (m) reflectance bands\n are two quality layers and four observation bands. For each pixel, a value\n is selected from all the acquisitions within the 8-day composite period. The\n criteria for the pixel choice include cloud and solar zenith. When several\n acquisitions meet the criteria the pixel with the minimum channel 3 (blue)\n value is used.\\\",\\\"item_assets\\\":{\\\"hdf\\\":{\\\"type\\\":\\\"application/x-hdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Source\n data containing all bands\\\"},\\\"metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Federal\n Geographic Data Committee (FGDC) Metadata\\\"},\\\"sur_refl_b01\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Surface\n Reflectance Band 1 (620-670 nm)\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"sur_refl_b01\\\",\\\"common_name\\\":\\\"red\\\",\\\"center_wavelength\\\":0.645,\\\"full_width_half_max\\\":0.5}],\\\"raster:bands\\\":[{\\\"scale\\\":0.0001,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"sur_refl_b02\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"],\\\"title\\\":\\\"Surface\n Reflectance Band 2 (841-876 nm)\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"sur_refl_b02\\\",\\\"common_name\\\":\\\"nir08\\\",\\\"center_wavelength\\\":0.8585,\\\"full_width_half_max\\\":0.35}],\\\"raster:bands\\\":[{\\\"scale\\\":0.0001,\\\"data_type\\\":\\\"int16\\\",\\\"spatial_resolution\\\":500}]},\\\"sur_refl_b03\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; 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profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Day\n of the year for the pixel\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"Julian Day\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":500}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"modis\\\",\\\"msft:container\\\":\\\"modis-061\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/eo/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.5067/MODIS/MOD09A1.061\\\",\\\"citation\\\":\\\"Vermote,\n E. 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NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD09A1.061\\\"}],\\\"msft:storage_account\\\":\\\"modiseuwest\\\",\\\"msft:short_description\\\":\\\"MODIS\n Surface Reflectance 8-Day (500m)\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"alos-dem\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-dem/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-dem\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://earth.jaxa.jp/policy/en.html\\\",\\\"title\\\":\\\"JAXA\n Terms of Use of Research Data\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/alos-dem\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"ALOS\n World 3D-30m\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/alos-dem.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"ALOS\n DEM\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/alos-dem.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2016-12-07T00:00:00Z\\\",\\\"2016-12-07T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"ALOS\\\",\\\"PRISM\\\",\\\"JAXA\\\",\\\"DEM\\\",\\\"DSM\\\",\\\"Elevation\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.eorc.jaxa.jp/ALOS/en/aw3d30/index.htm\\\",\\\"name\\\":\\\"Japan\n Aerospace Exploration Agency\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://doi.org/10.5069/G94M92HB\\\",\\\"name\\\":\\\"OpenTopography\\\",\\\"roles\\\":[\\\"host\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[30],\\\"platform\\\":[\\\"alos\\\"],\\\"instruments\\\":[\\\"prism\\\"]},\\\"description\\\":\\\"The\n \\\\\\\"ALOS World 3D-30m\\\\\\\" (AW3D30) dataset is a 30 meter resolution global\n digital surface model (DSM), developed by the Japan Aerospace Exploration\n Agency (JAXA). AWD30 was constructed from the Panchromatic Remote-sensing\n Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite\n (ALOS), operated from 2006 to 2011.\\\\n\\\\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/aw3d30/aw3d30v3.2_product_e_e1.2.pdf)\n for more details.\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"alos-dem\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"ai4edataeuwest\\\",\\\"msft:short_description\\\":\\\"ALOS\n World 3D is a global digital surface model (DSM) with a horizontal resolution\n of approximately 30 meters, constructed from the Panchromatic Remote-sensing\n Instrument for Stereo Mapping (PRISM) on board Advanced Land Observing Satellite\n (ALOS).\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"alos-palsar-mosaic\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-palsar-mosaic/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/alos-palsar-mosaic\\\"},{\\\"rel\\\":\\\"handbook\\\",\\\"href\\\":\\\"https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_Mosaic_V200.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Global\n 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)\\\",\\\"description\\\":\\\"Also includes\n data usage information\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://earth.jaxa.jp/policy/en.html\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"JAXA\n Terms of Use of Research Data\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/alos-palsar-mosaic\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"ALOS\n PALSAR Annual Mosaic\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/alos-palsar-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"ALOS\n PALSAR MOSAIC\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/alos-palsar-mosaic.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,85.0,180.0,-56.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2015-01-01T00:00:00Z\\\",\\\"2021-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"version\\\":\\\"2.0.0\\\",\\\"keywords\\\":[\\\"ALOS\\\",\\\"JAXA\\\",\\\"Remote\n Sensing\\\",\\\"Global\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm\\\",\\\"name\\\":\\\"Japan\n Aerospace Exploration Agency\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://developmentseed.org\\\",\\\"name\\\":\\\"Development\n Seed\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\n Planetary Computer\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"ALOS\\\",\\\"ALOS-2\\\"],\\\"instruments\\\":[\\\"PALSAR\\\",\\\"PALSAR-2\\\"],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"sar:polarizations\\\":[[\\\"HH\\\",\\\"HV\\\"],[\\\"HH\\\",\\\"HV\\\",\\\"VH\\\",\\\"VV\\\"]],\\\"sar:instrument_mode\\\":[\\\"F\\\",\\\"U\\\"],\\\"sar:observation_direction\\\":[\\\"left\\\",\\\"right\\\"],\\\"palsar:number_of_polarizations\\\":[\\\"D\\\",\\\"Q\\\"]},\\\"description\\\":\\\"Global\n 25 m Resolution PALSAR-2/PALSAR Mosaic (MOS)\\\",\\\"item_assets\\\":{\\\"HH\\\":{\\\"role\\\":\\\"data\\\",\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"HH\\\",\\\"description\\\":\\\"HH\n polarization backscattering coefficient, 16-bit DN.\\\"},\\\"HV\\\":{\\\"role\\\":\\\"data\\\",\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"HV\\\",\\\"description\\\":\\\"HV\n polarization backscattering coefficient, 16-bit DN.\\\"},\\\"VH\\\":{\\\"role\\\":\\\"data\\\",\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"VH\\\",\\\"description\\\":\\\"VH\n polarization backscattering coefficient, 16-bit DN (high-sensitive beam quad-mode\n only).\\\"},\\\"VV\\\":{\\\"role\\\":\\\"data\\\",\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"title\\\":\\\"VV\\\",\\\"description\\\":\\\"VV polarization\n backscattering coefficient, 16-bit DN (high-sensitive beam quad-mode only).\\\"},\\\"date\\\":{\\\"role\\\":\\\"date\\\",\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"date\\\",\\\"description\\\":\\\"Observation\n date (days since Jan 1, 1970).\\\"},\\\"mask\\\":{\\\"role\\\":\\\"data-mask\\\",\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"mask\\\",\\\"description\\\":\\\"Quality\n Mask\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\"}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"no_data\\\",\\\"value\\\":0,\\\"description\\\":\\\"No\n data\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":50,\\\"description\\\":\\\"Water\\\"},{\\\"name\\\":\\\"lay_over\\\",\\\"value\\\":100,\\\"description\\\":\\\"Lay\n over\\\"},{\\\"name\\\":\\\"shadowing\\\",\\\"value\\\":150,\\\"description\\\":\\\"Shadowing\\\"},{\\\"name\\\":\\\"land\\\",\\\"value\\\":255,\\\"description\\\":\\\"Land\\\"}]},\\\"linci\\\":{\\\"role\\\":\\\"local-incidence-angle\\\",\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"linci\\\",\\\"description\\\":\\\"Local\n incidence angle (degrees).\\\"},\\\"metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"title\\\":\\\"metadata\\\",\\\"description\\\":\\\"Product\n metadata file\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"palsar\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/sar/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/version/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/classification/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"https://pceo.blob.core.windows.net/\\\",\\\"msft:short_description\\\":\\\"Global\n 25m resolution SAR image mosaic.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"deltares-water-availability\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-water-availability/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-water-availability\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"User\n Guide\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://cdla.dev/permissive-1-0/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Community\n Data License Agreement - Permissive, Version 1.0\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/deltares-water-availability\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Deltares\n Global Water Availability\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.azureedge.net/assets/pc_thumbnails/additional_datasets/deltares-reservoir.jpg\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,90.0,180.0,-90.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1970-01-01T00:00:00Z\\\",\\\"2020-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CDLA-Permissive-1.0\\\",\\\"keywords\\\":[\\\"Deltares\\\",\\\"Water\n availability\\\",\\\"Reservoir\\\",\\\"Water\\\",\\\"Precipitation\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.deltares.nl/en/\\\",\\\"name\\\":\\\"Deltares\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"deltares:reservoir\\\":[\\\"ERA5\\\",\\\"CHIRPS\\\",\\\"EOBS\\\",\\\"NLDAS\\\",\\\"BOM\\\"]},\\\"description\\\":\\\"[Deltares](https://www.deltares.nl/en/)\n has produced a hydrological model approach to simulate historical daily reservoir\n variations for 3,236 locations across the globe for the period 1970-2020 using\n the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/)\n model. The model outputs long-term daily information on reservoir volume,\n inflow and outflow dynamics, as well as information on upstream hydrological\n forcing.\\\\n\\\\nThey hydrological model was forced with 5 different precipitation\n products. Two products (ERA5 and CHIRPS) are available at the global scale,\n while for Europe, USA and Australia a regional product was use (i.e. EOBS,\n NLDAS and BOM, respectively). Using these different precipitation products,\n it becomes possible to assess the impact of uncertainty in the model forcing.\n A different number of basins upstream of reservoirs are simulated, given the\n spatial coverage of each precipitation product.\\\\n\\\\nSee the complete [methodology\n documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf)\n for more information.\\\\n\\\\n## Dataset coverages\\\\n\\\\n| Name | Scale |\n Period | Number of basins |\\\\n|--------|--------------------------|-----------|------------------|\\\\n|\n ERA5 | Global | 1967-2020 | 3236 |\\\\n| CHIRPS\n | Global (+/- 50 latitude) | 1981-2020 | 2951 |\\\\n| EOBS | Europe/North\n Africa | 1979-2020 | 682 |\\\\n| NLDAS | USA |\n 1979-2020 | 1090 |\\\\n| BOM | Australia | 1979-2020\n | 116 |\\\\n\\\\n## STAC Metadata\\\\n\\\\nThis STAC collection includes\n one STAC item per dataset. The item includes a `deltares:reservoir` property\n that can be used to query for the URL of a specific dataset.\\\\n\\\\n## Contact\\\\n\\\\nFor\n questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Flood\n Map\\\",\\\"description\\\":\\\"Inundation maps of flood depth using a model that\n takes into account water level attenuation and is forced by sea level.\\\"},\\\"index\\\":{\\\"type\\\":\\\"application/json\\\",\\\"roles\\\":[\\\"index\\\"],\\\"title\\\":\\\"Index\n file\\\",\\\"description\\\":\\\"Kerchunk index file.\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"cube:variables\\\":{\\\"P\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\n per day\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm per day\\\",\\\"description\\\":\\\"Average precipitation\n upstream of reservoir\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Average\n precipitation upstream of reservoir\\\"},\\\"ETa\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\n per day\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm per day\\\",\\\"description\\\":\\\"Average simulated\n actual evapotransporation upstream of reservoir\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Average\n simulated actual evapotransporation upstream of reservoir\\\"},\\\"PET\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\n per day\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm per day\\\",\\\"description\\\":\\\"Average potential\n evapotranspiration upstream of reservoir\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Average\n potential evapotranspiration upstream of reservoir\\\"},\\\"Melt\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\n per day\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm per day\\\",\\\"description\\\":\\\"Average simulated\n snow melt upstream of reservoir\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Average\n simulated snow melt upstream of reservoir\\\"},\\\"Snow\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm\\\",\\\"description\\\":\\\"Average\n simulated snow depth upstream of reservoir\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Average\n simulated snow depth upstream of reservoir\\\"},\\\"Temp\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"degrees\n C\\\",\\\"attrs\\\":{\\\"units\\\":\\\"degrees C\\\",\\\"description\\\":\\\"Average surface temperature\n upstream of reservoir\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Average\n surface temperature upstream of reservoir\\\"},\\\"P_res\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\n per day\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm per day\\\",\\\"description\\\":\\\"Precipitation\n reservoir\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Precipitation\n reservoir\\\"},\\\"S_res\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"m3\\\",\\\"attrs\\\":{\\\"units\\\":\\\"m3\\\",\\\"description\\\":\\\"Simulated\n reservoir volume\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Simulated\n reservoir volume\\\"},\\\"Ea_res\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm per day\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm\n per day\\\",\\\"description\\\":\\\"Simulated actual evaporation reservoir\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Simulated\n actual evaporation reservoir\\\"},\\\"Qin_res\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"m3\n per s\\\",\\\"attrs\\\":{\\\"units\\\":\\\"m3 per s\\\",\\\"description\\\":\\\"Simulated reservoir\n inflow (surface+subsurface)\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Simulated\n reservoir inflow (surface+subsurface)\\\"},\\\"FracFull\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"m3\\\",\\\"attrs\\\":{\\\"units\\\":\\\"m3\\\",\\\"description\\\":\\\"Simulated\n reservoir volume\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Simulated\n reservoir volume\\\"},\\\"Qout_res\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"m3 per s\\\",\\\"attrs\\\":{\\\"units\\\":\\\"m3\n per s\\\",\\\"description\\\":\\\"Simulated reservoir outflow\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"GrandID\\\",\\\"ksathorfrac\\\"],\\\"description\\\":\\\"Simulated\n reservoir 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Census Bureau\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://makepath.com/\\\",\\\"name\\\":\\\"makepath\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"description\\\":\\\"The\n [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html)\n counted every person living in the United States and the five U.S. territories.\n It marked the 24th census in U.S. history and the first time that households\n were invited to respond to the census online.\\\\n\\\\nThe tables included on\n the Planetary Computer provide information on population and geographic boundaries\n at various levels of cartographic aggregation.\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Dataset\n root\\\",\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"ai4edataeuwest\\\"}}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"table:tables\\\":[{\\\"name\\\":\\\"American\n Indian/Alaska Native Areas/Hawaiian Home Lands (AIANNH)\\\",\\\"description\\\":\\\"This\n file contains data for legal and statistical [American Indian/Alaska Native\n Areas/Hawaiian Home Lands (AIANNH)](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1)\n entities published by the US Census Bureau.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_aiannh_500k\\\"},{\\\"name\\\":\\\"American\n Indian Tribal Subdivisions (AITSN)\\\",\\\"description\\\":\\\"This file contains\n data on [American Indian Tribal Subdivisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1).\n These areas are the legally defined subdivisions of American Indian Reservations\n (AIR), Oklahoma Tribal Statistical Areas (OTSA), and Off-Reservation Trust\n Land (ORTL).\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_aitsn_500k\\\"},{\\\"name\\\":\\\"Alaska\n Native Regional Corporations (ANRC)\\\",\\\"description\\\":\\\"This file contains\n data on [Alaska Native Regional Corporations](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1),\n which are corporations created according to the Alaska Native Claims Settlement\n Act. \\\",\\\"msft:item_name\\\":\\\"cb_2020_02_anrc_500k\\\"},{\\\"name\\\":\\\"Tribal Block\n Groups (TBG)\\\",\\\"description\\\":\\\"This file includes data on [Tribal Block\n Groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_26),\n which are subdivisions of Tribal Census Tracts. These block groups can extend\n over multiple AIRs and ORTLs due to areas not meeting Block Group minimum\n population thresholds.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_tbg_500k\\\"},{\\\"name\\\":\\\"Tribal\n Census Tracts (TTRACT)\\\",\\\"description\\\":\\\"This file includes data on [Tribal\n Census Tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_27)\n which are relatively small statistical subdivisions of AIRs and ORTLs defined\n by federally recognized tribal government officials in partnership with the\n Census Bureau. Due to population thresholds, the Tracts may consist of multiple\n non-contiguous areas.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_ttract_500k\\\"},{\\\"name\\\":\\\"Census\n Block Groups (BG)\\\",\\\"description\\\":\\\"This file contains data on [Census Block\n Groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_4).\n These groups are the second smallest geographic grouping. They consist of\n clusters of blocks within the same census tract that share the same first\n digit of their 4-character census block number. Census Block Groups generally\n contain between 600 and 3,000 people and generally cover contiguous areas.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_bg_500k\\\"},{\\\"name\\\":\\\"Census\n Tracts (TRACT)\\\",\\\"description\\\":\\\"This file contains data on [Census Tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13)\n which are small and relatively permanent statistical subdivisions of a county\n or equivalent entity. Tract population size is generally between 1,200 and\n 8,000 people with an ideal size of 4,000. Boundaries tend to follow visible\n and identifiable features and are usually contiguous areas.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_tract_500k\\\"},{\\\"name\\\":\\\"Congressional\n Districts: 116th Congress (CD116)\\\",\\\"description\\\":\\\"This file contains data\n on the [Congressional Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_9)\n for the 116th Congress. \\\",\\\"msft:item_name\\\":\\\"cb_2020_us_cd116_500k\\\"},{\\\"name\\\":\\\"Consolidated\n Cities (CONCITY)\\\",\\\"description\\\":\\\"This file contains data on [Consolidated\n Cities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_8).\n These are areas where one or several other incorporated places in a county\n or Minor Civil Division are included in a consolidated government but still\n exist as separate legal entities.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_concity_500k\\\"},{\\\"name\\\":\\\"Counties\n (COUNTY)\\\",\\\"description\\\":\\\"This file contains data on [Counties and Equivalent\n Entities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_12).\n These are the primary legal divisions of states. Most states use the term\n \\\\\\\"counties,\\\\\\\" but other terms such as \\\\\\\"Parishes,\\\\\\\" \\\\\\\"Municipios,\\\\\\\"\n or \\\\\\\"Independent Cities\\\\\\\" may be used. \\\",\\\"msft:item_name\\\":\\\"cb_2020_us_county_500k\\\"},{\\\"name\\\":\\\"Counties\n within Congressional Districts: 116th Congress (COUNTY_within_CD116)\\\",\\\"description\\\":\\\"This\n file contains data on Counties within Congressional Districts.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_county_within_cd116_500k\\\"},{\\\"name\\\":\\\"County\n Subdivisions (COUSUB)\\\",\\\"description\\\":\\\"This file contains [County Subdivisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_11),\n which are the primary divisions of counties and equivalent entities. These\n divisions vary from state to state and include Barrios, Purchases, Townships,\n and other types of legal and statistical entities. \\\",\\\"msft:item_name\\\":\\\"cb_2020_us_cousub_500k\\\"},{\\\"name\\\":\\\"Divisions\n (DIVISION)\\\",\\\"description\\\":\\\"This file contains data on [Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_10)\n of the US. This file is similar to the Regions file but contains more divisions\n and encompasses several states per division.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_division_500k\\\"},{\\\"name\\\":\\\"Core\n Based Statistical Areas (CBSAs)\\\",\\\"description\\\":\\\"This file contains data\n on [Core Based Statistical Areas (CBSAs)](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7).\n This encompasses all metropolitan and micropolitan statistical areas.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_cbsa_500k\\\"},{\\\"name\\\":\\\"Combined\n Statistical Areas (CSA)\\\",\\\"description\\\":\\\"This file contains data on [Combined\n Statistical Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7),\n which are areas that consist of two or more adjacent CBSAs that have significant\n employment interchanges.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_csa_500k\\\"},{\\\"name\\\":\\\"Metropolitan\n Divisions (METDIV)\\\",\\\"description\\\":\\\"This file contains data on [Metropolitan\n Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7).\n These areas are groupings of counties or equivalent entities within a metropolitan\n statistical area with a core of 2.5 million inhabitants and one or more main\n counties that represent employment centers, plus adjacent counties with commuting\n ties.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_metdiv_500k\\\"},{\\\"name\\\":\\\"New England\n City and Town Areas (NECTA)\\\",\\\"description\\\":\\\"This file contains [New England\n City and Town Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7),\n which encompass metropolitan and micropolitan statistical areas and urban\n clusters in New England.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_necta_500k\\\"},{\\\"name\\\":\\\"New\n England City and Town Area Division (NECTADIV)\\\",\\\"description\\\":\\\"This file\n contains [New England City and Town Areas Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7),\n which are smaller groupings of cities and towns in New England that contain\n a single core of 2.5 million inhabitants. Each division must have a total\n population of 100,000 or more.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_nectadiv_500k\\\"},{\\\"name\\\":\\\"Combined\n New England City and Town Areas (CNECTA)\\\",\\\"description\\\":\\\"This file contains\n data on [Combined New England City and Town Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7),\n consisting of two or more adjacent NECTAs that have significant employment\n interchanges.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_cnecta_500k\\\"},{\\\"name\\\":\\\"Places\n (PLACE)\\\",\\\"description\\\":\\\"This file contains [Places](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_14)\n which are Incorporated Places (legal entities) and Census Designated Places\n (CDPs, statistical entities). An incorporated place usually is a city, town,\n village, or borough but can have other legal descriptions. CDPs are settled\n concentrations of population that are identifiable by name but are not legally\n incorporated.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_place_500k\\\"},{\\\"name\\\":\\\"Regions\n (REGION)\\\",\\\"description\\\":\\\"This file contains [Regions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_10)\n of the US and encompasses several states per division.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_region_500k\\\"},{\\\"name\\\":\\\"School\n Districts - Elementary (ELSD)\\\",\\\"description\\\":\\\"This file contains [Elementary\n School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23),\n referring to districts with elementary schools.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_elsd_500k\\\"},{\\\"name\\\":\\\"School\n Districts - Secondary (SCSD)\\\",\\\"description\\\":\\\"This file contains [Secondary\n School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23),\n referring to districts with secondary schools.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_scsd_500k\\\"},{\\\"name\\\":\\\"School\n Districts - Unified (UNSD)\\\",\\\"description\\\":\\\"This file contains [Unified\n School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23),\n referring to districts that provide education to children of all school ages.\n Unified school districts can have both secondary and elementary schools.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_unsd_500k\\\"},{\\\"name\\\":\\\"State\n Legislative Districts - Lower Chamber (SLDL)\\\",\\\"description\\\":\\\"This file\n contains [Lower Chamber State Legislative Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_24).\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_sldl_500k\\\"},{\\\"name\\\":\\\"State\n Legislative Districts - Upper Chamber (SLDU)\\\",\\\"description\\\":\\\"This file\n contains [Upper Chamber State Legislative Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_24).\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_sldu_500k\\\"},{\\\"name\\\":\\\"States\n (STATE)\\\",\\\"description\\\":\\\"This file contains the [US States and State Equivalent\n Entities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_25).\n Within Census Bureau datasets, the District of Columbia, Puerto Rico, and\n the Island Areas (American Samoa, the Commonwealth of the Northern Mariana\n Islands, Guam, and the US Virgin Islands) are treated as statistical equivalents\n of states alongside the 50 US states.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_state_500k\\\"},{\\\"name\\\":\\\"Subbarrios\n (SUBBARRIO)\\\",\\\"description\\\":\\\"This file contains [Subbarrios](https://www.census.gov/programs-surveys/geography/about/glossary.html#pr),\n which are legally defined subdivisions of Minor Civil Division in Puerto Rico.\n They don\\\\\\\"t exist within every Minor Civil Division and don\\\\\\\"t always\n cover the entire Minor Civil Division where they do exist.\\\",\\\"msft:item_name\\\":\\\"cb_2020_72_subbarrio_500k\\\"},{\\\"name\\\":\\\"United\n States Outline\\\",\\\"description\\\":\\\"This file contains the [United States Outline](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_30)\n shapefile. This contains all 50 US states plus the District of Columbia, Puerto\n Rico, and the Island Areas (American Samoa, the Commonwealth of the Northern\n Mariana Islands, Guam, and the US Virgin Islands). There is only one feature\n within this dataset.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_nation_5m\\\"},{\\\"name\\\":\\\"Voting\n Districts (VTD)\\\",\\\"description\\\":\\\"This file contains all [US Voting Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_31),\n which are geographic features established by state, local and tribal governments\n to conduct elections.\\\",\\\"msft:item_name\\\":\\\"cb_2020_us_vtd_500k\\\"}],\\\"msft:container\\\":\\\"us-census\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"ai4edataeuwest\\\",\\\"msft:short_description\\\":\\\"United\n States population counts at various levels of cartographic aggregation\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"jrc-gsw\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/jrc-gsw/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/jrc-gsw\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.1038/nature20584\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.copernicus.eu/en/about-copernicus/international-cooperation\\\",\\\"title\\\":\\\"Copernicus\n Open Access Policy\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/jrc-gsw\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"JRC\n Global Surface Water\\\",\\\"assets\\\":{\\\"guide\\\":{\\\"href\\\":\\\"https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Users\n Guide\\\",\\\"description\\\":\\\"Data users guide and description of the JRC GSW\n datasets\\\"},\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/jrc-gsw.png\\\",\\\"title\\\":\\\"European\n Commission Joint Research Centre - Global Surface Water\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/jrc-gsw.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-56.0,180.0,78.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1984-03-01T00:00:00Z\\\",\\\"2020-12-31T11:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.1038/nature20584\\\",\\\"keywords\\\":[\\\"Global\\\",\\\"Water\\\",\\\"Landsat\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://global-surface-water.appspot.com/\\\",\\\"name\\\":\\\"European\n Commission Joint Research Centre\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://developers.google.com/earth-engine/datasets/tags/jrc\\\",\\\"name\\\":\\\"Google\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"description\\\":\\\"Global\n surface water products from the European Commission Joint Research Centre,\n based on Landsat 5, 7, and 8 imagery. Layers in this collection describe\n the occurrence, change, and seasonality of surface water from 1984-2020. Complete\n documentation for each layer is available in the [Data Users Guide](https://storage.cloud.google.com/global-surface-water/downloads_ancillary/DataUsersGuidev2020.pdf).\\\\n\\\",\\\"item_assets\\\":{\\\"change\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Occurrence\n Change Intensity\\\",\\\"description\\\":\\\"Change in water occurrence between the\n two periods (1984-1999) and (2000-2020)\\\"},\\\"extent\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Maximum\n Water Extent\\\",\\\"description\\\":\\\"Binary indicator of whether water was ever\n present (from 1984-2020)\\\",\\\"file:values\\\":[{\\\"values\\\":[0],\\\"summary\\\":\\\"Not\n water\\\"},{\\\"values\\\":[1],\\\"summary\\\":\\\"Water\\\"}]},\\\"occurrence\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Occurrence\\\",\\\"description\\\":\\\"Frequency\n with which water was present from March 1984 to December 2020\\\"},\\\"recurrence\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Recurrence\\\",\\\"description\\\":\\\"Frequency\n with which water returns from year to year\\\"},\\\"seasonality\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Seasonality\\\",\\\"description\\\":\\\"Number\n of months that water was present from January 2020 to December 2020\\\"},\\\"transitions\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Transitions\\\",\\\"description\\\":\\\"Categorical\n change in surface water status from 1984 to 2020\\\",\\\"file:values\\\":[{\\\"values\\\":[1],\\\"summary\\\":\\\"Permanent\\\"},{\\\"values\\\":[2],\\\"summary\\\":\\\"New\n permanent\\\"},{\\\"values\\\":[3],\\\"summary\\\":\\\"Lost permanent\\\"},{\\\"values\\\":[4],\\\"summary\\\":\\\"Seasonal\\\"},{\\\"values\\\":[5],\\\"summary\\\":\\\"New\n seasonal\\\"},{\\\"values\\\":[6],\\\"summary\\\":\\\"Lost seasonal\\\"},{\\\"values\\\":[7],\\\"summary\\\":\\\"Seasonal\n to permanent\\\"},{\\\"values\\\":[8],\\\"summary\\\":\\\"Permanent to seasonal\\\"},{\\\"values\\\":[9],\\\"summary\\\":\\\"Ephemeral\n permanent\\\"},{\\\"values\\\":[10],\\\"summary\\\":\\\"Ephemeral seasonal\\\"}]}},\\\"sci:citation\\\":\\\"Jean-Francois\n Pekel, Andrew Cottam, Noel Gorelick, Alan S. Belward, High-resolution mapping\n of global surface water and its long-term changes. Nature 540, 418-422 (2016)\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"jrc-gsw\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/file/v2.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"ai4edataeuwest\\\",\\\"msft:short_description\\\":\\\"Global\n surface water distribution and dynamics from 1984 to 2020\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"deltares-floods\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-floods/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/deltares-floods\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"User\n Guide\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://cdla.dev/permissive-1-0/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Community\n Data License Agreement - Permissive, Version 1.0\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/deltares-floods\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Deltares\n Global Flood Maps\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.azureedge.net/assets/pc_thumbnails/additional_datasets/deltares-flood.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,90.0,180.0,-90.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2018-01-01T00:00:00Z\\\",\\\"2018-12-31T00:00:00Z\\\"],[\\\"2050-01-01T00:00:00Z\\\",\\\"2050-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CDLA-Permissive-1.0\\\",\\\"keywords\\\":[\\\"Deltares\\\",\\\"Flood\\\",\\\"Sea\n level rise\\\",\\\"Water\\\",\\\"Global\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.deltares.nl/en/\\\",\\\"name\\\":\\\"Deltares\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"deltares:dem_name\\\":[\\\"NASADEM\\\",\\\"MERITDEM\\\",\\\"LIDAR\\\"],\\\"deltares:resolution\\\":[\\\"90m\\\",\\\"1km\\\",\\\"5km\\\"],\\\"deltares:return_period\\\":[0,2,5,10,25,50,100,250],\\\"deltares:sea_level_year\\\":[2018,2050]},\\\"description\\\":\\\"[Deltares](https://www.deltares.nl/en/)\n has produced inundation maps of flood depth using a model that takes into\n account water level attenuation and is forced by sea level. At the coastline,\n the model is forced by extreme water levels containing surge and tide from\n GTSMip6. The water level at the coastline is extended landwards to all areas\n that are hydrodynamically connected to the coast following a \\u2018bathtub\\u2019\n like approach and calculates the flood depth as the difference between the\n water level and the topography. Unlike a simple 'bathtub' model, this model\n attenuates the water level over land with a maximum attenuation factor of\n 0.5\\u2009m\\u2009km-1. The attenuation factor simulates the dampening of the\n flood levels due to the roughness over land.\\\\n\\\\nIn its current version,\n the model does not account for varying roughness over land and permanent water\n bodies such as rivers and lakes, and it does not account for the compound\n effects of waves, rainfall, and river discharge on coastal flooding. It also\n does not include the mitigating effect of coastal flood protection. Flood\n extents must thus be interpreted as the area that is potentially exposed to\n flooding without coastal protection.\\\\n\\\\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/11206409-003-ZWS-0003_v0.1-Planetary-Computer-Deltares-global-flood-docs.pdf)\n for more information.\\\\n\\\\n## Digital elevation models (DEMs)\\\\n\\\\nThis documentation\n will refer to three DEMs:\\\\n\\\\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem)\n product.\\\\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/),\n derived from SRTM and AW3D.\\\\n* `LIDAR` is the [Global LiDAR Lowland DTM (GLL_DTM_v1)](https://data.mendeley.com/datasets/v5x4vpnzds/1).\\\\n\\\\n##\n Global datasets\\\\n\\\\nThis collection includes multiple global flood datasets\n derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different\n resolutions. Not all DEMs have all resolutions:\\\\n\\\\n* `NASADEM` and `MERITDEM`\n are available at `90m` and `1km` resolutions\\\\n* `LIDAR` is available at `5km`\n resolution\\\\n\\\\n## Historic event datasets\\\\n\\\\nThis collection also includes\n historical storm event data files that follow similar DEM and resolution conventions.\n Not all storms events are available for each DEM and resolution combination,\n but generally follow the format of:\\\\n\\\\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\\\\n\\\\nFor\n example, a flood map for the MERITDEM-derived 90m flood data for the \\\\\\\"Omar\\\\\\\"\n storm in 2008 is available at:\\\\n\\\\n\\\\n\\\\n##\n Contact\\\\n\\\\nFor questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=deltares-floods%20question).\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Flood\n 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(2021). MODIS/Terra Vegetation Indices 16-Day L3 Global 500m SIN Grid\n V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD13A1.061\\\"},{\\\"doi\\\":\\\"10.5067/MODIS/MYD13A1.061\\\",\\\"citation\\\":\\\"Didan,\n K. (2021). MODIS/Aqua Vegetation Indices 16-Day L3 Global 500m SIN Grid\n V061 [Data set]. 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lands\\\",\\\"Urban\\\",\\\"Water\\\",\\\"Snow\n and Ice\\\"]}]},\\\"description\\\":\\\"Collection of Land Cover products for Canada\n as produced by Natural Resources Canada using Landsat satellite imagery. This\n collection of cartographic products offers classified Land Cover of Canada\n at a 30 metre scale, updated on a 5 year basis.\\\",\\\"item_assets\\\":{\\\"metadata\\\":{\\\"type\\\":\\\"application/json\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Land\n cover of Canada metadata\\\"},\\\"landcover\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\",\\\"labels\\\",\\\"labels-raster\\\"],\\\"title\\\":\\\"Land\n cover of Canada COG\\\",\\\"proj:epsg\\\":3978,\\\"file:values\\\":[{\\\"values\\\":[1],\\\"summary\\\":\\\"Temperate\n or sub-polar needleleaf forest\\\"},{\\\"values\\\":[2],\\\"summary\\\":\\\"Sub-polar\n taiga needleleaf forest\\\"},{\\\"values\\\":[5],\\\"summary\\\":\\\"Temperate or sub-polar\n broadleaf deciduous forest\\\"},{\\\"values\\\":[6],\\\"summary\\\":\\\"Mixed forest\\\"},{\\\"values\\\":[8],\\\"summary\\\":\\\"Temperate\n or sub-polar shrubland\\\"},{\\\"values\\\":[10],\\\"summary\\\":\\\"Temperate or sub-polar\n grassland\\\"},{\\\"values\\\":[11],\\\"summary\\\":\\\"Sub-polar or polar shrubland-lichen-moss\\\"},{\\\"values\\\":[12],\\\"summary\\\":\\\"Sub-polar\n or polar grassland-lichen-moss\\\"},{\\\"values\\\":[13],\\\"summary\\\":\\\"Sub-polar\n or polar barren-lichen-moss\\\"},{\\\"values\\\":[14],\\\"summary\\\":\\\"Wetland\\\"},{\\\"values\\\":[15],\\\"summary\\\":\\\"Cropland\\\"},{\\\"values\\\":[16],\\\"summary\\\":\\\"Barren\n lands\\\"},{\\\"values\\\":[17],\\\"summary\\\":\\\"Urban\\\"},{\\\"values\\\":[18],\\\"summary\\\":\\\"Water\\\"},{\\\"values\\\":[19],\\\"summary\\\":\\\"Snow\n and Ice\\\"}],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"landcover\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/label/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/file/v2.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"nrcanlandcovereuwest\\\",\\\"msft:short_description\\\":\\\"Collection\n of Land Cover products for Canada as produced by Natural Resources Canada\n using Landsat satellite imagery. This collection of cartographic products\n offers classified Land Cover of Canada at a 30 metre scale, updated on a 5\n year basis.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"modis-10A2-061\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-10A2-061\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5067/MODIS/MOD10A2.061\\\",\\\"title\\\":\\\"MODIS/Terra\n Snow Cover 8-Day L3 Global 500m SIN Grid, Version 61\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5067/MODIS/MYD10A2.061\\\",\\\"title\\\":\\\"MODIS/Aqua\n Snow Cover 8-Day L3 Global 500m SIN Grid, Version 61\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://nsidc.org/data/data-programs/nsidc-daac/citing-nsidc-daac\\\",\\\"title\\\":\\\"Use\n and Copyright | National Snow and Ice Data Center\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/modis-10A2-061\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"MODIS\n Snow Cover 8-day\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-10A2-061.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"MODIS\n Snow Cover 8-day thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/modis-10A2-061.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"MS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2000-02-18T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"NASA\\\",\\\"MODIS\\\",\\\"Satellite\\\",\\\"Global\\\",\\\"Snow\\\",\\\"MOD10A2\\\",\\\"MYD10A2\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://nsidc.org\\\",\\\"name\\\":\\\"National\n Snow and Ice Data Center\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"aqua\\\",\\\"terra\\\"],\\\"instruments\\\":[\\\"modis\\\"]},\\\"description\\\":\\\"This\n global Level-3 (L3) data set provides the maximum snow cover extent observed\n over an eight-day period within 10degx10deg MODIS sinusoidal grid tiles. Tiles\n are generated by compositing 500 m observations from the 'MODIS Snow Cover\n Daily L3 Global 500m Grid' data set. A bit flag index is used to track the\n eight-day snow/no-snow chronology for each 500 m cell.\\\",\\\"item_assets\\\":{\\\"hdf\\\":{\\\"type\\\":\\\"application/x-hdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Source\n data containing all bands\\\"},\\\"metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Federal\n Geographic Data Committee (FGDC) Metadata\\\"},\\\"Maximum_Snow_Extent\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Maximum\n snow extent over the 8-day period.\\\",\\\"raster:bands\\\":[{\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":500}],\\\"classification:classes\\\":[{\\\"value\\\":0,\\\"description\\\":\\\"missing\n data\\\"},{\\\"value\\\":1,\\\"description\\\":\\\"no decision\\\"},{\\\"value\\\":11,\\\"description\\\":\\\"night\\\"},{\\\"value\\\":25,\\\"description\\\":\\\"no\n snow\\\"},{\\\"value\\\":37,\\\"description\\\":\\\"lake\\\"},{\\\"value\\\":39,\\\"description\\\":\\\"ocean\\\"},{\\\"value\\\":50,\\\"description\\\":\\\"cloud\\\"},{\\\"value\\\":100,\\\"description\\\":\\\"lake\n ice\\\"},{\\\"value\\\":200,\\\"description\\\":\\\"snow\\\"},{\\\"value\\\":254,\\\"description\\\":\\\"detector\n saturated\\\"}]},\\\"Eight_Day_Snow_Cover\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Snow chronology\n bit flags for each day in the eight-day observation period.\\\",\\\"raster:bands\\\":[{\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":500}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"modis\\\",\\\"msft:container\\\":\\\"modis-061\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/classification/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.5067/MODIS/MOD10A2.061\\\",\\\"citation\\\":\\\"Hall,\n D. K., & Riggs, G. A. (2021). MODIS/Terra Snow Cover 8-Day L3 Global\n 500m Grid, Version 61 [Data set]. NASA National Snow and Ice Data Center\n DAAC. https://doi.org/10.5067/MODIS/MOD10A2.061\\\"},{\\\"doi\\\":\\\"10.5067/MODIS/MYD10A2.061\\\",\\\"citation\\\":\\\"Hall,\n D. K., & Riggs, G. A. (2021). MODIS/Aqua Snow Cover 8-Day L3 Global\n 500m Grid, Version 61 [Data set]. NASA National Snow and Ice Data Center\n DAAC. https://doi.org/10.5067/MODIS/MYD10A2.061\\\"}],\\\"msft:storage_account\\\":\\\"modiseuwest\\\",\\\"msft:short_description\\\":\\\"MODIS\n Snow Cover 8-day\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"ecmwf-forecast\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ecmwf-forecast\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://creativecommons.org/licenses/by/4.0/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"CC-BY-4.0\n license\\\"},{\\\"rel\\\":\\\"documentation\\\",\\\"href\\\":\\\"https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"ECMWF\n Open Data (Real Time) documentation\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/ecmwf-forecast\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"ECMWF\n Open Data (real-time)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/ecmwf-forecast-thumbnail-proj.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,90,180,-90]]},\\\"temporal\\\":{\\\"interval\\\":[[null,null]]}},\\\"license\\\":\\\"CC-BY-4.0\\\",\\\"keywords\\\":[\\\"ECMWF\\\",\\\"forecast\\\",\\\"weather\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ecmwf.int/\\\",\\\"name\\\":\\\"ECMWF\\\",\\\"roles\\\":[\\\"producer\\\"]}],\\\"summaries\\\":{\\\"ecmwf:types\\\":[\\\"ef\\\",\\\"ep\\\",\\\"fc\\\",\\\"tf\\\"],\\\"ecmwf:streams\\\":[\\\"enfo\\\",\\\"mmsf\\\",\\\"oper\\\",\\\"scda\\\",\\\"scwv\\\",\\\"waef\\\",\\\"wave\\\"],\\\"ecmwf:pressure_levels\\\":[1000,925,850,700,500,300,250,200,50],\\\"ecmwf:reference_times\\\":[\\\"00\\\",\\\"06\\\",\\\"12\\\",\\\"18\\\"]},\\\"description\\\":\\\"The\n [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products)\n offers real-time meterological and oceanographic productions from the ECMWF\n forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction)\n for detailed information on each of the products.\\\\n\\\\n## Overview of products\\\\n\\\\nThe\n following diagram shows the publishing schedule of the various products.\\\\n\\\\n\\\\n\\\\nThe vertical axis shows the various products,\n defined below, which are grouped by combinations of `stream`, `forecast type`,\n and `reference time`. The horizontal axis shows *forecast times* in 3-hour\n intervals out from the reference time. A black square over a particular forecast\n time, or step, indicates that a forecast is made for that forecast time, for\n that particular `stream`, `forecast type`, `reference time` combination.\\\\n\\\\n*\n **stream** is the forecasting system that produced the data. The values are\n available in the `ecmwf:stream` summary of the STAC collection. They are:\\\\n\n \\ * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts),\n atmospheric fields\\\\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast)\n fields from the ECMWF model only.\\\\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast),\n atmospheric fields \\\\n * `scda`: short cut-off high-resolution forecast,\n atmospheric fields (also known as \\\\\\\"high-frequency products\\\\\\\")\\\\n * `scwv`:\n short cut-off high-resolution forecast, ocean wave fields (also known as \\\\\\\"high-frequency\n products\\\\\\\") and\\\\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts),\n ocean wave fields,\\\\n * `wave`: wave model\\\\n* **type** is the forecast type.\n The values are available in the `ecmwf:type` summary of the STAC collection.\n They are:\\\\n * `fc`: forecast\\\\n * `ef`: ensemble forecast\\\\n * `pf`: ensemble\n probabilities\\\\n * `tf`: trajectory forecast for tropical cyclone tracks\\\\n*\n **reference time** is the hours after midnight when the model was run. Each\n stream / type will produce assets for different forecast times (steps from\n the reference datetime) depending on the reference time.\\\\n\\\\nVisit the [ECMWF's\n User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time)\n for more details on each of the various products.\\\\n\\\\nAssets are available\n for the previous 30 days.\\\\n\\\\n## Asset overview\\\\n\\\\nThe data are provided\n as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\\\\nAdditionally,\n [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles)\n are provided, which can be used to read subsets of the data from Azure Blob\n Storage.\\\\n\\\\nWithin each `stream`, `forecast type`, `reference time`, the\n structure of the data are mostly consistent. Each GRIB2 file will have the\\\\nsame\n data variables, coordinates (aside from `time` as the *reference time* changes\n and `step` as the *forecast time* changes). The exception\\\\nis the `enfo-ep`\n and `waef-ep` products, which have more `step`s in the 240-hour forecast than\n in the 360-hour forecast. \\\\n\\\\nSee the example notebook for more on how to\n access the data.\\\\n\\\\n## STAC metadata\\\\n\\\\nThe Planetary Computer provides\n a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so\n every item has the same\\\\n`bbox` and `geometry`.\\\\n\\\\nA few custom properties\n are available on each STAC item, which can be used in searches to narrow down\n the data to items of interest:\\\\n\\\\n* `ecmwf:stream`: The forecasting system\n (see above for definitions). The full set of values is available in the Collection's\n summaries.\\\\n* `ecmwf:type`: The forecast type (see above for definitions).\n The full set of values is available in the Collection's summaries.\\\\n* `ecmwf:step`:\n The offset from the reference datetime, expressed as ``, for\n example `\\\\\\\"3h\\\\\\\"` means \\\\\\\"3 hours from the reference datetime\\\\\\\". \\\\n*\n `ecmwf:reference_datetime`: The datetime when the model was run. This indicates\n when the forecast *was made*, rather than when it's valid for.\\\\n* `ecmwf:forecast_datetime`:\n The datetime for which the forecast is valid. This is also set as the item's\n `datetime`.\\\\n\\\\nSee the example notebook for more on how to use the STAC\n metadata to query for particular data.\\\\n\\\\n## Attribution\\\\n\\\\nThe products\n listed and described on this page are available to the public and their use\n is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms\n of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that\n the data may be redistributed and used commercially, subject to appropriate\n attribution.\\\\n\\\\nThe following wording should be attached to the use of this\n ECMWF dataset: \\\\n\\\\n1. Copyright statement: Copyright \\\\\\\"\\xA9 [year] European\n Centre for Medium-Range Weather Forecasts (ECMWF)\\\\\\\".\\\\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\\\\n3.\n License Statement: This data is published under a Creative Commons Attribution\n 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\\\\n4.\n Disclaimer: ECMWF does not accept any liability whatsoever for any error or\n omission in the data, their availability, or for any loss or damage arising\n from their use.\\\\n5. Where applicable, an indication if the material has been\n modified and an indication of previous modifications.\\\\n\\\\nThe following wording\n shall be attached to services created with this ECMWF dataset:\\\\n\\\\n1. Copyright\n statement: Copyright \\\\\\\"This service is based on data and products of the\n European Centre for Medium-Range Weather Forecasts (ECMWF)\\\\\\\".\\\\n2. Source\n www.ecmwf.int\\\\n3. License Statement: This ECMWF data is published under a\n Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\\\\n4.\n Disclaimer: ECMWF does not accept any liability whatsoever for any error or\n omission in the data, their availability, or for any loss or damage arising\n from their use.\\\\n5. Where applicable, an indication if the material has been\n modified and an indication of previous modifications\\\\n\\\\n## More information\\\\n\\\\nFor\n more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time)\n and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/wmo-GRIB2\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"GRIB2\n data file\\\",\\\"description\\\":\\\"The forecast data, as a grib2 file. 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value not used\\\"},{\\\"name\\\":\\\"high\\\",\\\"value\\\":3,\\\"description\\\":\\\"High\n confidence cloud\\\"}],\\\"description\\\":\\\"Cloud confidence levels\\\"}]},\\\"qa_radsat\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"saturation\\\"],\\\"title\\\":\\\"Radiometric\n Saturation and Dropped Pixel Quality Assessment Band\\\",\\\"description\\\":\\\"Collection\n 2 Level-1 Radiometric Saturation and Dropped Pixel Quality Assessment Band\n (QA_RADSAT)\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"bit index\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":60}],\\\"classification:bitfields\\\":[{\\\"name\\\":\\\"band1\\\",\\\"length\\\":1,\\\"offset\\\":0,\\\"classes\\\":[{\\\"name\\\":\\\"not_saturated\\\",\\\"value\\\":0,\\\"description\\\":\\\"Band\n 1 not saturated\\\"},{\\\"name\\\":\\\"saturated\\\",\\\"value\\\":1,\\\"description\\\":\\\"Band\n 1 saturated\\\"}],\\\"description\\\":\\\"Band 1 radiometric saturation\\\"},{\\\"name\\\":\\\"band2\\\",\\\"length\\\":1,\\\"offset\\\":1,\\\"classes\\\":[{\\\"name\\\":\\\"not_saturated\\\",\\\"value\\\":0,\\\"description\\\":\\\"Band\n 2 not saturated\\\"},{\\\"name\\\":\\\"saturated\\\",\\\"value\\\":1,\\\"description\\\":\\\"Band\n 2 saturated\\\"}],\\\"description\\\":\\\"Band 2 radiometric saturation\\\"},{\\\"name\\\":\\\"band3\\\",\\\"length\\\":1,\\\"offset\\\":2,\\\"classes\\\":[{\\\"name\\\":\\\"not_saturated\\\",\\\"value\\\":0,\\\"description\\\":\\\"Band\n 3 not saturated\\\"},{\\\"name\\\":\\\"saturated\\\",\\\"value\\\":1,\\\"description\\\":\\\"Band\n 3 saturated\\\"}],\\\"description\\\":\\\"Band 3 radiometric saturation\\\"},{\\\"name\\\":\\\"band4\\\",\\\"length\\\":1,\\\"offset\\\":3,\\\"classes\\\":[{\\\"name\\\":\\\"not_saturated\\\",\\\"value\\\":0,\\\"description\\\":\\\"Band\n 4 not saturated\\\"},{\\\"name\\\":\\\"saturated\\\",\\\"value\\\":1,\\\"description\\\":\\\"Band\n 4 saturated\\\"}],\\\"description\\\":\\\"Band 4 radiometric saturation\\\"},{\\\"name\\\":\\\"band5\\\",\\\"length\\\":1,\\\"offset\\\":4,\\\"classes\\\":[{\\\"name\\\":\\\"not_saturated\\\",\\\"value\\\":0,\\\"description\\\":\\\"Band\n 5 not saturated\\\"},{\\\"name\\\":\\\"saturated\\\",\\\"value\\\":1,\\\"description\\\":\\\"Band\n 5 saturated\\\"}],\\\"description\\\":\\\"Band 5 radiometric saturation\\\"},{\\\"name\\\":\\\"band6\\\",\\\"length\\\":1,\\\"offset\\\":5,\\\"classes\\\":[{\\\"name\\\":\\\"not_saturated\\\",\\\"value\\\":0,\\\"description\\\":\\\"Band\n 6 not saturated\\\"},{\\\"name\\\":\\\"saturated\\\",\\\"value\\\":1,\\\"description\\\":\\\"Band\n 6 saturated\\\"}],\\\"description\\\":\\\"Band 6 radiometric saturation\\\"},{\\\"name\\\":\\\"band7\\\",\\\"length\\\":1,\\\"offset\\\":6,\\\"classes\\\":[{\\\"name\\\":\\\"not_saturated\\\",\\\"value\\\":0,\\\"description\\\":\\\"Band\n 7 not saturated\\\"},{\\\"name\\\":\\\"saturated\\\",\\\"value\\\":1,\\\"description\\\":\\\"Band\n 7 saturated\\\"}],\\\"description\\\":\\\"Band 7 radiometric saturation\\\"},{\\\"name\\\":\\\"dropped\\\",\\\"length\\\":1,\\\"offset\\\":9,\\\"classes\\\":[{\\\"name\\\":\\\"not_dropped\\\",\\\"value\\\":0,\\\"description\\\":\\\"Detector\n has a value - pixel present\\\"},{\\\"name\\\":\\\"dropped\\\",\\\"value\\\":1,\\\"description\\\":\\\"Detector\n does not have a value - no data\\\"}],\\\"description\\\":\\\"Dropped pixel\\\"}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"landsat\\\",\\\"msft:container\\\":\\\"landsat-c2\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/view/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"landsateuwest\\\",\\\"msft:short_description\\\":\\\"Landsat\n Collection 2 Level-1 data from the Multispectral Scanner System (MSS) onboard\n Landsat 1 through Landsat 5.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"drcog-lulc\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/drcog-lulc/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/drcog-lulc\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://drcog.org/legal-terms\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"DRCOG\n Legal Terms\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://gis.drcog.org/rdc/supplemental/lulc_pilot_report.zip\\\",\\\"type\\\":\\\"application/zip\\\",\\\"title\\\":\\\"Supplemental\n Information\\\"},{\\\"rel\\\":\\\"original\\\",\\\"href\\\":\\\"https://landcoverarchive.s3.amazonaws.com/2018/lulc_pilot_raster_2018.zip\\\",\\\"type\\\":\\\"application/zip\\\",\\\"title\\\":\\\"Land_Cover_Raster_Data_2018\\\"},{\\\"rel\\\":\\\"original\\\",\\\"href\\\":\\\"https://landcoverarchive.s3.amazonaws.com/2020/DRCOG_2020_Landcover.zip\\\",\\\"type\\\":\\\"application/zip\\\",\\\"title\\\":\\\"Land_Cover_Raster_Data_2020\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/drcog-lulc\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Denver\n Regional Council of Governments Land Use Land Cover\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/drcog-lulc.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"DRCOG\n LULC\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/drcog-lulc.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-105.93962510864995,39.10438697007073,-103.66801443832743,40.320593119647256],[-105.54671456161505,39.54013841830152,-104.46335720577567,39.94430501943824]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2018-01-01T00:00:00Z\\\",\\\"2020-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Land\n Cover\\\",\\\"Land Use\\\",\\\"NAIP\\\",\\\"USDA\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project\\\",\\\"name\\\":\\\"Denver\n Regional Council of Governments\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"summaries\\\":{\\\"mission\\\":[\\\"2018\n DRCOG LULC pilot study covering 1,000 square miles\\\",\\\"2020 DRCOG LULC study\n covering 6,000 square miles\\\"]},\\\"description\\\":\\\"The [Denver Regional Council\n of Governments (DRCOG) Land Use/Land Cover (LULC)](https://drcog.org/services-and-resources/data-maps-and-modeling/regional-land-use-land-cover-project)\n datasets are developed in partnership with the [Babbit Center for Land and\n Water Policy](https://www.lincolninst.edu/our-work/babbitt-center-land-water-policy)\n and the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)'s\n Conservation Innovation Center (CIC). DRCOG LULC includes 2018 data at 3.28ft\n (1m) resolution covering 1,000 square miles and 2020 data at 1ft resolution\n covering 6,000 square miles of the Denver, Colorado region. The classification\n data is derived from the USDA's 1m National Agricultural Imagery Program (NAIP)\n aerial imagery and leaf-off aerial ortho-imagery captured as part of the [Denver\n Regional Aerial Photography Project](https://drcog.org/services-and-resources/data-maps-and-modeling/denver-regional-aerial-photography-project)\n (6in resolution everywhere except the mountainous regions to the west, which\n are 1ft resolution).\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"DRCOG LULC\\\",\\\"description\\\":\\\"Denver\n Regional Council of Governments (DRCOG) Land Use Land Cover (LULC) Classifications\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"usft\\\",\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"description\\\":\\\"Classification\n values\\\"}],\\\"classification:classes\\\":[{\\\"value\\\":1,\\\"color_hint\\\":\\\"FF0000\\\",\\\"description\\\":\\\"Structures\\\"},{\\\"value\\\":2,\\\"color_hint\\\":\\\"B2B2B2\\\",\\\"description\\\":\\\"Impervious\n Surfaces\\\"},{\\\"value\\\":3,\\\"color_hint\\\":\\\"00A9E6\\\",\\\"description\\\":\\\"Water\\\"},{\\\"value\\\":4,\\\"color_hint\\\":\\\"C7D79E\\\",\\\"description\\\":\\\"Prairie/Grassland/Natural\n Ground Cover\\\"},{\\\"value\\\":5,\\\"color_hint\\\":\\\"267300\\\",\\\"description\\\":\\\"Tree\n Canopy\\\"},{\\\"value\\\":6,\\\"color_hint\\\":\\\"70A800\\\",\\\"description\\\":\\\"Turf/Irrigated\n Land\\\"},{\\\"value\\\":7,\\\"color_hint\\\":\\\"FFEBAF\\\",\\\"description\\\":\\\"Barren 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Guide\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.chesapeakeconservancy.org/conservation-innovation-center/precision-conservation/chesapeake-bay-program/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Data\n Availability Description\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/chesapeake-lc-7\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Chesapeake\n Land Cover (7-class)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/chesapeake-lc-7-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Chesapeake\n Land Cover (7-class) Thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-81.14658496196135,36.21291717905733,-73.27357561029186,44.77821441524524]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2013-01-01T00:00:00Z\\\",\\\"2014-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Land\n Cover\\\",\\\"Chesapeake Bay Watershed\\\",\\\"Chesapeake Conservancy\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/\\\",\\\"name\\\":\\\"Chesapeake\n Conservancy Conservation Innovation Center\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"description\\\":\\\"A\n high-resolution 1-meter [land cover data product](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/)\n 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The product\n area encompasses over 250,000 square kilometers in New York, Pennsylvania,\n Maryland, Delaware, West Virginia, Virginia, and the District of Columbia.\n The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)\n [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/)\n for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is\n a regional partnership of EPA, other federal, state, and local agencies and\n governments, nonprofits, and academic institutions, that leads and directs\n Chesapeake Bay restoration efforts. \\\\n\\\\nThe dataset is composed of a uniform\n set of 7 land cover classes. 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The product\n area encompasses over 250,000 square kilometers in New York, Pennsylvania,\n Maryland, Delaware, West Virginia, Virginia, and the District of Columbia.\n The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/)\n [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/)\n for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is\n a regional partnership of EPA, other federal, state, and local agencies and\n governments, nonprofits, and academic institutions, that leads and directs\n Chesapeake Bay restoration efforts. \\\\n\\\\nThe dataset is composed of 13 land\n cover classes, although not all classes are used in all areas. Additional\n information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf)\n and [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf)\n document. 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The dataset was\n created by modifying the 2013-2014 high-resolution [land cover dataset](https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/)\n using 13 ancillary datasets including data on zoning, land use, parcel boundaries,\n landfills, floodplains, and wetlands. The product area encompasses over 250,000\n square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia,\n Virginia, and the District of Columbia. The dataset was created by the [Chesapeake\n Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation\n Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/)\n for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is\n a regional partnership of EPA, other federal, state, and local agencies and\n governments, nonprofits, and academic institutions that leads and directs\n Chesapeake Bay restoration efforts.\\\\n\\\\nThe dataset is composed of 17 land\n use classes in Virginia and 16 classes in all other jurisdictions. Additional\n information is available in a land use [Class Description](https://www.chesapeakeconservancy.org/wp-content/uploads/2018/11/2013-Phase-6-Mapped-Land-Use-Definitions-Updated-PC-11302018.pdf)\n document. 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The\n products are automatically generated through integration of data from multiple\n radars and radar networks, surface and satellite observations, numerical weather\n prediction (NWP) models, and climatology. The products are updated hourly\n at the top of the hour.\\\\n\\\\nMRMS QPE is available as a \\\\\\\"Pass 1\\\\\\\" or\n \\\\\\\"Pass 2\\\\\\\" product. The Pass 1 product is available with a 60-minute latency\n and includes 60-65% of gauges. The Pass 2 product has a higher latency of\n 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are\n broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\\\\n\\\\nThis\n Collection contains the **1-Hour Pass 1** sub-product, i.e., 1-hour cumulative\n precipitation accumulation with a 1-hour latency. The data are available in\n [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original\n source GRIB2 format files. The GRIB2 files are delivered to Azure as part\n of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\n\\\",\\\"item_assets\\\":{\\\"cog\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Processed\n Cloud Optimized GeoTIFF file\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"mm\\\",\\\"data_type\\\":\\\"float64\\\",\\\"spatial_resolution\\\":1000}]},\\\"grib2\\\":{\\\"type\\\":\\\"application/wmo-GRIB2\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Original\n GRIB2 file\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"mm\\\",\\\"data_type\\\":\\\"float64\\\",\\\"spatial_resolution\\\":1000}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"noaa-mrms-qpe\\\",\\\"msft:container\\\":\\\"mrms-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/noaa-mrms-qpe/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"mrms\\\",\\\"msft:short_description\\\":\\\"Integrated\n multi-sensor cumulative precipation estimate for the past hour with a 1-hour\n latency.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"noaa-mrms-qpe-1h-pass2\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-1h-pass2/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-1h-pass2\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.nssl.noaa.gov/projects/mrms/nmq_data_policy_OGCrevised.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"MRMS\n Dataset Sharing Policy\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://mrms.nssl.noaa.gov\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"MRMS\n Homepage\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://vlab.noaa.gov/web/wdtd/-/multi-sensor-qpe-1?selectedFolder=9234881\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"MRMS\n QPE Technical Product Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-mrms-qpe-1h-pass2\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"NOAA\n MRMS QPE 1-Hour Pass 2\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-mrms-qpe-1h-pass2-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"NOAA\n MRMS QPE 1-Hour Pass 2 Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-mrms-qpe-1h-pass2.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"QS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-176.0,9.0,150.0,72.0],[-130.0,20.0,-60.0,55.0],[-164.0,15.0,-151.0,26.0],[140.0,9.0,150.0,18.0],[-176.0,50.0,-126.0,72.0],[-90.0,10.0,-60.0,25.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2022-07-21T20:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"NOAA\\\",\\\"MRMS\\\",\\\"QPE\\\",\\\"Precipitation\\\",\\\"Weather\\\",\\\"United\n States\\\",\\\"Guam\\\",\\\"Caribbean\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.nssl.noaa.gov/projects/mrms\\\",\\\"name\\\":\\\"NOAA\n National Severe Storms Laboratory\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"summaries\\\":{\\\"noaa_mrms_qpe:pass\\\":[2],\\\"noaa_mrms_qpe:period\\\":[1],\\\"noaa_mrms_qpe:region\\\":[\\\"CONUS\\\",\\\"HAWAII\\\",\\\"GUAM\\\",\\\"ALASKA\\\",\\\"CARIB\\\"]},\\\"description\\\":\\\"The\n [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/)\n products are seamless 1-km mosaics of precipitation accumulation covering\n the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The\n products are automatically generated through integration of data from multiple\n radars and radar networks, surface and satellite observations, numerical weather\n prediction (NWP) models, and climatology. The products are updated hourly\n at the top of the hour.\\\\n\\\\nMRMS QPE is available as a \\\\\\\"Pass 1\\\\\\\" or\n \\\\\\\"Pass 2\\\\\\\" product. The Pass 1 product is available with a 60-minute latency\n and includes 60-65% of gauges. The Pass 2 product has a higher latency of\n 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are\n broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\\\\n\\\\nThis\n Collection contains the **1-Hour Pass 2** sub-product, i.e., 1-hour cumulative\n precipitation accumulation with a 2-hour latency. The data are available in\n [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original\n source GRIB2 format files. The GRIB2 files are delivered to Azure as part\n of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\n\\\",\\\"item_assets\\\":{\\\"cog\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Processed\n Cloud Optimized GeoTIFF file\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"mm\\\",\\\"data_type\\\":\\\"float64\\\",\\\"spatial_resolution\\\":1000}]},\\\"grib2\\\":{\\\"type\\\":\\\"application/wmo-GRIB2\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Original\n GRIB2 file\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"mm\\\",\\\"data_type\\\":\\\"float64\\\",\\\"spatial_resolution\\\":1000}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"noaa-mrms-qpe\\\",\\\"msft:container\\\":\\\"mrms-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/noaa-mrms-qpe/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"mrms\\\",\\\"msft:short_description\\\":\\\"Integrated\n multi-sensor cumulative precipation estimate for the past hour with a 2-hour\n latency.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"noaa-nclimgrid-monthly\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-nclimgrid-monthly/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-nclimgrid-monthly\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.7289/V5SX6B56\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NOAA\n Monthly U.S. Climate Gridded Dataset (NClimGrid)\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332#Constraints\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NClimGrid\n Data Use and Access Constraints\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Product\n Landing Page\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-nclimgrid-monthly\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Monthly\n NOAA U.S. Climate Gridded Dataset (NClimGrid)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-nclimgrid-monthly-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Monthly\n NOAA NClimGrid Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-nclimgrid-monthly.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-124.708333,24.541666,-66.999995,49.375001]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1895-01-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.7289/V5SX6B56\\\",\\\"keywords\\\":[\\\"United\n States\\\",\\\"NOAA\\\",\\\"NClimGrid\\\",\\\"Climate\\\",\\\"Precipitation\\\",\\\"Temperature\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ncei.noaa.gov/\\\",\\\"name\\\":\\\"NOAA\n National Centers for Environmental Information\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"description\\\":\\\"The\n [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332)\n consists of four climate variables derived from the [Global Historical Climatology\n Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily)\n dataset: maximum temperature, minimum temperature, average temperature, and\n precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer)\n grids for the Continental United States (CONUS). \\\\n\\\\nNClimGrid data is available\n in monthly and daily temporal intervals, with the daily data further differentiated\n as \\\\\\\"prelim\\\\\\\" (preliminary) or \\\\\\\"scaled\\\\\\\". Preliminary daily data\n is available within approximately three days of collection. Once a calendar\n month of preliminary daily data has been collected, it is scaled to match\n the corresponding monthly value. Monthly data is available from 1895 to the\n present. Daily preliminary and daily scaled data is available from 1951 to\n the present. \\\\n\\\\nThis Collection contains **Monthly** data. See the journal\n publication [\\\\\\\"Improved Historical Temperature and Precipitation Time Series\n for U.S. Climate Divisions\\\\\\\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml)\n for more information about monthly gridded data.\\\\n\\\\nUsers of all NClimGrid\n data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332)\n that:\\\\n>\\\\\\\"On an annual basis, approximately one year of 'final' NClimGrid\n data is submitted to replace the initially supplied 'preliminary' data for\n the same time period. Users should be sure to ascertain which level of data\n is required for their research.\\\\\\\"\\\\n\\\\nThe source NetCDF files are delivered\n to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\n\\\\n*Note*:\n The Planetary Computer currently has STAC metadata for just the monthly collection.\n We'll have STAC metadata for daily data in our next release. In the meantime,\n you can access the daily NetCDF data directly from Blob Storage using the\n storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`.\n See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns\n for more.*\\\\n\\\",\\\"item_assets\\\":{\\\"prcp\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Monthly Precipitation\n (mm)\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"mm\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tavg\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Monthly\n Average Temperature (degree Celsius)\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmax\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Monthly\n Maximmum Temperature (degree Celsius)\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmin\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Monthly\n Minimum Temperature (degree Celsius)\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]}},\\\"sci:citation\\\":\\\"Vose,\n Russell S., Applequist, Scott, Squires, Mike, Durre, Imke, Menne, Matthew\n J., Williams, Claude N. Jr., Fenimore, Chris, Gleason, Karin, and Arndt, Derek\n (2014): NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid), Version 1.\n NOAA National Centers for Environmental Information. DOI:10.7289/V5SX6B56.\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"noaa-nclimgrid\\\",\\\"msft:container\\\":\\\"nclimgrid-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.1175/JAMC-D-13-0248.1\\\",\\\"citation\\\":\\\"Vose,\n R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C.\n N., Jr., Fenimore, C., Gleason, K., & Arndt, D. (2014). Improved Historical\n Temperature and Precipitation Time Series for U.S. Climate Divisions, Journal\n of Applied Meteorology and Climatology, 53(5), 1232-1251.\\\"}],\\\"msft:storage_account\\\":\\\"nclimgridwesteurope\\\",\\\"msft:short_description\\\":\\\"Gridded\n surface observations of monthly temperature and precipitation for the continental\n United States\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"usda-cdl\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usda-cdl/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usda-cdl\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php\\\",\\\"title\\\":\\\"Product\n Landing Page\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php#Section3_5.0\\\",\\\"title\\\":\\\"Redistribution\n information\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/usda-cdl\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USDA\n Cropland Data Layers (CDLs)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/usda-cdl-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"USDA\n Cropland Data Layer (CDL) thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-127.887212,22.94027,-65.345507,51.603492]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2008-01-01T00:00:00Z\\\",\\\"2021-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USDA\\\",\\\"United\n States\\\",\\\"Land Cover\\\",\\\"Land Use\\\",\\\"Agriculture\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.nass.usda.gov/\\\",\\\"name\\\":\\\"United\n States Department of Agriculture - National Agricultural Statistics Service\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"usda_cdl:type\\\":[\\\"cropland\\\",\\\"frequency\\\",\\\"cultivated\\\"]},\\\"description\\\":\\\"The\n Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics\n Service (NASS) with the mission \\\\\\\"to provide timely, accurate and useful\n statistics in service to U.S. agriculture\\\\\\\" (Johnson and Mueller, 2010,\n p. 1204). The CDL is a crop-specific land cover classification product of\n more than 100 crop categories grown in the United States. CDLs are derived\n using a supervised land cover classification of satellite imagery. The supervised\n classification relies on first manually identifying pixels within certain\n images, often called training sites, which represent the same crop or land\n cover type. Using these training sites, a spectral signature is developed\n for each crop type that is then used by the analysis software to identify\n all other pixels in the satellite image representing the same crop. Using\n this method, a new CDL is compiled annually and released to the public a few\n months after the end of the growing season.\\\\n\\\\nThis collection includes\n Cropland, Confidence, Cultivated, and Frequency products.\\\\n\\\\n- Cropland:\n Crop-specific land cover data created annually. There are currently four individual\n crop frequency data layers that represent four major crops: corn, cotton,\n soybeans, and wheat.\\\\n- Confidence: The predicted confidence associated with\n an output pixel. A value of zero indicates low confidence, while a value of\n 100 indicates high confidence.\\\\n- Cultivated: cultivated and non-cultivated\n land cover for CONUS based on land cover information derived from the 2017\n through 2021 Cropland products.\\\\n- Frequency: crop specific planting frequency\n based on land cover information derived from the 2008 through 2021 Cropland\n products.\\\\n\\\\nFor more, visit the [Cropland Data Layer homepage](https://www.nass.usda.gov/Research_and_Science/Cropland/SARS1a.php).\\\",\\\"item_assets\\\":{\\\"corn\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"nodata\\\":255,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"wheat\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"nodata\\\":255,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"cotton\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"nodata\\\":255,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"cropland\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"value\\\":1,\\\"color_hint\\\":\\\"FFD200\\\",\\\"description\\\":\\\"Corn\\\"},{\\\"value\\\":2,\\\"color_hint\\\":\\\"FF2525\\\",\\\"description\\\":\\\"Cotton\\\"},{\\\"value\\\":3,\\\"color_hint\\\":\\\"00A8E3\\\",\\\"description\\\":\\\"Rice\\\"},{\\\"value\\\":4,\\\"color_hint\\\":\\\"FF9E0A\\\",\\\"description\\\":\\\"Sorghum\\\"},{\\\"value\\\":5,\\\"color_hint\\\":\\\"256F00\\\",\\\"description\\\":\\\"Soybeans\\\"},{\\\"value\\\":6,\\\"color_hint\\\":\\\"FFFF00\\\",\\\"description\\\":\\\"Sunflower\\\"},{\\\"value\\\":10,\\\"color_hint\\\":\\\"6FA400\\\",\\\"description\\\":\\\"Peanuts\\\"},{\\\"value\\\":11,\\\"color_hint\\\":\\\"00AE4A\\\",\\\"description\\\":\\\"Tobacco\\\"},{\\\"value\\\":12,\\\"color_hint\\\":\\\"DDA40A\\\",\\\"description\\\":\\\"Sweet\n 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profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"value\\\":1,\\\"color_hint\\\":\\\"000000\\\",\\\"description\\\":\\\"Non-Cultivated\\\"},{\\\"value\\\":2,\\\"color_hint\\\":\\\"006300\\\",\\\"description\\\":\\\"Cultivated\\\"}]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"usda-cdl\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/classification/v1.1.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"landcoverdata\\\",\\\"msft:short_description\\\":\\\"The\n UDA Cropland Data Layer is an annual raster, geo-referenced, crop-specific\n land cover data layer produced using satellite imagery and extensive agricultural\n ground truth collected during the current growing season.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"eclipse\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/eclipse/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/eclipse\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Microsoft%20Project%20Eclipse%20API%20Terms%20of%20Use_Mar%202022.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Terms\n of use\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Eclipse:\n An End-to-End Platform for Low-Cost, Hyperlocal Environment Sensing in Cities\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/eclipse\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Urban\n Innovation Eclipse Sensor Data\\\",\\\"assets\\\":{\\\"data\\\":{\\\"href\\\":\\\"abfs://eclipse/Chicago/\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Full\n dataset\\\",\\\"description\\\":\\\"Full parquet dataset\\\",\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"ai4edataeuwest\\\"}},\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/eclipse-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Urban\n Innovation Chicago Sensors\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/eclipse.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-87.94011408252348,41.64454312178303,-87.5241371038952,42.023038586147585]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2021-01-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Eclipse\\\",\\\"PM25\\\",\\\"air\n pollution\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.microsoft.com/en-us/research/urban-innovation-research/\\\",\\\"name\\\":\\\"Urban\n Innovation\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\",\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"description\\\":\\\"The\n [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/)\n Network is a low-cost air quality sensing network for cities and a research\n project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/)\n at Microsoft Research.\\\\n\\\\nProject Eclipse currently includes over 100 locations\n in Chicago, Illinois, USA.\\\\n\\\\nThis network was deployed starting in July,\n 2021, through a collaboration with the City of Chicago, the Array of Things\n Project, JCDecaux Chicago, and the Environmental Law and Policy Center as\n well as local environmental justice organizations in the city. [This talk](\n https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/)\n documents the network design and data calibration strategy.\\\\n\\\\n## Storage\n resources\\\\n\\\\nData are stored in [Parquet](https://parquet.apache.org/) files\n in Azure Blob Storage in the West Europe Azure region, in the following blob\n container:\\\\n\\\\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\\\\n\\\\nWithin\n that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`,\n where `YYYY-MM-DD` corresponds to the date of the snapshot.\\\\nEach snapshot\n contains a sensor readings from the next 7-days in Parquet format starting\n with date on the folder name YYYY-MM-DD.\\\\nTherefore, the data files for the\n first snapshot are at\\\\n\\\\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_*.parquet\\\\n\\\\nThe\n Parquet file schema is as described below. \\\\n\\\\n## Additional Documentation\\\\n\\\\nFor\n details on Calibration of Pm2.5, O3 and NO2, please see [this PDF](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/Calibration_Doc_v1.1.pdf).\\\\n\\\\n##\n License and attribution\\\\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022)\n [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing\n in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy.](https://www.microsoft.com/en-us/research/uploads/prod/2022/05/ACM_2022-IPSN_FINAL_Eclipse.pdf)\\\\n\\\\n##\n Contact\\\\n\\\\nFor questions about this dataset, contact [`msrurbanops@microsoft.com`](mailto:msrurbanops@microsoft.com?subject=eclipse%20question)\n \\\\n\\\\n\\\\n## Learn more\\\\n\\\\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/)\n contains an overview of the Project Eclipse at Microsoft Research.\\\\n\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Weekly\n dataset\\\",\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"ai4edataeuwest\\\"}}},\\\"sci:citation\\\":\\\"Daepp,\n Cabral, Ranganathan et al. (2022) Eclipse: An End-to-End Platform for Low-Cost,\n Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing\n in Sensor Networks. Milan, Italy. 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2.1\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://datastore.copernicus-climate.eu/documents/satellite-land-cover/D3.3.11-v1.0_PUGS_CDR_LC-CCI_v2.0.7cds_Products_v1.0.1_APPROVED_Ver1.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Product\n user guide for version 2.0\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.24381/cds.006f2c9a\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"ESA\n Climate Change Initiative Land Cover Maps (NetCDF)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/esa-cci-lc-netcdf-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"ESA\n CCI Land Cover NetCDF Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/esa-cci-lc-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-90.0,180.0,90.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1992-01-01T00:00:00Z\\\",\\\"2020-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.24381/cds.006f2c9a\\\",\\\"keywords\\\":[\\\"Land\n Cover\\\",\\\"ESA\\\",\\\"CCI\\\",\\\"Global\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://vito.be\\\",\\\"name\\\":\\\"VITO\\\",\\\"roles\\\":[\\\"licensor\\\"],\\\"description\\\":\\\"Provides\n the PROBA-V source data (for v2.0).\\\"},{\\\"url\\\":\\\"https://uclouvain.be\\\",\\\"name\\\":\\\"UCLouvain\\\",\\\"roles\\\":[\\\"producer\\\"],\\\"description\\\":\\\"UCLouvain\n produces the dataset (v2.1) for the ESA Climate Change Initiative.\\\"},{\\\"url\\\":\\\"https://brockmann-consult.de\\\",\\\"name\\\":\\\"Brockmann\n Consult\\\",\\\"roles\\\":[\\\"processor\\\"],\\\"description\\\":\\\"Brockmann Consult is\n responsible for the required pre-processing and the distribution of the dataset\n (v2.1).\\\"},{\\\"url\\\":\\\"http://esa-landcover-cci.org\\\",\\\"name\\\":\\\"ESA Climate\n Change Initiative\\\",\\\"roles\\\":[\\\"licensor\\\"],\\\"description\\\":\\\"The ESA Climate\n Change Initiative (CCI) is leading the product creation.\\\"},{\\\"url\\\":\\\"https://copernicus.eu\\\",\\\"name\\\":\\\"Copernicus\\\",\\\"roles\\\":[\\\"licensor\\\"],\\\"description\\\":\\\"Hosts\n the data on the Copernicus Climate Data Store (CDS).\\\"},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"esa_cci_lc:version\\\":[\\\"2.0.7cds\\\",\\\"2.1.1\\\"]},\\\"description\\\":\\\"The\n ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview)\n provides consistent global annual land cover maps at 300m spatial resolution\n from 1992 to 2020. The land cover classes are defined using the United Nations\n Food and Agriculture Organization's (UN FAO) [Land Cover Classification System](https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1036361/)\n (LCCS). In addition to the land cover maps, four quality flags are produced\n to document the reliability of the classification and change detection. \\\\n\\\\nThe\n data in this Collection are the original NetCDF files accessed from the [Copernicus\n Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend\n users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc),\n which provides the data as Cloud Optimized GeoTIFFs.\\\",\\\"item_assets\\\":{\\\"netcdf\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\",\\\"quality\\\"],\\\"title\\\":\\\"ESA\n CCI Land Cover NetCDF 4 File\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"esa-cci-lc\\\",\\\"msft:container\\\":\\\"esa-cci-lc\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"landcoverdata\\\",\\\"msft:short_description\\\":\\\"ESA\n CCI global land cover maps in NetCDF format\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"fws-nwi\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fws-nwi/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/fws-nwi\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://www.fws.gov/wetlands/Data/metadata/FWS_Wetlands.xml\\\",\\\"type\\\":\\\"application/xml\\\",\\\"title\\\":\\\"Wetlands\n metadata\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.fws.gov/sites/default/files/documents/national-wetlands-inventory-fact-sheet.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Project\n Fact Sheet\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.fws.gov/program/national-wetlands-inventory\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Project\n Landing Page\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"http://www.usa.gov/publicdomain/label/1.0/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"US\n Public Domain\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/fws-nwi\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"FWS\n National Wetlands Inventory\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/fws-nwi-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"FWS\n National Wetlands Inventory thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-64.54958,13.16667,144.6,71.99633],[144.6,13.16667,180.0,71.99633],[-180.0,13.16667,-64.54958,71.99633]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2022-10-01T00:00:00Z\\\",\\\"2022-10-01T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"USFWS\\\",\\\"Wetlands\\\",\\\"United\n States\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.fws.gov\\\",\\\"name\\\":\\\"U.S.\n Fish and Wildlife Service\\\",\\\"email\\\":\\\"wetlands_team@fws.gov\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"],\\\"description\\\":\\\"The\n U.S. Fish and Wildlife Service is the principal federal agency tasked with\n providing information to the public on the extent and status of the nation's\n wetland and deepwater habitats, as well as changes to these habitats over\n time.\\\"},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"fws_nwi:state\\\":[\\\"Alabama\\\",\\\"Alaska\\\",\\\"Arizona\\\",\\\"Arkansas\\\",\\\"California\\\",\\\"Colorado\\\",\\\"Connecticut\\\",\\\"Delaware\\\",\\\"District\n of Columbia\\\",\\\"Florida\\\",\\\"Georgia\\\",\\\"Hawaii\\\",\\\"Idaho\\\",\\\"Illinois\\\",\\\"Indiana\\\",\\\"Iowa\\\",\\\"Kansas\\\",\\\"Kentucky\\\",\\\"Louisiana\\\",\\\"Maine\\\",\\\"Maryland\\\",\\\"Massachusetts\\\",\\\"Michigan\\\",\\\"Minnesota\\\",\\\"Mississippi\\\",\\\"Missouri\\\",\\\"Montana\\\",\\\"Nebraska\\\",\\\"Nevada\\\",\\\"New\n Hampshire\\\",\\\"New Jersey\\\",\\\"New Mexico\\\",\\\"New York\\\",\\\"North Carolina\\\",\\\"North\n Dakota\\\",\\\"Ohio\\\",\\\"Oklahoma\\\",\\\"Oregon\\\",\\\"Pacific Trust Islands\\\",\\\"Pennsylvania\\\",\\\"Puerto\n Rico and Virgin Islands\\\",\\\"Rhode Island\\\",\\\"South Carolina\\\",\\\"South Dakota\\\",\\\"Tennessee\\\",\\\"Texas\\\",\\\"Utah\\\",\\\"Vermont\\\",\\\"Virginia\\\",\\\"Washington\\\",\\\"West\n Virginia\\\",\\\"Wisconsin\\\",\\\"Wyoming\\\"],\\\"fws_nwi:content\\\":[\\\"riparian\\\",\\\"historic_wetlands\\\",\\\"wetlands\\\"],\\\"fws_nwi:state_code\\\":[\\\"AL\\\",\\\"AK\\\",\\\"AZ\\\",\\\"AR\\\",\\\"CA\\\",\\\"CO\\\",\\\"CT\\\",\\\"DE\\\",\\\"DC\\\",\\\"FL\\\",\\\"GA\\\",\\\"HI\\\",\\\"ID\\\",\\\"IL\\\",\\\"IN\\\",\\\"IA\\\",\\\"KS\\\",\\\"KY\\\",\\\"LA\\\",\\\"ME\\\",\\\"MD\\\",\\\"MA\\\",\\\"MI\\\",\\\"MN\\\",\\\"MS\\\",\\\"MO\\\",\\\"MT\\\",\\\"NE\\\",\\\"NV\\\",\\\"NH\\\",\\\"NJ\\\",\\\"NM\\\",\\\"NY\\\",\\\"NC\\\",\\\"ND\\\",\\\"OH\\\",\\\"OK\\\",\\\"OR\\\",\\\"PacTrust\\\",\\\"PA\\\",\\\"PRVI\\\",\\\"RI\\\",\\\"SC\\\",\\\"SD\\\",\\\"TN\\\",\\\"TX\\\",\\\"UT\\\",\\\"VT\\\",\\\"VA\\\",\\\"WA\\\",\\\"WV\\\",\\\"WI\\\",\\\"WY\\\"]},\\\"description\\\":\\\"The\n Wetlands Data Layer is the product of over 45 years of work by the National\n Wetlands Inventory (NWI) and its collaborators and currently contains more\n than 35 million wetland and deepwater features. This dataset, covering the\n conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam,\n the major Northern Mariana Islands and Alaska, continues to grow at a rate\n of 50 to 100 million acres annually as data are updated.\\\\n\\\\n**NOTE:** Due\n to the variation in use and analysis of this data by the end user, each state's\n wetlands data extends beyond the state boundary. Each state includes wetlands\n data that intersect the 1:24,000 quadrangles that contain part of that state\n (1:2,000,000 source data). This allows the user to clip the data to their\n specific analysis datasets. Beware that two adjacent states will contain some\n of the same data along their borders.\\\\n\\\\nFor more information, visit the\n National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\\\\n\\\\n##\n STAC Metadata\\\\n\\\\nIn addition to the `zip` asset in every STAC item, each\n item has its own assets unique to its wetlands. In general, each item will\n have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet)\n asset with data for the entire region or a sub-region within that state. Use\n the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles)\n to select just the geoparquet assets. See the Example Notebook for more.\\\",\\\"item_assets\\\":{\\\"zip\\\":{\\\"type\\\":\\\"application/zip\\\",\\\"roles\\\":[\\\"data\\\",\\\"archive\\\",\\\"source\\\"]}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"fws-nwi\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"ai4edataeuwest\\\",\\\"msft:short_description\\\":\\\"Vector\n dataset containing wetlands boundaries and identification across the United\n States.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"usgs-lcmap-conus-v13\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-conus-v13/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-conus-v13\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"LCMAP\n CONUS Science Products\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Proprietary,\n Unrestricted\\\"},{\\\"rel\\\":\\\"source\\\",\\\"href\\\":\\\"https://www.usgs.gov/special-topics/lcmap/lcmap-data-access\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"USGS\n Data Access Options\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.1016/j.rse.2019.111356\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.1016/j.rse.2014.01.011\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5066/P9C46NG0\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/usgs-lcmap-conus-v13\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n LCMAP CONUS Collection 1.3\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/usgs-lcmap-conus-v13-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"USGS\n LCMAP CONUS 1.3 Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/usgs-lcmap-conus-v13.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-129.27732,21.805095,-63.11843,52.92172]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1985-01-01T00:00:00Z\\\",\\\"2021-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.5066/P9C46NG0\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"LCMAP\\\",\\\"Land\n Cover\\\",\\\"Land Cover Change\\\",\\\"CONUS\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.usgs.gov/special-topics/lcmap\\\",\\\"name\\\":\\\"United\n States Geological Survey\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"summaries\\\":{\\\"usgs_lcmap:vertical_tile\\\":{\\\"maximum\\\":20,\\\"minimum\\\":0},\\\"usgs_lcmap:horizontal_tile\\\":{\\\"maximum\\\":32,\\\"minimum\\\":1}},\\\"description\\\":\\\"The\n [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap)\n (LCMAP) product provides land cover mapping and change monitoring from the\n U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros)\n (EROS) Center. LCMAP's Science Products are developed by applying time-series\n modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data)\n (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011)\n (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations\n are fit to a harmonic model to predict future Landsat-like surface reflectance.\n Where Landsat surface reflectance observations differ significantly from those\n predictions, a change is identified. Attributes of the resulting model sequences\n (e.g., start/end dates, residuals, model coefficients) are then used to produce\n a set of land surface change products and as inputs to the subsequent classification\n to thematic land cover. \\\\n\\\\nThis [STAC](https://stacspec.org/en) Collection\n contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products),\n which was released in August 2022 for years 1985-2021. The data are tiled\n according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/)\n (COGs) and corresponding metadata files. Note that the provided COGs differ\n slightly from those in the USGS source data. They have been reprocessed to\n add overviews, \\\\\\\"nodata\\\\\\\" values where appropriate, and an updated projection\n definition.\\\\n\\\",\\\"item_assets\\\":{\\\"dates\\\":{\\\"type\\\":\\\"text/plain\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Landsat\n Observation Dates\\\",\\\"description\\\":\\\"Landsat observation dates used as input\n to the CCDC algorithm.\\\"},\\\"lcpri\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Primary Land Cover\\\",\\\"description\\\":\\\"Land\n cover classification consisting of eight general land cover types. The most\n likely land cover according to the modeling process.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"nodata\\\",\\\"value\\\":0,\\\"color_hint\\\":\\\"000000\\\",\\\"description\\\":\\\"No\n Data\\\"},{\\\"name\\\":\\\"developed\\\",\\\"value\\\":1,\\\"color_hint\\\":\\\"ff3232\\\",\\\"description\\\":\\\"Developed\\\"},{\\\"name\\\":\\\"cropland\\\",\\\"value\\\":2,\\\"color_hint\\\":\\\"be8c5a\\\",\\\"description\\\":\\\"Cropland\\\"},{\\\"name\\\":\\\"grass_shrub\\\",\\\"value\\\":3,\\\"color_hint\\\":\\\"e6f0d2\\\",\\\"description\\\":\\\"Grass/Shrub\\\"},{\\\"name\\\":\\\"tree_cover\\\",\\\"value\\\":4,\\\"color_hint\\\":\\\"1c6330\\\",\\\"description\\\":\\\"Tree\n Cover\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":5,\\\"color_hint\\\":\\\"0070ff\\\",\\\"description\\\":\\\"Water\\\"},{\\\"name\\\":\\\"wetlands\\\",\\\"value\\\":6,\\\"color_hint\\\":\\\"b3d9ff\\\",\\\"description\\\":\\\"Wetlands\\\"},{\\\"name\\\":\\\"ice_snow\\\",\\\"value\\\":7,\\\"color_hint\\\":\\\"ffffff\\\",\\\"description\\\":\\\"Snow/Ice\\\"},{\\\"name\\\":\\\"barren\\\",\\\"value\\\":8,\\\"color_hint\\\":\\\"b3aea3\\\",\\\"description\\\":\\\"Barren\\\"}]},\\\"lcsec\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Secondary\n Land Cover\\\",\\\"description\\\":\\\"Land cover classification consisting of eight\n general land cover types. The second most likely land cover according to the\n modeling process.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"nodata\\\",\\\"value\\\":0,\\\"color_hint\\\":\\\"000000\\\",\\\"description\\\":\\\"No\n Data\\\"},{\\\"name\\\":\\\"developed\\\",\\\"value\\\":1,\\\"color_hint\\\":\\\"ff3232\\\",\\\"description\\\":\\\"Developed\\\"},{\\\"name\\\":\\\"cropland\\\",\\\"value\\\":2,\\\"color_hint\\\":\\\"be8c5a\\\",\\\"description\\\":\\\"Cropland\\\"},{\\\"name\\\":\\\"grass_shrub\\\",\\\"value\\\":3,\\\"color_hint\\\":\\\"e6f0d2\\\",\\\"description\\\":\\\"Grass/Shrub\\\"},{\\\"name\\\":\\\"tree_cover\\\",\\\"value\\\":4,\\\"color_hint\\\":\\\"1c6330\\\",\\\"description\\\":\\\"Tree\n Cover\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":5,\\\"color_hint\\\":\\\"0070ff\\\",\\\"description\\\":\\\"Water\\\"},{\\\"name\\\":\\\"wetlands\\\",\\\"value\\\":6,\\\"color_hint\\\":\\\"b3d9ff\\\",\\\"description\\\":\\\"Wetlands\\\"},{\\\"name\\\":\\\"ice_snow\\\",\\\"value\\\":7,\\\"color_hint\\\":\\\"ffffff\\\",\\\"description\\\":\\\"Snow/Ice\\\"},{\\\"name\\\":\\\"barren\\\",\\\"value\\\":8,\\\"color_hint\\\":\\\"b3aea3\\\",\\\"description\\\":\\\"Barren\\\"}]},\\\"scmag\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Change\n Magnitude\\\",\\\"description\\\":\\\"The spectral strength or intensity of a time\n series model 'break' when spectral observations have diverged from the model\n predictions.\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":30}]},\\\"scmqa\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Model\n Quality\\\",\\\"description\\\":\\\"Information regarding the type of time series\n model applied to the current product year.\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"no_model\\\",\\\"value\\\":0,\\\"description\\\":\\\"No\n model established for July 1st of current year.\\\"},{\\\"name\\\":\\\"simple_model\\\",\\\"value\\\":4,\\\"description\\\":\\\"A\n partial, 4-coefficient harmonic model.\\\"},{\\\"name\\\":\\\"advanced_model\\\",\\\"value\\\":6,\\\"description\\\":\\\"A\n partial, 6-coefficient harmonic model.\\\"},{\\\"name\\\":\\\"full_model\\\",\\\"value\\\":8,\\\"description\\\":\\\"A\n full, 8-coefficient harmonic model.\\\"},{\\\"name\\\":\\\"start_fit\\\",\\\"value\\\":14,\\\"description\\\":\\\"A\n simple model at the beginning of a time series where sparse and/or highly\n variable spectral measurements prevent establishment of a harmonic model.\\\"},{\\\"name\\\":\\\"end_fit\\\",\\\"value\\\":24,\\\"description\\\":\\\"A\n simple model at the end of a time series where there are insufficient observations\n and/or time to establish a new harmonic model following a model break.\\\"},{\\\"name\\\":\\\"insufficient_clear\\\",\\\"value\\\":44,\\\"description\\\":\\\"A\n simple model for the entire time series in cases where fewer than 25% of input\n observations are labeled as 'Clear' or 'Water' by the U.S. Landsat ARD per-pixel\n quality band (PIXELQA).\\\"},{\\\"name\\\":\\\"persistent_snow\\\",\\\"value\\\":54,\\\"description\\\":\\\"A\n simple model for the entire time series in cases where 75% or more of input\n observations are labeled as 'Snow' by the U.S. Landsat ARD per-pixel quality\n band (PIXELQA).\\\"}]},\\\"browse\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"USGS Browse Image\\\",\\\"description\\\":\\\"Image\n generated by USGS for viewing LCMAP classification data in web applications.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"lcachg\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Annual\n Land Cover Change\\\",\\\"description\\\":\\\"Synthesis of Primary Land Cover of current\n and previous year identifying changes in land cover class.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"nodata\\\",\\\"value\\\":0,\\\"color_hint\\\":\\\"000000\\\",\\\"description\\\":\\\"No\n Data\\\"},{\\\"name\\\":\\\"developed\\\",\\\"value\\\":1,\\\"color_hint\\\":\\\"ff3232\\\",\\\"description\\\":\\\"Developed\\\"},{\\\"name\\\":\\\"cropland\\\",\\\"value\\\":2,\\\"color_hint\\\":\\\"be8c5a\\\",\\\"description\\\":\\\"Cropland\\\"},{\\\"name\\\":\\\"grass_shrub\\\",\\\"value\\\":3,\\\"color_hint\\\":\\\"e6f0d2\\\",\\\"description\\\":\\\"Grass/Shrub\\\"},{\\\"name\\\":\\\"tree_cover\\\",\\\"value\\\":4,\\\"color_hint\\\":\\\"1c6330\\\",\\\"description\\\":\\\"Tree\n Cover\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":5,\\\"color_hint\\\":\\\"0070ff\\\",\\\"description\\\":\\\"Water\\\"},{\\\"name\\\":\\\"wetlands\\\",\\\"value\\\":6,\\\"color_hint\\\":\\\"b3d9ff\\\",\\\"description\\\":\\\"Wetlands\\\"},{\\\"name\\\":\\\"ice_snow\\\",\\\"value\\\":7,\\\"color_hint\\\":\\\"ffffff\\\",\\\"description\\\":\\\"Snow/Ice\\\"},{\\\"name\\\":\\\"barren\\\",\\\"value\\\":8,\\\"color_hint\\\":\\\"b3aea3\\\",\\\"description\\\":\\\"Barren\\\"},{\\\"name\\\":\\\"1_to_2\\\",\\\"value\\\":12,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Cropland\\\"},{\\\"name\\\":\\\"1_to_3\\\",\\\"value\\\":13,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Grass/Shrub\\\"},{\\\"name\\\":\\\"1_to_4\\\",\\\"value\\\":14,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Tree Cover\\\"},{\\\"name\\\":\\\"1_to_5\\\",\\\"value\\\":15,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Water\\\"},{\\\"name\\\":\\\"1_to_6\\\",\\\"value\\\":16,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Wetlands\\\"},{\\\"name\\\":\\\"1_to_7\\\",\\\"value\\\":17,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Snow/Ice\\\"},{\\\"name\\\":\\\"1_to_8\\\",\\\"value\\\":18,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Barren\\\"},{\\\"name\\\":\\\"2_to_1\\\",\\\"value\\\":21,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Developed\\\"},{\\\"name\\\":\\\"2_to_3\\\",\\\"value\\\":23,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Grass/Shrub\\\"},{\\\"name\\\":\\\"2_to_4\\\",\\\"value\\\":24,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Tree Cover\\\"},{\\\"name\\\":\\\"2_to_5\\\",\\\"value\\\":25,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Water\\\"},{\\\"name\\\":\\\"2_to_6\\\",\\\"value\\\":26,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Wetlands\\\"},{\\\"name\\\":\\\"2_to_7\\\",\\\"value\\\":27,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Snow/Ice\\\"},{\\\"name\\\":\\\"2_to_8\\\",\\\"value\\\":28,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Barren\\\"},{\\\"name\\\":\\\"3_to_1\\\",\\\"value\\\":31,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Developed\\\"},{\\\"name\\\":\\\"3_to_2\\\",\\\"value\\\":32,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Cropland\\\"},{\\\"name\\\":\\\"3_to_4\\\",\\\"value\\\":34,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Tree Cover\\\"},{\\\"name\\\":\\\"3_to_5\\\",\\\"value\\\":35,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Water\\\"},{\\\"name\\\":\\\"3_to_6\\\",\\\"value\\\":36,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Wetlands\\\"},{\\\"name\\\":\\\"3_to_7\\\",\\\"value\\\":37,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Snow/Ice\\\"},{\\\"name\\\":\\\"3_to_8\\\",\\\"value\\\":38,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Barren\\\"},{\\\"name\\\":\\\"4_to_1\\\",\\\"value\\\":41,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Developed\\\"},{\\\"name\\\":\\\"4_to_2\\\",\\\"value\\\":42,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Cropland\\\"},{\\\"name\\\":\\\"4_to_3\\\",\\\"value\\\":43,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Grass/Shrub\\\"},{\\\"name\\\":\\\"4_to_5\\\",\\\"value\\\":45,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Water\\\"},{\\\"name\\\":\\\"4_to_6\\\",\\\"value\\\":46,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Wetlands\\\"},{\\\"name\\\":\\\"4_to_7\\\",\\\"value\\\":47,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Snow/Ice\\\"},{\\\"name\\\":\\\"4_to_8\\\",\\\"value\\\":48,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Barren\\\"},{\\\"name\\\":\\\"5_to_1\\\",\\\"value\\\":51,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Developed\\\"},{\\\"name\\\":\\\"5_to_2\\\",\\\"value\\\":52,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Cropland\\\"},{\\\"name\\\":\\\"5_to_3\\\",\\\"value\\\":53,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Grass/Shrub\\\"},{\\\"name\\\":\\\"5_to_4\\\",\\\"value\\\":54,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Tree Cover\\\"},{\\\"name\\\":\\\"5_to_6\\\",\\\"value\\\":56,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Wetlands\\\"},{\\\"name\\\":\\\"5_to_7\\\",\\\"value\\\":57,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Snow/Ice\\\"},{\\\"name\\\":\\\"5_to_8\\\",\\\"value\\\":58,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Barren\\\"},{\\\"name\\\":\\\"6_to_1\\\",\\\"value\\\":61,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Developed\\\"},{\\\"name\\\":\\\"6_to_2\\\",\\\"value\\\":62,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Cropland\\\"},{\\\"name\\\":\\\"6_to_3\\\",\\\"value\\\":63,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Grass/Shrub\\\"},{\\\"name\\\":\\\"6_to_4\\\",\\\"value\\\":64,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Tree Cover\\\"},{\\\"name\\\":\\\"6_to_5\\\",\\\"value\\\":65,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Water\\\"},{\\\"name\\\":\\\"6_to_7\\\",\\\"value\\\":67,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Snow/Ice\\\"},{\\\"name\\\":\\\"6_to_8\\\",\\\"value\\\":68,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Barren\\\"},{\\\"name\\\":\\\"7_to_1\\\",\\\"value\\\":71,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Developed\\\"},{\\\"name\\\":\\\"7_to_2\\\",\\\"value\\\":72,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Cropland\\\"},{\\\"name\\\":\\\"7_to_3\\\",\\\"value\\\":73,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Grass/Shrub\\\"},{\\\"name\\\":\\\"7_to_4\\\",\\\"value\\\":74,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Tree Cover\\\"},{\\\"name\\\":\\\"7_to_5\\\",\\\"value\\\":75,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Water\\\"},{\\\"name\\\":\\\"7_to_6\\\",\\\"value\\\":76,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Wetlands\\\"},{\\\"name\\\":\\\"7_to_8\\\",\\\"value\\\":78,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Barren\\\"},{\\\"name\\\":\\\"8_to_1\\\",\\\"value\\\":81,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Developed\\\"},{\\\"name\\\":\\\"8_to_2\\\",\\\"value\\\":82,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Cropland\\\"},{\\\"name\\\":\\\"8_to_3\\\",\\\"value\\\":83,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Grass/Shrub\\\"},{\\\"name\\\":\\\"8_to_4\\\",\\\"value\\\":84,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Tree Cover\\\"},{\\\"name\\\":\\\"8_to_5\\\",\\\"value\\\":85,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Water\\\"},{\\\"name\\\":\\\"8_to_6\\\",\\\"value\\\":86,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Wetlands\\\"},{\\\"name\\\":\\\"8_to_7\\\",\\\"value\\\":87,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Snow/Ice\\\"}]},\\\"sclast\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Time Since Last\n Change\\\",\\\"description\\\":\\\"Time, in days, since the last identified Spectral\n Change (SCTIME).\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"scstab\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Spectral\n Stability Period\\\",\\\"description\\\":\\\"Measure of the amount of time in days\n that a pixel has been in its current spectral state as of July 1st. Current\n spectral state can refer to both during stable time series segments or a period\n outside of stable time series segments.\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"sctime\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Time\n of Spectral Change\\\",\\\"description\\\":\\\"Represents the timing of a spectral\n change within the current product year as the day of year the change occurred.\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"lcpconf\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Primary\n Land Cover Confidence\\\",\\\"description\\\":\\\"Provides provenance tracking and\n a measure of confidence that the Primary Land Cover label matches the training\n data.\\\",\\\"file:values\\\":[{\\\"values\\\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],\\\"summary\\\":\\\"Measure\n of confidence that the Primary Land Cover label matches the training data.\\\"},{\\\"values\\\":[151],\\\"summary\\\":\\\"Time\n series model identified as transition from a Grass/Shrub class to a Tree Cover\n class. Primary Land Cover class assignment based on secondary analysis.\\\"},{\\\"values\\\":[152],\\\"summary\\\":\\\"Time\n series model identified as transition from a Tree Cover class to a Grass/Shrub\n class. Primary Land cover class assignment based on secondary analysis.\\\"},{\\\"values\\\":[201],\\\"summary\\\":\\\"No\n stable time series models were produced for this location. Primary Land Cover\n was assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP\n Level 1 classification schema, see LCMAP Science Product Guide for more information).\\\"},{\\\"values\\\":[202],\\\"summary\\\":\\\"Insufficient\n data available to extend most recent time series model past July 1st of current\n year. Land cover assigned the last identified cover class from earlier year.\\\"},{\\\"values\\\":[211],\\\"summary\\\":\\\"July\n 1st falls in a gap between two stable time series models of the same land\n cover class. Primary Land Cover assigned the primary land cover class of those\n before/after models.\\\"},{\\\"values\\\":[212],\\\"summary\\\":\\\"July 1st falls in\n a gap between two stable time series models of differing land cover class.\n If July 1st is before the 'break date' of the earlier model, Primary Land\n Cover is assigned the primary land cover class of that earlier model. Otherwise,\n Primary Land Cover is assigned the primary land cover class of the subsequent,\n later model.\\\"},{\\\"values\\\":[213],\\\"summary\\\":\\\"Insufficient data available\n to establish a stable time series model at the beginning of the time series\n prior to July 1st of the current year. Primary Land Cover assigned the primary\n land cover class of 1st subsequent model.\\\"},{\\\"values\\\":[214],\\\"summary\\\":\\\"Insufficient\n data available to establish a new stable time series model following a break\n near the end of the time series prior to July 1st of the current year. Primary\n Land Cover assigned the last identified primary land cover class from earlier\n year.\\\"}],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"lcsconf\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Secondary\n Land Cover Confidence\\\",\\\"description\\\":\\\"Provides provenance tracking and\n a measure of confidence that the Secondary Land Cover label matches the training\n data.\\\",\\\"file:values\\\":[{\\\"values\\\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],\\\"summary\\\":\\\"Measure\n of confidence that the Secondary Land Cover label matches the training data.\\\"},{\\\"values\\\":[151],\\\"summary\\\":\\\"Time\n series model identified as transition from a Grass/Shrub class to a Tree Cover\n class in Primary Land Cover. Primary Land Cover class assignment based on\n secondary analysis and Secondary Land Cover class assigned logical opposite\n of Primary.\\\"},{\\\"values\\\":[152],\\\"summary\\\":\\\"Time series model identified\n as transition from a Tree Cover class to a Grass/Shrub class in Primary Land\n Cover. Primary Land Cover class assignment based on secondary analysis and\n Secondary Land Cover class assigned logical opposite of Primary.\\\"},{\\\"values\\\":[201],\\\"summary\\\":\\\"No\n stable time series models were produced for this location. Secondary Land\n Cover assigned the land cover class present in NLCD-2001 (cross-walked to\n LCMAP classification schema, see LCMAP Science Product Guide for more information).\\\"},{\\\"values\\\":[202],\\\"summary\\\":\\\"Insufficient\n data available to extend most recent time series model past July 1st of current\n year. Secondary Land Cover assigned the last identified secondary cover class\n from earlier year.\\\"},{\\\"values\\\":[211],\\\"summary\\\":\\\"July 1st falls in a\n gap between two stable time series models of the same secondary land cover\n class. Secondary Land Cover assigned the land cover class of those before/after\n models.\\\"},{\\\"values\\\":[212],\\\"summary\\\":\\\"July 1st falls in a gap between\n two stable time series models of differing secondary land cover classes. If\n July 1st is before the 'break date' of the earlier model, Secondary Land Cover\n is assigned the secondary land cover class of the earlier model. Otherwise,\n Secondary Land Cover is assigned the secondary land cover class of the subsequent\n model.\\\"},{\\\"values\\\":[213],\\\"summary\\\":\\\"Insufficient data available to establish\n a stable time series model at the beginning of the time series prior to July\n 1st of the current year. Secondary Land Cover assigned the secondary land\n cover class of 1st subsequent model.\\\"},{\\\"values\\\":[214],\\\"summary\\\":\\\"Insufficient\n data available to establish a new stable time series model following a break\n near the end of the time series prior to July 1st of the current year. Secondary\n Land Cover assigned the last identified secondary cover class from earlier\n year.\\\"}],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"lcpri_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Primary\n Land Cover Metadata\\\",\\\"description\\\":\\\"Primary Land Cover product metadata\\\"},\\\"lcsec_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Secondary\n Land Cover Metadata\\\",\\\"description\\\":\\\"Secondary Land Cover product metadata\\\"},\\\"scmag_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Change\n Magnitude Metadata\\\",\\\"description\\\":\\\"Change Magnitude product metadata\\\"},\\\"scmqa_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Model\n Quality Metadata\\\",\\\"description\\\":\\\"Model Quality product metadata\\\"},\\\"lcachg_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Annual\n Land Cover Change Metadata\\\",\\\"description\\\":\\\"Annual Land Cover Change product\n metadata\\\"},\\\"sclast_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Time\n Since Last Change Metadata\\\",\\\"description\\\":\\\"Time Since Last Change product\n metadata\\\"},\\\"scstab_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Spectral\n Stability Period Metadata\\\",\\\"description\\\":\\\"Spectral Stability Period product\n metadata\\\"},\\\"sctime_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Time\n of Spectral Change Metadata\\\",\\\"description\\\":\\\"Time of Spectral Change product\n metadata\\\"},\\\"lcpconf_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Primary\n Land Cover Confidence Metadata\\\",\\\"description\\\":\\\"Primary Land Cover Confidence\n product metadata\\\"},\\\"lcsconf_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Secondary\n Land Cover Confidence Metadata\\\",\\\"description\\\":\\\"Secondary Land Cover Confidence\n product metadata\\\"}},\\\"sci:citation\\\":\\\"U.S. Geological Survey (USGS), 2022,\n Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3\n Science Products for the Conterminous United States: USGS data release\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"usgs-lcmap\\\",\\\"msft:container\\\":\\\"lcmap\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/classification/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/file/v2.1.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.1016/j.rse.2019.111356\\\",\\\"citation\\\":\\\"Brown,\n J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E.,\n Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith,\n K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant,\n A.L., Zelenak, D., Reker, R.R., and Rover, J., 2020, Lessons learned implementing\n an operational continuous United States national land change monitoring capability-The\n Land Change Monitoring, Assessment, and Projection (LCMAP) approach: Remote\n Sensing of Environment, v. 238, article 111356\\\"},{\\\"doi\\\":\\\"10.1016/j.rse.2014.01.011\\\",\\\"citation\\\":\\\"Zhu,\n Z., and Woodcock, C.E., 2014, Continuous change detection and classification\n of land cover using all available Landsat data: Remote Sensing of Environment,\n v. 144, p. 152-171\\\"}],\\\"msft:storage_account\\\":\\\"landcoverdata\\\",\\\"msft:short_description\\\":\\\"USGS\n Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3\n Science Products for the Conterminous United States.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"usgs-lcmap-hawaii-v10\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-hawaii-v10/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/usgs-lcmap-hawaii-v10\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"LCMAP\n Hawaii Science Products\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Proprietary,\n Unrestricted\\\"},{\\\"rel\\\":\\\"source\\\",\\\"href\\\":\\\"https://www.usgs.gov/special-topics/lcmap/lcmap-data-access\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"USGS\n Data Access Options\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.1016/j.jag.2022.103015\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.1016/j.rse.2019.111356\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.5066/P91E8M23\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/usgs-lcmap-hawaii-v10\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"USGS\n LCMAP Hawaii Collection 1.0\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/usgs-lcmap-hawaii-v10-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"USGS\n LCMAP Hawaii 1.0 Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/usgs-lcmap-hawaii-v10.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-161.27577,18.505136,-154.058649,22.624478]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2000-01-01T00:00:00Z\\\",\\\"2020-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.5066/P91E8M23\\\",\\\"keywords\\\":[\\\"USGS\\\",\\\"LCMAP\\\",\\\"Land\n Cover\\\",\\\"Land Cover Change\\\",\\\"Hawaii\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.usgs.gov/special-topics/lcmap\\\",\\\"name\\\":\\\"United\n States Geological Survey\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"summaries\\\":{\\\"usgs_lcmap:vertical_tile\\\":{\\\"maximum\\\":2,\\\"minimum\\\":0},\\\"usgs_lcmap:horizontal_tile\\\":{\\\"maximum\\\":4,\\\"minimum\\\":0}},\\\"description\\\":\\\"The\n [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap)\n (LCMAP) product provides land cover mapping and change monitoring from the\n U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros)\n (EROS) Center. LCMAP's Science Products are developed by applying time-series\n modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data)\n (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011)\n (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations\n are fit to a harmonic model to predict future Landsat-like surface reflectance.\n Where Landsat surface reflectance observations differ significantly from those\n predictions, a change is identified. Attributes of the resulting model sequences\n (e.g., start/end dates, residuals, model coefficients) are then used to produce\n a set of land surface change products and as inputs to the subsequent classification\n to thematic land cover. \\\\n\\\\nThis [STAC](https://stacspec.org/en) Collection\n contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products),\n which was released in January 2022 for years 2000-2020. The data are tiled\n according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/)\n (COGs) and corresponding metadata files. Note that the provided COGs differ\n slightly from those in the USGS source data. They have been reprocessed to\n add overviews, \\\\\\\"nodata\\\\\\\" values where appropriate, and an updated projection\n definition.\\\\n\\\",\\\"item_assets\\\":{\\\"dates\\\":{\\\"type\\\":\\\"text/plain\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Landsat\n Observation Dates\\\",\\\"description\\\":\\\"Landsat observation dates used as input\n to the CCDC algorithm.\\\"},\\\"lcpri\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Primary Land Cover\\\",\\\"description\\\":\\\"Land\n cover classification consisting of eight general land cover types. The most\n likely land cover according to the modeling process.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"nodata\\\",\\\"value\\\":0,\\\"color_hint\\\":\\\"000000\\\",\\\"description\\\":\\\"No\n Data\\\"},{\\\"name\\\":\\\"developed\\\",\\\"value\\\":1,\\\"color_hint\\\":\\\"ff3232\\\",\\\"description\\\":\\\"Developed\\\"},{\\\"name\\\":\\\"cropland\\\",\\\"value\\\":2,\\\"color_hint\\\":\\\"be8c5a\\\",\\\"description\\\":\\\"Cropland\\\"},{\\\"name\\\":\\\"grass_shrub\\\",\\\"value\\\":3,\\\"color_hint\\\":\\\"e6f0d2\\\",\\\"description\\\":\\\"Grass/Shrub\\\"},{\\\"name\\\":\\\"tree_cover\\\",\\\"value\\\":4,\\\"color_hint\\\":\\\"1c6330\\\",\\\"description\\\":\\\"Tree\n Cover\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":5,\\\"color_hint\\\":\\\"0070ff\\\",\\\"description\\\":\\\"Water\\\"},{\\\"name\\\":\\\"wetlands\\\",\\\"value\\\":6,\\\"color_hint\\\":\\\"b3d9ff\\\",\\\"description\\\":\\\"Wetlands\\\"},{\\\"name\\\":\\\"ice_snow\\\",\\\"value\\\":7,\\\"color_hint\\\":\\\"ffffff\\\",\\\"description\\\":\\\"Snow/Ice\\\"},{\\\"name\\\":\\\"barren\\\",\\\"value\\\":8,\\\"color_hint\\\":\\\"b3aea3\\\",\\\"description\\\":\\\"Barren\\\"}]},\\\"lcsec\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Secondary\n Land Cover\\\",\\\"description\\\":\\\"Land cover classification consisting of eight\n general land cover types. The second most likely land cover according to the\n modeling process.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"nodata\\\",\\\"value\\\":0,\\\"color_hint\\\":\\\"000000\\\",\\\"description\\\":\\\"No\n Data\\\"},{\\\"name\\\":\\\"developed\\\",\\\"value\\\":1,\\\"color_hint\\\":\\\"ff3232\\\",\\\"description\\\":\\\"Developed\\\"},{\\\"name\\\":\\\"cropland\\\",\\\"value\\\":2,\\\"color_hint\\\":\\\"be8c5a\\\",\\\"description\\\":\\\"Cropland\\\"},{\\\"name\\\":\\\"grass_shrub\\\",\\\"value\\\":3,\\\"color_hint\\\":\\\"e6f0d2\\\",\\\"description\\\":\\\"Grass/Shrub\\\"},{\\\"name\\\":\\\"tree_cover\\\",\\\"value\\\":4,\\\"color_hint\\\":\\\"1c6330\\\",\\\"description\\\":\\\"Tree\n Cover\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":5,\\\"color_hint\\\":\\\"0070ff\\\",\\\"description\\\":\\\"Water\\\"},{\\\"name\\\":\\\"wetlands\\\",\\\"value\\\":6,\\\"color_hint\\\":\\\"b3d9ff\\\",\\\"description\\\":\\\"Wetlands\\\"},{\\\"name\\\":\\\"ice_snow\\\",\\\"value\\\":7,\\\"color_hint\\\":\\\"ffffff\\\",\\\"description\\\":\\\"Snow/Ice\\\"},{\\\"name\\\":\\\"barren\\\",\\\"value\\\":8,\\\"color_hint\\\":\\\"b3aea3\\\",\\\"description\\\":\\\"Barren\\\"}]},\\\"scmag\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Change\n Magnitude\\\",\\\"description\\\":\\\"The spectral strength or intensity of a time\n series model 'break' when spectral observations have diverged from the model\n predictions.\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":30}]},\\\"scmqa\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Model\n Quality\\\",\\\"description\\\":\\\"Information regarding the type of time series\n model applied to the current product year.\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"no_model\\\",\\\"value\\\":0,\\\"description\\\":\\\"No\n model established for July 1st of current year.\\\"},{\\\"name\\\":\\\"simple_model\\\",\\\"value\\\":4,\\\"description\\\":\\\"A\n partial, 4-coefficient harmonic model.\\\"},{\\\"name\\\":\\\"advanced_model\\\",\\\"value\\\":6,\\\"description\\\":\\\"A\n partial, 6-coefficient harmonic model.\\\"},{\\\"name\\\":\\\"full_model\\\",\\\"value\\\":8,\\\"description\\\":\\\"A\n full, 8-coefficient harmonic model.\\\"},{\\\"name\\\":\\\"start_fit\\\",\\\"value\\\":14,\\\"description\\\":\\\"A\n simple model at the beginning of a time series where sparse and/or highly\n variable spectral measurements prevent establishment of a harmonic model.\\\"},{\\\"name\\\":\\\"end_fit\\\",\\\"value\\\":24,\\\"description\\\":\\\"A\n simple model at the end of a time series where there are insufficient observations\n and/or time to establish a new harmonic model following a model break.\\\"},{\\\"name\\\":\\\"insufficient_clear\\\",\\\"value\\\":44,\\\"description\\\":\\\"A\n simple model for the entire time series in cases where fewer than 25% of input\n observations are labeled as 'Clear' or 'Water' by the U.S. Landsat ARD per-pixel\n quality band (PIXELQA).\\\"},{\\\"name\\\":\\\"persistent_snow\\\",\\\"value\\\":54,\\\"description\\\":\\\"A\n simple model for the entire time series in cases where 75% or more of input\n observations are labeled as 'Snow' by the U.S. Landsat ARD per-pixel quality\n band (PIXELQA).\\\"}]},\\\"browse\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"USGS Browse Image\\\",\\\"description\\\":\\\"Image\n generated by USGS for viewing LCMAP classification data in web applications.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"lcachg\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Annual\n Land Cover Change\\\",\\\"description\\\":\\\"Synthesis of Primary Land Cover of current\n and previous year identifying changes in land cover class.\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}],\\\"classification:classes\\\":[{\\\"name\\\":\\\"nodata\\\",\\\"value\\\":0,\\\"color_hint\\\":\\\"000000\\\",\\\"description\\\":\\\"No\n Data\\\"},{\\\"name\\\":\\\"developed\\\",\\\"value\\\":1,\\\"color_hint\\\":\\\"ff3232\\\",\\\"description\\\":\\\"Developed\\\"},{\\\"name\\\":\\\"cropland\\\",\\\"value\\\":2,\\\"color_hint\\\":\\\"be8c5a\\\",\\\"description\\\":\\\"Cropland\\\"},{\\\"name\\\":\\\"grass_shrub\\\",\\\"value\\\":3,\\\"color_hint\\\":\\\"e6f0d2\\\",\\\"description\\\":\\\"Grass/Shrub\\\"},{\\\"name\\\":\\\"tree_cover\\\",\\\"value\\\":4,\\\"color_hint\\\":\\\"1c6330\\\",\\\"description\\\":\\\"Tree\n Cover\\\"},{\\\"name\\\":\\\"water\\\",\\\"value\\\":5,\\\"color_hint\\\":\\\"0070ff\\\",\\\"description\\\":\\\"Water\\\"},{\\\"name\\\":\\\"wetlands\\\",\\\"value\\\":6,\\\"color_hint\\\":\\\"b3d9ff\\\",\\\"description\\\":\\\"Wetlands\\\"},{\\\"name\\\":\\\"ice_snow\\\",\\\"value\\\":7,\\\"color_hint\\\":\\\"ffffff\\\",\\\"description\\\":\\\"Snow/Ice\\\"},{\\\"name\\\":\\\"barren\\\",\\\"value\\\":8,\\\"color_hint\\\":\\\"b3aea3\\\",\\\"description\\\":\\\"Barren\\\"},{\\\"name\\\":\\\"1_to_2\\\",\\\"value\\\":12,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Cropland\\\"},{\\\"name\\\":\\\"1_to_3\\\",\\\"value\\\":13,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Grass/Shrub\\\"},{\\\"name\\\":\\\"1_to_4\\\",\\\"value\\\":14,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Tree Cover\\\"},{\\\"name\\\":\\\"1_to_5\\\",\\\"value\\\":15,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Water\\\"},{\\\"name\\\":\\\"1_to_6\\\",\\\"value\\\":16,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Wetlands\\\"},{\\\"name\\\":\\\"1_to_7\\\",\\\"value\\\":17,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Snow/Ice\\\"},{\\\"name\\\":\\\"1_to_8\\\",\\\"value\\\":18,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Developed to Barren\\\"},{\\\"name\\\":\\\"2_to_1\\\",\\\"value\\\":21,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Developed\\\"},{\\\"name\\\":\\\"2_to_3\\\",\\\"value\\\":23,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Grass/Shrub\\\"},{\\\"name\\\":\\\"2_to_4\\\",\\\"value\\\":24,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Tree Cover\\\"},{\\\"name\\\":\\\"2_to_5\\\",\\\"value\\\":25,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Water\\\"},{\\\"name\\\":\\\"2_to_6\\\",\\\"value\\\":26,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Wetlands\\\"},{\\\"name\\\":\\\"2_to_7\\\",\\\"value\\\":27,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Snow/Ice\\\"},{\\\"name\\\":\\\"2_to_8\\\",\\\"value\\\":28,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Cropland to Barren\\\"},{\\\"name\\\":\\\"3_to_1\\\",\\\"value\\\":31,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Developed\\\"},{\\\"name\\\":\\\"3_to_2\\\",\\\"value\\\":32,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Cropland\\\"},{\\\"name\\\":\\\"3_to_4\\\",\\\"value\\\":34,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Tree Cover\\\"},{\\\"name\\\":\\\"3_to_5\\\",\\\"value\\\":35,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Water\\\"},{\\\"name\\\":\\\"3_to_6\\\",\\\"value\\\":36,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Wetlands\\\"},{\\\"name\\\":\\\"3_to_7\\\",\\\"value\\\":37,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Snow/Ice\\\"},{\\\"name\\\":\\\"3_to_8\\\",\\\"value\\\":38,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Grass/Shrub to Barren\\\"},{\\\"name\\\":\\\"4_to_1\\\",\\\"value\\\":41,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Developed\\\"},{\\\"name\\\":\\\"4_to_2\\\",\\\"value\\\":42,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Cropland\\\"},{\\\"name\\\":\\\"4_to_3\\\",\\\"value\\\":43,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Grass/Shrub\\\"},{\\\"name\\\":\\\"4_to_5\\\",\\\"value\\\":45,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Water\\\"},{\\\"name\\\":\\\"4_to_6\\\",\\\"value\\\":46,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Wetlands\\\"},{\\\"name\\\":\\\"4_to_7\\\",\\\"value\\\":47,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Snow/Ice\\\"},{\\\"name\\\":\\\"4_to_8\\\",\\\"value\\\":48,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Tree Cover to Barren\\\"},{\\\"name\\\":\\\"5_to_1\\\",\\\"value\\\":51,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Developed\\\"},{\\\"name\\\":\\\"5_to_2\\\",\\\"value\\\":52,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Cropland\\\"},{\\\"name\\\":\\\"5_to_3\\\",\\\"value\\\":53,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Grass/Shrub\\\"},{\\\"name\\\":\\\"5_to_4\\\",\\\"value\\\":54,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Tree Cover\\\"},{\\\"name\\\":\\\"5_to_6\\\",\\\"value\\\":56,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Wetlands\\\"},{\\\"name\\\":\\\"5_to_7\\\",\\\"value\\\":57,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Snow/Ice\\\"},{\\\"name\\\":\\\"5_to_8\\\",\\\"value\\\":58,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Water to Barren\\\"},{\\\"name\\\":\\\"6_to_1\\\",\\\"value\\\":61,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Developed\\\"},{\\\"name\\\":\\\"6_to_2\\\",\\\"value\\\":62,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Cropland\\\"},{\\\"name\\\":\\\"6_to_3\\\",\\\"value\\\":63,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Grass/Shrub\\\"},{\\\"name\\\":\\\"6_to_4\\\",\\\"value\\\":64,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Tree Cover\\\"},{\\\"name\\\":\\\"6_to_5\\\",\\\"value\\\":65,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Water\\\"},{\\\"name\\\":\\\"6_to_7\\\",\\\"value\\\":67,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Snow/Ice\\\"},{\\\"name\\\":\\\"6_to_8\\\",\\\"value\\\":68,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Wetlands to Barren\\\"},{\\\"name\\\":\\\"7_to_1\\\",\\\"value\\\":71,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Developed\\\"},{\\\"name\\\":\\\"7_to_2\\\",\\\"value\\\":72,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Cropland\\\"},{\\\"name\\\":\\\"7_to_3\\\",\\\"value\\\":73,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Grass/Shrub\\\"},{\\\"name\\\":\\\"7_to_4\\\",\\\"value\\\":74,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Tree Cover\\\"},{\\\"name\\\":\\\"7_to_5\\\",\\\"value\\\":75,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Water\\\"},{\\\"name\\\":\\\"7_to_6\\\",\\\"value\\\":76,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Wetlands\\\"},{\\\"name\\\":\\\"7_to_8\\\",\\\"value\\\":78,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Snow/Ice to Barren\\\"},{\\\"name\\\":\\\"8_to_1\\\",\\\"value\\\":81,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Developed\\\"},{\\\"name\\\":\\\"8_to_2\\\",\\\"value\\\":82,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Cropland\\\"},{\\\"name\\\":\\\"8_to_3\\\",\\\"value\\\":83,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Grass/Shrub\\\"},{\\\"name\\\":\\\"8_to_4\\\",\\\"value\\\":84,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Tree Cover\\\"},{\\\"name\\\":\\\"8_to_5\\\",\\\"value\\\":85,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Water\\\"},{\\\"name\\\":\\\"8_to_6\\\",\\\"value\\\":86,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Wetlands\\\"},{\\\"name\\\":\\\"8_to_7\\\",\\\"value\\\":87,\\\"color_hint\\\":\\\"ab00d6\\\",\\\"description\\\":\\\"Cover\n change from Barren to Snow/Ice\\\"}]},\\\"sclast\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Time Since Last\n Change\\\",\\\"description\\\":\\\"Time, in days, since the last identified Spectral\n Change (SCTIME).\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"scstab\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Spectral\n Stability Period\\\",\\\"description\\\":\\\"Measure of the amount of time in days\n that a pixel has been in its current spectral state as of July 1st. Current\n spectral state can refer to both during stable time series segments or a period\n outside of stable time series segments.\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"sctime\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Time\n of Spectral Change\\\",\\\"description\\\":\\\"Represents the timing of a spectral\n change within the current product year as the day of year the change occurred.\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":30}]},\\\"lcpconf\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Primary\n Land Cover Confidence\\\",\\\"description\\\":\\\"Provides provenance tracking and\n a measure of confidence that the Primary Land Cover label matches the training\n data.\\\",\\\"file:values\\\":[{\\\"values\\\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],\\\"summary\\\":\\\"Measure\n of confidence that the Primary Land Cover label matches the training data.\\\"},{\\\"values\\\":[151],\\\"summary\\\":\\\"Time\n series model identified as transition from a Grass/Shrub class to a Tree Cover\n class. Primary Land Cover class assignment based on secondary analysis.\\\"},{\\\"values\\\":[152],\\\"summary\\\":\\\"Time\n series model identified as transition from a Tree Cover class to a Grass/Shrub\n class. Primary Land cover class assignment based on secondary analysis.\\\"},{\\\"values\\\":[201],\\\"summary\\\":\\\"No\n stable time series models were produced for this location. Primary Land Cover\n was assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP\n Level 1 classification schema, see LCMAP Science Product Guide for more information).\\\"},{\\\"values\\\":[202],\\\"summary\\\":\\\"Insufficient\n data available to extend most recent time series model past July 1st of current\n year. Land cover assigned the last identified cover class from earlier year.\\\"},{\\\"values\\\":[211],\\\"summary\\\":\\\"July\n 1st falls in a gap between two stable time series models of the same land\n cover class. Primary Land Cover assigned the primary land cover class of those\n before/after models.\\\"},{\\\"values\\\":[212],\\\"summary\\\":\\\"July 1st falls in\n a gap between two stable time series models of differing land cover class.\n If July 1st is before the 'break date' of the earlier model, Primary Land\n Cover is assigned the primary land cover class of that earlier model. Otherwise,\n Primary Land Cover is assigned the primary land cover class of the subsequent,\n later model.\\\"},{\\\"values\\\":[213],\\\"summary\\\":\\\"Insufficient data available\n to establish a stable time series model at the beginning of the time series\n prior to July 1st of the current year. Primary Land Cover assigned the primary\n land cover class of 1st subsequent model.\\\"},{\\\"values\\\":[214],\\\"summary\\\":\\\"Insufficient\n data available to establish a new stable time series model following a break\n near the end of the time series prior to July 1st of the current year. Primary\n Land Cover assigned the last identified primary land cover class from earlier\n year.\\\"}],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"lcsconf\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Secondary\n Land Cover Confidence\\\",\\\"description\\\":\\\"Provides provenance tracking and\n a measure of confidence that the Secondary Land Cover label matches the training\n data.\\\",\\\"file:values\\\":[{\\\"values\\\":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],\\\"summary\\\":\\\"Measure\n of confidence that the Secondary Land Cover label matches the training data.\\\"},{\\\"values\\\":[151],\\\"summary\\\":\\\"Time\n series model identified as transition from a Grass/Shrub class to a Tree Cover\n class in Primary Land Cover. Primary Land Cover class assignment based on\n secondary analysis and Secondary Land Cover class assigned logical opposite\n of Primary.\\\"},{\\\"values\\\":[152],\\\"summary\\\":\\\"Time series model identified\n as transition from a Tree Cover class to a Grass/Shrub class in Primary Land\n Cover. Primary Land Cover class assignment based on secondary analysis and\n Secondary Land Cover class assigned logical opposite of Primary.\\\"},{\\\"values\\\":[201],\\\"summary\\\":\\\"No\n stable time series models were produced for this location. Secondary Land\n Cover assigned the land cover class present in NLCD-2001 (cross-walked to\n LCMAP classification schema, see LCMAP Science Product Guide for more information).\\\"},{\\\"values\\\":[202],\\\"summary\\\":\\\"Insufficient\n data available to extend most recent time series model past July 1st of current\n year. Secondary Land Cover assigned the last identified secondary cover class\n from earlier year.\\\"},{\\\"values\\\":[211],\\\"summary\\\":\\\"July 1st falls in a\n gap between two stable time series models of the same secondary land cover\n class. Secondary Land Cover assigned the land cover class of those before/after\n models.\\\"},{\\\"values\\\":[212],\\\"summary\\\":\\\"July 1st falls in a gap between\n two stable time series models of differing secondary land cover classes. If\n July 1st is before the 'break date' of the earlier model, Secondary Land Cover\n is assigned the secondary land cover class of the earlier model. Otherwise,\n Secondary Land Cover is assigned the secondary land cover class of the subsequent\n model.\\\"},{\\\"values\\\":[213],\\\"summary\\\":\\\"Insufficient data available to establish\n a stable time series model at the beginning of the time series prior to July\n 1st of the current year. Secondary Land Cover assigned the secondary land\n cover class of 1st subsequent model.\\\"},{\\\"values\\\":[214],\\\"summary\\\":\\\"Insufficient\n data available to establish a new stable time series model following a break\n near the end of the time series prior to July 1st of the current year. Secondary\n Land Cover assigned the last identified secondary cover class from earlier\n year.\\\"}],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"lcpri_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Primary\n Land Cover Metadata\\\",\\\"description\\\":\\\"Primary Land Cover product metadata\\\"},\\\"lcsec_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Secondary\n Land Cover Metadata\\\",\\\"description\\\":\\\"Secondary Land Cover product metadata\\\"},\\\"scmag_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Change\n Magnitude Metadata\\\",\\\"description\\\":\\\"Change Magnitude product metadata\\\"},\\\"scmqa_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Model\n Quality Metadata\\\",\\\"description\\\":\\\"Model Quality product metadata\\\"},\\\"lcachg_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Annual\n Land Cover Change Metadata\\\",\\\"description\\\":\\\"Annual Land Cover Change product\n metadata\\\"},\\\"sclast_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Time\n Since Last Change Metadata\\\",\\\"description\\\":\\\"Time Since Last Change product\n metadata\\\"},\\\"scstab_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Spectral\n Stability Period Metadata\\\",\\\"description\\\":\\\"Spectral Stability Period product\n metadata\\\"},\\\"sctime_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Time\n of Spectral Change Metadata\\\",\\\"description\\\":\\\"Time of Spectral Change product\n metadata\\\"},\\\"lcpconf_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Primary\n Land Cover Confidence Metadata\\\",\\\"description\\\":\\\"Primary Land Cover Confidence\n product metadata\\\"},\\\"lcsconf_metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Secondary\n Land Cover Confidence Metadata\\\",\\\"description\\\":\\\"Secondary Land Cover Confidence\n product metadata\\\"}},\\\"sci:citation\\\":\\\"U.S. Geological Survey (USGS), 2022,\n Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0\n Science Products for Hawaii: USGS data release\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"usgs-lcmap\\\",\\\"msft:container\\\":\\\"lcmap\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/classification/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/file/v2.1.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.1016/j.jag.2022.103015\\\",\\\"citation\\\":\\\"Li,\n C., Xian, G., Wellington, D., Smith, K., Horton, J., & Zhou, Q., 2022,\n Development of the LCMAP annual land cover product across Hawaii: International\n Journal of Applied Earth Observation and Geoinformation, v. 113, article 103015.\\\"},{\\\"doi\\\":\\\"10.1016/j.rse.2019.111356\\\",\\\"citation\\\":\\\"Brown,\n J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E.,\n Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith,\n K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant,\n A.L., Zelenak, D., Reker, R.R., and Rover, J., 2020, Lessons learned implementing\n an operational continuous United States national land change monitoring capability-The\n Land Change Monitoring, Assessment, and Projection (LCMAP) approach: Remote\n Sensing of Environment, v. 238, article 111356\\\"}],\\\"msft:storage_account\\\":\\\"landcoverdata\\\",\\\"msft:short_description\\\":\\\"USGS\n Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0\n Science Products for Hawaii\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"noaa-climate-normals-tabular\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-tabular/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-tabular\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.1175/BAMS-D-11-00197.1\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.1175/BAMS-D-11-00173.1\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NOAA\n U.S. Climate Normals Landing Page\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.noaa.gov/information-technology/open-data-dissemination\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NOAA\n Open Data Dissemination\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-tabular\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"NOAA\n US Tabular Climate Normals\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-climate-normals-tabular-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"NOAA\n U.S. Tabular Climate Normals Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-climate-normals-tabular.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-177.38333,-14.3306,174.1,71.3214]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1981-01-01T00:00:00Z\\\",\\\"2020-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"NOAA\\\",\\\"Climate\n Normals\\\",\\\"Weather\\\",\\\"Surface Observations\\\",\\\"Climatology\\\",\\\"CONUS\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ncei.noaa.gov/\\\",\\\"name\\\":\\\"NOAA\n National Centers for Environmental Information\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"summaries\\\":{\\\"noaa_climate_normals:period\\\":[\\\"1981-2010\\\",\\\"1991-2020\\\",\\\"2006-2020\\\"],\\\"noaa_climate_normals:frequency\\\":[\\\"hourly\\\",\\\"daily\\\",\\\"monthly\\\",\\\"annualseasonal\\\"]},\\\"description\\\":\\\"The\n [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals)\n provide information about typical climate conditions for thousands of weather\n station locations across the United States. Normals act both as a ruler to\n compare current weather and as a predictor of conditions in the near future.\n The official normals are calculated for a uniform 30 year period, and consist\n of annual/seasonal, monthly, daily, and hourly averages and statistics of\n temperature, precipitation, and other climatological variables for each weather\n station. \\\\n\\\\nNOAA produces Climate Normals in accordance with the [World\n Meteorological Organization](https://public.wmo.int/en) (WMO), of which the\n United States is a member. The WMO requires each member nation to compute\n 30-year meteorological quantity averages at least every 30 years, and recommends\n an update each decade, in part to incorporate newer weather stations. The\n 1991\\u20132020 U.S. Climate Normals are the latest in a series of decadal\n normals first produced in the 1950s. \\\\n\\\\nThis Collection contains tabular\n weather variable data at weather station locations in GeoParquet format, converted\n from the source CSV files. The source NetCDF files are delivered to Azure\n as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\n\\\\nData\n are provided for annual/seasonal, monthly, daily, and hourly frequencies for\n the following time periods:\\\\n\\\\n- Legacy 30-year normals (1981\\u20132010)\\\\n-\n Supplemental 15-year normals (2006\\u20132020)\\\\n\\\",\\\"item_assets\\\":{\\\"geoparquet\\\":{\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Dataset\n root\\\",\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"noaanormals\\\"}}},\\\"msft:region\\\":\\\"eastus\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"table:tables\\\":[{\\\"name\\\":\\\"1981_2010-hourly\\\",\\\"description\\\":\\\"Hourly\n Climate Normals for Period 1981-2010\\\"},{\\\"name\\\":\\\"1981_2010-daily\\\",\\\"description\\\":\\\"Daily\n Climate Normals for Period 1981-2010\\\"},{\\\"name\\\":\\\"1981_2010-monthly\\\",\\\"description\\\":\\\"Monthly\n Climate Normals for Period 1981-2010\\\"},{\\\"name\\\":\\\"1981_2010-annualseasonal\\\",\\\"description\\\":\\\"Annual/Seasonal\n Climate Normals for Period 1981-2010\\\"},{\\\"name\\\":\\\"1991_2020-hourly\\\",\\\"description\\\":\\\"Hourly\n Climate Normals for Period 1991-2020\\\"},{\\\"name\\\":\\\"1991_2020-daily\\\",\\\"description\\\":\\\"Daily\n Climate Normals for Period 1991-2020\\\"},{\\\"name\\\":\\\"1991_2020-monthly\\\",\\\"description\\\":\\\"Monthly\n Climate Normals for Period 1991-2020\\\"},{\\\"name\\\":\\\"1991_2020-annualseasonal\\\",\\\"description\\\":\\\"Annual/Seasonal\n Climate Normals for Period 1991-2020\\\"},{\\\"name\\\":\\\"2006_2020-hourly\\\",\\\"description\\\":\\\"Hourly\n Climate Normals for Period 2006-2020\\\"},{\\\"name\\\":\\\"2006_2020-daily\\\",\\\"description\\\":\\\"Daily\n Climate Normals for Period 2006-2020\\\"},{\\\"name\\\":\\\"2006_2020-monthly\\\",\\\"description\\\":\\\"Monthly\n Climate Normals for Period 2006-2020\\\"},{\\\"name\\\":\\\"2006_2020-annualseasonal\\\",\\\"description\\\":\\\"Annual/Seasonal\n Climate Normals for Period 2006-2020\\\"}],\\\"msft:group_id\\\":\\\"noaa-climate-normals\\\",\\\"msft:container\\\":\\\"climate-normals-geoparquet\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"sci:publications\\\":[{\\\"doi\\\":\\\"10.1175/BAMS-D-11-00197.1\\\",\\\"citation\\\":\\\"Arguez,\n A., I. Durre, S. Applequist, R. Vose, M. Squires, X. Yin, R. Heim, and T.\n Owen, 2012: NOAA's 1981-2010 climate normals: An overview. Bull. Amer. Meteor.\n Soc., 93, 1687-1697. DOI: 10.1175/BAMS-D-11-00197.1.\\\"},{\\\"doi\\\":\\\"10.1175/BAMS-D-11-00173.1\\\",\\\"citation\\\":\\\"Applequist,\n S., A. Arguez, I. Durre, M. Squires, R. Vose, and X. Yin, 2012: 1981-2010\n U.S. Hourly Normals. Bulletin of the American Meteorological Society, 93,\n 1637-1640. DOI: 10.1175/BAMS-D-11-00173.1.\\\"}],\\\"msft:storage_account\\\":\\\"noaanormals\\\",\\\"msft:short_description\\\":\\\"Tabular\n Climate Normal data for over 15,000 United States weather station locations\n in GeoParquet format.\\\"},{\\\"id\\\":\\\"noaa-climate-normals-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-netcdf\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NOAA\n U.S. Climate Normals Landing Page\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.noaa.gov/information-technology/open-data-dissemination\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NOAA\n Open Data Dissemination\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/sites/default/files/2022-04/Readme_Monthly_Gridded_Normals.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Readme\n for Monthly Gridded Normals\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/sites/default/files/2022-04/Documentation_Monthly_Gridded_Normals.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Documentation\n for Monthly Gridded Normals\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"NOAA\n US Gridded Climate Normals (NetCDF)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-climate-normals-netcdf-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"NOAA\n U.S. Gridded Climate Normals (NetCDF) Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-climate-normals-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-124.708333,24.541666,-66.999995,49.375001]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1901-01-01T00:00:00Z\\\",\\\"2020-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"NOAA\\\",\\\"Climate\n Normals\\\",\\\"Weather\\\",\\\"Surface Observations\\\",\\\"Climatology\\\",\\\"CONUS\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ncei.noaa.gov/\\\",\\\"name\\\":\\\"NOAA\n National Centers for Environmental Information\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"noaa_climate_normals:period\\\":[\\\"1901-2000\\\",\\\"1991-2020\\\",\\\"2006-2020\\\"],\\\"noaa_climate_normals:frequency\\\":[\\\"daily\\\",\\\"monthly\\\"]},\\\"description\\\":\\\"The\n [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027)\n provide a continuous grid of temperature and precipitation data across the\n contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid\n dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid),\n and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore\n match that of NClimGrid. Monthly, seasonal, and annual gridded normals are\n computed from simple averages of the NClimGrid data and are provided for three\n time-periods: 1901\\u20132020, 1991\\u20132020, and 2006\\u20132020. Daily gridded\n normals are smoothed for a smooth transition from one day to another and are\n provided for two time-periods: 1991\\u20132020, and 2006\\u20132020.\\\\n\\\\nNOAA\n produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en)\n (WMO), of which the United States is a member. The WMO requires each member\n nation to compute 30-year meteorological quantity averages at least every\n 30 years, and recommends an update each decade, in part to incorporate newer\n weather stations. The 1991\\u20132020 U.S. Climate Normals are the latest in\n a series of decadal normals first produced in the 1950s. \\\\n\\\\nThe data in\n this Collection are the original NetCDF files provided by NOAA's National\n Centers for Environmental Information. This Collection contains gridded data\n for the following frequencies and time periods:\\\\n\\\\n- Annual, seasonal, and\n monthly normals\\\\n - 100-year (1901\\u20132000)\\\\n - 30-year (1991\\u20132020)\\\\n\n \\ - 15-year (2006\\u20132020)\\\\n- Daily normals\\\\n - 30-year (1991\\u20132020)\\\\n\n \\ - 15-year (2006\\u20132020)\\\\n\\\\nFor most use-cases, we recommend using\n the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded)\n collection, which contains the same data in Cloud Optimized GeoTIFF format.\n The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination\n (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\n\\\",\\\"item_assets\\\":{\\\"netcdf\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"]}},\\\"msft:region\\\":\\\"eastus\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"noaa-climate-normals\\\",\\\"msft:container\\\":\\\"gridded-normals\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"noaanormals\\\",\\\"msft:short_description\\\":\\\"Gridded\n Climate Normals for the contiguous United States in the original NetCDF format\n provided by NOAA's National Centers for Environmental Information.\\\"},{\\\"id\\\":\\\"goes-glm\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/goes-glm\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01527\\\",\\\"title\\\":\\\"License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01527\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Product\n Landing Page\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.goes-r.gov/users/docs/PUG-main-vol1.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Product\n Definition and Users' Guide (PUG) Vol.1 Main\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Product\n Definition and Users' Guide (PUG) Vol.5 Level 2+ Products\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.7289/V5KH0KK6\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/goes-glm\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"GOES-R\n Lightning Detection\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/goes-glm-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"GOES\n GLM Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/goes-glm.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"QS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-66.56,-8.44,66.56],[156.44,-66.56,180.0,66.56]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2018-02-13T16:10:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.7289/V5KH0KK6\\\",\\\"keywords\\\":[\\\"GOES\\\",\\\"NOAA\\\",\\\"NASA\\\",\\\"Satellite\\\",\\\"Lightning\\\",\\\"Weather\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.nasa.gov/content/goes\\\",\\\"name\\\":\\\"NASA\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://www.goes-r.gov/\\\",\\\"name\\\":\\\"NOAA\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[8000],\\\"mission\\\":[\\\"GOES\\\"],\\\"platform\\\":[\\\"GOES-16\\\",\\\"GOES-17\\\",\\\"GOES-18\\\",\\\"GOES-19\\\"],\\\"instruments\\\":[\\\"FM1\\\",\\\"FM2\\\"],\\\"constellation\\\":[\\\"GOES\\\"],\\\"processing:level\\\":[\\\"L2\\\"],\\\"goes:orbital-slot\\\":[\\\"West\\\",\\\"East\\\",\\\"Test\\\"]},\\\"description\\\":\\\"The\n [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html)\n is a single-channel, near-infrared optical transient detector that can detect\n the momentary changes in an optical scene, indicating the presence of lightning.\n GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground)\n activity continuously over the Americas and adjacent ocean regions with near-uniform\n spatial resolution of approximately 10 km. GLM collects information such as\n the frequency, location and extent of lightning discharges to identify intensifying\n thunderstorms and tropical cyclones. Trends in total lightning available from\n the GLM provide critical information to forecasters, allowing them to focus\n on developing severe storms much earlier and before these storms produce damaging\n winds, hail or even tornadoes.\\\\n\\\\nThe GLM data product consists of a hierarchy\n of earth-located lightning radiant energy measures including events, groups,\n and flashes:\\\\n\\\\n- Lightning events are detected by the instrument.\\\\n- Lightning\n groups are a collection of one or more lightning events that satisfy temporal\n and spatial coincidence thresholds.\\\\n- Similarly, lightning flashes are a\n collection of one or more lightning groups that satisfy temporal and spatial\n coincidence thresholds.\\\\n\\\\nThe product includes the relationship among lightning\n events, groups, and flashes, and the area coverage of lightning groups and\n flashes. The product also includes processing and data quality metadata, and\n satellite state and location information. \\\\n\\\\nThe NetCDF files are delivered\n to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\",\\\"item_assets\\\":{\\\"netcdf\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Original\n NetCDF4 file\\\"}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"sci:citation\\\":\\\"GOES-R\n Algorithm Working Group and GOES-R Series Program, (2018): NOAA GOES-R Series\n Geostationary Lightning Mapper (GLM) Level 2 Lightning Detection: Events,\n Groups, and Flashes. NOAA National Centers for Environmental Information.\n doi:10.7289/V5KH0KK6.\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"goes\\\",\\\"msft:container\\\":\\\"noaa-goes-geoparquet\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/goes/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/processing/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"goeseuwest\\\",\\\"msft:short_description\\\":\\\"Continuous\n lightning detection over the Western Hemisphere from the Geostationary Lightning\n Mapper (GLM) instrument.\\\"},{\\\"id\\\":\\\"sentinel-1-grd\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-1-grd\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"title\\\":\\\"Copernicus\n Sentinel data terms\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel\n 1 Level-1 Ground Range Detected (GRD)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-1-grd.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Sentinel\n 1 GRD\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-1-grd.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"MS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2014-10-10T00:28:21Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"ESA\\\",\\\"Copernicus\\\",\\\"Sentinel\\\",\\\"C-Band\\\",\\\"SAR\\\",\\\"GRD\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"SENTINEL-1A\\\",\\\"SENTINEL-1B\\\",\\\"SENTINEL-1C\\\"],\\\"constellation\\\":[\\\"Sentinel-1\\\"],\\\"s1:resolution\\\":[\\\"full\\\",\\\"high\\\",\\\"medium\\\"],\\\"s1:orbit_source\\\":[\\\"DOWNLINK\\\",\\\"POEORB\\\",\\\"PREORB\\\",\\\"RESORB\\\"],\\\"sar:looks_range\\\":[5,6,3,2],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"sar:product_type\\\":[\\\"GRD\\\"],\\\"sar:looks_azimuth\\\":[1,6,2],\\\"sar:polarizations\\\":[[\\\"VV\\\",\\\"VH\\\"],[\\\"HH\\\",\\\"HV\\\"],[\\\"VV\\\"],[\\\"VH\\\"],[\\\"HH\\\"],[\\\"HV\\\"]],\\\"sar:frequency_band\\\":[\\\"C\\\"],\\\"s1:processing_level\\\":[\\\"1\\\"],\\\"sar:instrument_mode\\\":[\\\"IW\\\",\\\"EW\\\",\\\"SM\\\"],\\\"sar:center_frequency\\\":[5.405],\\\"sar:resolution_range\\\":[20,23,50,93,9],\\\"s1:product_timeliness\\\":[\\\"NRT-10m\\\",\\\"NRT-1h\\\",\\\"NRT-3h\\\",\\\"Fast-24h\\\",\\\"Off-line\\\",\\\"Reprocessing\\\"],\\\"sar:resolution_azimuth\\\":[22,23,50,87,9],\\\"sar:pixel_spacing_range\\\":[10,25,40,3.5],\\\"sar:observation_direction\\\":[\\\"right\\\"],\\\"sar:pixel_spacing_azimuth\\\":[10,25,40,3.5],\\\"sar:looks_equivalent_number\\\":[4.4,29.7,2.7,10.7,3.7],\\\"sat:platform_international_designator\\\":[\\\"2014-016A\\\",\\\"2016-025A\\\",\\\"0000-000A\\\"]},\\\"description\\\":\\\"The\n [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission\n is a constellation of two polar-orbiting satellites, operating day and night\n performing C-band synthetic aperture radar imaging. The Level-1 Ground Range\n Detected (GRD) products in this Collection consist of focused SAR data that\n has been detected, multi-looked and projected to ground range using the Earth\n ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected\n using the terrain height specified in the product general annotation. The\n terrain height used varies in azimuth but is constant in range (but can be\n different for each IW/EW sub-swath).\\\\n\\\\nGround range coordinates are the\n slant range coordinates projected onto the ellipsoid of the Earth. Pixel values\n represent detected amplitude. Phase information is lost. The resulting product\n has approximately square resolution pixels and square pixel spacing with reduced\n speckle at a cost of reduced spatial resolution.\\\\n\\\\nFor the IW and EW GRD\n products, multi-looking is performed on each burst individually. All bursts\n in all sub-swaths are then seamlessly merged to form a single, contiguous,\n ground range, detected image per polarization.\\\\n\\\\nFor more information see\n the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\\\\n\\\\n###\n Terrain Correction\\\\n\\\\nUsers might want to geometrically or radiometrically\n terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC\n Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc)\n collection is a global radiometrically terrain corrected dataset derived from\n Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any\n DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM).\n See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/)\n for more.\\\",\\\"item_assets\\\":{\\\"hh\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"HH: horizontal\n transmit, horizontal receive\\\",\\\"description\\\":\\\"Amplitude of signal transmitted\n with horizontal polarization and received with horizontal polarization with\n radiometric terrain correction applied.\\\"},\\\"hv\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"HV:\n horizontal transmit, vertical receive\\\",\\\"description\\\":\\\"Amplitude of signal\n transmitted with horizontal polarization and received with vertical polarization\n with radiometric terrain correction applied.\\\"},\\\"vh\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VH:\n vertical transmit, horizontal receive\\\",\\\"description\\\":\\\"Amplitude of signal\n transmitted with vertical polarization and received with horizontal polarization\n with radiometric terrain correction applied.\\\"},\\\"vv\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VV:\n vertical transmit, vertical receive\\\",\\\"description\\\":\\\"Amplitude of signal\n transmitted with vertical polarization and received with vertical polarization\n with radiometric terrain correction applied.\\\"},\\\"thumbnail\\\":{\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Preview\n Image\\\",\\\"description\\\":\\\"An averaged, decimated preview image in PNG format.\n Single polarisation products are represented with a grey scale image. Dual\n polarisation products are represented by a single composite colour image in\n RGB with the red channel (R) representing the co-polarisation VV or HH),\n the green channel (G) represents the cross-polarisation (VH or HV) and the\n blue channel (B) represents the ratio of the cross an co-polarisations.\\\"},\\\"safe-manifest\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Manifest\n File\\\",\\\"description\\\":\\\"General product metadata in XML format. Contains\n a high-level textual description of the product and references to all of product's\n components, the product metadata, including the product identification and\n the resource references, and references to the physical location of each component\n file contained in the product.\\\"},\\\"schema-noise-hh\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Noise\n Schema\\\",\\\"description\\\":\\\"Estimated thermal noise look-up tables\\\"},\\\"schema-noise-hv\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Noise\n Schema\\\",\\\"description\\\":\\\"Estimated thermal noise look-up tables\\\"},\\\"schema-noise-vh\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Noise\n Schema\\\",\\\"description\\\":\\\"Estimated thermal noise look-up tables\\\"},\\\"schema-noise-vv\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Noise\n Schema\\\",\\\"description\\\":\\\"Estimated thermal noise look-up tables\\\"},\\\"schema-product-hh\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Product\n Schema\\\",\\\"description\\\":\\\"Describes the main characteristics corresponding\n to the band: state of the platform during acquisition, image properties, Doppler\n information, geographic location, etc.\\\"},\\\"schema-product-hv\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Product\n Schema\\\",\\\"description\\\":\\\"Describes the main characteristics corresponding\n to the band: state of the platform during acquisition, image properties, Doppler\n information, geographic location, etc.\\\"},\\\"schema-product-vh\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Product\n Schema\\\",\\\"description\\\":\\\"Describes the main characteristics corresponding\n to the band: state of the platform during acquisition, image properties, Doppler\n information, geographic location, etc.\\\"},\\\"schema-product-vv\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Product\n Schema\\\",\\\"description\\\":\\\"Describes the main characteristics corresponding\n to the band: state of the platform during acquisition, image properties, Doppler\n information, geographic location, etc.\\\"},\\\"schema-calibration-hh\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Calibration\n Schema\\\",\\\"description\\\":\\\"Calibration metadata including calibration information\n and the beta nought, sigma nought, gamma and digital number look-up tables\n that can be used for absolute product calibration.\\\"},\\\"schema-calibration-hv\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Calibration\n Schema\\\",\\\"description\\\":\\\"Calibration metadata including calibration information\n and the beta nought, sigma nought, gamma and digital number look-up tables\n that can be used for absolute product calibration.\\\"},\\\"schema-calibration-vh\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Calibration\n Schema\\\",\\\"description\\\":\\\"Calibration metadata including calibration information\n and the beta nought, sigma nought, gamma and digital number look-up tables\n that can be used for absolute product calibration.\\\"},\\\"schema-calibration-vv\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"Calibration\n Schema\\\",\\\"description\\\":\\\"Calibration metadata including calibration information\n and the beta nought, sigma nought, gamma and digital number look-up tables\n that can be used for absolute product calibration.\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-1\\\",\\\"msft:container\\\":\\\"s1-grd\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sar/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel1euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-1\n Level-1 Ground Range Detected (GRD) products consist of focused SAR data that\n has been detected, multi-looked and projected to ground range using an Earth\n ellipsoid model.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"noaa-climate-normals-gridded\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-gridded/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-climate-normals-gridded\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NOAA\n U.S. Climate Normals Landing Page\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.noaa.gov/information-technology/open-data-dissemination\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"NOAA\n Open Data Dissemination\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/sites/default/files/2022-04/Readme_Monthly_Gridded_Normals.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Readme\n for Monthly Gridded Normals\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/sites/default/files/2022-04/Documentation_Monthly_Gridded_Normals.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Documentation\n for Monthly Gridded Normals\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"NOAA\n US Gridded Climate Normals (Cloud-Optimized GeoTIFF)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-climate-normals-gridded-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"NOAA\n U.S. Gridded Climate Normals (Cloud-Optimized GeoTIFF) Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-climate-normals-gridded.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-124.708333,24.541666,-66.999995,49.375001]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1901-01-01T00:00:00Z\\\",\\\"2020-12-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"NOAA\\\",\\\"Climate\n Normals\\\",\\\"Weather\\\",\\\"Surface Observations\\\",\\\"Climatology\\\",\\\"CONUS\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ncei.noaa.gov/\\\",\\\"name\\\":\\\"NOAA\n National Centers for Environmental Information\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"summaries\\\":{\\\"noaa_climate_normals:period\\\":[\\\"1901-2000\\\",\\\"1991-2020\\\",\\\"2006-2020\\\"],\\\"noaa_climate_normals:frequency\\\":[\\\"daily\\\",\\\"monthly\\\",\\\"seasonal\\\",\\\"annual\\\"]},\\\"description\\\":\\\"The\n [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027)\n provide a continuous grid of temperature and precipitation data across the\n contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid\n dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid),\n and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore\n match that of NClimGrid. Monthly, seasonal, and annual gridded normals are\n computed from simple averages of the NClimGrid data and are provided for three\n time-periods: 1901\\u20132020, 1991\\u20132020, and 2006\\u20132020. Daily gridded\n normals are smoothed for a smooth transition from one day to another and are\n provided for two time-periods: 1991\\u20132020, and 2006\\u20132020.\\\\n\\\\nNOAA\n produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en)\n (WMO), of which the United States is a member. The WMO requires each member\n nation to compute 30-year meteorological quantity averages at least every\n 30 years, and recommends an update each decade, in part to incorporate newer\n weather stations. The 1991\\u20132020 U.S. Climate Normals are the latest in\n a series of decadal normals first produced in the 1950s. \\\\n\\\\nThis Collection\n contains gridded data for the following frequencies and time periods:\\\\n\\\\n-\n Annual, seasonal, and monthly normals\\\\n - 100-year (1901\\u20132000)\\\\n\n \\ - 30-year (1991\\u20132020)\\\\n - 15-year (2006\\u20132020)\\\\n- Daily\n normals\\\\n - 30-year (1991\\u20132020)\\\\n - 15-year (2006\\u20132020)\\\\n\\\\nThe\n data in this Collection have been converted from the original NetCDF format\n to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered\n to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\n\\\\n##\n STAC Metadata\\\\n\\\\nThe STAC items in this collection contain several custom\n fields that can be used to further filter the data.\\\\n\\\\n* `noaa_climate_normals:period`:\n Climate normal time period. This can be \\\\\\\"1901-2000\\\\\\\", \\\\\\\"1991-2020\\\\\\\",\n or \\\\\\\"2006-2020\\\\\\\".\\\\n* `noaa_climate_normals:frequency`: Climate normal\n temporal interval (frequency). This can be \\\\\\\"daily\\\\\\\", \\\\\\\"monthly\\\\\\\",\n \\\\\\\"seasonal\\\\\\\" , or \\\\\\\"annual\\\\\\\"\\\\n* `noaa_climate_normals:time_index`:\n Time step index, e.g., month of year (1-12).\\\\n\\\\nThe `description` field\n of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions\n are\\\\n\\\\n* annual: \\\\\\\"Annual precipitation normals from monthly precipitation\n normal values\\\\\\\"\\\\n* seasonal: \\\\\\\"Seasonal precipitation normals (WSSF)\n from monthly normals\\\\\\\"\\\\n* monthly: \\\\\\\"Monthly precipitation normals from\n monthly precipitation values\\\\\\\"\\\\n* daily: \\\\\\\"Precipitation normals from\n daily averages\\\\\\\"\\\\n\\\\nCheck the assets on individual items for the appropriate\n description.\\\\n\\\\nThe STAC keys for most assets consist of two abbreviations.\n A \\\\\\\"variable\\\\\\\":\\\\n\\\\n\\\\n| Abbreviation | Description |\\\\n|\n ------------ | ---------------------------------------- |\\\\n| prcp |\n Precipitation over the time period |\\\\n| tavg | Mean temperature\n over the time period |\\\\n| tmax | Maximum temperature over the\n time period |\\\\n| tmin | Minimum temperature over the time period\n |\\\\n\\\\nAnd an \\\\\\\"aggregation\\\\\\\":\\\\n\\\\n| Abbreviation | Description\n \\ |\\\\n| ------------ | ------------------------------------------------------------------------------\n |\\\\n| max | Maximum of the variable over the time period |\\\\n|\n min | Minimum of the variable over the time period |\\\\n|\n std | Standard deviation of the value over the time period |\\\\n|\n flag | An count of the number of inputs (months, years, etc.) to calculate\n the normal |\\\\n| norm | The normal for the variable over the time\n period |\\\\n\\\\nSo, for example, `prcp_max` for\n monthly data is the \\\\\\\"Maximum values of all input monthly precipitation\n normal values\\\\\\\".\\\\n\\\",\\\"item_assets\\\":{\\\"prcp_max\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeter\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"prcp_min\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeter\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"prcp_std\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeter\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tavg_max\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tavg_min\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tavg_std\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmax_max\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmax_min\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmax_std\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmin_max\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmin_min\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmin_std\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"prcp_flag\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"months\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"prcp_norm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeter\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tavg_flag\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"months\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tavg_norm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmax_flag\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"months\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmax_norm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmin_flag\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"months\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"tmin_norm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"m2dprcp_norm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Month-To-Day\n Precipitation normals\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeter\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]},\\\"y2dprcp_norm\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Year-To-Day\n Precipitation normals\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeter\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":5000}]}},\\\"msft:region\\\":\\\"eastus\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"noaa-climate-normals\\\",\\\"msft:container\\\":\\\"gridded-normals-cogs\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"noaanormals\\\",\\\"msft:short_description\\\":\\\"Gridded\n Climate Normals for the contiguous United States in Cloud Optimized GeoTIFF\n (COG) format.\\\"},{\\\"id\\\":\\\"aster-l1t\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.usgs.gov/core-science-systems/hdds/data-policy\\\",\\\"title\\\":\\\"Public\n Domain\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/aster-l1t\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/aster-l1t\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"ASTER\n L1T\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/aster.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"ASTER\n L1T\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/aster-l1t.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"AS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2000-03-04T12:00:00Z\\\",\\\"2006-12-31T12:00:00Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"ASTER\\\",\\\"USGS\\\",\\\"NASA\\\",\\\"Satellite\\\",\\\"Global\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://terra.nasa.gov/about/terra-instruments/aster\\\",\\\"name\\\":\\\"NASA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/aster-overview/\\\",\\\"name\\\":\\\"USGS\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\",\\\"processor\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[15,30,90],\\\"eo:bands\\\":[{\\\"gsd\\\":15,\\\"name\\\":\\\"VNIR_Band1\\\",\\\"common_name\\\":\\\"green\\\",\\\"description\\\":\\\"visible\n yellow/green\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.08},{\\\"gsd\\\":15,\\\"name\\\":\\\"VNIR_Band2\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"visible\n red\\\",\\\"center_wavelength\\\":0.66,\\\"full_width_half_max\\\":0.06},{\\\"gsd\\\":15,\\\"name\\\":\\\"VNIR_Band3N\\\",\\\"common_name\\\":\\\"nir08\\\",\\\"description\\\":\\\"near\n infrared\\\",\\\"center_wavelength\\\":0.82,\\\"full_width_half_max\\\":0.08},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band4\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":1.65,\\\"full_width_half_max\\\":0.1},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band5\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.165,\\\"full_width_half_max\\\":0.04},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band6\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.205,\\\"full_width_half_max\\\":0.04},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band7\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.26,\\\"full_width_half_max\\\":0.05},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band8\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.339,\\\"full_width_half_max\\\":0.07},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band9\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.395,\\\"full_width_half_max\\\":0.07},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band10\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":8.3,\\\"full_width_half_max\\\":0.35},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band11\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":8.65,\\\"full_width_half_max\\\":0.35},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band12\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":9.11,\\\"full_width_half_max\\\":0.35},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band13\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":10.6,\\\"full_width_half_max\\\":0.7},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band14\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":11.3,\\\"full_width_half_max\\\":0.7}],\\\"platform\\\":[\\\"terra\\\"],\\\"instruments\\\":[\\\"aster\\\"]},\\\"description\\\":\\\"The\n [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument,\n launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999,\n provides multispectral images of the Earth at 15m-90m resolution. ASTER images\n provide information about land surface temperature, color, elevation, and\n mineral composition.\\\\n\\\\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/)\n data from 2000-2006. L1T images have been terrain-corrected and rotated to\n a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/)\n format.\\\\n\\\",\\\"item_assets\\\":{\\\"TIR\\\":{\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"TIR\n Swath data\\\",\\\"eo:bands\\\":[{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band10\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":8.3,\\\"full_width_half_max\\\":0.35},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band11\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":8.65,\\\"full_width_half_max\\\":0.35},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band12\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":9.11,\\\"full_width_half_max\\\":0.35},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band13\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":10.6,\\\"full_width_half_max\\\":0.7},{\\\"gsd\\\":90,\\\"name\\\":\\\"TIR_Band14\\\",\\\"common_name\\\":\\\"lwir\\\",\\\"description\\\":\\\"thermal\n infrared\\\",\\\"center_wavelength\\\":11.3,\\\"full_width_half_max\\\":0.7}],\\\"raster:bands\\\":[{\\\"name\\\":\\\"TIR_Band10\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":90},{\\\"name\\\":\\\"TIR_Band11\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":90},{\\\"name\\\":\\\"TIR_Band12\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":90},{\\\"name\\\":\\\"TIR_Band13\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":90},{\\\"name\\\":\\\"TIR_Band14\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"spatial_resolution\\\":90}]},\\\"xml\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"XML\n metadata\\\"},\\\"SWIR\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"SWIR\n Swath data\\\",\\\"eo:bands\\\":[{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band4\\\",\\\"common_name\\\":\\\"swir\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":1.65,\\\"full_width_half_max\\\":0.1},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band5\\\",\\\"common_name\\\":\\\"swir\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.165,\\\"full_width_half_max\\\":0.04},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band6\\\",\\\"common_name\\\":\\\"swir\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.205,\\\"full_width_half_max\\\":0.04},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band7\\\",\\\"common_name\\\":\\\"swir\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.26,\\\"full_width_half_max\\\":0.05},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band8\\\",\\\"common_name\\\":\\\"swir\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.339,\\\"full_width_half_max\\\":0.07},{\\\"gsd\\\":30,\\\"name\\\":\\\"SWIR_Band9\\\",\\\"common_name\\\":\\\"swir\\\",\\\"description\\\":\\\"short-wave\n infrared\\\",\\\"center_wavelength\\\":2.395,\\\"full_width_half_max\\\":0.07}],\\\"raster:bands\\\":[{\\\"name\\\":\\\"SWIR_Band4\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30},{\\\"name\\\":\\\"SWIR_Band5\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30},{\\\"name\\\":\\\"SWIR_Band6\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30},{\\\"name\\\":\\\"SWIR_Band7\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30},{\\\"name\\\":\\\"SWIR_Band8\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30},{\\\"name\\\":\\\"SWIR_Band9\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":30}]},\\\"VNIR\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VNIR\n Swath data\\\",\\\"eo:bands\\\":[{\\\"gsd\\\":15,\\\"name\\\":\\\"VNIR_Band1\\\",\\\"common_name\\\":\\\"yellow/green\\\",\\\"description\\\":\\\"visible\n yellow/green\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.08},{\\\"gsd\\\":15,\\\"name\\\":\\\"VNIR_Band2\\\",\\\"common_name\\\":\\\"red\\\",\\\"description\\\":\\\"visible\n red\\\",\\\"center_wavelength\\\":0.66,\\\"full_width_half_max\\\":0.06},{\\\"gsd\\\":15,\\\"name\\\":\\\"VNIR_Band3N\\\",\\\"common_name\\\":\\\"near\n infrared\\\",\\\"description\\\":\\\"near infrared\\\",\\\"center_wavelength\\\":0.82,\\\"full_width_half_max\\\":0.08}],\\\"raster:bands\\\":[{\\\"name\\\":\\\"VNIR_Band1\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":15},{\\\"name\\\":\\\"VNIR_Band2\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":15},{\\\"name\\\":\\\"VNIR_Band3N\\\",\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint8\\\",\\\"spatial_resolution\\\":15}]},\\\"qa-txt\\\":{\\\"type\\\":\\\"text/plain\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"title\\\":\\\"QA\n browse file\\\",\\\"description\\\":\\\"Geometric quality assessment report.\\\"},\\\"qa-browse\\\":{\\\"type\\\":\\\"image/jpeg\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"QA\n browse file\\\",\\\"description\\\":\\\"Single-band black and white reduced resolution\n browse overlaid with red, green, and blue (RGB) markers for GCPs used during\n the geometric verification quality check.\\\"},\\\"tir-browse\\\":{\\\"type\\\":\\\"image/jpeg\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Standalone\n reduced resolution TIR\\\"},\\\"vnir-browse\\\":{\\\"type\\\":\\\"image/jpeg\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"VNIR\n browse file\\\",\\\"description\\\":\\\"Standalone reduced resolution VNIR\\\"}},\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"aster\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"astersa\\\",\\\"msft:short_description\\\":\\\"The\n ASTER instrument, launched on-board NASA's Terra satellite in 1999, provides\n multispectral images of the Earth at 15m-90m resolution. This dataset contains\n ASTER data from 2000-2006.\\\",\\\"msft:region\\\":\\\"westeurope\\\"},{\\\"id\\\":\\\"cil-gdpcir-cc-by-sa\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by-sa/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by-sa\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://spdx.org/licenses/CC-BY-SA-4.0.html\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Creative\n Commons Attribution Share Alike 4.0 International\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://zenodo.org/record/6403794\\\",\\\"type\\\":\\\"text/html\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://github.com/ClimateImpactLab/downscaleCMIP6/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Project\n homepage\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"CIL\n Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"title\\\":\\\"Thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1950-01-01T00:00:00Z\\\",\\\"2100-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CC-BY-SA-4.0\\\",\\\"sci:doi\\\":\\\"10.5194/egusphere-2022-1513\\\",\\\"keywords\\\":[\\\"CMIP6\\\",\\\"Climate\n Impact Lab\\\",\\\"Rhodium Group\\\",\\\"Precipitation\\\",\\\"Temperature\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://impactlab.org/\\\",\\\"name\\\":\\\"Climate\n Impact Lab\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"cmip6:variable\\\":[\\\"pr\\\",\\\"tasmax\\\",\\\"tasmin\\\"],\\\"cmip6:source_id\\\":[\\\"CanESM5\\\"],\\\"cmip6:experiment_id\\\":[\\\"historical\\\",\\\"ssp126\\\",\\\"ssp245\\\",\\\"ssp370\\\",\\\"ssp585\\\"],\\\"cmip6:institution_id\\\":[\\\"BCC\\\",\\\"CAS\\\",\\\"CCCma\\\",\\\"CMCC\\\",\\\"CSIRO\\\",\\\"CSIRO-ARCCSS\\\",\\\"DKRZ\\\",\\\"EC-Earth-Consortium\\\",\\\"INM\\\",\\\"MIROC\\\",\\\"MOHC\\\",\\\"MPI-M\\\",\\\"NCC\\\",\\\"NOAA-GFDL\\\",\\\"NUIST\\\"]},\\\"description\\\":\\\"The\n World Climate Research Programme's [6th Coupled Model Intercomparison Project\n (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an\n enormous advance in the quality, detail, and scope of climate modeling.\\\\n\\\\nThe\n [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6)\n dataset makes this modeling more applicable to understanding the impacts of\n changes in the climate on humans and society with two key developments: trend-preserving\n bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org)\n provides global, daily minimum and maximum air temperature at the surface\n (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`)\n corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5\n scenarios for 25 global climate models on a 1/4-degree regular global grid.\\\\n\\\\n##\n Accessing the data\\\\n\\\\nGDPCIR data can be accessed on the Microsoft Planetary\n Computer. The dataset is made of of three collections, distinguished by data\n license:\\\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\\\n*\n [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\\\n*\n [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\\\\n\\\\nEach\n modeling center with bias corrected and downscaled data in this collection\n falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by-sa#available-institutions-models-and-scenarios-by-license-collection)\n to see which model is in each collection, and see the section below on [Citing,\n Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project)\n for citations and additional information about each license.\\\\n\\\\n## Data\n format & contents\\\\n\\\\nThe data is stored as partitioned zarr stores (see\n [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which\n includes thousands of data and metadata files covering the full time span\n of the experiment. Historical zarr stores contain just over 50 GB, while SSP\n zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with\n dimensions time (daily datetime), lat (float latitude), and lon (float longitude).\n The data is chunked at each interval of 365 days and 90 degree interval of\n latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each\n chunk occupying approximately 179MB in memory.\\\\n\\\\nHistorical data is daily,\n excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily,\n excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100,\n depending on data availability in the source GCM.\\\\n\\\\nThe spatial domain\n covers all 0.25-degree grid cells, indexed by the grid center, with grid edges\n on the quarter-degree, using a -180 to 180 longitude convention. Thus, the\n \\u201Clon\\u201D coordinate extends from -179.875 to 179.875, and the \\u201Clat\\u201D\n coordinate extends from -89.875 to 89.875, with intermediate values at each\n 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\\\n\\\\n##\n Available institutions, models, and scenarios by license collection\\\\n\\\\n|\n Modeling institution | Source model | Available experiments\n \\ | License collection |\\\\n| -------------------- | -----------------\n | ------------------------------------------ | ---------------------- |\\\\n|\n CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5\n \\ | Public domain datasets |\\\\n| INM | INM-CM4-8\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets\n |\\\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0,\n and SSP5-8.5 | Public domain datasets |\\\\n| BCC | BCC-CSM2-MR\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\\\n|\n CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5\n \\ | CC-BY-40] |\\\\n| CMCC | CMCC-ESM2 |\n ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40] |\\\\n|\n CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 |\n CC-BY-40] |\\\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6,\n SSP2-4.5, and SSP3-7.0 | CC-BY-40] |\\\\n| MIROC |\n MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]\n \\ |\\\\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5,\n SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\\\n| MOHC |\n HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40]\n \\ |\\\\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5,\n SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\\\n| MPI-M |\n MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]\n \\ |\\\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and\n SSP5-8.5 | CC-BY-40] |\\\\n| NCC |\n NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40]\n \\ |\\\\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5,\n SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\\\n| NOAA-GFDL |\n GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40]\n \\ |\\\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5,\n SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\\\n| NUIST |\n NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40]\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5,\n ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-AerChem | ssp370 | CC-BY-40]\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and\n ssp585 | CC-BY-40] |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40]\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5,\n ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\\\\n| CCCma |\n CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-SA-40]\n \\ |\\\\n\\\\n*Notes:*\\\\n\\\\n[^1]: At the time of running, no ssp1-2.6\n precipitation data was available. Therefore, we provide `tasmin` and `tamax`\n for this model and experiment, but not `pr`. All other model/experiment combinations\n in the above table include all three variables.\\\\n\\\\n[^2]: The institution\n which ran MPI-ESM1-2-HR\\u2019s historical (CMIP) simulations is `MPI-M`, while\n the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution\n component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP`\n scenarios.\\\\n\\\\n## Project methods\\\\n\\\\nThis project makes use of statistical\n bias correction and downscaling algorithms, which are specifically designed\n to accurately represent changes in the extremes. For this reason, we selected\n Quantile Delta Mapping (QDM), following the method introduced by [Cannon et\n al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific\n trends from the GCM while fitting the full distribution for a given day-of-year\n to a reference dataset (ERA5).\\\\n\\\\nWe then introduce a similar method tailored\n to increase spatial resolution while preserving extreme behavior, Quantile-Preserving\n Localized-Analog Downscaling (QPLAD).\\\\n\\\\nTogether, these methods provide\n a robust means to handle both the central and tail behavior seen in climate\n model output, while aligning the full distribution to a state-of-the-art reanalysis\n dataset and providing the spatial granularity needed to study surface impacts.\\\\n\\\\nFor\n further documentation, see [Global downscaled projections for climate impacts\n research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/)\n (EGUsphere, 2022 [preprint]).\\\\n\\\\n## Citing, licensing, and using data produced\n by this project\\\\n\\\\nProjects making use of the data produced as part of the\n Climate Impact Lab Global Downscaled Projections for Climate Impacts Research\n (CIL GDPCIR) project are requested to cite both this project and the source\n datasets from which these results are derived. Additionally, the use of data\n derived from some GCMs *requires* citations, and some modeling centers impose\n licensing restrictions & requirements on derived works. See each GCM's license\n info in the links below for more information.\\\\n\\\\n### CIL GDPCIR\\\\n\\\\nUsers\n are requested to cite this project in derived works. Our method documentation\n paper may be cited using the following:\\\\n\\\\n> Gergel, D. R., Malevich, S.\n B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp,\n R. E.: Global downscaled projections for climate impacts research (GDPCIR):\n preserving extremes for modeling future climate impacts, EGUsphere [preprint],\n https://doi.org/10.5194/egusphere-2022-1513, 2023. \\\\n\\\\nThe code repository\n may be cited using the following:\\\\n\\\\n> Diana Gergel, Kelly McCusker, Brewster\n Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6:\n (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\\\n\\\\n### ERA5\\\\n\\\\nAdditionally,\n we request you cite the historical dataset used in bias correction and downscaling,\n ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\\\\n\\\\n>\n Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146:\n 1999\\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present.\n Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed\n on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus\n Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4,\n 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n\\\\n###\n GCM-specific citations & licenses\\\\n\\\\nThe CMIP6 simulation data made available\n through the Earth System Grid Federation (ESGF) are subject to Creative Commons\n [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA\n 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate\n Impact Lab has reached out to each of the modeling institutions to request\n waivers from these terms so the outputs of this project may be used with fewer\n restrictions, and has been granted permission to release the data using the\n licenses listed here.\\\\n\\\\n#### Public Domain Datasets\\\\n\\\\nThe following\n bias corrected and downscaled model simulations are available in the public\n domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/).\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\\\n\\\\n*\n **FGOALS-g3**\\\\n\\\\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output\n prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1783\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6\n ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0\n version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version\n 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\\\n\\\\n\\\\n*\n **INM-CM4-8**\\\\n\\\\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8\n model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\\\n\\\\n\\\\n* **INM-CM5-0**\\\\n\\\\n\n \\ License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5\n version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\\\n\\\\n\\\\n####\n CC-BY-4.0\\\\n\\\\nThe following bias corrected and downscaled model simulations\n are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).\n Note that this license requires citation of the source model output (included\n here). Please see https://creativecommons.org/licenses/by/4.0/ for more information.\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\\\n\\\\n*\n **ACCESS-CM2**\\\\n\\\\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler,\n Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell,\n Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire;\n Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman,\n Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen,\n Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans,\n Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;\n Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2\n model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman,\n Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid,\n Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter;\n Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias,\n Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita,\n Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards,\n Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate;\n Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output\n prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2285\\\\n\\\\n\\\\n* **ACCESS-ESM1-5**\\\\n\\\\n\n \\ License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew;\n Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter;\n Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan,\n Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5\n model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman,\n Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan;\n Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell,\n Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared\n for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\\\n\\\\n\\\\n*\n **BCC-CSM2-MR**\\\\n\\\\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen;\n Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang;\n Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*.\n Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang,\n Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei,\n Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318;\n SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\\\n\\\\n\\\\n*\n **CMCC-CM2-SR5**\\\\n\\\\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC\n CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5\n version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\\\n\\\\n\\\\n*\n **CMCC-ESM2**\\\\n\\\\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n, Momme\n **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version\n 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n,\n Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202;\n SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\\\n\\\\n\\\\n*\n **EC-Earth3-AerChem**\\\\n\\\\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*.\n Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\\\n\\\\n\\\\n*\n **EC-Earth3-CC**\\\\n\\\\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium\n EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg-LR**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123;\n SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\\\n\\\\n\\\\n*\n **EC-Earth3**\\\\n\\\\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\\\n\\\\n\\\\n*\n **GFDL-CM4**\\\\n\\\\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh,\n Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh,\n Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne,\n Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen\n M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev,\n Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey;\n Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew;\n Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair;\n Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly,\n P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**.\n *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1402\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei;\n Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta,\n Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth;\n Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies,\n Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay,\n Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi;\n Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.;\n Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova,\n Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4\n model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\\\n\\\\n\\\\n* **GFDL-ESM4**\\\\n\\\\n\n \\ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris;\n McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher;\n Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier,\n Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;\n Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter,\n David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman,\n Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne,\n John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry\n W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael;\n Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4\n model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna;\n Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne,\n John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers,\n Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL\n GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\\\n\\\\n\\\\n*\n **HadGEM3-GC31-LL**\\\\n\\\\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till;\n Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output\n prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.419\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for\n CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908;\n SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\\\n\\\\n\\\\n*\n **MIROC-ES2L**\\\\n\\\\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki,\n Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto,\n Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki,\n Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri,\n Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*.\n Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro;\n Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio;\n Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito,\n Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya,\n Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*.\n Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\\\n\\\\n\\\\n*\n **MIROC6**\\\\n\\\\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**.\n *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**.\n *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-HR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco;\n Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz,\n Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens,\n J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina;\n Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster,\n Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem;\n Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Schupfner, Martin; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann;\n Fr\\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke,\n Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp;\n Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\\xF6rg;\n Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler,\n Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia;\n Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela;\n Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang;\n Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann,\n Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner;\n Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich\n **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*.\n Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-LR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus,\n Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie;\n Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de\n Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song;\n Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast,\n Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas;\n Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke,\n Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco;\n Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler,\n Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten;\n von Storch, Jin-Song; Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin;\n Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger,\n Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh,\n Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz,\n Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von\n Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast,\n Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;\n Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR\n model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\\\n\\\\n\\\\n* **NESM3**\\\\n\\\\n\n \\ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model\n output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2021\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\\\n\\\\n\\\\n*\n **NorESM2-LM**\\\\n\\\\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk\n Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model\n output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.502\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\\\n\\\\n\\\\n*\n **NorESM2-MM**\\\\n\\\\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland,\n \\xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model\n output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.506\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland, \\xD8yvind; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\\\n\\\\n\\\\n*\n **UKESM1-0-LL**\\\\n\\\\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley,\n Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**.\n *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Good, Peter; Sellar, Alistair; Tang, Yongming;\n Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy\n **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726;\n SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\\\n\\\\n\\\\n####\n CC-BY-SA-4.0\\\\n\\\\nThe following bias corrected and downscaled model simulations\n are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International\n License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this\n license requires citation of the source model output (included here) and requires\n that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/\n for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\\\\n\\\\n*\n **CanESM5**\\\\n\\\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav\n V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora,\n Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad;\n Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim,\n Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**.\n *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav\n V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora,\n Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad;\n Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim,\n Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**.\n *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\\\n\\\\n##\n Acknowledgements\\\\n\\\\nThis work is the result of many years worth of work\n by members of the [Climate Impact Lab](https://impactlab.org), but would not\n have been possible without many contributions from across the wider scientific\n and computing communities.\\\\n\\\\nSpecifically, we would like to acknowledge\n the World Climate Research Programme's Working Group on Coupled Modeling,\n which is responsible for CMIP, and we would like to thank the climate modeling\n groups for producing and making their model output available. We would particularly\n like to thank the modeling institutions whose results are included as an input\n to this repository (listed above) for their contributions to the CMIP6 project\n and for responding to and granting our requests for license waivers.\\\\n\\\\nWe\n would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data)\n (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#))\n and Google Cloud and the Google Public Datasets program for making the [CMIP6\n Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6)\n possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat),\n [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca),\n [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke),\n and [Charles Stern](https://github.com/cisaacstern) for the huge amount of\n work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted,\n analysis-ready zarr stores on Google Cloud.\\\\n\\\\nWe're also grateful to the\n [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors)\n ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in\n particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard),\n and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM\n bias correction method in the xclim python package, supporting our QPLAD implementation\n into the package, and ongoing support in integrating dask into downscaling\n workflows. For method advice and useful conversations, we would like to thank\n Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\\\n\\\\n##\n Financial support\\\\n\\\\nThis research has been supported by The Rockefeller\n Foundation and the Microsoft AI for Earth Initiative.\\\\n\\\\n## Additional links:\\\\n\\\\n*\n CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\\\n*\n Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\\\n* Climate\n Impact Lab homepage: [impactlab.org](https://impactlab.org)\\\",\\\"item_assets\\\":{\\\"pr\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Precipitation\\\",\\\"description\\\":\\\"Precipitation\\\"},\\\"tasmax\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Daily\n Maximum Near-Surface Air Temperature\\\",\\\"description\\\":\\\"Daily Maximum 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high-resolution aerial imagery, with four spectral\n bands (R, G, B, IR). \\\\nNAIP is administered by the [Aerial Field Photography\n Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/)\n (AFPO) \\\\nwithin the [US Department of Agriculture](https://www.usda.gov/)\n (USDA). \\\\nData are captured at least once every three years for each state.\n \\\\nThis dataset represents NAIP data from 2010-present, in [cloud-optimized\n GeoTIFF](https://www.cogeo.org/) format.\\\\nYou can visualize the coverage\n of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/).\n \\\\n\\\",\\\"item_assets\\\":{\\\"image\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"RGBIR COG 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This mature version\n of the map remains available for use in existing applications. This item will\n be retired in December 2024. There is 2023 data available in the newer [9-class\n v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\\\\n\\\\nTime\n series of annual global maps of land use and land cover (LULC). It currently\n has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery\n at 10m resolution. Each map is a composite of LULC predictions for 9 classes\n throughout the year in order to generate a representative snapshot of each\n year.\\\\n\\\\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/),\n who used billions of human-labeled pixels (curated by the National Geographic\n Society) to train a deep learning model for land classification. The global\n map was produced by applying this model to the Sentinel-2 annual scene collections\n on the Planetary Computer. Each of the maps has an assessed average accuracy\n of over 75%.\\\\n\\\\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc)\n and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single\n Rangeland class (class 11). The original Esri 2020 Land Cover collection uses\n 10 classes (Grass and Scrub separate) and an older version of the underlying\n deep learning model. The Esri 2020 Land Cover map was also produced by Impact\n Observatory. The map remains available for use in existing applications.\n New applications should use the updated version of 2020 once it is available\n in this collection, especially when using data from multiple years of this\n time series, to ensure consistent classification.\\\\n\\\\nAll years are available\n under a Creative Commons BY-4.0.\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Global\n land cover data\\\",\\\"file:values\\\":[{\\\"values\\\":[0],\\\"summary\\\":\\\"No Data\\\"},{\\\"values\\\":[1],\\\"summary\\\":\\\"Water\\\"},{\\\"values\\\":[2],\\\"summary\\\":\\\"Trees\\\"},{\\\"values\\\":[4],\\\"summary\\\":\\\"Flooded\n vegetation\\\"},{\\\"values\\\":[5],\\\"summary\\\":\\\"Crops\\\"},{\\\"values\\\":[7],\\\"summary\\\":\\\"Built\n area\\\"},{\\\"values\\\":[8],\\\"summary\\\":\\\"Bare 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resolution\\\"},{\\\"id\\\":\\\"io-biodiversity\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-biodiversity/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-biodiversity\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://creativecommons.org/licenses/by/4.0/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"CC\n BY 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Observatory\\\",\\\"roles\\\":[\\\"processor\\\",\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://www.vizzuality.com/\\\",\\\"name\\\":\\\"Vizzuality\\\",\\\"roles\\\":[\\\"processor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"version\\\":[\\\"v1\\\"]},\\\"description\\\":\\\"Generated\n by [Impact Observatory](https://www.impactobservatory.com/), in collaboration\n with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial\n Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\\\\n\\\\nMaps\n depicting the intactness of global biodiversity have become a critical tool\n for spatial planning and management, monitoring the extent of biodiversity\n across Earth, and identifying critical remaining intact habitat. Yet, these\n maps are often years out of date by the time they are available to scientists\n and policy-makers. The datasets in this STAC Collection build on past studies\n that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579)\n of spatially referenced observations of biodiversity across 32,000 sites from\n over 750 studies. The approach differs from previous work by modeling the\n relationship between observed biodiversity metrics and contemporary, global,\n geospatial layers of human pressures, with the intention of providing a high\n resolution monitoring product into the future.\\\\n\\\\nBiodiversity intactness\n is estimated as a combination of two metrics: Abundance, the quantity of individuals,\n and Compositional Similarity, how similar the composition of species is to\n an intact baseline. Linear mixed effects models are fit to estimate the predictive\n capacity of spatial datasets of human pressures on each of these metrics and\n project results spatially across the globe. These methods, as well as comparisons\n to other leading datasets and guidance on interpreting results, are further\n explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf)\n entitled \\u201CGlobal 100m Projections of Biodiversity Intactness for the\n years 2017-2020.\\u201D\\\\n\\\\nAll years are available under a Creative Commons\n BY-4.0 license.\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Biodiversity Intactness\\\",\\\"description\\\":\\\"Terrestrial\n biodiversity intactness at 100m resolution\\\",\\\"raster:bands\\\":[{\\\"sampling\\\":\\\"area\\\",\\\"data_type\\\":\\\"float32\\\",\\\"spatial_resolution\\\":100}]}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"impact\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"pcdata01euw\\\",\\\"msft:short_description\\\":\\\"Global\n terrestrial biodiversity intactness at 100m resolution for years 2017-2020\\\"},{\\\"id\\\":\\\"noaa-cdr-sea-surface-temperature-whoi\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Sea%20Surface%20Temperature%20-%20WHOI/UseAgreement_01B-27a.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"NOAA\n CDR Sea Surface Temperature - WHOI Use Agreement\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/products/climate-data-records/sea-surface-temperature-whoi\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sea\n Surface Temperature - WHOI CDR\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.7289/V5FB510W\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-cdr-sea-surface-temperature-whoi\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sea\n Surface Temperature - WHOI CDR\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-cdr-sea-surface-temperature-whoi-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Sea\n Surface Temperature - WHOI CDR thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-cdr-sea-surface-temperature-whoi.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1988-01-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.7289/V5FB510W\\\",\\\"keywords\\\":[\\\"Global\\\",\\\"Climate\\\",\\\"NOAA\\\",\\\"Ocean\\\",\\\"Temperature\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ncei.noaa.gov/\\\",\\\"name\\\":\\\"National\n Centers for Environmental Information\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"],\\\"description\\\":\\\"NCEI\n is the Nation's leading authority for environmental data, and manage one of\n the largest archives of atmospheric, coastal, geophysical, and oceanic research\n in the world. NCEI contributes to the NESDIS mission by developing new products\n and services that span the science disciplines and enable better data discovery.\\\"},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"description\\\":\\\"The\n Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate\n Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean\n Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data\n are produced through modeling the diurnal variability in combination with\n AVHRR SST observations. The final record is output to a 3-hourly 0.25\\xB0\n resolution grid over the global ice-free oceans from January 1988\\u2014present.\\\\n\\\\nThese\n Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered\n to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\nFor\n the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\\\\n\\\",\\\"item_assets\\\":{\\\"fill_missing_qc\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\"}]},\\\"sea_surface_temperature\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degree\n Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\"}]}},\\\"msft:region\\\":\\\"eastus\\\",\\\"sci:citation\\\":\\\"Clayson,\n Carol Anne; Brown, Jeremiah; and NOAA CDR Program (2016). NOAA Climate Data\n Record (CDR) of Sea Surface Temperature - WHOI, Version 2. NOAA National Climatic\n Data Center. doi:10.7289/V5FB510W\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"noaa-cdr\\\",\\\"msft:container\\\":\\\"sea-surface-temperature-whoi\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"noaacdr\\\",\\\"msft:short_description\\\":\\\"The\n Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate\n Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean\n Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data\n are produced through modeling the diurnal variability in combination with\n AVHRR SST observations.\\\"},{\\\"id\\\":\\\"noaa-cdr-ocean-heat-content\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-ocean-heat-content/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-ocean-heat-content\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Ocean_Heat_Content/UseAgreement_01B-41.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"NOAA\n CDR Ocean Heat Content Use Agreement\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/products/climate-data-records/global-ocean-heat-content\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Global\n Ocean Heat Content CDR\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.7289/v53f4mvp\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-cdr-ocean-heat-content\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Global\n Ocean Heat Content CDR\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-cdr-ocean-heat-content-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Global\n Ocean Heat Content CDR thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-cdr-ocean-heat-content.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-90.0,180.0,90.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1972-03-01T00:00:00Z\\\",\\\"2022-03-31T23:59:59Z\\\"]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.7289/v53f4mvp\\\",\\\"keywords\\\":[\\\"Global\\\",\\\"Climate\\\",\\\"NOAA\\\",\\\"Temperature\\\",\\\"Ocean\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ncei.noaa.gov/\\\",\\\"name\\\":\\\"National\n Centers for Environmental Information\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"],\\\"description\\\":\\\"NCEI\n is the Nation's leading authority for environmental data, and manage one of\n the largest archives of atmospheric, coastal, geophysical, and oceanic research\n in the world. NCEI contributes to the NESDIS mission by developing new products\n and services that span the science disciplines and enable better data discovery.\\\"},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"summaries\\\":{\\\"noaa_cdr:interval\\\":[\\\"monthly\\\",\\\"seasonal\\\",\\\"yearly\\\",\\\"pentadal\\\"],\\\"noaa_cdr:max_depth\\\":[100,700,2000]},\\\"description\\\":\\\"The\n Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content\n anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal\n (five-yearly) scales. This CDR quantifies ocean heat content change over time,\n which is an essential metric for understanding climate change and the Earth's\n energy budget. It provides time-series for multiple depth ranges in the global\n ocean and each of the major basins (Atlantic, Pacific, and Indian) divided\n by hemisphere (Northern, Southern).\\\\n\\\\nThese Cloud Optimized GeoTIFFs (COGs)\n were created from NetCDF files which are delivered to Azure as part of the\n [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\nFor\n the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\\\\n\\\",\\\"item_assets\\\":{\\\"heat_content\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Ocean\n Heat Content anomalies from WOA09\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"10^18 joules\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\"}]},\\\"mean_salinity\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Mean\n salinity anomalies from WOA09\\\",\\\"raster:bands\\\":[{\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\"}]},\\\"mean_temperature\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Mean\n temperature anomalies from WOA09\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"degrees Celsius\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\"}]},\\\"mean_halosteric_sea_level\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Mean\n halosteric sea level anomalies from WOA09\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeters\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\"}]},\\\"mean_thermosteric_sea_level\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Mean\n thermosteric sea level anomalies from WOA09\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeters\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\"}]},\\\"mean_total_steric_sea_level\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Mean\n total steric sea level anomalies from WOA09\\\",\\\"raster:bands\\\":[{\\\"unit\\\":\\\"millimeters\\\",\\\"nodata\\\":\\\"nan\\\",\\\"data_type\\\":\\\"float32\\\"}]}},\\\"msft:region\\\":\\\"eastus\\\",\\\"sci:citation\\\":\\\"Levitus,\n Sydney; Antonov, John I.; Boyer, Tim P.; Baranova, Olga K.; Garc\\xEDa, Hern\\xE1n\n E.; Locarnini, Ricardo A.; Mishonov, Alexey V.; Reagan, James R.; [Seidov,\n Dan; Yarosh, Evgeney; Zweng, Melissa M. (2017). NCEI ocean heat content, temperature\n anomalies, salinity anomalies, thermosteric sea level anomalies, halosteric\n sea level anomalies, and total steric sea level anomalies from 1955 to present\n calculated from in situ oceanographic subsurface profile data (NCEI Accession\n 0164586). NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.7289/v53f4mvp.\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"noaa-cdr\\\",\\\"msft:container\\\":\\\"ocean-heat-content\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/raster/v1.1.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"noaacdr\\\",\\\"msft:short_description\\\":\\\"The\n Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content\n anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal\n (five-yearly) scales.\\\"},{\\\"id\\\":\\\"cil-gdpcir-cc0\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc0/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc0\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://spdx.org/licenses/CC0-1.0.html\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Creative\n Commons Zero v1.0 Universal\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://zenodo.org/record/6403794\\\",\\\"type\\\":\\\"text/html\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://github.com/ClimateImpactLab/downscaleCMIP6/\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Project\n homepage\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"CIL\n Global Downscaled Projections for Climate Impacts Research (CC0-1.0)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"title\\\":\\\"Thumbnail\\\"}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1950-01-01T00:00:00Z\\\",\\\"2100-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CC0-1.0\\\",\\\"sci:doi\\\":\\\"10.5194/egusphere-2022-1513\\\",\\\"keywords\\\":[\\\"CMIP6\\\",\\\"Climate\n Impact Lab\\\",\\\"Rhodium Group\\\",\\\"Precipitation\\\",\\\"Temperature\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://impactlab.org/\\\",\\\"name\\\":\\\"Climate\n Impact Lab\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"cmip6:variable\\\":[\\\"pr\\\",\\\"tasmax\\\",\\\"tasmin\\\"],\\\"cmip6:source_id\\\":[\\\"FGOALS-g3\\\",\\\"INM-CM4-8\\\",\\\"INM-CM5-0\\\"],\\\"cmip6:experiment_id\\\":[\\\"historical\\\",\\\"ssp126\\\",\\\"ssp245\\\",\\\"ssp370\\\",\\\"ssp585\\\"],\\\"cmip6:institution_id\\\":[\\\"BCC\\\",\\\"CAS\\\",\\\"CCCma\\\",\\\"CMCC\\\",\\\"CSIRO\\\",\\\"CSIRO-ARCCSS\\\",\\\"DKRZ\\\",\\\"EC-Earth-Consortium\\\",\\\"INM\\\",\\\"MIROC\\\",\\\"MOHC\\\",\\\"MPI-M\\\",\\\"NCC\\\",\\\"NOAA-GFDL\\\",\\\"NUIST\\\"]},\\\"description\\\":\\\"The\n World Climate Research Programme's [6th Coupled Model Intercomparison Project\n (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an\n enormous advance in the quality, detail, and scope of climate modeling.\\\\n\\\\nThe\n [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6)\n dataset makes this modeling more applicable to understanding the impacts of\n changes in the climate on humans and society with two key developments: trend-preserving\n bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org)\n provides global, daily minimum and maximum air temperature at the surface\n (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`)\n corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5\n scenarios for 25 global climate models on a 1/4-degree regular global grid.\\\\n\\\\n##\n Accessing the data\\\\n\\\\nGDPCIR data can be accessed on the Microsoft Planetary\n Computer. The dataset is made of of three collections, distinguished by data\n license:\\\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\\\n*\n [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\\\n\\\\nEach\n modeling center with bias corrected and downscaled data in this collection\n falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection)\n to see which model is in each collection, and see the section below on [Citing,\n Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project)\n for citations and additional information about each license.\\\\n\\\\n## Data\n format & contents\\\\n\\\\nThe data is stored as partitioned zarr stores (see\n [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which\n includes thousands of data and metadata files covering the full time span\n of the experiment. Historical zarr stores contain just over 50 GB, while SSP\n zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with\n dimensions time (daily datetime), lat (float latitude), and lon (float longitude).\n The data is chunked at each interval of 365 days and 90 degree interval of\n latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each\n chunk occupying approximately 180MB in memory.\\\\n\\\\nHistorical data is daily,\n excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily,\n excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100,\n depending on data availability in the source GCM.\\\\n\\\\nThe spatial domain\n covers all 0.25-degree grid cells, indexed by the grid center, with grid edges\n on the quarter-degree, using a -180 to 180 longitude convention. Thus, the\n \\u201Clon\\u201D coordinate extends from -179.875 to 179.875, and the \\u201Clat\\u201D\n coordinate extends from -89.875 to 89.875, with intermediate values at each\n 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\\\n\\\\n##\n Available institutions, models, and scenarios by license collection\\\\n\\\\n|\n Modeling institution | Source model | Available experiments\n \\ | License collection |\\\\n| -------------------- | -----------------\n | ------------------------------------------ | ---------------------- |\\\\n|\n CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5\n \\ | Public domain datasets |\\\\n| INM | INM-CM4-8\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets\n |\\\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0,\n and SSP5-8.5 | Public domain datasets |\\\\n| BCC | BCC-CSM2-MR\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n|\n CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5\n \\ | CC-BY-40 |\\\\n| CMCC | CMCC-ESM2 |\n ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\\\\n|\n CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 |\n CC-BY-40 |\\\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6,\n SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\\\\n| MIROC |\n MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MIROC | MIROC6 | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| MOHC |\n HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MOHC | UKESM1-0-LL | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| MPI-M |\n MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and\n SSP5-8.5 | CC-BY-40 |\\\\n| NCC |\n NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| NCC | NorESM2-MM | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| NOAA-GFDL\n \\ | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 |\n CC-BY-40 |\\\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| NUIST |\n NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6,\n ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-AerChem | ssp370 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and\n ssp585 | CC-BY-40 |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6,\n ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\\\n| CCCma |\n CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3]\n \\ |\\\\n\\\\n*Notes:*\\\\n\\\\n[^1]: At the time of running, no ssp1-2.6\n precipitation data was available. Therefore, we provide `tasmin` and `tamax`\n for this model and experiment, but not `pr`. All other model/experiment combinations\n in the above table include all three variables.\\\\n\\\\n[^2]: The institution\n which ran MPI-ESM1-2-HR\\u2019s historical (CMIP) simulations is `MPI-M`, while\n the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution\n component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP`\n scenarios.\\\\n\\\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),\n but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0)\n in March, 2023. \\\\n\\\\n## Project methods\\\\n\\\\nThis project makes use of statistical\n bias correction and downscaling algorithms, which are specifically designed\n to accurately represent changes in the extremes. For this reason, we selected\n Quantile Delta Mapping (QDM), following the method introduced by [Cannon et\n al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific\n trends from the GCM while fitting the full distribution for a given day-of-year\n to a reference dataset (ERA5).\\\\n\\\\nWe then introduce a similar method tailored\n to increase spatial resolution while preserving extreme behavior, Quantile-Preserving\n Localized-Analog Downscaling (QPLAD).\\\\n\\\\nTogether, these methods provide\n a robust means to handle both the central and tail behavior seen in climate\n model output, while aligning the full distribution to a state-of-the-art reanalysis\n dataset and providing the spatial granularity needed to study surface impacts.\\\\n\\\\nFor\n further documentation, see [Global downscaled projections for climate impacts\n research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/)\n (EGUsphere, 2022 [preprint]).\\\\n\\\\n\\\\n## Citing, licensing, and using data\n produced by this project\\\\n\\\\nProjects making use of the data produced as\n part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts\n Research (CIL GDPCIR) project are requested to cite both this project and\n the source datasets from which these results are derived. Additionally, the\n use of data derived from some GCMs *requires* citations, and some modeling\n centers impose licensing restrictions & requirements on derived works. See\n each GCM's license info in the links below for more information.\\\\n\\\\n###\n CIL GDPCIR\\\\n\\\\nUsers are requested to cite this project in derived works.\n Our method documentation paper may be cited using the following:\\\\n\\\\n> Gergel,\n D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish,\n M. A., and Kopp, R. E.: Global downscaled projections for climate impacts\n research (GDPCIR): preserving extremes for modeling future climate impacts,\n EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \\\\n\\\\nThe\n code repository may be cited using the following:\\\\n\\\\n> Diana Gergel, Kelly\n McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado\n (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\\\n\\\\n###\n ERA5\\\\n\\\\nAdditionally, we request you cite the historical dataset used in\n bias correction and downscaling, ERA5. See the [ECMWF guide to citing a dataset\n on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\\\\n\\\\n>\n Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146:\n 1999\\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present.\n Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed\n on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus\n Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4,\n 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n\\\\n###\n GCM-specific citations & licenses\\\\n\\\\nThe CMIP6 simulation data made available\n through the Earth System Grid Federation (ESGF) are subject to Creative Commons\n [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA\n 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate\n Impact Lab has reached out to each of the modeling institutions to request\n waivers from these terms so the outputs of this project may be used with fewer\n restrictions, and has been granted permission to release the data using the\n licenses listed here.\\\\n\\\\n#### Public Domain Datasets\\\\n\\\\nThe following\n bias corrected and downscaled model simulations are available in the public\n domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/).\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\\\n\\\\n*\n **FGOALS-g3**\\\\n\\\\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output\n prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1783\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6\n ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0\n version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version\n 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\\\n\\\\n\\\\n*\n **INM-CM4-8**\\\\n\\\\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8\n model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\\\n\\\\n\\\\n* **INM-CM5-0**\\\\n\\\\n\n \\ License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5\n version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\\\n\\\\n\\\\n####\n CC-BY-4.0\\\\n\\\\nThe following bias corrected and downscaled model simulations\n are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).\n Note that this license requires citation of the source model output (included\n here). Please see https://creativecommons.org/licenses/by/4.0/ for more information.\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\\\n\\\\n*\n **ACCESS-CM2**\\\\n\\\\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler,\n Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell,\n Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire;\n Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman,\n Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen,\n Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans,\n Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;\n Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2\n model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman,\n Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid,\n Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter;\n Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias,\n Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita,\n Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards,\n Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate;\n Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output\n prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2285\\\\n\\\\n\\\\n* **ACCESS-ESM1-5**\\\\n\\\\n\n \\ License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew;\n Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter;\n Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan,\n Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5\n model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman,\n Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan;\n Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell,\n Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared\n for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\\\n\\\\n\\\\n*\n **BCC-CSM2-MR**\\\\n\\\\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen;\n Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang;\n Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*.\n Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang,\n Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei,\n Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318;\n SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\\\n\\\\n\\\\n*\n **CMCC-CM2-SR5**\\\\n\\\\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC\n CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5\n version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\\\n\\\\n\\\\n*\n **CMCC-ESM2**\\\\n\\\\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n, Momme\n **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version\n 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n,\n Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202;\n SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\\\n\\\\n\\\\n*\n **EC-Earth3-AerChem**\\\\n\\\\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*.\n Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\\\n\\\\n\\\\n*\n **EC-Earth3-CC**\\\\n\\\\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium\n EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg-LR**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123;\n SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\\\n\\\\n\\\\n*\n **EC-Earth3**\\\\n\\\\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\\\n\\\\n\\\\n*\n **GFDL-CM4**\\\\n\\\\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh,\n Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh,\n Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne,\n Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen\n M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev,\n Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey;\n Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew;\n Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair;\n Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly,\n P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**.\n *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1402\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei;\n Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta,\n Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth;\n Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies,\n Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay,\n Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi;\n Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.;\n Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova,\n Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4\n model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\\\n\\\\n\\\\n* **GFDL-ESM4**\\\\n\\\\n\n \\ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris;\n McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher;\n Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier,\n Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;\n Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter,\n David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman,\n Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne,\n John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry\n W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael;\n Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4\n model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna;\n Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne,\n John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers,\n Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL\n GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\\\n\\\\n\\\\n*\n **HadGEM3-GC31-LL**\\\\n\\\\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till;\n Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output\n prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.419\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for\n CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908;\n SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\\\n\\\\n\\\\n*\n **MIROC-ES2L**\\\\n\\\\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki,\n Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto,\n Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki,\n Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri,\n Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*.\n Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro;\n Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio;\n Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito,\n Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya,\n Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*.\n Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\\\n\\\\n\\\\n*\n **MIROC6**\\\\n\\\\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**.\n *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**.\n *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-HR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco;\n Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz,\n Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens,\n J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina;\n Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster,\n Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem;\n Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Schupfner, Martin; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann;\n Fr\\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke,\n Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp;\n Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\\xF6rg;\n Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler,\n Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia;\n Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela;\n Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang;\n Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann,\n Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner;\n Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich\n **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*.\n Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-LR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus,\n Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie;\n Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de\n Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song;\n Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast,\n Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas;\n Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke,\n Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco;\n Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler,\n Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten;\n von Storch, Jin-Song; Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin;\n Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger,\n Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh,\n Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz,\n Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von\n Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast,\n Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;\n Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR\n model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\\\n\\\\n\\\\n* **NESM3**\\\\n\\\\n\n \\ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model\n output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2021\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\\\n\\\\n\\\\n*\n **NorESM2-LM**\\\\n\\\\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk\n Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model\n output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.502\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\\\n\\\\n\\\\n*\n **NorESM2-MM**\\\\n\\\\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland,\n \\xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model\n output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.506\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland, \\xD8yvind; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\\\n\\\\n\\\\n*\n **UKESM1-0-LL**\\\\n\\\\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley,\n Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**.\n *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Good, Peter; Sellar, Alistair; Tang, Yongming;\n Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy\n **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726;\n SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\\\n\\\\n\\\\n*\n **CanESM5**\\\\n\\\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt).\n Note: this dataset was previously licensed\\\\n under CC BY-SA 4.0, but was\n relicensed as CC BY 4.0 in March, 2023.\\\\n\\\\n CMIP Citation:\\\\n\\\\n > Swart,\n Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca,\n John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James\n R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler,\n Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut;\n Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model\n output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1303\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare,\n Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek;\n Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko,\n Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von\n Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma\n CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\\\n\\\\n## Acknowledgements\\\\n\\\\nThis\n work is the result of many years worth of work by members of the [Climate\n Impact Lab](https://impactlab.org), but would not have been possible without\n many contributions from across the wider scientific and computing communities.\\\\n\\\\nSpecifically,\n we would like to acknowledge the World Climate Research Programme's Working\n Group on Coupled Modeling, which is responsible for CMIP, and we would like\n to thank the climate modeling groups for producing and making their model\n output available. We would particularly like to thank the modeling institutions\n whose results are included as an input to this repository (listed above) for\n their contributions to the CMIP6 project and for responding to and granting\n our requests for license waivers.\\\\n\\\\nWe would also like to thank Lamont-Doherty\n Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data)\n (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#))\n and Google Cloud and the Google Public Datasets program for making the [CMIP6\n Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6)\n possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat),\n [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca),\n [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke),\n and [Charles Stern](https://github.com/cisaacstern) for the huge amount of\n work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted,\n analysis-ready zarr stores on Google Cloud.\\\\n\\\\nWe're also grateful to the\n [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors)\n ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in\n particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard),\n and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM\n bias correction method in the xclim python package, supporting our QPLAD implementation\n into the package, and ongoing support in integrating dask into downscaling\n workflows. For method advice and useful conversations, we would like to thank\n Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\\\n\\\\n##\n Financial support\\\\n\\\\nThis research has been supported by The Rockefeller\n Foundation and the Microsoft AI for Earth Initiative.\\\\n\\\\n## Additional links:\\\\n\\\\n*\n CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\\\n*\n Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\\\n* Climate\n Impact Lab homepage: [impactlab.org](https://impactlab.org)\\\",\\\"item_assets\\\":{\\\"pr\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Precipitation\\\",\\\"description\\\":\\\"Precipitation\\\"},\\\"tasmax\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Daily\n Maximum Near-Surface Air Temperature\\\",\\\"description\\\":\\\"Daily Maximum 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(CC-BY-4.0)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gdpcir.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"title\\\":\\\"Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/cil-gdpcir-cc-by.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1950-01-01T00:00:00Z\\\",\\\"2100-12-31T00:00:00Z\\\"]]}},\\\"license\\\":\\\"CC-BY-4.0\\\",\\\"sci:doi\\\":\\\"10.5194/egusphere-2022-1513\\\",\\\"keywords\\\":[\\\"CMIP6\\\",\\\"Climate\n Impact Lab\\\",\\\"Rhodium Group\\\",\\\"Precipitation\\\",\\\"Temperature\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://impactlab.org/\\\",\\\"name\\\":\\\"Climate\n Impact Lab\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com/\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"cmip6:variable\\\":[\\\"pr\\\",\\\"tasmax\\\",\\\"tasmin\\\"],\\\"cmip6:source_id\\\":[\\\"BCC-CSM2-MR\\\",\\\"ACCESS-ESM1-5\\\",\\\"ACCESS-CM2\\\",\\\"MIROC-ES2L\\\",\\\"MIROC6\\\",\\\"NorESM2-LM\\\",\\\"NorESM2-MM\\\",\\\"GFDL-CM4\\\",\\\"GFDL-ESM4\\\",\\\"NESM3\\\",\\\"MPI-ESM1-2-HR\\\",\\\"HadGEM3-GC31-LL\\\",\\\"UKESM1-0-LL\\\",\\\"MPI-ESM1-2-LR\\\",\\\"EC-Earth3\\\",\\\"EC-Earth3-AerChem\\\",\\\"EC-Earth3-CC\\\",\\\"EC-Earth3-Veg\\\",\\\"EC-Earth3-Veg-LR\\\",\\\"CMCC-CM2-SR5\\\",\\\"CMCC-ESM2\\\"],\\\"cmip6:experiment_id\\\":[\\\"historical\\\",\\\"ssp126\\\",\\\"ssp245\\\",\\\"ssp370\\\",\\\"ssp585\\\"],\\\"cmip6:institution_id\\\":[\\\"BCC\\\",\\\"CAS\\\",\\\"CCCma\\\",\\\"CMCC\\\",\\\"CSIRO\\\",\\\"CSIRO-ARCCSS\\\",\\\"DKRZ\\\",\\\"EC-Earth-Consortium\\\",\\\"INM\\\",\\\"MIROC\\\",\\\"MOHC\\\",\\\"MPI-M\\\",\\\"NCC\\\",\\\"NOAA-GFDL\\\",\\\"NUIST\\\"]},\\\"description\\\":\\\"The\n World Climate Research Programme's [6th Coupled Model Intercomparison Project\n (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an\n enormous advance in the quality, detail, and scope of climate modeling.\\\\n\\\\nThe\n [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6)\n dataset makes this modeling more applicable to understanding the impacts of\n changes in the climate on humans and society with two key developments: trend-preserving\n bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org)\n provides global, daily minimum and maximum air temperature at the surface\n (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`)\n corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5\n scenarios for 25 global climate models on a 1/4-degree regular global grid.\\\\n\\\\n##\n Accessing the data\\\\n\\\\nGDPCIR data can be accessed on the Microsoft Planetary\n Computer. The dataset is made of of three collections, distinguished by data\n license:\\\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\\\n*\n [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\\\n\\\\nEach\n modeling center with bias corrected and downscaled data in this collection\n falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection)\n to see which model is in each collection, and see the section below on [Citing,\n Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project)\n for citations and additional information about each license.\\\\n\\\\n## Data\n format & contents\\\\n\\\\nThe data is stored as partitioned zarr stores (see\n [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which\n includes thousands of data and metadata files covering the full time span\n of the experiment. Historical zarr stores contain just over 50 GB, while SSP\n zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with\n dimensions time (daily datetime), lat (float latitude), and lon (float longitude).\n The data is chunked at each interval of 365 days and 90 degree interval of\n latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each\n chunk occupying approximately 180MB in memory.\\\\n\\\\nHistorical data is daily,\n excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily,\n excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100,\n depending on data availability in the source GCM.\\\\n\\\\nThe spatial domain\n covers all 0.25-degree grid cells, indexed by the grid center, with grid edges\n on the quarter-degree, using a -180 to 180 longitude convention. Thus, the\n \\u201Clon\\u201D coordinate extends from -179.875 to 179.875, and the \\u201Clat\\u201D\n coordinate extends from -89.875 to 89.875, with intermediate values at each\n 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\\\n\\\\n##\n Available institutions, models, and scenarios by license collection\\\\n\\\\n|\n Modeling institution | Source model | Available experiments\n \\ | License collection |\\\\n| -------------------- | -----------------\n | ------------------------------------------ | ---------------------- |\\\\n|\n CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5\n \\ | Public domain datasets |\\\\n| INM | INM-CM4-8\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets\n |\\\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0,\n and SSP5-8.5 | Public domain datasets |\\\\n| BCC | BCC-CSM2-MR\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n|\n CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5\n \\ | CC-BY-40 |\\\\n| CMCC | CMCC-ESM2 |\n ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\\\\n|\n CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 |\n CC-BY-40 |\\\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6,\n SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\\\\n| MIROC |\n MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MIROC | MIROC6 | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| MOHC |\n HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MOHC | UKESM1-0-LL | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| MPI-M |\n MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and\n SSP5-8.5 | CC-BY-40 |\\\\n| NCC |\n NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| NCC | NorESM2-MM | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| NOAA-GFDL\n \\ | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 |\n CC-BY-40 |\\\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| NUIST |\n NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6,\n ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-AerChem | ssp370 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and\n ssp585 | CC-BY-40 |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6,\n ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\\\n| CCCma |\n CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3]\n \\ |\\\\n\\\\n*Notes:*\\\\n\\\\n[^1]: At the time of running, no ssp1-2.6\n precipitation data was available. Therefore, we provide `tasmin` and `tamax`\n for this model and experiment, but not `pr`. All other model/experiment combinations\n in the above table include all three variables.\\\\n\\\\n[^2]: The institution\n which ran MPI-ESM1-2-HR\\u2019s historical (CMIP) simulations is `MPI-M`, while\n the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution\n component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP`\n scenarios.\\\\n\\\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),\n but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0)\n in March, 2023. \\\\n\\\\n## Project methods\\\\n\\\\nThis project makes use of statistical\n bias correction and downscaling algorithms, which are specifically designed\n to accurately represent changes in the extremes. For this reason, we selected\n Quantile Delta Mapping (QDM), following the method introduced by [Cannon et\n al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific\n trends from the GCM while fitting the full distribution for a given day-of-year\n to a reference dataset (ERA5).\\\\n\\\\nWe then introduce a similar method tailored\n to increase spatial resolution while preserving extreme behavior, Quantile-Preserving\n Localized-Analog Downscaling (QPLAD).\\\\n\\\\nTogether, these methods provide\n a robust means to handle both the central and tail behavior seen in climate\n model output, while aligning the full distribution to a state-of-the-art reanalysis\n dataset and providing the spatial granularity needed to study surface impacts.\\\\n\\\\nFor\n further documentation, see [Global downscaled projections for climate impacts\n research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/)\n (EGUsphere, 2022 [preprint]).\\\\n\\\\n## Citing, licensing, and using data produced\n by this project\\\\n\\\\nProjects making use of the data produced as part of the\n Climate Impact Lab Global Downscaled Projections for Climate Impacts Research\n (CIL GDPCIR) project are requested to cite both this project and the source\n datasets from which these results are derived. Additionally, the use of data\n derived from some GCMs *requires* citations, and some modeling centers impose\n licensing restrictions & requirements on derived works. See each GCM's license\n info in the links below for more information.\\\\n\\\\n### CIL GDPCIR\\\\n\\\\nUsers\n are requested to cite this project in derived works. Our method documentation\n paper may be cited using the following:\\\\n\\\\n> Gergel, D. R., Malevich, S.\n B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp,\n R. E.: Global downscaled projections for climate impacts research (GDPCIR):\n preserving extremes for modeling future climate impacts, EGUsphere [preprint],\n https://doi.org/10.5194/egusphere-2022-1513, 2023. \\\\n\\\\nThe code repository\n may be cited using the following:\\\\n\\\\n> Diana Gergel, Kelly McCusker, Brewster\n Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6:\n (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\\\n\\\\n### ERA5\\\\n\\\\nAdditionally,\n we request you cite the historical dataset used in bias correction and downscaling,\n ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\\\\n\\\\n>\n Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146:\n 1999\\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present.\n Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed\n on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus\n Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4,\n 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n\\\\n###\n GCM-specific citations & licenses\\\\n\\\\nThe CMIP6 simulation data made available\n through the Earth System Grid Federation (ESGF) are subject to Creative Commons\n [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA\n 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate\n Impact Lab has reached out to each of the modeling institutions to request\n waivers from these terms so the outputs of this project may be used with fewer\n restrictions, and has been granted permission to release the data using the\n licenses listed here.\\\\n\\\\n#### Public Domain Datasets\\\\n\\\\nThe following\n bias corrected and downscaled model simulations are available in the public\n domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/).\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\\\n\\\\n*\n **FGOALS-g3**\\\\n\\\\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output\n prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1783\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6\n ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0\n version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version\n 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\\\n\\\\n\\\\n*\n **INM-CM4-8**\\\\n\\\\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8\n model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\\\n\\\\n\\\\n* **INM-CM5-0**\\\\n\\\\n\n \\ License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5\n version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\\\n\\\\n\\\\n####\n CC-BY-4.0\\\\n\\\\nThe following bias corrected and downscaled model simulations\n are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).\n Note that this license requires citation of the source model output (included\n here). Please see https://creativecommons.org/licenses/by/4.0/ for more information.\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\\\n\\\\n*\n **ACCESS-CM2**\\\\n\\\\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler,\n Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell,\n Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire;\n Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman,\n Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen,\n Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans,\n Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;\n Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2\n model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman,\n Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid,\n Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter;\n Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias,\n Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita,\n Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards,\n Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate;\n Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output\n prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2285\\\\n\\\\n\\\\n* **ACCESS-ESM1-5**\\\\n\\\\n\n \\ License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew;\n Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter;\n Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan,\n Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5\n model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman,\n Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan;\n Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell,\n Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared\n for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\\\n\\\\n\\\\n*\n **BCC-CSM2-MR**\\\\n\\\\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen;\n Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang;\n Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*.\n Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang,\n Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei,\n Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318;\n SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\\\n\\\\n\\\\n*\n **CMCC-CM2-SR5**\\\\n\\\\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC\n CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5\n version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\\\n\\\\n\\\\n*\n **CMCC-ESM2**\\\\n\\\\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n, Momme\n **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version\n 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n,\n Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202;\n SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\\\n\\\\n\\\\n*\n **EC-Earth3-AerChem**\\\\n\\\\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*.\n Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\\\n\\\\n\\\\n*\n **EC-Earth3-CC**\\\\n\\\\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium\n EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg-LR**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123;\n SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\\\n\\\\n\\\\n*\n **EC-Earth3**\\\\n\\\\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\\\n\\\\n\\\\n*\n **GFDL-CM4**\\\\n\\\\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh,\n Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh,\n Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne,\n Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen\n M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev,\n Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey;\n Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew;\n Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair;\n Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly,\n P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**.\n *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1402\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei;\n Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta,\n Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth;\n Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies,\n Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay,\n Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi;\n Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.;\n Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova,\n Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4\n model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\\\n\\\\n\\\\n* **GFDL-ESM4**\\\\n\\\\n\n \\ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris;\n McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher;\n Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier,\n Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;\n Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter,\n David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman,\n Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne,\n John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry\n W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael;\n Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4\n model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna;\n Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne,\n John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers,\n Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL\n GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\\\n\\\\n\\\\n*\n **HadGEM3-GC31-LL**\\\\n\\\\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till;\n Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output\n prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.419\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for\n CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908;\n SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\\\n\\\\n\\\\n*\n **MIROC-ES2L**\\\\n\\\\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki,\n Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto,\n Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki,\n Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri,\n Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*.\n Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro;\n Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio;\n Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito,\n Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya,\n Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*.\n Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\\\n\\\\n\\\\n*\n **MIROC6**\\\\n\\\\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**.\n *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**.\n *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-HR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco;\n Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz,\n Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens,\n J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina;\n Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster,\n Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem;\n Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Schupfner, Martin; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann;\n Fr\\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke,\n Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp;\n Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\\xF6rg;\n Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler,\n Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia;\n Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela;\n Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang;\n Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann,\n Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner;\n Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich\n **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*.\n Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-LR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus,\n Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie;\n Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de\n Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song;\n Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast,\n Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas;\n Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke,\n Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco;\n Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler,\n Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten;\n von Storch, Jin-Song; Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin;\n Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger,\n Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh,\n Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz,\n Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von\n Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast,\n Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;\n Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR\n model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\\\n\\\\n\\\\n* **NESM3**\\\\n\\\\n\n \\ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model\n output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2021\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\\\n\\\\n\\\\n*\n **NorESM2-LM**\\\\n\\\\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk\n Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model\n output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.502\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\\\n\\\\n\\\\n*\n **NorESM2-MM**\\\\n\\\\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland,\n \\xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model\n output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.506\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland, \\xD8yvind; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\\\n\\\\n\\\\n*\n **UKESM1-0-LL**\\\\n\\\\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley,\n Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**.\n *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Good, Peter; Sellar, Alistair; Tang, Yongming;\n Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy\n **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726;\n SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\\\n\\\\n*\n **CanESM5**\\\\n\\\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt).\n Note: this dataset was previously licensed\\\\n under CC BY-SA 4.0, but was\n relicensed as CC BY 4.0 in March, 2023.\\\\n\\\\n CMIP Citation:\\\\n\\\\n > Swart,\n Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca,\n John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James\n R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler,\n Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut;\n Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model\n output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1303\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare,\n Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek;\n Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko,\n Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von\n Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma\n CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\\\n\\\\n## Acknowledgements\\\\n\\\\nThis\n work is the result of many years worth of work by members of the [Climate\n Impact Lab](https://impactlab.org), but would not have been possible without\n many contributions from across the wider scientific and computing communities.\\\\n\\\\nSpecifically,\n we would like to acknowledge the World Climate Research Programme's Working\n Group on Coupled Modeling, which is responsible for CMIP, and we would like\n to thank the climate modeling groups for producing and making their model\n output available. We would particularly like to thank the modeling institutions\n whose results are included as an input to this repository (listed above) for\n their contributions to the CMIP6 project and for responding to and granting\n our requests for license waivers.\\\\n\\\\nWe would also like to thank Lamont-Doherty\n Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data)\n (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#))\n and Google Cloud and the Google Public Datasets program for making the [CMIP6\n Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6)\n possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat),\n [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca),\n [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke),\n and [Charles Stern](https://github.com/cisaacstern) for the huge amount of\n work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted,\n analysis-ready zarr stores on Google Cloud.\\\\n\\\\nWe're also grateful to the\n [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors)\n ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in\n particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard),\n and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM\n bias correction method in the xclim python package, supporting our QPLAD implementation\n into the package, and ongoing support in integrating dask into downscaling\n workflows. For method advice and useful conversations, we would like to thank\n Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\\\n\\\\n##\n Financial support\\\\n\\\\nThis research has been supported by The Rockefeller\n Foundation and the Microsoft AI for Earth Initiative.\\\\n\\\\n## Additional links:\\\\n\\\\n*\n CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\\\n*\n Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\\\n* Climate\n Impact Lab homepage: [impactlab.org](https://impactlab.org)\\\",\\\"item_assets\\\":{\\\"pr\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Precipitation\\\",\\\"description\\\":\\\"Precipitation\\\"},\\\"tasmax\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Daily\n Maximum Near-Surface Air Temperature\\\",\\\"description\\\":\\\"Daily Maximum Near-Surface\n Air Temperature\\\"},\\\"tasmin\\\":{\\\"type\\\":\\\"application/vnd+zarr\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Daily\n Minimum Near-Surface Air Temperature\\\",\\\"description\\\":\\\"Daily Minimum Near-Surface\n Air Temperature\\\"}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"cil-gdpcir\\\",\\\"cube:variables\\\":{\\\"pr\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"mm\n day-1\\\",\\\"attrs\\\":{\\\"units\\\":\\\"mm day-1\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"]},\\\"tasmax\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"K\\\",\\\"attrs\\\":{\\\"units\\\":\\\"K\\\",\\\"comment\\\":\\\"maximum\n near-surface (usually, 2 meter) air temperature (add cell_method attribute\n 'time: max')\\\",\\\"long_name\\\":\\\"Daily Maximum Near-Surface Air Temperature\\\",\\\"coordinates\\\":\\\"height\\\",\\\"cell_methods\\\":\\\"area:\n mean time: maximum (interval: 5 minutes)\\\",\\\"cell_measures\\\":\\\"area: areacella\\\",\\\"original_name\\\":\\\"TREFHTMX\\\",\\\"standard_name\\\":\\\"air_temperature\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n Maximum Near-Surface Air Temperature\\\"},\\\"tasmin\\\":{\\\"type\\\":\\\"data\\\",\\\"unit\\\":\\\"K\\\",\\\"attrs\\\":{\\\"units\\\":\\\"K\\\",\\\"comment\\\":\\\"minimum\n near-surface (usually, 2 meter) air temperature (add cell_method attribute\n 'time: min')\\\",\\\"long_name\\\":\\\"Daily Minimum Near-Surface Air Temperature\\\",\\\"coordinates\\\":\\\"height\\\",\\\"cell_methods\\\":\\\"area:\n mean time: minimum (interval: 5 minutes)\\\",\\\"cell_measures\\\":\\\"area: areacella\\\",\\\"original_name\\\":\\\"TREFHTMN\\\",\\\"standard_name\\\":\\\"air_temperature\\\"},\\\"dimensions\\\":[\\\"time\\\",\\\"lat\\\",\\\"lon\\\"],\\\"description\\\":\\\"Daily\n Minimum Near-Surface Air Temperature\\\"}},\\\"msft:container\\\":\\\"cil-gdpcir\\\",\\\"cube:dimensions\\\":{\\\"lat\\\":{\\\"axis\\\":\\\"y\\\",\\\"step\\\":0.25,\\\"type\\\":\\\"spatial\\\",\\\"extent\\\":[-89.875,89.875],\\\"reference_system\\\":\\\"epsg:4326\\\"},\\\"lon\\\":{\\\"axis\\\":\\\"x\\\",\\\"step\\\":0.25,\\\"type\\\":\\\"spatial\\\",\\\"extent\\\":[-179.875,179.875],\\\"reference_system\\\":\\\"epsg:4326\\\"},\\\"time\\\":{\\\"step\\\":\\\"P1DT0H0M0S\\\",\\\"type\\\":\\\"temporal\\\",\\\"extent\\\":[\\\"1950-01-01T12:00:00Z\\\",\\\"2100-12-31T12:00:00Z\\\"],\\\"description\\\":\\\"time\\\"}},\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"rhgeuwest\\\",\\\"msft:short_description\\\":\\\"Climate\n Impact Lab Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)\\\"},{\\\"id\\\":\\\"noaa-cdr-sea-surface-temperature-whoi-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-cdr-sea-surface-temperature-whoi-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/pub/data/sds/cdr/CDRs/Sea%20Surface%20Temperature%20-%20WHOI/UseAgreement_01B-27a.pdf\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"NOAA\n CDR Sea Surface Temperature - WHOI Use Agreement\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://www.ncei.noaa.gov/products/climate-data-records/sea-surface-temperature-whoi\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sea\n Surface Temperature - WHOI CDR\\\"},{\\\"rel\\\":\\\"cite-as\\\",\\\"href\\\":\\\"https://doi.org/10.7289/V5FB510W\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/noaa-cdr-sea-surface-temperature-whoi-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sea\n Surface Temperature - WHOI CDR NetCDFs\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-cdr-sea-surface-temperature-whoi-netcdf-thumb.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"title\\\":\\\"Sea\n Surface Temperature - WHOI (NetCDF) CDR thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/noaa-cdr-sea-surface-temperature-whoi-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":false},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"1988-01-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"sci:doi\\\":\\\"10.7289/V5FB510W\\\",\\\"keywords\\\":[\\\"Global\\\",\\\"Climate\\\",\\\"NOAA\\\",\\\"Ocean\\\",\\\"Temperature\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.ncei.noaa.gov/\\\",\\\"name\\\":\\\"National\n Centers for Environmental Information\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"],\\\"description\\\":\\\"NCEI\n is the Nation's leading authority for environmental data, and manage one of\n the largest archives of atmospheric, coastal, geophysical, and oceanic research\n in the world. NCEI contributes to the NESDIS mission by developing new products\n and services that span the science disciplines and enable better data discovery.\\\"},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"processor\\\",\\\"host\\\"]}],\\\"description\\\":\\\"The\n Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate\n Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean\n Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data\n are produced through modeling the diurnal variability in combination with\n AVHRR SST observations. The final record is output to a 3-hourly 0.25\\xB0\n resolution grid over the global ice-free oceans from January 1988\\u2014present.\\\\n\\\\nThis\n is a NetCDF-only collection, for Cloud-Optimized GeoTIFFs use collection `noaa-cdr-sea-surface-temperature-whoi`.\\\\nThe\n NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination\n (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\\\n\\\",\\\"item_assets\\\":{\\\"netcdf\\\":{\\\"type\\\":\\\"application/netcdf\\\",\\\"roles\\\":[\\\"data\\\"]}},\\\"msft:region\\\":\\\"eastus\\\",\\\"sci:citation\\\":\\\"Clayson,\n Carol Anne; Brown, Jeremiah; and NOAA CDR Program (2016). NOAA Climate Data\n Record (CDR) of Sea Surface Temperature - WHOI, Version 2. 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The mission consists of one satellite carrying the [TROPOspheric\n Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies\n in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html)\n spacecraft, allowing utilization of co-located cloud mask data provided by\n the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging)\n (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane\n product.\\\\n\\\\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric\n data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat)\n and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions\n and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5)\n mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products)\n provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide\n and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone\n and cloud & aerosol information. These measurements are used for improving\n air quality forecasts and monitoring the concentrations of atmospheric constituents.\\\\n\\\\nThis\n STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format,\n since April 2018 for the following products:\\\\n\\\\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index):\n Ultraviolet aerosol index\\\\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height):\n Aerosol layer height\\\\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane):\n Methane (CH4) total column\\\\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud):\n Cloud fraction, albedo, and top pressure\\\\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide):\n Carbon monoxide (CO) total column\\\\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde):\n Formaldehyde (HCHO) total column\\\\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide):\n Nitrogen dioxide (NO2) total column\\\\n* [`L2__O3____`](http://www.tropomi.eu/data-products/total-ozone-column):\n Ozone (O3) total column\\\\n* [`L2__O3_TCL`](http://www.tropomi.eu/data-products/tropospheric-ozone-column):\n Ozone (O3) tropospheric column\\\\n* [`L2__SO2___`](http://www.tropomi.eu/data-products/sulphur-dioxide):\n Sulfur dioxide (SO2) total column\\\\n* [`L2__NP_BD3`](http://www.tropomi.eu/data-products/auxiliary):\n Cloud from the Suomi NPP mission, band 3\\\\n* [`L2__NP_BD6`](http://www.tropomi.eu/data-products/auxiliary):\n Cloud from the Suomi NPP mission, band 6\\\\n* [`L2__NP_BD7`](http://www.tropomi.eu/data-products/auxiliary):\n Cloud from the Suomi NPP mission, band 7\\\\n\\\",\\\"item_assets\\\":{\\\"co\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Carbon\n Monoxide Total Column\\\"},\\\"o3\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Ozone\n Total Column\\\"},\\\"ch4\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Methane\n Total Column\\\"},\\\"no2\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Nitrogen\n Dioxide Total Column\\\"},\\\"so2\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Sulphur\n Dioxide Total Column\\\"},\\\"hcho\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Formaldehyde\n Total Column\\\"},\\\"cloud\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cloud\n Fraction, Albedo, and Top Pressure\\\"},\\\"aer-ai\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Ultraviolet\n Aerosol Index\\\"},\\\"aer-lh\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Aerosol\n Layer Height\\\"},\\\"np-bd3\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VIIRS/NPP\n Band 3 Cloud Mask\\\"},\\\"np-bd6\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VIIRS/NPP\n Band 6 Cloud Mask\\\"},\\\"np-bd7\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VIIRS/NPP\n Band 7 Cloud Mask\\\"},\\\"o3-tcl\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Ozone\n Tropospheric Column\\\"}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:container\\\":\\\"sentinel-5p\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel5euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-5P\n Level 2 atmospheric monitoring products in NetCDF format\\\"},{\\\"id\\\":\\\"sentinel-3-olci-wfr-l2-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-olci-wfr-l2-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-olci-wfr-l2-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Sentinel\n Data License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sentinel-3\n Water (LRR and LFR) Product User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-olci-wfr-l2-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel-3\n Water (Full Resolution)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-olci-wfr-l2-netcdf-thumb.png\\\",\\\"title\\\":\\\"Sentinel-3\n OLCI WFR L2 NetCDF Thumbnail\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-3-olci-wfr-l2-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC Items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"MS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2017-11-01T00:07:01.738487Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"ESA\\\",\\\"Copernicus\\\",\\\"Sentinel\\\",\\\"Water\\\",\\\"Ocean\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa01\\\",\\\"description\\\":\\\"Band\n 1 - Aerosol correction, improved water constituent retrieval\\\",\\\"center_wavelength\\\":0.4,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa02\\\",\\\"description\\\":\\\"Band\n 2 - Yellow substance and detrital pigments (turbidity)\\\",\\\"center_wavelength\\\":0.4125,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa03\\\",\\\"description\\\":\\\"Band\n 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa04\\\",\\\"description\\\":\\\"Band\n 4 - Chlorophyll\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa05\\\",\\\"description\\\":\\\"Band\n 5 - Chlorophyll, sediment, turbidity, red tide\\\",\\\"center_wavelength\\\":0.51,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa06\\\",\\\"description\\\":\\\"Band\n 6 - Chlorophyll reference (minimum)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa07\\\",\\\"description\\\":\\\"Band\n 7 - Sediment loading\\\",\\\"center_wavelength\\\":0.62,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa08\\\",\\\"description\\\":\\\"Band\n 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa09\\\",\\\"description\\\":\\\"Band\n 9 - Improved fluorescence retrieval\\\",\\\"center_wavelength\\\":0.67375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa10\\\",\\\"description\\\":\\\"Band\n 10 - Chlorophyll fluorescence peak, red edge\\\",\\\"center_wavelength\\\":0.68125,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa11\\\",\\\"description\\\":\\\"Band\n 11 - Chlorophyll fluorescence baseline, red edge transition\\\",\\\"center_wavelength\\\":0.70875,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa12\\\",\\\"description\\\":\\\"Band\n 12 - O2 absorption / clouds, vegetation\\\",\\\"center_wavelength\\\":0.75375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa16\\\",\\\"description\\\":\\\"Band\n 16 - Atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":0.77875,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa17\\\",\\\"description\\\":\\\"Band\n 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. Common reference band with SLSTR.\n Vegetation monitoring\\\",\\\"center_wavelength\\\":0.885,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa19\\\",\\\"description\\\":\\\"Band\n 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\\\",\\\"center_wavelength\\\":0.9,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa21\\\",\\\"description\\\":\\\"Band\n 21 - Water vapour absorption, atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":1.02,\\\"full_width_half_max\\\":0.04}],\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"OLCI\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"olci-wfr\\\"],\\\"s3:product_type\\\":[\\\"OL_2_WFR___\\\"],\\\"sat:orbit_state\\\":[\\\"descending\\\",\\\"ascending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2]\n products containing data on water-leaving reflectance, ocean color, and more.\\\\n\\\\n##\n Data files\\\\n\\\\nThis dataset includes data on:\\\\n\\\\n- Surface directional\n reflectance\\\\n- Chlorophyll-a concentration\\\\n- Suspended matter concentration\\\\n-\n Energy flux\\\\n- Aerosol load\\\\n- Integrated water vapor column\\\\n\\\\nEach variable\n is contained within NetCDF files. Error estimates are available for each product.\\\\n\\\\n##\n Processing overview\\\\n\\\\nThe values in the data files have been converted\n from Top of Atmosphere radiance to reflectance, and include various corrections\n for gaseous absorption and pixel classification. More information about the\n product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-water)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from November 2017 to present.\\\\n\\\\n[olci-l2]:\n https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/ocean-products\\\\n\\\",\\\"item_assets\\\":{\\\"iwv\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. Common reference band with SLSTR.\n Vegetation monitoring\\\",\\\"center_wavelength\\\":0.885,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa19\\\",\\\"description\\\":\\\"Band\n 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\\\",\\\"center_wavelength\\\":0.9,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Integrated\n water vapour column\\\"},\\\"par\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Photosynthetically\n active radiation\\\"},\\\"trsp\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa04\\\",\\\"description\\\":\\\"Band\n 4 - Chlorophyll\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa06\\\",\\\"description\\\":\\\"Band\n 6 - Chlorophyll reference (minimum)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Transparency\n properties of water\\\"},\\\"wqsf\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Water\n quality and science flags\\\"},\\\"w-aer\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa05\\\",\\\"description\\\":\\\"Band\n 5 - Chlorophyll, sediment, turbidity, red tide\\\",\\\"center_wavelength\\\":0.51,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa06\\\",\\\"description\\\":\\\"Band\n 6 - Chlorophyll reference (minimum)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa17\\\",\\\"description\\\":\\\"Band\n 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Aerosol\n over water\\\"},\\\"chl-nn\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa01\\\",\\\"description\\\":\\\"Band\n 1 - Aerosol correction, improved water constituent retrieval\\\",\\\"center_wavelength\\\":0.4,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa02\\\",\\\"description\\\":\\\"Band\n 2 - Yellow substance and detrital pigments (turbidity)\\\",\\\"center_wavelength\\\":0.4125,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa03\\\",\\\"description\\\":\\\"Band\n 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa04\\\",\\\"description\\\":\\\"Band\n 4 - Chlorophyll\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa05\\\",\\\"description\\\":\\\"Band\n 5 - Chlorophyll, sediment, turbidity, red tide\\\",\\\"center_wavelength\\\":0.51,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa06\\\",\\\"description\\\":\\\"Band\n 6 - Chlorophyll reference (minimum)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa07\\\",\\\"description\\\":\\\"Band\n 7 - Sediment loading\\\",\\\"center_wavelength\\\":0.62,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa08\\\",\\\"description\\\":\\\"Band\n 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa09\\\",\\\"description\\\":\\\"Band\n 9 - Improved fluorescence retrieval\\\",\\\"center_wavelength\\\":0.67375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa10\\\",\\\"description\\\":\\\"Band\n 10 - Chlorophyll fluorescence peak, red edge\\\",\\\"center_wavelength\\\":0.68125,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa11\\\",\\\"description\\\":\\\"Band\n 11 - Chlorophyll fluorescence baseline, red edge transition\\\",\\\"center_wavelength\\\":0.70875,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa12\\\",\\\"description\\\":\\\"Band\n 12 - O2 absorption / clouds, vegetation\\\",\\\"center_wavelength\\\":0.75375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa16\\\",\\\"description\\\":\\\"Band\n 16 - Atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":0.77875,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa17\\\",\\\"description\\\":\\\"Band\n 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. 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Temperatures](https://sentinels.copernicus.eu/web/sentinel/user-guides/Sentinel-3-slstr/data-formats/level-1)\n products. The dataset provides both retrieved and diagnostic global aerosol\n parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF\n file for all regions over land and ocean free of snow/ice cover, excluding\n high cloud fraction data. The retrieved and derived aerosol parameters are:\\\\n\\\\n-\n Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\\\\n- Error\n estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and\n 2250 nm\\\\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and\n 2250 nm\\\\n- Fine-mode AOD at 550nm\\\\n- Aerosol Angstrom parameter between\n 550 and 865nm\\\\n- Dust AOD at 550nm\\\\n- Aerosol absorption optical depth at\n 550nm\\\\n\\\\nAtmospherically corrected nadir surface directional reflectances\n at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution\n are also provided. More information about the product and data processing\n can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from April 2020 to present.\\\\n\\\",\\\"item_assets\\\":{\\\"ntc-aod\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN_440\\\",\\\"description\\\":\\\"OLCI\n channel Oa03\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN_550\\\",\\\"description\\\":\\\"SLSTR\n nadir and oblique channel S1\\\",\\\"center_wavelength\\\":0.55,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"SYN_670\\\",\\\"description\\\":\\\"SLSTR\n nadir and oblique channel 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Oa07, Oa08, Oa09, Oa10\\\",\\\"center_wavelength\\\":0.645,\\\"full_width_half_max\\\":0.035},{\\\"name\\\":\\\"B3\\\",\\\"description\\\":\\\"OLCI\n channels Oa16, Oa17, Oa18, Oa21\\\",\\\"center_wavelength\\\":0.835,\\\"full_width_half_max\\\":0.055},{\\\"name\\\":\\\"MIR\\\",\\\"description\\\":\\\"SLSTR\n nadir and oblique channels S5, S6\\\",\\\"center_wavelength\\\":1.665,\\\"full_width_half_max\\\":0.085}],\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"OLCI\\\",\\\"SLSTR\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"synergy-v10\\\"],\\\"s3:product_type\\\":[\\\"SY_2_V10___\\\"],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance\n and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10)\n products, which are SPOT VEGETATION Continuity Products similar to those obtained\n from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments)\n onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum\n Normalized Difference Vegetation Index (NDVI) composite, which is derived\n from ground reflectance during a 10-day window, and four surface reflectance\n bands:\\\\n\\\\n- B0 (Blue, 450nm)\\\\n- B2 (Red, 645nm)\\\\n- B3 (NIR, 835nm)\\\\n-\n MIR (SWIR, 1665nm)\\\\n\\\\nThe four reflectance bands have center wavelengths\n matching those on the original SPOT VEGETATION instrument. The NDVI variable,\n which is an indicator of the amount of vegetation, is derived from the B3\n and B2 bands.\\\\n\\\\n## Data files\\\\n\\\\nThe four reflectance bands and NDVI\n values are each contained in dedicated NetCDF files. Additional metadata are\n delivered in annotation NetCDF files, each containing a single variable, including\n the geometric viewing and illumination conditions, the total water vapour\n and ozone columns, and the aerosol optical depth.\\\\n\\\\nEach 10-day product\n is delivered as a set of 10 rectangular scenes:\\\\n\\\\n- AFRICA\\\\n- NORTH_AMERICA\\\\n-\n SOUTH_AMERICA\\\\n- CENTRAL_AMERICA\\\\n- NORTH_ASIA\\\\n- WEST_ASIA\\\\n- SOUTH_EAST_ASIA\\\\n-\n ASIAN_ISLANDS\\\\n- AUSTRALASIA\\\\n- EUROPE\\\\n\\\\nMore information about the product\n and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from September 2018 to present.\\\\n\\\",\\\"item_assets\\\":{\\\"ag\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Aerosol\n optical thickness data\\\"},\\\"b0\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"B0\\\",\\\"description\\\":\\\"OLCI\n channels Oa02, Oa03\\\",\\\"center_wavelength\\\":0.45,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Surface\n Reflectance Data Set associated with VGT-B0 channel\\\"},\\\"b2\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"B2\\\",\\\"description\\\":\\\"OLCI\n channels Oa06, Oa07, Oa08, Oa09, Oa10\\\",\\\"center_wavelength\\\":0.645,\\\"full_width_half_max\\\":0.035}],\\\"description\\\":\\\"Surface\n Reflectance Data Set associated with VGT-B2 channel\\\"},\\\"b3\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"B3\\\",\\\"description\\\":\\\"OLCI\n channels Oa16, Oa17, Oa18, Oa21\\\",\\\"center_wavelength\\\":0.835,\\\"full_width_half_max\\\":0.055}],\\\"description\\\":\\\"Surface\n Reflectance Data Set associated with VGT-B3 channel\\\"},\\\"og\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Total\n Ozone column data\\\"},\\\"sm\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Status\n Map data\\\"},\\\"tg\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Synthesis\n time data\\\"},\\\"mir\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"MIR\\\",\\\"description\\\":\\\"SLSTR\n nadir and oblique channels S5, S6\\\",\\\"center_wavelength\\\":1.665,\\\"full_width_half_max\\\":0.085}],\\\"description\\\":\\\"Surface\n Reflectance Data Set associated with VGT-MIR channel\\\"},\\\"saa\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Solar\n azimuth angle data\\\"},\\\"sza\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Solar\n zenith angle data\\\"},\\\"vaa\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"View\n azimuth angle data\\\"},\\\"vza\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"View\n zenith angle data\\\"},\\\"wvg\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Total\n column Water vapour data\\\"},\\\"ndvi\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"B2\\\",\\\"description\\\":\\\"OLCI\n channels Oa06, Oa07, Oa08, Oa09, Oa10\\\",\\\"center_wavelength\\\":0.645,\\\"full_width_half_max\\\":0.035},{\\\"name\\\":\\\"B3\\\",\\\"description\\\":\\\"OLCI\n channels Oa16, Oa17, Oa18, Oa21\\\",\\\"center_wavelength\\\":0.835,\\\"full_width_half_max\\\":0.055}],\\\"description\\\":\\\"Normalised\n difference vegetation index\\\"},\\\"safe-manifest\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"SAFE\n product manifest\\\"}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-3\\\",\\\"msft:container\\\":\\\"sentinel-3\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel3euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-3\n 10-day surface reflectance and NDVI (SYNERGY V10, a SPOT VEGETATION Continuity\n Product).\\\"},{\\\"id\\\":\\\"sentinel-3-olci-lfr-l2-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-olci-lfr-l2-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-olci-lfr-l2-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Sentinel\n Data License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sentinel-3\n Land (LRR and LFR) Product User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-olci-lfr-l2-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel-3\n Land (Full Resolution)\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-olci-lfr-l2-netcdf-thumb.png\\\",\\\"title\\\":\\\"Sentinel-5P\n Level-2 NetCDF Thumbnail\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-3-olci-lfr-l2-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC Items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"MS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2016-04-25T11:33:47.368562Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"ESA\\\",\\\"Copernicus\\\",\\\"Sentinel\\\",\\\"Land\\\",\\\"Biomass\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa03\\\",\\\"description\\\":\\\"Band\n 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa10\\\",\\\"description\\\":\\\"Band\n 10 - Chlorophyll fluorescence peak, red edge\\\",\\\"center_wavelength\\\":0.68125,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa11\\\",\\\"description\\\":\\\"Band\n 11 - Chlorophyll fluorescence baseline, red edge transition\\\",\\\"center_wavelength\\\":0.70875,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa12\\\",\\\"description\\\":\\\"Band\n 12 - O2 absorption / clouds, vegetation\\\",\\\"center_wavelength\\\":0.75375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa17\\\",\\\"description\\\":\\\"Band\n 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. Common reference band with SLSTR.\n Vegetation monitoring\\\",\\\"center_wavelength\\\":0.885,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa19\\\",\\\"description\\\":\\\"Band\n 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\\\",\\\"center_wavelength\\\":0.9,\\\"full_width_half_max\\\":0.01}],\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"OLCI\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"olci-lfr\\\"],\\\"s3:product_type\\\":[\\\"OL_2_LFR___\\\"],\\\"sat:orbit_state\\\":[\\\"descending\\\",\\\"ascending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2]\n products containing data on global vegetation, chlorophyll, and water vapor.\\\\n\\\\n##\n Data files\\\\n\\\\nThis dataset includes data on three primary variables:\\\\n\\\\n*\n OLCI global vegetation index file\\\\n* terrestrial Chlorophyll index file\\\\n*\n integrated water vapor over water file.\\\\n\\\\nEach variable is contained within\n a separate NetCDF file, and is cataloged as an asset in each Item.\\\\n\\\\nSeveral\n associated variables are also provided in the annotations data files:\\\\n\\\\n*\n rectified reflectance for red and NIR channels (RC681 and RC865)\\\\n* classification,\n quality and science flags (LQSF)\\\\n* common data such as the ortho-geolocation\n of land pixels, solar and satellite angles, atmospheric and meteorological\n data, time stamp or instrument information. These variables are inherited\n from Level-1B products.\\\\n\\\\nThis full resolution product offers a spatial\n sampling of approximately 300 m.\\\\n\\\\n## Processing overview\\\\n\\\\nThe values\n in the data files have been converted from Top of Atmosphere radiance to reflectance,\n and include various corrections for gaseous absorption and pixel classification.\n More information about the product and data processing can be found in the\n [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-olci/product-types/level-2-land)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from April 2016 to present.\\\\n\\\\n[olci-l2]:\n https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/land-products\\\\n\\\",\\\"item_assets\\\":{\\\"iwv\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. Common reference band with SLSTR.\n Vegetation monitoring\\\",\\\"center_wavelength\\\":0.885,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa19\\\",\\\"description\\\":\\\"Band\n 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\\\",\\\"center_wavelength\\\":0.9,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Integrated\n water vapour column\\\"},\\\"lqsf\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Land\n quality and science flags\\\"},\\\"ogvi\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa03\\\",\\\"description\\\":\\\"Band\n 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa10\\\",\\\"description\\\":\\\"Band\n 10 - Chlorophyll fluorescence peak, red 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annotations\\\"}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-3\\\",\\\"msft:container\\\":\\\"sentinel-3\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel3euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-3\n Land Full Resolution vegetation index, chlorophyll index, and water vapor\n (OLCI LFR).\\\"},{\\\"id\\\":\\\"sentinel-3-sral-lan-l2-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-lan-l2-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-lan-l2-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Sentinel\n Data License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/product-types\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sentinel-3\n Land Radar Altimetry Product User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-sral-lan-l2-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel-3\n Land Radar Altimetry\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-sral-lan-l2-netcdf-thumb.png\\\",\\\"title\\\":\\\"Sentinel-3\n Land Radar Altimetry Thumbnail\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-3-sral-lan-l2-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC Items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"QS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-81.5,180,81.5]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2016-03-01T14:07:51.632846Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Sentinel\\\",\\\"Copernicus\\\",\\\"ESA\\\",\\\"Satellite\\\",\\\"Radar\\\",\\\"Altimetry\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"SRAL\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"sral-lan\\\"],\\\"s3:product_type\\\":[\\\"SR_2_LAN___\\\"],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n Collection provides Sentinel-3 [SRAL Level-2 Land Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products)\n products, which contain data on land radar altimetry measurements. Each product\n contains three NetCDF files:\\\\n\\\\n- A reduced data file containing a subset\n of the 1 Hz Ku-band parameters.\\\\n- A standard data file containing the standard\n 1 Hz and 20 Hz Ku- and C-band parameters.\\\\n- An enhanced data file containing\n the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms\n and parameters necessary to reprocess the data.\\\\n\\\\nMore information about\n the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from March 2016 to present.\\\\n\\\",\\\"item_assets\\\":{\\\"safe-manifest\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"SAFE\n product manifest\\\"},\\\"reduced-measurement\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Reduced\n measurement data file\\\",\\\"s3:altimetry_bands\\\":[{\\\"band_width\\\":0.32,\\\"description\\\":\\\"Band\n Ku - Range measurements\\\",\\\"frequency_band\\\":\\\"Ku\\\",\\\"center_frequency\\\":13.575000064}]},\\\"enhanced-measurement\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Enhanced\n measurement data file\\\",\\\"s3:altimetry_bands\\\":[{\\\"band_width\\\":0.29,\\\"description\\\":\\\"Band\n C - Ionospheric correction\\\",\\\"frequency_band\\\":\\\"C\\\",\\\"center_frequency\\\":5.409999872},{\\\"band_width\\\":0.32,\\\"description\\\":\\\"Band\n Ku - Range measurements\\\",\\\"frequency_band\\\":\\\"Ku\\\",\\\"center_frequency\\\":13.575000064}]},\\\"standard-measurement\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Standard\n measurement data file\\\",\\\"s3:altimetry_bands\\\":[{\\\"band_width\\\":0.29,\\\"description\\\":\\\"Band\n C - Ionospheric correction\\\",\\\"frequency_band\\\":\\\"C\\\",\\\"center_frequency\\\":5.409999872},{\\\"band_width\\\":0.32,\\\"description\\\":\\\"Band\n Ku - Range measurements\\\",\\\"frequency_band\\\":\\\"Ku\\\",\\\"center_frequency\\\":13.575000064}]}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-3\\\",\\\"msft:container\\\":\\\"sentinel-3\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel3euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-3\n radar altimetry over land (SRAL LAN).\\\"},{\\\"id\\\":\\\"sentinel-3-slstr-lst-l2-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-lst-l2-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-lst-l2-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Sentinel\n Data License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sentinel-3\n Land Surface Temperature Product User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-lst-l2-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel-3\n Land Surface Temperature\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-slstr-lst-l2-netcdf-thumb.png\\\",\\\"title\\\":\\\"Sentinel-3\n Land Surface Temperature Thumbnail\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-3-slstr-lst-l2-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC Items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"W\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2016-04-19T01:35:17.188500Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Sentinel\\\",\\\"Copernicus\\\",\\\"ESA\\\",\\\"Satellite\\\",\\\"Temperature\\\",\\\"Land\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"eo:bands\\\":[{\\\"name\\\":\\\"S8\\\",\\\"description\\\":\\\"Band\n 8 - SST, LST, Active fire\\\",\\\"center_wavelength\\\":10.854,\\\"full_width_half_max\\\":0.776},{\\\"name\\\":\\\"S9\\\",\\\"description\\\":\\\"Band\n 9 - SST, LST\\\",\\\"center_wavelength\\\":12.0225,\\\"full_width_half_max\\\":0.905}],\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"SLSTR\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"slstr-lst\\\"],\\\"s3:product_type\\\":[\\\"SL_2_LST___\\\"],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n Collection provides Sentinel-3 [SLSTR Level-2 Land Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst)\n products containing data on land surface temperature measurements on a 1km\n grid. Radiance is measured in two channels to determine the temperature of\n the Earth's surface skin in the instrument field of view, where the term \\\\\\\"skin\\\\\\\"\n refers to the top surface of bare soil or the effective emitting temperature\n of vegetation canopies as viewed from above.\\\\n\\\\n## Data files\\\\n\\\\nThe dataset\n includes data on the primary measurement variable, land surface temperature,\n in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains\n several ancillary variables:\\\\n\\\\n- Normalized Difference Vegetation Index\\\\n-\n Surface biome classification\\\\n- Fractional vegetation cover\\\\n- Total water\n vapor column\\\\n\\\\nIn addition to the primary and ancillary data files, a standard\n set of annotation data files provide meteorological information, geolocation\n and time coordinates, geometry information, and quality flags. More information\n about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-lst)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from April 2016 to present.\\\\n\\\\n##\n STAC Item geometries\\\\n\\\\nThe Collection contains small \\\\\\\"chips\\\\\\\" and\n long \\\\\\\"stripes\\\\\\\" of data collected along the satellite direction of travel.\n Approximately five percent of the STAC Items describing long stripes of data\n contain geometries that encompass a larger area than an exact concave hull\n of the data extents. This may require additional filtering when searching\n the Collection for Items that spatially intersect an area of interest.\\\\n\\\",\\\"item_assets\\\":{\\\"lst-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"S8\\\",\\\"description\\\":\\\"Band\n 8 - SST, LST, Active fire\\\",\\\"center_wavelength\\\":10.854,\\\"full_width_half_max\\\":0.776},{\\\"name\\\":\\\"S9\\\",\\\"description\\\":\\\"Band\n 9 - SST, LST\\\",\\\"center_wavelength\\\":12.0225,\\\"full_width_half_max\\\":0.905}],\\\"description\\\":\\\"Land\n Surface Temperature (LST) values\\\"},\\\"slstr-met-tx\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Meteorological\n parameters regridded onto the 16km tie points\\\"},\\\"safe-manifest\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"SAFE\n product manifest\\\"},\\\"slstr-time-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Time\n annotations for the 1km grid\\\"},\\\"slstr-flags-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Global\n flags for the 1km TIR grid, nadir view\\\"},\\\"lst-ancillary-ds\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"LST\n ancillary measurement dataset\\\"},\\\"slstr-indices-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Scan,\n pixel and detector indices annotations for the 1km TIR grid, nadir view\\\"},\\\"slstr-geodetic-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Full\n resolution geodetic coordinates for the 1km TIR grid, nadir view\\\"},\\\"slstr-geodetic-tx\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"16km\n geodetic coordinates\\\"},\\\"slstr-geometry-tn\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"16km\n solar and satellite geometry annotations, nadir view\\\"},\\\"slstr-cartesian-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Full\n resolution cartesian coordinates for the 1km TIR grid, nadir view\\\"},\\\"slstr-cartesian-tx\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"16km\n cartesian coordinates\\\"}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-3\\\",\\\"msft:container\\\":\\\"sentinel-3\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel3euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-3\n land surface temperature (SLSTR LST).\\\"},{\\\"id\\\":\\\"sentinel-3-slstr-wst-l2-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-wst-l2-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-wst-l2-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Sentinel\n Data License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sentinel-3\n Sea Surface Temperature Product User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-wst-l2-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel-3\n Sea Surface Temperature\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-slstr-wst-l2-netcdf-thumb.png\\\",\\\"title\\\":\\\"Sentinel-3\n Sea Surface Temperature Thumbnail\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-3-slstr-wst-l2-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC Items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"MS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2017-10-31T23:59:57.451604Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Sentinel\\\",\\\"Copernicus\\\",\\\"ESA\\\",\\\"Satellite\\\",\\\"Temperature\\\",\\\"Ocean\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"eo:bands\\\":[{\\\"name\\\":\\\"S7\\\",\\\"description\\\":\\\"Band\n 7 - SST, LST, Active fire\\\",\\\"center_wavelength\\\":3.742,\\\"full_width_half_max\\\":0.398},{\\\"name\\\":\\\"S8\\\",\\\"description\\\":\\\"Band\n 8 - SST, LST, Active fire\\\",\\\"center_wavelength\\\":10.854,\\\"full_width_half_max\\\":0.776},{\\\"name\\\":\\\"S9\\\",\\\"description\\\":\\\"Band\n 9 - SST, LST\\\",\\\"center_wavelength\\\":12.0225,\\\"full_width_half_max\\\":0.905}],\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"SLSTR\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"slstr-wst\\\"],\\\"s3:product_type\\\":[\\\"SL_2_WST___\\\"],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n Collection provides Sentinel-3 [SLSTR Level-2 Water Surface Temperature](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst)\n products containing data on sea surface temperature measurements on a 1km\n grid. Each product consists of a single NetCDF file containing all data variables:\\\\n\\\\n-\n Sea Surface Temperature (SST) value\\\\n- SST total uncertainty\\\\n- Latitude\n and longitude coordinates\\\\n- SST time deviation\\\\n- Single Sensor Error Statistic\n (SSES) bias and standard deviation estimate\\\\n- Contextual parameters such\n as wind speed at 10 m and fractional sea-ice contamination\\\\n- Quality flag\\\\n-\n Satellite zenith angle\\\\n- Top Of Atmosphere (TOA) Brightness Temperature\n (BT)\\\\n- TOA noise equivalent BT\\\\n\\\\nMore information about the product and\n data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-wst)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from October 2017 to present.\\\\n\\\",\\\"item_assets\\\":{\\\"l2p\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"S7\\\",\\\"description\\\":\\\"Band\n 7 - SST, LST, Active fire\\\",\\\"center_wavelength\\\":3.742,\\\"full_width_half_max\\\":0.398},{\\\"name\\\":\\\"S8\\\",\\\"description\\\":\\\"Band\n 8 - SST, LST, Active fire\\\",\\\"center_wavelength\\\":10.854,\\\"full_width_half_max\\\":0.776},{\\\"name\\\":\\\"S9\\\",\\\"description\\\":\\\"Band\n 9 - SST, LST\\\",\\\"center_wavelength\\\":12.0225,\\\"full_width_half_max\\\":0.905}],\\\"description\\\":\\\"Skin\n Sea Surface Temperature (SST) values\\\"},\\\"browse-jpg\\\":{\\\"type\\\":\\\"image/jpeg\\\",\\\"roles\\\":[\\\"thumbnail\\\"],\\\"description\\\":\\\"Preview\n image produced by the European Organisation for the Exploitation of Meteorological\n Satellites (EUMETSAT)\\\"},\\\"eop-metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"Metadata\n produced by the European Organisation for the Exploitation of Meteorological\n Satellites (EUMETSAT)\\\"},\\\"safe-manifest\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"SAFE\n product manifest\\\"}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-3\\\",\\\"msft:container\\\":\\\"sentinel-3\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel3euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-3\n sea surface temperature (SLSTR WST).\\\"},{\\\"id\\\":\\\"sentinel-3-sral-wat-l2-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-wat-l2-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-sral-wat-l2-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Sentinel\n Data License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/product-types\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sentinel-3\n Ocean Radar Altimetry Product User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-sral-wat-l2-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel-3\n Ocean Radar Altimetry\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-sral-wat-l2-netcdf-thumb.png\\\",\\\"title\\\":\\\"Sentinel-3\n Ocean Radar Altimetry Thumbnail\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-3-sral-wat-l2-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC Items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"QS\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-81.5,180,81.5]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2017-01-28T00:59:14.149496Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Sentinel\\\",\\\"Copernicus\\\",\\\"ESA\\\",\\\"Satellite\\\",\\\"Radar\\\",\\\"Altimetry\\\",\\\"Ocean\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"SRAL\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"sral-wat\\\"],\\\"s3:product_type\\\":[\\\"SR_2_WAT___\\\"],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products)\n products, which contain data on ocean radar altimetry measurements. Each product\n contains three NetCDF files:\\\\n\\\\n- A reduced data file containing a subset\n of the 1 Hz Ku-band parameters.\\\\n- A standard data file containing the standard\n 1 Hz and 20 Hz Ku- and C-band parameters.\\\\n- An enhanced data file containing\n the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms\n and parameters necessary to reprocess the data.\\\\n\\\\nMore information about\n the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from January 2017 to present.\\\\n\\\",\\\"item_assets\\\":{\\\"eop-metadata\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"Product\n metadata file produced by the European Organisation for the Exploitation of\n Meteorological Satellites (EUMETSAT)\\\"},\\\"safe-manifest\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"SAFE\n product manifest\\\"},\\\"reduced-measurement\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Reduced\n measurement data file\\\",\\\"s3:altimetry_bands\\\":[{\\\"band_width\\\":0.32,\\\"description\\\":\\\"Band\n Ku - Range measurements\\\",\\\"frequency_band\\\":\\\"Ku\\\",\\\"center_frequency\\\":13.575000064}]},\\\"enhanced-measurement\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Enhanced\n measurement data file\\\",\\\"s3:altimetry_bands\\\":[{\\\"band_width\\\":0.29,\\\"description\\\":\\\"Band\n C - Ionospheric correction\\\",\\\"frequency_band\\\":\\\"C\\\",\\\"center_frequency\\\":5.409999872},{\\\"band_width\\\":0.32,\\\"description\\\":\\\"Band\n Ku - Range measurements\\\",\\\"frequency_band\\\":\\\"Ku\\\",\\\"center_frequency\\\":13.575000064}]},\\\"standard-measurement\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Standard\n measurement data file\\\",\\\"s3:altimetry_bands\\\":[{\\\"band_width\\\":0.29,\\\"description\\\":\\\"Band\n C - Ionospheric correction\\\",\\\"frequency_band\\\":\\\"C\\\",\\\"center_frequency\\\":5.409999872},{\\\"band_width\\\":0.32,\\\"description\\\":\\\"Band\n Ku - Range measurements\\\",\\\"frequency_band\\\":\\\"Ku\\\",\\\"center_frequency\\\":13.575000064}]}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-3\\\",\\\"msft:container\\\":\\\"sentinel-3\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel3euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-3\n radar altimetry over ocean (SRAL WAT).\\\"},{\\\"id\\\":\\\"hls2-s30\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hls2-s30/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/hls2-s30\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\\\",\\\"title\\\":\\\"LP\n DAAC - Data Citation and Policies\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/hls2-s30\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Harmonized\n Landsat Sentinel-2 (HLS) Version 2.0, Sentinel-2 Data\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/hls2-s30.webp\\\",\\\"type\\\":\\\"image/webp\\\",\\\"title\\\":\\\"HLS2\n Sentinel Collection Thumbnail\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/hls2-s30.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"W-MON\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2020-01-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Sentinel\\\",\\\"Landsat\\\",\\\"HLS\\\",\\\"Satellite\\\",\\\"Global\\\",\\\"Imagery\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\"]},{\\\"url\\\":\\\"https://doi.org/10.5067/HLS/HLSL30.002\\\",\\\"name\\\":\\\"LP\n DAAC\\\",\\\"roles\\\":[\\\"producer\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"gsd\\\":[30],\\\"platform\\\":[\\\"Sentinel-2A\\\",\\\"Sentinel-2B\\\",\\\"Sentinel-2C\\\"]},\\\"description\\\":\\\"Harmonized\n Landsat Sentinel-2 (HLS) Version 2.0 provides consistent surface reflectance\n (SR)\\\\nand top of atmosphere (TOA) brightness data from the Operational Land\n Imager (OLI) aboard the\\\\njoint NASA/USGS Landsat 8 and Landsat 9 satellites\n and the Multi-Spectral Instrument (MSI)\\\\naboard the ESA (European Space Agency)\n Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites.\\\\nThe combined measurement\n enables global observations of the land every 2-3 days at 30 meter\\\\n(m) spatial\n resolution. The HLS-S30 and HLS-L30 products are gridded to the same resolution\n and\\\\nMilitary Grid Reference System (MGRS) tiling system and are \\\\\\\"stackable\\\\\\\"\n for time series analysis.\\\\nThis dataset is in the form of cloud-optimized\n GeoTIFFs. The HLS v2.0 data is generated by NASA's\\\\nIMPACT team at Marshall\n Space Flight Center. The product latency is 1.7 days, from the satellite\\\\noverpass\n to the HLS data availability at NASA's Land Processes Distributed Active Archive\n Center\\\\n(LP DAAC). For more information see LP DAAC's\\\\n[HLS Overview](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/).\\\\n\\\\nThis\n collection contains HLS data collected from Sentinel-2A, Sentinel-2B, and\n Sentinel-2C.\\\\nHLS data generated from Landsat can be found in a separate\n collection.\\\\n\\\",\\\"item_assets\\\":{\\\"B01\\\":{\\\"type\\\":\\\"image/tiff; application=geotiff;\n profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Coastal Aerosol\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B01\\\",\\\"common_name\\\":\\\"coastal\\\",\\\"center_wavelength\\\":0.4439,\\\"full_width_half_max\\\":0.027}]},\\\"B02\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Blue\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B02\\\",\\\"common_name\\\":\\\"blue\\\",\\\"center_wavelength\\\":0.4966,\\\"full_width_half_max\\\":0.098}]},\\\"B03\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Green\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B03\\\",\\\"common_name\\\":\\\"green\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.045}]},\\\"B04\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Red\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B04\\\",\\\"common_name\\\":\\\"red\\\",\\\"center_wavelength\\\":0.6645,\\\"full_width_half_max\\\":0.038}]},\\\"B05\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Red-Edge\n 1\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B05\\\",\\\"center_wavelength\\\":0.7039,\\\"full_width_half_max\\\":0.019}]},\\\"B06\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Red-Edge\n 2\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B06\\\",\\\"center_wavelength\\\":0.7402,\\\"full_width_half_max\\\":0.018}]},\\\"B07\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Red-Edge\n 3\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B07\\\",\\\"center_wavelength\\\":0.7825,\\\"full_width_half_max\\\":0.028}]},\\\"B08\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"NIR\n Broad\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B08\\\",\\\"common_name\\\":\\\"nir\\\",\\\"center_wavelength\\\":0.8351,\\\"full_width_half_max\\\":0.145}]},\\\"B09\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Water\n Vapor\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B09\\\",\\\"center_wavelength\\\":0.945,\\\"full_width_half_max\\\":0.026}]},\\\"B10\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Cirrus\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B10\\\",\\\"common_name\\\":\\\"cirrus\\\",\\\"center_wavelength\\\":1.3735,\\\"full_width_half_max\\\":0.075}]},\\\"B11\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"SWIR\n 1\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B11\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"center_wavelength\\\":1.6137,\\\"full_width_half_max\\\":0.143}]},\\\"B12\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"SWIR\n 2\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B12\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"center_wavelength\\\":2.22024,\\\"full_width_half_max\\\":0.242}]},\\\"B8A\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"NIR\n Narrow\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"B8A\\\",\\\"center_wavelength\\\":0.8648,\\\"full_width_half_max\\\":0.033}]},\\\"SAA\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"SAA\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"SAA\\\"}]},\\\"SZA\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"SZA\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"SZA\\\"}]},\\\"VAA\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VAA\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"VAA\\\"}]},\\\"VZA\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"VZA\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"VZA\\\"}]},\\\"Fmask\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Fmask\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"Fmask\\\"}]}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"hls2\\\",\\\"msft:container\\\":\\\"hls2\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/projection/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/view/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"hls2euwest\\\",\\\"msft:short_description\\\":\\\"Harmonized\n Landsat Sentinel-2 (HLS) Version 2.0, Sentinel-2 Data\\\"},{\\\"id\\\":\\\"ms-buildings\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/ms-buildings\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://github.com/microsoft/GlobalMLBuildingFootprints/blob/main/LICENSE\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"ODbL-1.0\n License\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/ms-buildings\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Microsoft\n Building Footprints\\\",\\\"assets\\\":{\\\"delta\\\":{\\\"href\\\":\\\"az://footprints/delta/2023-04-25/ml-buildings.parquet/\\\",\\\"title\\\":\\\"Delta\n table\\\",\\\"description\\\":\\\"Delta table with latest buildings footprints partitioned\n by Region and quadkey.\\\",\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"bingmlbuildings\\\"}},\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/msbuildings-thumbnail.png\\\",\\\"type\\\":\\\"image/png\\\",\\\"title\\\":\\\"Thumbnail\\\"},\\\"global-footprints\\\":{\\\"title\\\":\\\"Global\n Bing ML building footprints\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/data/v1/vector/collections/ms-buildings/tilesets/global-footprints/tilejson.json\\\",\\\"type\\\":\\\"application/json\\\",\\\"roles\\\":[\\\"tiles\\\"]}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180.0,90.0,180.0,-90.0]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2014-01-01T00:00:00Z\\\",null]]}},\\\"license\\\":\\\"ODbL-1.0\\\",\\\"keywords\\\":[\\\"Bing\n Maps\\\",\\\"Buildings\\\",\\\"geoparquet\\\",\\\"Microsoft\\\",\\\"Footprint\\\",\\\"Delta\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"host\\\"]}],\\\"summaries\\\":{\\\"msbuildings:processing-date\\\":[\\\"2023-04-25\\\"]},\\\"description\\\":\\\"Bing\n Maps is releasing open building footprints around the world. We have detected\n over 999 million buildings from Bing Maps imagery between 2014 and 2021 including\n Maxar and Airbus imagery. The data is freely available for download and use\n under ODbL. This dataset complements our other releases.\\\\n\\\\nFor more information,\n see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/)\n repository on GitHub.\\\\n\\\\n## Building footprint creation\\\\n\\\\nThe building\n extraction is done in two stages:\\\\n\\\\n1. Semantic Segmentation \\u2013 Recognizing\n building pixels on an aerial image using deep neural networks (DNNs)\\\\n2.\n Polygonization \\u2013 Converting building pixel detections into polygons\\\\n\\\\n**Stage\n 1: Semantic Segmentation**\\\\n\\\\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\\\\n\\\\n**Stage\n 2: Polygonization**\\\\n\\\\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\\\\n\\\\n##\n Data assets\\\\n\\\\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet)\n datasets in [Delta][delta] table format.\\\\nThe data are partitioned by\\\\n\\\\n1.\n Region\\\\n2. quadkey at [Bing Map Tiles][tiles] level 9\\\\n\\\\nEach `(Region,\n quadkey)` pair will have one or more geoparquet files, depending on the density\n of the of the buildings in that area.\\\\n\\\\nNote that older items in this dataset\n are *not* spatially partitioned. We recommend using data with a processing\n date\\\\nof 2023-04-25 or newer. This processing date is part of the URL for\n each parquet file and is captured in the STAC metadata\\\\nfor each item (see\n below).\\\\n\\\\n## Delta Format\\\\n\\\\nThe collection-level asset under the `delta`\n key gives you the fsspec-style URL\\\\nto the Delta table. This can be used\n to efficiently query for matching partitions\\\\nby `Region` and `quadkey`.\n See the notebook for an example using Python.\\\\n\\\\n## STAC metadata\\\\n\\\\nThis\n STAC collection has one STAC item per region. The `msbuildings:region`\\\\nproperty\n can be used to filter items to a specific region, and the `msbuildings:quadkey`\\\\nproperty\n can be used to filter items to a specific quadkey (though you can also search\\\\nby\n the `geometry`).\\\\n\\\\nNote that older STAC items are not spatially partitioned.\n We recommend filtering on\\\\nitems with an `msbuildings:processing-date` of\n `2023-04-25` or newer. See the collection\\\\nsummary for `msbuildings:processing-date`\n for a list of valid values.\\\\n\\\\n[delta]: https://delta.io/\\\\n[tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system\\\\n\\\",\\\"item_assets\\\":{\\\"data\\\":{\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"data\\\"],\\\"title\\\":\\\"Building\n Footprints\\\",\\\"description\\\":\\\"Parquet dataset with the building footprints\n for this region.\\\",\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"bingmlbuildings\\\"}}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"table:columns\\\":[{\\\"name\\\":\\\"geometry\\\",\\\"type\\\":\\\"byte_array\\\",\\\"description\\\":\\\"Building\n footprint polygons\\\"}],\\\"msft:container\\\":\\\"footprints\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"bingmlbuildings\\\",\\\"msft:short_description\\\":\\\"Machine\n learning detected buildings footprints.\\\"},{\\\"id\\\":\\\"sentinel-3-slstr-frp-l2-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-frp-l2-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-slstr-frp-l2-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Sentinel\n Data License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sentinel-3\n Fire Radiative Power Product User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-frp-l2-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel-3\n Fire Radiative Power\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-slstr-frp-l2-netcdf-thumb.png\\\",\\\"title\\\":\\\"Sentinel-3\n Fire Radiative Power Thumbnail\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-3-slstr-frp-l2-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC Items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"W\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2020-08-08T23:11:15.617203Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Sentinel\\\",\\\"Copernicus\\\",\\\"ESA\\\",\\\"Satellite\\\",\\\"Temperature\\\",\\\"Fire\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"eo:bands\\\":[{\\\"name\\\":\\\"F1\\\",\\\"description\\\":\\\"Band\n 10 - Active fire\\\",\\\"center_wavelength\\\":3.742,\\\"full_width_half_max\\\":0.398},{\\\"name\\\":\\\"S5\\\",\\\"description\\\":\\\"Band\n 5 - Cloud clearing, ice, snow, vegetation monitoring\\\",\\\"center_wavelength\\\":1.6134,\\\"full_width_half_max\\\":0.06068},{\\\"name\\\":\\\"S6\\\",\\\"description\\\":\\\"Band\n 6 - Vegetation state and cloud clearing\\\",\\\"center_wavelength\\\":2.2557,\\\"full_width_half_max\\\":0.05015},{\\\"name\\\":\\\"S7\\\",\\\"description\\\":\\\"Band\n 7 - SST, LST, Active fire\\\",\\\"center_wavelength\\\":3.742,\\\"full_width_half_max\\\":0.398}],\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"SLSTR\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"slstr-frp\\\"],\\\"s3:product_type\\\":[\\\"SL_2_FRP___\\\"],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n Collection provides Sentinel-3 [SLSTR Level-2 Fire Radiative Power](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp)\n (FRP) products containing data on fires detected over land and ocean.\\\\n\\\\n##\n Data files\\\\n\\\\nThe primary measurement data is contained in the `FRP_in.nc`\n file and provides FRP and uncertainties, projected onto a 1km grid, for fires\n detected in the thermal infrared (TIR) spectrum over land. Since February\n 2022, FRP and uncertainties are also provided for fires detected in the short\n wave infrared (SWIR) spectrum over both land and ocean, with the delivered\n data projected onto a 500m grid. The latter SWIR-detected fire data is only\n available for night-time measurements and is contained in the `FRP_an.nc`\n or `FRP_bn.nc` files.\\\\n\\\\nIn addition to the measurement data files, a standard\n set of annotation data files provide meteorological information, geolocation\n and time coordinates, geometry information, and quality flags.\\\\n\\\\n## Processing\\\\n\\\\nThe\n TIR fire detection is based on measurements from the S7 and F1 bands of the\n [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument);\n SWIR fire detection is based on the S5 and S6 bands. More information about\n the product and data processing can be found in the [User Guide](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/product-types/level-2-frp)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from August 2020 to present.\\\\n\\\",\\\"item_assets\\\":{\\\"frp-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"S5\\\",\\\"description\\\":\\\"Band\n 5 - Cloud clearing, ice, snow, vegetation monitoring\\\",\\\"center_wavelength\\\":1.6134,\\\"full_width_half_max\\\":0.06068},{\\\"name\\\":\\\"S6\\\",\\\"description\\\":\\\"Band\n 6 - Vegetation state and cloud clearing\\\",\\\"center_wavelength\\\":2.2557,\\\"full_width_half_max\\\":0.05015},{\\\"name\\\":\\\"S7\\\",\\\"description\\\":\\\"Band\n 7 - SST, LST, Active fire\\\",\\\"center_wavelength\\\":3.742,\\\"full_width_half_max\\\":0.398},{\\\"name\\\":\\\"F1\\\",\\\"description\\\":\\\"Band\n 10 - Active fire\\\",\\\"center_wavelength\\\":3.742,\\\"full_width_half_max\\\":0.398}],\\\"description\\\":\\\"Fire\n Radiative Power (FRP) dataset\\\"},\\\"slstr-met-tx\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Meteorological\n parameters regridded onto the 16km tie points\\\"},\\\"safe-manifest\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"SAFE\n product manifest\\\"},\\\"slstr-time-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Time\n annotations for the 1 KM grid\\\"},\\\"slstr-flags-fn\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Global\n flags for the 1km F1 grid, nadir view\\\"},\\\"slstr-flags-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Global\n flags for the 1km TIR grid, nadir view\\\"},\\\"slstr-indices-fn\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Scan,\n pixel and detector annotations for the 1km F1 grid, nadir view\\\"},\\\"slstr-indices-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Scan,\n pixel and detector annotations for the 1km TIR grid, nadir view\\\"},\\\"slstr-geodetic-fn\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Full\n resolution geodetic coordinates for the 1km F1 grid, nadir view\\\"},\\\"slstr-geodetic-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Full\n resolution geodetic coordinates for the 1km TIR grid, nadir view\\\"},\\\"slstr-geodetic-tx\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"16km\n geodetic coordinates\\\"},\\\"slstr-geometry-tn\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"16km\n solar and satellite geometry annotations, nadir view\\\"},\\\"slstr-cartesian-fn\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Full\n resolution cartesian coordinates for the 1km F1 grid, nadir view\\\"},\\\"slstr-cartesian-in\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Full\n resolution cartesian coordinates for the 1km TIR grid, nadir view\\\"},\\\"slstr-cartesian-tx\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"16km\n cartesian coordinates\\\"}},\\\"msft:region\\\":\\\"westeurope\\\",\\\"stac_version\\\":\\\"1.0.0\\\",\\\"msft:group_id\\\":\\\"sentinel-3\\\",\\\"msft:container\\\":\\\"sentinel-3\\\",\\\"stac_extensions\\\":[\\\"https://stac-extensions.github.io/sat/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/table/v1.2.0/schema.json\\\",\\\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\\\",\\\"https://stac-extensions.github.io/eo/v1.1.0/schema.json\\\"],\\\"msft:storage_account\\\":\\\"sentinel3euwest\\\",\\\"msft:short_description\\\":\\\"Sentinel-3\n fire detection over land (SLSTR FRP).\\\"},{\\\"id\\\":\\\"sentinel-3-synergy-syn-l2-netcdf\\\",\\\"type\\\":\\\"Collection\\\",\\\"links\\\":[{\\\"rel\\\":\\\"items\\\",\\\"type\\\":\\\"application/geo+json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-syn-l2-netcdf/items\\\"},{\\\"rel\\\":\\\"parent\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"root\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/\\\"},{\\\"rel\\\":\\\"self\\\",\\\"type\\\":\\\"application/json\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-syn-l2-netcdf\\\"},{\\\"rel\\\":\\\"license\\\",\\\"href\\\":\\\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\\\",\\\"type\\\":\\\"application/pdf\\\",\\\"title\\\":\\\"Sentinel\n Data License\\\"},{\\\"rel\\\":\\\"about\\\",\\\"href\\\":\\\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn\\\",\\\"type\\\":\\\"text/html\\\",\\\"title\\\":\\\"Sentinel-3\n Land Surface Reflectance and Aerosol Product User Guide\\\"},{\\\"rel\\\":\\\"describedby\\\",\\\"href\\\":\\\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-syn-l2-netcdf\\\",\\\"title\\\":\\\"Human\n readable dataset overview and reference\\\",\\\"type\\\":\\\"text/html\\\"}],\\\"title\\\":\\\"Sentinel-3\n Land Surface Reflectance and Aerosol\\\",\\\"assets\\\":{\\\"thumbnail\\\":{\\\"href\\\":\\\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-synergy-syn-l2-netcdf-thumb.png\\\",\\\"title\\\":\\\"Sentinel-3\n Land Surface Reflectance and Aerosol Thumbnail\\\",\\\"media_type\\\":\\\"image/png\\\"},\\\"geoparquet-items\\\":{\\\"href\\\":\\\"abfs://items/sentinel-3-synergy-syn-l2-netcdf.parquet\\\",\\\"type\\\":\\\"application/x-parquet\\\",\\\"roles\\\":[\\\"stac-items\\\"],\\\"title\\\":\\\"GeoParquet\n STAC Items\\\",\\\"description\\\":\\\"Snapshot of the collection's STAC items exported\n to GeoParquet format.\\\",\\\"msft:partition_info\\\":{\\\"is_partitioned\\\":true,\\\"partition_frequency\\\":\\\"W\\\"},\\\"table:storage_options\\\":{\\\"account_name\\\":\\\"pcstacitems\\\"}}},\\\"extent\\\":{\\\"spatial\\\":{\\\"bbox\\\":[[-180,-90,180,90]]},\\\"temporal\\\":{\\\"interval\\\":[[\\\"2018-09-22T16:51:00.001276Z\\\",null]]}},\\\"license\\\":\\\"proprietary\\\",\\\"keywords\\\":[\\\"Sentinel\\\",\\\"Copernicus\\\",\\\"ESA\\\",\\\"Satellite\\\",\\\"Land\\\",\\\"Reflectance\\\",\\\"Aerosol\\\"],\\\"providers\\\":[{\\\"url\\\":\\\"https://earth.esa.int/web/guest/home\\\",\\\"name\\\":\\\"ESA\\\",\\\"roles\\\":[\\\"producer\\\",\\\"processor\\\",\\\"licensor\\\"]},{\\\"url\\\":\\\"https://planetarycomputer.microsoft.com\\\",\\\"name\\\":\\\"Microsoft\\\",\\\"roles\\\":[\\\"host\\\"]}],\\\"summaries\\\":{\\\"eo:bands\\\":[{\\\"name\\\":\\\"Oa01\\\",\\\"description\\\":\\\"Band\n 1 - Aerosol correction, improved water constituent retrieval\\\",\\\"center_wavelength\\\":0.4,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa02\\\",\\\"description\\\":\\\"Band\n 2 - Yellow substance and detrital pigments (turbidity)\\\",\\\"center_wavelength\\\":0.4125,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa03\\\",\\\"description\\\":\\\"Band\n 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa04\\\",\\\"description\\\":\\\"Band\n 4 - Chlorophyll\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa05\\\",\\\"description\\\":\\\"Band\n 5 - Chlorophyll, sediment, turbidity, red tide\\\",\\\"center_wavelength\\\":0.51,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa06\\\",\\\"description\\\":\\\"Band\n 6 - Chlorophyll reference (minimum)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa07\\\",\\\"description\\\":\\\"Band\n 7 - Sediment loading\\\",\\\"center_wavelength\\\":0.62,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa08\\\",\\\"description\\\":\\\"Band\n 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa09\\\",\\\"description\\\":\\\"Band\n 9 - Improved fluorescence retrieval\\\",\\\"center_wavelength\\\":0.67375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa10\\\",\\\"description\\\":\\\"Band\n 10 - Chlorophyll fluorescence peak, red edge\\\",\\\"center_wavelength\\\":0.68125,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa11\\\",\\\"description\\\":\\\"Band\n 11 - Chlorophyll fluorescence baseline, red edge transition\\\",\\\"center_wavelength\\\":0.70875,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa12\\\",\\\"description\\\":\\\"Band\n 12 - O2 absorption / clouds, vegetation\\\",\\\"center_wavelength\\\":0.75375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa13\\\",\\\"description\\\":\\\"Band\n 13 - O2 absorption / aerosol correction\\\",\\\"center_wavelength\\\":0.76125,\\\"full_width_half_max\\\":0.0025},{\\\"name\\\":\\\"Oa16\\\",\\\"description\\\":\\\"Band\n 16 - Atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":0.77875,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa17\\\",\\\"description\\\":\\\"Band\n 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. Common reference band with SLSTR.\n Vegetation monitoring\\\",\\\"center_wavelength\\\":0.885,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa19\\\",\\\"description\\\":\\\"Band\n 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\\\",\\\"center_wavelength\\\":0.9,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa21\\\",\\\"description\\\":\\\"Band\n 21 - Water vapour absorption, atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":1.02,\\\"full_width_half_max\\\":0.04},{\\\"name\\\":\\\"S1\\\",\\\"description\\\":\\\"Band\n 1 - Cloud screening, vegetation monitoring, aerosol\\\",\\\"center_wavelength\\\":0.55427,\\\"full_width_half_max\\\":0.01926},{\\\"name\\\":\\\"S2\\\",\\\"description\\\":\\\"Band\n 2 - NDVI, vegetation monitoring, aerosol\\\",\\\"center_wavelength\\\":0.65947,\\\"full_width_half_max\\\":0.01925},{\\\"name\\\":\\\"S3\\\",\\\"description\\\":\\\"Band\n 3 - NDVI, cloud flagging, pixel co-registration\\\",\\\"center_wavelength\\\":0.868,\\\"full_width_half_max\\\":0.0206},{\\\"name\\\":\\\"S4\\\",\\\"description\\\":\\\"Band\n 4 - Cirrus detection over land\\\",\\\"center_wavelength\\\":1.3748,\\\"full_width_half_max\\\":0.0208},{\\\"name\\\":\\\"S5\\\",\\\"description\\\":\\\"Band\n 5 - Cloud clearing, ice, snow, vegetation monitoring\\\",\\\"center_wavelength\\\":1.6134,\\\"full_width_half_max\\\":0.06068},{\\\"name\\\":\\\"S6\\\",\\\"description\\\":\\\"Band\n 6 - Vegetation state and cloud clearing\\\",\\\"center_wavelength\\\":2.2557,\\\"full_width_half_max\\\":0.05015},{\\\"name\\\":\\\"SYN01\\\",\\\"description\\\":\\\"OLCI\n channel Oa01\\\",\\\"center_wavelength\\\":0.4,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"SYN02\\\",\\\"description\\\":\\\"OLCI\n channel Oa02\\\",\\\"center_wavelength\\\":0.4125,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN03\\\",\\\"description\\\":\\\"OLCI\n channel Oa03\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN04\\\",\\\"description\\\":\\\"OLCI\n channel Oa04\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN05\\\",\\\"description\\\":\\\"OLCI\n channel Oa05\\\",\\\"center_wavelength\\\":0.51,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN06\\\",\\\"description\\\":\\\"OLCI\n channel Oa06\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN07\\\",\\\"description\\\":\\\"OLCI\n channel Oa07\\\",\\\"center_wavelength\\\":0.62,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN08\\\",\\\"description\\\":\\\"OLCI\n channel Oa08\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN09\\\",\\\"description\\\":\\\"OLCI\n channel Oa09\\\",\\\"center_wavelength\\\":0.67375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"SYN10\\\",\\\"description\\\":\\\"OLCI\n channel Oa10\\\",\\\"center_wavelength\\\":0.68125,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"SYN11\\\",\\\"description\\\":\\\"OLCI\n channel Oa11\\\",\\\"center_wavelength\\\":0.70875,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN12\\\",\\\"description\\\":\\\"OLCI\n channel Oa12\\\",\\\"center_wavelength\\\":0.75375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"SYN13\\\",\\\"description\\\":\\\"OLCI\n channel Oa16\\\",\\\"center_wavelength\\\":0.7785,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"SYN14\\\",\\\"description\\\":\\\"OLCI\n channel Oa17\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"SYN15\\\",\\\"description\\\":\\\"OLCI\n channel Oa18\\\",\\\"center_wavelength\\\":0.885,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"SYN16\\\",\\\"description\\\":\\\"OLCI\n channel Oa21\\\",\\\"center_wavelength\\\":1.02,\\\"full_width_half_max\\\":0.04},{\\\"name\\\":\\\"SYN17\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S1\\\",\\\"center_wavelength\\\":0.555,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"SYN18\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S2\\\",\\\"center_wavelength\\\":0.659,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"SYN19\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S3\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"SYN20\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S5\\\",\\\"center_wavelength\\\":1.61,\\\"full_width_half_max\\\":0.06},{\\\"name\\\":\\\"SYN21\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S6\\\",\\\"center_wavelength\\\":2.25,\\\"full_width_half_max\\\":0.05},{\\\"name\\\":\\\"SYN22\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S1\\\",\\\"center_wavelength\\\":0.555,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"SYN23\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S2\\\",\\\"center_wavelength\\\":0.659,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"SYN24\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S3\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"SYN25\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S5\\\",\\\"center_wavelength\\\":1.61,\\\"full_width_half_max\\\":0.06},{\\\"name\\\":\\\"SYN26\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S6\\\",\\\"center_wavelength\\\":2.25,\\\"full_width_half_max\\\":0.05}],\\\"platform\\\":[\\\"Sentinel-3A\\\",\\\"Sentinel-3B\\\"],\\\"instruments\\\":[\\\"OLCI\\\",\\\"SLSTR\\\"],\\\"constellation\\\":[\\\"Sentinel-3\\\"],\\\"s3:product_name\\\":[\\\"synergy-syn\\\"],\\\"s3:product_type\\\":[\\\"SY_2_SYN___\\\"],\\\"sat:orbit_state\\\":[\\\"ascending\\\",\\\"descending\\\"],\\\"s3:processing_timeliness\\\":[\\\"NT\\\"],\\\"sat:platform_international_designator\\\":[\\\"2016-011A\\\",\\\"2018-039A\\\"]},\\\"description\\\":\\\"This\n Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance\n and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn)\n product, which contains data on Surface Directional Reflectance, Aerosol Optical\n Thickness, and an Angstrom coefficient estimate over land.\\\\n\\\\n## Data Files\\\\n\\\\nIndividual\n NetCDF files for the following variables:\\\\n\\\\n- Surface Directional Reflectance\n (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr)\n channels (S1 to S6 for both nadir and oblique views, except S4) and for all\n [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci)\n channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the\n water vapor bands Oa19 and Oa20.\\\\n- Aerosol optical thickness at 550nm with\n error estimates.\\\\n- Angstrom coefficient at 550nm.\\\\n\\\\nMore information\n about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from September 2018 to present.\\\\n\\\",\\\"item_assets\\\":{\\\"time\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Time\n stamps annotation\\\"},\\\"syn-amin\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"L2\n Aerosol model index number data\\\"},\\\"syn-flags\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Classification\n and quality Flags associated with OLCI, SLSTR and SYNERGY products\\\"},\\\"syn-ato550\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"S1\\\",\\\"description\\\":\\\"Band\n 1 - Cloud screening, vegetation monitoring, aerosol\\\",\\\"center_wavelength\\\":0.55427,\\\"full_width_half_max\\\":0.01926},{\\\"name\\\":\\\"S2\\\",\\\"description\\\":\\\"Band\n 2 - NDVI, vegetation monitoring, aerosol\\\",\\\"center_wavelength\\\":0.65947,\\\"full_width_half_max\\\":0.01925},{\\\"name\\\":\\\"S3\\\",\\\"description\\\":\\\"Band\n 3 - NDVI, cloud flagging, pixel co-registration\\\",\\\"center_wavelength\\\":0.868,\\\"full_width_half_max\\\":0.0206},{\\\"name\\\":\\\"S5\\\",\\\"description\\\":\\\"Band\n 5 - Cloud clearing, ice, snow, vegetation monitoring\\\",\\\"center_wavelength\\\":1.6134,\\\"full_width_half_max\\\":0.06068},{\\\"name\\\":\\\"S6\\\",\\\"description\\\":\\\"Band\n 6 - Vegetation state and cloud clearing\\\",\\\"center_wavelength\\\":2.2557,\\\"full_width_half_max\\\":0.05015},{\\\"name\\\":\\\"Oa01\\\",\\\"description\\\":\\\"Band\n 1 - Aerosol correction, improved water constituent retrieval\\\",\\\"center_wavelength\\\":0.4,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa02\\\",\\\"description\\\":\\\"Band\n 2 - Yellow substance and detrital pigments (turbidity)\\\",\\\"center_wavelength\\\":0.4125,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa03\\\",\\\"description\\\":\\\"Band\n 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa04\\\",\\\"description\\\":\\\"Band\n 4 - Chlorophyll\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa05\\\",\\\"description\\\":\\\"Band\n 5 - Chlorophyll, sediment, turbidity, red tide\\\",\\\"center_wavelength\\\":0.51,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa06\\\",\\\"description\\\":\\\"Band\n 6 - Chlorophyll reference (minimum)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa07\\\",\\\"description\\\":\\\"Band\n 7 - Sediment loading\\\",\\\"center_wavelength\\\":0.62,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa08\\\",\\\"description\\\":\\\"Band\n 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa09\\\",\\\"description\\\":\\\"Band\n 9 - Improved fluorescence retrieval\\\",\\\"center_wavelength\\\":0.67375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa10\\\",\\\"description\\\":\\\"Band\n 10 - Chlorophyll fluorescence peak, red edge\\\",\\\"center_wavelength\\\":0.68125,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa11\\\",\\\"description\\\":\\\"Band\n 11 - Chlorophyll fluorescence baseline, red edge transition\\\",\\\"center_wavelength\\\":0.70875,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa12\\\",\\\"description\\\":\\\"Band\n 12 - O2 absorption / clouds, vegetation\\\",\\\"center_wavelength\\\":0.75375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa16\\\",\\\"description\\\":\\\"Band\n 16 - Atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":0.77875,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa17\\\",\\\"description\\\":\\\"Band\n 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. Common reference band with SLSTR.\n Vegetation monitoring\\\",\\\"center_wavelength\\\":0.885,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa21\\\",\\\"description\\\":\\\"Band\n 21 - Water vapour absorption, atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":1.02,\\\"full_width_half_max\\\":0.04}],\\\"description\\\":\\\"Aerosol\n Optical Thickness Data Set\\\"},\\\"geolocation\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"High\n resolution georeferencing data\\\"},\\\"safe-manifest\\\":{\\\"type\\\":\\\"application/xml\\\",\\\"roles\\\":[\\\"metadata\\\"],\\\"description\\\":\\\"SAFE\n product manifest\\\"},\\\"tiepoints-olci\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Low\n resolution georeferencing data and Sun and View angles associated with OLCI\n products\\\"},\\\"tiepoints-meteo\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"ECMWF\n meteorology data\\\"},\\\"tiepoints-slstr-n\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Low\n resolution georeferencing data and Sun and View angles associated with SLSTR\n nadir view products\\\"},\\\"tiepoints-slstr-o\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Low\n resolution georeferencing data and Sun and View angles associated with SLSTR\n oblique view products\\\"},\\\"syn-angstrom-exp550\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"S1\\\",\\\"description\\\":\\\"Band\n 1 - Cloud screening, vegetation monitoring, aerosol\\\",\\\"center_wavelength\\\":0.55427,\\\"full_width_half_max\\\":0.01926},{\\\"name\\\":\\\"S2\\\",\\\"description\\\":\\\"Band\n 2 - NDVI, vegetation monitoring, aerosol\\\",\\\"center_wavelength\\\":0.65947,\\\"full_width_half_max\\\":0.01925},{\\\"name\\\":\\\"S3\\\",\\\"description\\\":\\\"Band\n 3 - NDVI, cloud flagging, pixel co-registration\\\",\\\"center_wavelength\\\":0.868,\\\"full_width_half_max\\\":0.0206},{\\\"name\\\":\\\"S5\\\",\\\"description\\\":\\\"Band\n 5 - Cloud clearing, ice, snow, vegetation monitoring\\\",\\\"center_wavelength\\\":1.6134,\\\"full_width_half_max\\\":0.06068},{\\\"name\\\":\\\"S6\\\",\\\"description\\\":\\\"Band\n 6 - Vegetation state and cloud clearing\\\",\\\"center_wavelength\\\":2.2557,\\\"full_width_half_max\\\":0.05015},{\\\"name\\\":\\\"Oa01\\\",\\\"description\\\":\\\"Band\n 1 - Aerosol correction, improved water constituent retrieval\\\",\\\"center_wavelength\\\":0.4,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa02\\\",\\\"description\\\":\\\"Band\n 2 - Yellow substance and detrital pigments (turbidity)\\\",\\\"center_wavelength\\\":0.4125,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa03\\\",\\\"description\\\":\\\"Band\n 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa04\\\",\\\"description\\\":\\\"Band\n 4 - Chlorophyll\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa05\\\",\\\"description\\\":\\\"Band\n 5 - Chlorophyll, sediment, turbidity, red tide\\\",\\\"center_wavelength\\\":0.51,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa06\\\",\\\"description\\\":\\\"Band\n 6 - Chlorophyll reference (minimum)\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa07\\\",\\\"description\\\":\\\"Band\n 7 - Sediment loading\\\",\\\"center_wavelength\\\":0.62,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa08\\\",\\\"description\\\":\\\"Band\n 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa09\\\",\\\"description\\\":\\\"Band\n 9 - Improved fluorescence retrieval\\\",\\\"center_wavelength\\\":0.67375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa10\\\",\\\"description\\\":\\\"Band\n 10 - Chlorophyll fluorescence peak, red edge\\\",\\\"center_wavelength\\\":0.68125,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa11\\\",\\\"description\\\":\\\"Band\n 11 - Chlorophyll fluorescence baseline, red edge transition\\\",\\\"center_wavelength\\\":0.70875,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa12\\\",\\\"description\\\":\\\"Band\n 12 - O2 absorption / clouds, vegetation\\\",\\\"center_wavelength\\\":0.75375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa16\\\",\\\"description\\\":\\\"Band\n 16 - Atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":0.77875,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa17\\\",\\\"description\\\":\\\"Band\n 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. Common reference band with SLSTR.\n Vegetation monitoring\\\",\\\"center_wavelength\\\":0.885,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa21\\\",\\\"description\\\":\\\"Band\n 21 - Water vapour absorption, atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":1.02,\\\"full_width_half_max\\\":0.04}],\\\"description\\\":\\\"Aerosol\n Angstrom Exponent Data Set\\\"},\\\"syn-s1n-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN17\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S1\\\",\\\"center_wavelength\\\":0.555,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 01 acquired in nadir\n view\\\"},\\\"syn-s1o-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN22\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S1\\\",\\\"center_wavelength\\\":0.555,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 01 acquired in oblique\n view\\\"},\\\"syn-s2n-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN18\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S2\\\",\\\"center_wavelength\\\":0.659,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 02 acquired in nadir\n view\\\"},\\\"syn-s2o-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN23\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S2\\\",\\\"center_wavelength\\\":0.659,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 02 acquired in oblique\n view\\\"},\\\"syn-s3n-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN19\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S3\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 03 acquired in nadir\n view\\\"},\\\"syn-s3o-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN24\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S3\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 03 acquired in oblique\n view\\\"},\\\"syn-s5n-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN20\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S5\\\",\\\"center_wavelength\\\":1.61,\\\"full_width_half_max\\\":0.06}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 05 acquired in nadir\n view\\\"},\\\"syn-s5o-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN25\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S5\\\",\\\"center_wavelength\\\":1.61,\\\"full_width_half_max\\\":0.06}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 05 acquired in oblique\n view\\\"},\\\"syn-s6n-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN21\\\",\\\"description\\\":\\\"SLSTR\n nadir channel S6\\\",\\\"center_wavelength\\\":2.25,\\\"full_width_half_max\\\":0.05}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 06 acquired in nadir\n view\\\"},\\\"syn-s6o-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN26\\\",\\\"description\\\":\\\"SLSTR\n oblique channel S6\\\",\\\"center_wavelength\\\":2.25,\\\"full_width_half_max\\\":0.05}],\\\"description\\\":\\\"Surface\n directional reflectance associated with SLSTR channel 06 acquired in oblique\n view\\\"},\\\"syn-oa01-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN01\\\",\\\"description\\\":\\\"OLCI\n channel Oa01\\\",\\\"center_wavelength\\\":0.4,\\\"full_width_half_max\\\":0.015}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 01\\\"},\\\"syn-oa02-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN02\\\",\\\"description\\\":\\\"OLCI\n channel Oa02\\\",\\\"center_wavelength\\\":0.4125,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 02\\\"},\\\"syn-oa03-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN03\\\",\\\"description\\\":\\\"OLCI\n channel Oa03\\\",\\\"center_wavelength\\\":0.4425,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 03\\\"},\\\"syn-oa04-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN04\\\",\\\"description\\\":\\\"OLCI\n channel Oa04\\\",\\\"center_wavelength\\\":0.49,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 04\\\"},\\\"syn-oa05-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN05\\\",\\\"description\\\":\\\"OLCI\n channel Oa05\\\",\\\"center_wavelength\\\":0.51,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 05\\\"},\\\"syn-oa06-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN06\\\",\\\"description\\\":\\\"OLCI\n channel Oa06\\\",\\\"center_wavelength\\\":0.56,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 06\\\"},\\\"syn-oa07-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN07\\\",\\\"description\\\":\\\"OLCI\n channel Oa07\\\",\\\"center_wavelength\\\":0.62,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 07\\\"},\\\"syn-oa08-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN08\\\",\\\"description\\\":\\\"OLCI\n channel Oa08\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.01}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 08\\\"},\\\"syn-oa09-reflectance\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"SYN09\\\",\\\"description\\\":\\\"OLCI\n channel Oa09\\\",\\\"center_wavelength\\\":0.67375,\\\"full_width_half_max\\\":0.0075}],\\\"description\\\":\\\"Surface\n directional reflectance associated with OLCI channel 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Sediment loading\\\",\\\"center_wavelength\\\":0.62,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa08\\\",\\\"description\\\":\\\"Band\n 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\\\",\\\"center_wavelength\\\":0.665,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa09\\\",\\\"description\\\":\\\"Band\n 9 - Improved fluorescence retrieval\\\",\\\"center_wavelength\\\":0.67375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa10\\\",\\\"description\\\":\\\"Band\n 10 - Chlorophyll fluorescence peak, red edge\\\",\\\"center_wavelength\\\":0.68125,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa11\\\",\\\"description\\\":\\\"Band\n 11 - Chlorophyll fluorescence baseline, red edge transition\\\",\\\"center_wavelength\\\":0.70875,\\\"full_width_half_max\\\":0.01},{\\\"name\\\":\\\"Oa12\\\",\\\"description\\\":\\\"Band\n 12 - O2 absorption / clouds, vegetation\\\",\\\"center_wavelength\\\":0.75375,\\\"full_width_half_max\\\":0.0075},{\\\"name\\\":\\\"Oa13\\\",\\\"description\\\":\\\"Band\n 13 - O2 absorption / aerosol correction\\\",\\\"center_wavelength\\\":0.76125,\\\"full_width_half_max\\\":0.0025},{\\\"name\\\":\\\"Oa16\\\",\\\"description\\\":\\\"Band\n 16 - Atmospheric / aerosol correction\\\",\\\"center_wavelength\\\":0.77875,\\\"full_width_half_max\\\":0.015},{\\\"name\\\":\\\"Oa17\\\",\\\"description\\\":\\\"Band\n 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\\\",\\\"center_wavelength\\\":0.865,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"Oa18\\\",\\\"description\\\":\\\"Band\n 18 - Water vapour absorption reference. 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Oa03\\\",\\\"center_wavelength\\\":0.45,\\\"full_width_half_max\\\":0.02},{\\\"name\\\":\\\"B2\\\",\\\"description\\\":\\\"OLCI\n channels Oa06, Oa07, Oa08, Oa09, Oa10\\\",\\\"center_wavelength\\\":0.645,\\\"full_width_half_max\\\":0.035},{\\\"name\\\":\\\"B3\\\",\\\"description\\\":\\\"OLCI\n channels Oa16, Oa17, Oa18, Oa21\\\",\\\"center_wavelength\\\":0.835,\\\"full_width_half_max\\\":0.055},{\\\"name\\\":\\\"MIR\\\",\\\"description\\\":\\\"SLSTR\n nadir and oblique channels S5, 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The primary variables are four top\n of atmosphere reflectance bands:\\\\n\\\\n- B0 (Blue, 450nm)\\\\n- B2 (Red, 645nm)\\\\n-\n B3 (NIR, 835nm)\\\\n- MIR (SWIR, 1665nm)\\\\n\\\\nThe four reflectance bands have\n center wavelengths matching those on the original SPOT VEGETATION instrument\n and have been adapted for scientific applications requiring highly accurate\n physical measurements through correction for systematic errors and re-sampling\n to predefined geographic projections. The pixel brightness count is the ground\n area's apparent reflectance as seen at the top of atmosphere.\\\\n\\\\n## Data\n files\\\\n\\\\nNetCDF files are provided for the four reflectance bands. Additional\n metadata are delivered in annotation NetCDF files, each containing a single\n variable, including the geometric viewing and illumination conditions, the\n total water vapour and ozone columns, and the aerosol optical depth.\\\\n\\\\nMore\n information about the product and data processing can be found in the [User\n Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from October 2018 to present.\\\\n\\\",\\\"item_assets\\\":{\\\"ag\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Aerosol\n optical thickness data\\\"},\\\"b0\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"B0\\\",\\\"description\\\":\\\"OLCI\n channels 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channel\\\"},\\\"og\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Total\n ozone column data\\\"},\\\"sm\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Status\n map data\\\"},\\\"mir\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"eo:bands\\\":[{\\\"name\\\":\\\"MIR\\\",\\\"description\\\":\\\"SLSTR\n nadir and oblique channels S5, S6\\\",\\\"center_wavelength\\\":1.665,\\\"full_width_half_max\\\":0.085}],\\\"description\\\":\\\"Top\n of atmosphere Reflectance data set associated with the VGT-MIR channel\\\"},\\\"saa\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Solar\n azimuth angle data\\\"},\\\"sza\\\":{\\\"type\\\":\\\"application/x-netcdf\\\",\\\"roles\\\":[\\\"data\\\"],\\\"description\\\":\\\"Solar\n zenith angle 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The primary variables are a maximum\n Normalized Difference Vegetation Index (NDVI) composite, which is derived\n from daily ground reflecrtance, and four surface reflectance bands:\\\\n\\\\n-\n B0 (Blue, 450nm)\\\\n- B2 (Red, 645nm)\\\\n- B3 (NIR, 835nm)\\\\n- MIR (SWIR, 1665nm)\\\\n\\\\nThe\n four reflectance bands have center wavelengths matching those on the original\n SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the\n amount of vegetation, is derived from the B3 and B2 bands.\\\\n\\\\n## Data files\\\\n\\\\nThe\n four reflectance bands and NDVI values are each contained in dedicated NetCDF\n files. Additional metadata are delivered in annotation NetCDF files, each\n containing a single variable, including the geometric viewing and illumination\n conditions, the total water vapour and ozone columns, and the aerosol optical\n depth.\\\\n\\\\nEach 1-day product is delivered as a set of 10 rectangular scenes:\\\\n\\\\n-\n AFRICA\\\\n- NORTH_AMERICA\\\\n- SOUTH_AMERICA\\\\n- CENTRAL_AMERICA\\\\n- NORTH_ASIA\\\\n-\n WEST_ASIA\\\\n- SOUTH_EAST_ASIA\\\\n- ASIAN_ISLANDS\\\\n- AUSTRALASIA\\\\n- EUROPE\\\\n\\\\nMore\n information about the product and data processing can be found in the [User\n Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10)\n and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\\\\n\\\\nThis\n Collection contains Level-2 data in NetCDF files from October 2018 to 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* PLOT.CN = TREE.PLT_CN\n links the unique plot record to the tree records.

* PLOT.CN = SEEDLING.PLT_CN\n links the unique plot record to the seedling records.\",\"msft:item_name\":\"plot\"},{\"name\":\"Condition\n Table\",\"description\":\"Condition table. This table provides information on\n the discrete combination of landscape attributes that define the condition\n (a condition will have the same land class, reserved status, owner group,\n forest type, stand-size class, regeneration status, and stand density).

*\n PLOT.CN = COND.PLT_CN links the condition class record(s) to the plot table.\n

* COND.PLT_CN = SITETREE.PLT_CN and COND.CONDID = SITETREE.CONDID\n links the condition class record to the site tree data.

* COND.PLT_CN\n = TREE.PLT_CN and COND.CONDID = TREE.CONDID links the condition class record\n to the tree data.\",\"msft:item_name\":\"cond\"},{\"name\":\"Subplot Table\",\"description\":\"Subplot\n table. This table describes the features of a single subplot. There are multiple\n subplots per 1-acre field plot and there can be multiple conditions sampled\n on each subplot.

* PLOT.CN = SUBPLOT.PLT_CN links the unique plot\n record to the subplot records.

* SUBPLOT.PLT_CN = COND.PLT_CN and\n SUBPLOT.MACRCOND = COND.CONDID links the macroplot conditions to the condition\n class record.

* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.SUBPCOND\n = COND.CONDID links the subplot conditions to the condition class record.\n

* SUBPLOT.PLT_CN = COND.PLT_CN and SUBPLOT.MICRCOND = COND.CONDID\n links the microplot conditions to the condition class record.\",\"msft:item_name\":\"subplot\"},{\"name\":\"Subplot\n Condition Table\",\"description\":\"Subplot condition table. This table contains\n information about the proportion of a subplot in a condition.

* PLOT.CN\n = SUBP_COND.PLT_CN links the subplot condition class record to the plot table.\n

* SUBP_COND.PLT_CN = COND.PLT_CN and SUBP_COND.CONDID = COND.CONDID\n links the condition class records found on the four subplots to the subplot\n description.\",\"msft:item_name\":\"subp_cond\"},{\"name\":\"boundary\",\"description\":\"Boundary\n table. This table provides a description of the demarcation line between two\n conditions that occur on a single subplot\",\"msft:item_name\":\"boundary\"},{\"name\":\"Subplot\n Condition Change Matrix\",\"description\":\"Subplot condition change matrix table.\n This table contains information about the mix of current and previous conditions\n that occupy the same area on the subplot.

* PLOT.CN = SUBP_COND_CHNG_MTRX.PLT_CN\n links the subplot condition change matrix records to the unique plot record.\n

* PLOT.PREV_PLT_CN = SUBP_COND_CHNG_MTRX.PREV_PLT_CN links the subplot\n condition change matrix records to the unique previous plot record.\",\"msft:item_name\":\"subp_cond_chng_mtrx\"},{\"name\":\"Tree\n Table\",\"description\":\"Tree table. This table provides information for each\n tree 1 inch in diameter and larger found on a microplot, subplot, or core\n optional macroplot.

* PLOT.CN = TREE.PLT_CN links the tree records\n to the unique plot record.

* COND.PLT_CN = TREE.PLT_CN and COND.CONDID\n = TREE.CONDID links the tree records to the unique condition record.\",\"msft:item_name\":\"tree\"},{\"name\":\"Tree\n Woodland Stems Table\",\"description\":\"Tree woodland stems table. This table\n stores data for the individual stems of a woodland species tree. Individual\n woodland stem diameter measurements contribute to the calculation of the diameter\n stored on the parent TREE table record.

* TREE.CN = TREE_WOODLAND_STEMS.TRE_CN\n links a woodland stems record to the corresponding unique tree record.\",\"msft:item_name\":\"tree_woodland_stems\"},{\"name\":\"Tree\n Regional Biomass Table\",\"description\":\"Tree regional biomass table. This table\n contains biomass estimates computed using equations and methodology that varies\n by FIA work unit. This table retains valuable information for generating biomass\n estimates that match earlier published reports.

* TREE.CN = TREE_REGIONAL_BIOMASS.TRE_CN\n links a tree regional biomass record to the corresponding unique tree.\",\"msft:item_name\":\"tree_regional_biomass\"},{\"name\":\"Tree\n Net Growth, Removal, and Mortality Component Table\",\"description\":\"Tree net\n growth, removal, and mortality component table. This table stores information\n used to compute net growth, removals, and mortality estimates for remeasurement\n trees. Each remeasurement tree has a single record in this table.

*\n TREE_GRM_COMPONENT.TRE_CN = TREE.TRE_CN links the records in this table to\n the corresponding tree record in the TREE table.\",\"msft:item_name\":\"tree_grm_component\"},{\"name\":\"Tree\n Net Growth, Removal, and Mortality Midpoint Table\",\"description\":\"Tree net\n growth, removal, and mortality midpoint table. This table contains information\n about a remeasured tree at the midpoint of the remeasurement period. It does\n not contain a record for every tree. Midpoint estimates are computed for trees\n that experience mortality, removal, or land use diversion or reversion. The\n information in this table is used to compute net growth, removal, and mortality\n estimates on remeasurement trees.

* TREE_GRM_MIDPT.TRE_CN = TREE.TRE_CN\n links the records in this table to the corresponding tree record in the TREE\n table.\",\"msft:item_name\":\"tree_grm_midpt\"},{\"name\":\"Tree Net Growth, Removal,\n and Mortality Begin Table\",\"description\":\"Tree net growth, removal, and mortality\n begin table. This table contains information for remeasured trees where values\n have been calculated for the beginning of the remeasurement period. Only those\n trees where information was recalculated for time 1 (T1) are included. The\n information in this table is used to produce net growth, removal and mortality\n estimates on remeasured trees.

* TREE_GRM_BEGIN.TRE_CN = TREE.TRE_CN\n links the records in this table to the corresponding tree record in the TREE\n table.\",\"msft:item_name\":\"tree_grm_begin\"},{\"name\":\"Tree Net Growth, Removal,\n and Mortality Estimation Table\",\"description\":\"Tree net growth, removal, and\n mortality estimation table. This table contains information used to produce\n estimates of growth, removals and mortality.

* PLOT.CN = TREE_GRM_ESTN.PLT_CN\n links the tree GRM estimation records to the unique plot record.

*\n TREE.CN = TREE_GRM_ESTN.TRE_CN links the tree GRM estimation records to the\n unique tree record.\",\"msft:item_name\":\"tree_grm_estn\"},{\"name\":\"Seedling Table\",\"description\":\"Seedling\n table. This table provides a count of the number of live trees of a species\n found on a microplot that are less than 1 inch in diameter but at least 6\n inches in length for conifer species or at least 12 inches in length for hardwood\n species.

* PLOT.CN = SEEDLING.PLT_CN links the seedling records to\n the unique plot record.

* COND.PLT_CN = SEEDLING.PLT_CN and COND.CONDID\n = SEEDLING.CONDID links the condition record to the seedling record.\",\"msft:item_name\":\"seedling\"},{\"name\":\"Site\n Tree Table\",\"description\":\"Site tree table. This table provides information\n on the site tree(s) collected in order to calculate site index and/or site\n productivity information for a condition.

* PLOT.CN = SITETREE.PLT_CN\n links the site tree records to the unique plot record.

* SITETREE.PLT_CN\n = COND.PLT_CN and SITETREE.CONDID = COND.CONDID links the site tree record(s)\n to the unique condition class record.\",\"msft:item_name\":\"sitetree\"},{\"name\":\"Invasive\n Subplot Species Table\",\"description\":\"Invasive subplot species table. This\n table provides percent cover data of invasive species identified on the subplot.\n

* PLOT.CN = INVASIVE_SUBPLOT_SPP.PLT_CN links the invasive subplot\n species record(s) to the unique plot record.

* SUBP_COND.PLT_CN =\n INVASIVE_SUBPLOT_SPP.PLT_CN and SUBP_COND.CONDID = INVASIVE_SUBPLOT_SPP.CONDID\n and SUBP_COND.SUBP = INVASIVE_SUBPLOT_SPP.SUBP links the invasive subplot\n species record(s) to the unique subplot condition record.

* INVASIVE_SUBPLOT_SPP.VEG_SPCD\n = REF_PLANT_DICTIONARY.SYMBOL links the invasive vegetation subplot NRCS species\n code to the plant dictionary reference species code.\",\"msft:item_name\":\"invasive_subplot_spp\"},{\"name\":\"Phase\n 2 Vegetation Subplot Species Table\",\"description\":\"Phase 2 Vegetation subplot\n species table. This table provides percent cover data of vegetation species\n identified on the subplot.

* PLOT.CN = P2VEG_SUBPLOT_SPP.PLT_CN links\n the vegetation subplot species record(s) to the unique plot record.

*\n SUBP_COND.PLT_CN = P2VEG_SUBPLOT_SPP.PLT_CN and SUBP_COND.CONDID = P2VEG_SUBPLOT_SPP.CONDID\n and SUBP_COND.SUBP = P2VEG_SUBPLOT_SPP.SUBP links the vegetation subplot species\n record(s) to the unique subplot condition record.

* P2VEG_SUBPLOT_SPP.VEG_SPCD\n = REF_PLANT_DICTIONARY.SYMBOL links the P2 vegetation subplot NRCS species\n code to the plant dictionary reference species code.\",\"msft:item_name\":\"p2veg_subplot_spp\"},{\"name\":\"Phase\n 2 Vegetation Subplot Structure Table\",\"description\":\"Phase 2 Vegetation subplot\n structure table. This table provides percent cover by layer by growth habit.\n

* PLOT.CN = P2VEG_SUBP_STRUCTURE. PLT_CN links the subplot structure\n record(s) to the unique plot record.

* SUBP_COND.PLT_CN = P2VEG_SUBP_STRUCTURE.PLT_CN\n and SUBP_COND.CONDID = P2VEG_SUBP_STRUCTURE.CONDID and SUBP_COND.SUBP = P2VEG_SUBP_STRUCTURE.SUBP\n links the vegetation subplot structure record(s) to the unique subplot condition\n record.\",\"msft:item_name\":\"p2veg_subp_structure\"},{\"name\":\"Down Woody Material\n Visit Table\",\"description\":\"Down woody material visit table. This table provides\n general information on down woody material indicator visit, such as the date\n of the DWM survey.

* PLOT.CN = DWM_VISIT.PLT_CN links the down woody\n material indicator visit record to the unique plot record.\",\"msft:item_name\":\"dwm_visit\"},{\"name\":\"Down\n Woody Material Coarse Woody Debris Table\",\"description\":\"Down woody material\n coarse woody debris table. This table provides information for each piece\n of coarse woody debris measured along the transects.

* PLOT.CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN\n links the down woody material coarse woody debris records to the unique plot\n record.

* COND.PLT_CN = DWM_COARSE_WOODY_DEBRIS.PLT_CN and COND.CONDID=\n DWM_COARSE_WOODY_DEBRIS.CONDID links the coarse woody debris records to the\n unique condition record.\",\"msft:item_name\":\"dwm_coarse_woody_debris\"},{\"name\":\"Down\n Woody Material Duff, Litter, Fuel Table\",\"description\":\"Down woody material\n duff, litter, fuel table. This table provides information on the duff, litter,\n fuelbed depths measured at a point on the transects.

* PLOT.CN = DWM_DUFF_LITTER_FUEL.PLT_CN\n links the duff, litter, fuelbed records to the unique plot record.

*\n COND.PLT_CN = DWM_DUFF_LITTER_FUEL.PLT_CN and COND.CONDID= DWM_DUFF_LITTER_FUEL.CONDID\n links the duff, litter, fuel records to the unique condition record.\",\"msft:item_name\":\"dwm_duff_litter_fuel\"},{\"name\":\"Down\n Woody Material Fine Woody Debris Table\",\"description\":\"Down woody material\n fine woody debris table. This table provides information on the fine woody\n debris measured along a segment of the transects.

* PLOT.CN = DWM_FINE_WOODY_DEBRIS.PLT_CN\n links the fine woody debris records to the unique plot record.

* COND.PLT_CN\n = DWM_FINE_WOODY_DEBRIS.PLT_CN and COND.CONDID= DWM_FINE_WOODY_DEBRIS.CONDID\n links the fine woody debris records to the unique condition record.\",\"msft:item_name\":\"dwm_fine_woody_debris\"},{\"name\":\"Down\n Woody Material Microplot Fuel Table\",\"description\":\"Down woody material microplot\n fuel table. This table provides information on the fuel loads (shrubs and\n herbs) measured on the microplot.

* PLOT.CN = DWM_MICROPLOT_FUEL.PLT_CN\n links the microplot fuel records to the unique plot record.\",\"msft:item_name\":\"dwm_microplot_fuel\"},{\"name\":\"Down\n Woody Material Residual Pile Table\",\"description\":\"Down woody material residual\n pile table. This table provides information on the wood piles measured on\n the subplot.

* PLOT.CN = DWM_RESIDUAL_PILE.PLT_CN links the wood piles\n records to the unique plot record.

* COND.PLT_CN = DWM_RESIDUAL_PILE.PLT_CN\n and COND.CONDID= DWM_RESIDUAL_PILE.CONDID links the wood piles records to\n the unique condition record.\",\"msft:item_name\":\"dwm_residual_pile\"},{\"name\":\"Down\n Woody Material Transect Segment Table\",\"description\":\"Down woody material\n transect segment table. This table describes the down woody material transect\n segment lengths by condition class.

* PLOT.CN = DWM_TRANSECT_SEGMENT.PLT_CN\n links the down woody material transect length records to the unique plot record.\n

* COND.PLT_CN = DWM_TRANSECT_SEGMENT.PLT_CN and COND.CONDID= DWM_TRANSECT_SEGMENT.CONDID\n links the down woody material transect segment records to the unique condition\n record.\",\"msft:item_name\":\"dwm_transect_segment\"},{\"name\":\"Condition Down\n Woody Material Calculation Table\",\"description\":\"Condition down woody material\n calculation table. This table contains calculated values and condition-level\n estimates for down woody attributes by plot number (PLOT), condition class\n number (CONDID), and evaluation identifier (EVALID).

* PLOT.CN = COND_DWM_CALC.PLT_CN\n links the down woody material calculation records to the unique plot record.\n

* COND.CN = COND_DWM_CALC.CND_CN links the down woody material calculation\n records to the unique condition record.

* POP_STRATUM. CN = COND_DWM_CALC.STRATUM_CN\n links the down woody material calculation records to the unique population\n stratum record.\",\"msft:item_name\":\"cond_dwm_calc\"},{\"name\":\"Plot Regeneration\n Table\",\"description\":\"Plot regeneration table. This table contains the information\n for the four subplots describing the amount of animal browse pressure exerted\n on the regeneration of trees.

* PLOT.CN = PLOT_REGEN.PLT_CN links\n the unique plot record to the unique plot regeneration record.\",\"msft:item_name\":\"plot_regen\"},{\"name\":\"Subplot\n Regeneration Table\",\"description\":\"Subplot regeneration table. This table\n provides information on the subplot survey status and the site survey limitations,\n if any, for the tree regeneration study.

* PLOT.CN = SUBPLOT_REGEN.PLT_CN\n links the unique plot record to the subplot regeneration records.

*\n SUBPLOT.PLT_CN = SUBPLOT_REGEN.PLT_CN and SUBPLOT.SUBP = SUBPLOT_REGEN.SUBP\n links the subplot record to the subplot regeneration record.\",\"msft:item_name\":\"subplot_regen\"},{\"name\":\"Seedling\n Regeneration Table\",\"description\":\"Seedling regeneration. This table contains\n provides information on the seedling count by condition, species, source,\n and length class for the tree regeneration study.

* PLOT.CN = SEEDLING_REGEN.PLT_CN\n links the unique plot record to the seedling regeneration records.

*\n COND.PLT_CN = SEEDLING_REGEN.PLT_CN and COND.CONDID = SEEDLING_REGEN.CONDID\n links the regeneration seedling records to the unique condition record.\",\"msft:item_name\":\"seedling_regen\"},{\"name\":\"Population\n Estimation Unit Table\",\"description\":\"Population estimation unit table. This\n table contains information about estimation units. An estimation unit is a\n geographic area that can be drawn on a map. It has a known area, and the sampling\n intensity must be the same within a stratum within an estimation unit. Generally,\n estimation units are contiguous areas, but exceptions are made when certain\n ownerships, usually National Forests, are sampled at different intensities.\n One record in the POP_ESTN_UNIT table corresponds to a single estimation unit.\n POP_ESTN_UNIT.CN = POP_STRATUM.ESTN_UNIT_CN links the unique stratified geographical\n area (ESTN_UNIT) to the strata (STRATUMCD) that are assigned to each ESTN_UNIT.\",\"msft:item_name\":\"pop_estn_unit\"},{\"name\":\"Population\n Evaluation Table\",\"description\":\"Population evaluation table. This table provides\n information about evaluations. An evaluation is the combination of a set of\n plots (the sample) and a set of Phase 1 data (obtained through remote sensing,\n called a stratification) that can be used to produce population estimates\n for a State (an evaluation may be created to produce population estimates\n for a region other than a State, such as the Black Hills National Forest).\n A record in the POP_EVAL table identifies one evaluation and provides some\n descriptive information about how the evaluation may be used.

* POP_ESTN_UNIT.EVAL_CN\n = POP_EVAL.CN links the unique evaluation identifier (EVALID) in the POP_EVAL\n table to the unique geographical areas (ESTN_UNIT) that are stratified. Within\n a population evaluation (EVALID) there can be multiple population estimation\n units, or geographic areas across which there are a number of values being\n estimated (e.g., estimation of volume across counties for a given State).\",\"msft:item_name\":\"pop_eval\"},{\"name\":\"Population\n Evaluation Attribute Table\",\"description\":\"Population evaluation attribute\n table. This table provides information as to which population estimates can\n be provided by an evaluation. If an evaluation can produce only 22 of all\n the population estimates in the REF_POP_ATTRIBUTE table, there will be 22\n records in the POP_EVAL_ATTRIBUTE table (one per population estimate) for\n that evaluation.

* POP_EVAL.CN = POP_EVAL_ATTRIBUTE.EVAL_CN links\n the unique evaluation identifier to the list of population estimates that\n can be derived for that evaluation.\",\"msft:item_name\":\"pop_eval_attribute\"},{\"name\":\"Population\n Evaluation Group Table\",\"description\":\"Population evaluation group table.\n This table lists and describes the evaluation groups. One record in the POP_EVAL_GRP\n table can be linked to all the evaluations that were used in generating estimates\n for a State inventory report.

* POP_EVAL_GRP.CN = POP_EVAL_TYP.EVAL_GRP_CN\n links the evaluation group record to the evaluation type record.\",\"msft:item_name\":\"pop_eval_grp\"},{\"name\":\"Population\n Evaluation Type Table\",\"description\":\"Population evaluation type table. This\n table provides information on the type of evaluations that were used to generate\n a set of tables for an inventory report. In a typical State inventory report,\n one evaluation is used to generate an estimate of the total land area; a second\n evaluation is used to generate current estimates of volume, numbers of trees\n and biomass; and a third evaluation is used for estimating growth, removals\n and mortality.

* POP_EVAL_TYP.EVAL_CN = POP_EVAL.CN links the evaluation\n type record to the evaluation record.

* POP_EVAL_TYP.EVAL_GRP_CN =\n POP_EVAL_GRP.CN links the evaluation type record to the evaluation group record.\n

* POP_EVAL_TYP.EVAL_TYP = REF_POP_EVAL_TYP_DESCR.EVAL_TYP links an\n evaluation type record to an evaluation type description reference record.\",\"msft:item_name\":\"pop_eval_typ\"},{\"name\":\"Population\n Plot Stratum Assignment Table\",\"description\":\"Population plot stratum assignment\n table. This table provides a way to assign stratum information to a plot.\n Stratum information is assigned to a plot by overlaying the plot''s location\n on the Phase 1 imagery. Plots are linked to their appropriate stratum for\n an evaluation via the POP_PLOT_STRATUM_ASSGN table.

* POP_PLOT_STRATUM_ASSGN.PLT_CN\n = PLOT.CN links the stratum assigned to the plot record.\",\"msft:item_name\":\"pop_plot_stratum_assgn\"},{\"name\":\"Population\n Stratum Table\",\"description\":\"Population stratum table. This table provides\n information about individual strata. The area within an estimation unit is\n divided into strata. The area for each stratum can be calculated by determining\n the proportion of Phase 1 pixels/plots in each stratum and multiplying that\n proportion by the total area in the estimation unit. Information for a single\n stratum is stored in a single record of the POP_STRATUM table.

* POP_STRATUM.CN\n = POP_PLOT_STRATUM_ASSGN.STRATUM_CN links the defined stratum to each plot.\",\"msft:item_name\":\"pop_stratum\"},{\"name\":\"Plot\n Geometry Table\",\"description\":\"Plot geometry table. This table contains geometric\n attributes associated with the plot location, such as the hydrological unit\n and roadless codes.

* PLOTGEOM.CN = PLOT.CN links the unique plot\n record between the two tables.\",\"msft:item_name\":\"plotgeom\"},{\"name\":\"Plot\n Snapshot Table\",\"description\":\"Plot snapshot table. This table combines the\n information in the PLOT table with information in the PLOT_EVAL_GRP and POP_STRATUM\n tables to provide a snapshot of the plot records with their associated expansion\n and adjustment factors.

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World Climate Research Programme's [6th Coupled Model Intercomparison Project\n (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an\n enormous advance in the quality, detail, and scope of climate modeling.\\\\n\\\\nThe\n [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6)\n dataset makes this modeling more applicable to understanding the impacts of\n changes in the climate on humans and society with two key developments: trend-preserving\n bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org)\n provides global, daily minimum and maximum air temperature at the surface\n (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`)\n corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5\n scenarios for 25 global climate models on a 1/4-degree regular global grid.\\\\n\\\\n##\n Accessing the data\\\\n\\\\nGDPCIR data can be accessed on the Microsoft Planetary\n Computer. The dataset is made of of three collections, distinguished by data\n license:\\\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\\\n*\n [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\\\n\\\\nEach\n modeling center with bias corrected and downscaled data in this collection\n falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection)\n to see which model is in each collection, and see the section below on [Citing,\n Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project)\n for citations and additional information about each license.\\\\n\\\\n## Data\n format & contents\\\\n\\\\nThe data is stored as partitioned zarr stores (see\n [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which\n includes thousands of data and metadata files covering the full time span\n of the experiment. Historical zarr stores contain just over 50 GB, while SSP\n zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with\n dimensions time (daily datetime), lat (float latitude), and lon (float longitude).\n The data is chunked at each interval of 365 days and 90 degree interval of\n latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each\n chunk occupying approximately 180MB in memory.\\\\n\\\\nHistorical data is daily,\n excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily,\n excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100,\n depending on data availability in the source GCM.\\\\n\\\\nThe spatial domain\n covers all 0.25-degree grid cells, indexed by the grid center, with grid edges\n on the quarter-degree, using a -180 to 180 longitude convention. Thus, the\n \\u201Clon\\u201D coordinate extends from -179.875 to 179.875, and the \\u201Clat\\u201D\n coordinate extends from -89.875 to 89.875, with intermediate values at each\n 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\\\n\\\\n##\n Available institutions, models, and scenarios by license collection\\\\n\\\\n|\n Modeling institution | Source model | Available experiments\n \\ | License collection |\\\\n| -------------------- | -----------------\n | ------------------------------------------ | ---------------------- |\\\\n|\n CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5\n \\ | Public domain datasets |\\\\n| INM | INM-CM4-8\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets\n |\\\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0,\n and SSP5-8.5 | Public domain datasets |\\\\n| BCC | BCC-CSM2-MR\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n|\n CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5\n \\ | CC-BY-40 |\\\\n| CMCC | CMCC-ESM2 |\n ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\\\\n|\n CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 |\n CC-BY-40 |\\\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6,\n SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\\\\n| MIROC |\n MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MIROC | MIROC6 | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| MOHC |\n HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MOHC | UKESM1-0-LL | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| MPI-M |\n MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and\n SSP5-8.5 | CC-BY-40 |\\\\n| NCC |\n NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| NCC | NorESM2-MM | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| NOAA-GFDL\n \\ | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 |\n CC-BY-40 |\\\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| NUIST |\n NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6,\n ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-AerChem | ssp370 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and\n ssp585 | CC-BY-40 |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6,\n ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\\\n| CCCma |\n CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3]\n \\ |\\\\n\\\\n*Notes:*\\\\n\\\\n[^1]: At the time of running, no ssp1-2.6\n precipitation data was available. Therefore, we provide `tasmin` and `tamax`\n for this model and experiment, but not `pr`. All other model/experiment combinations\n in the above table include all three variables.\\\\n\\\\n[^2]: The institution\n which ran MPI-ESM1-2-HR\\u2019s historical (CMIP) simulations is `MPI-M`, while\n the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution\n component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP`\n scenarios.\\\\n\\\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),\n but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0)\n in March, 2023. \\\\n\\\\n## Project methods\\\\n\\\\nThis project makes use of statistical\n bias correction and downscaling algorithms, which are specifically designed\n to accurately represent changes in the extremes. For this reason, we selected\n Quantile Delta Mapping (QDM), following the method introduced by [Cannon et\n al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific\n trends from the GCM while fitting the full distribution for a given day-of-year\n to a reference dataset (ERA5).\\\\n\\\\nWe then introduce a similar method tailored\n to increase spatial resolution while preserving extreme behavior, Quantile-Preserving\n Localized-Analog Downscaling (QPLAD).\\\\n\\\\nTogether, these methods provide\n a robust means to handle both the central and tail behavior seen in climate\n model output, while aligning the full distribution to a state-of-the-art reanalysis\n dataset and providing the spatial granularity needed to study surface impacts.\\\\n\\\\nFor\n further documentation, see [Global downscaled projections for climate impacts\n research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/)\n (EGUsphere, 2022 [preprint]).\\\\n\\\\n## Citing, licensing, and using data produced\n by this project\\\\n\\\\nProjects making use of the data produced as part of the\n Climate Impact Lab Global Downscaled Projections for Climate Impacts Research\n (CIL GDPCIR) project are requested to cite both this project and the source\n datasets from which these results are derived. Additionally, the use of data\n derived from some GCMs *requires* citations, and some modeling centers impose\n licensing restrictions & requirements on derived works. See each GCM's license\n info in the links below for more information.\\\\n\\\\n### CIL GDPCIR\\\\n\\\\nUsers\n are requested to cite this project in derived works. Our method documentation\n paper may be cited using the following:\\\\n\\\\n> Gergel, D. R., Malevich, S.\n B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp,\n R. E.: Global downscaled projections for climate impacts research (GDPCIR):\n preserving extremes for modeling future climate impacts, EGUsphere [preprint],\n https://doi.org/10.5194/egusphere-2022-1513, 2023. \\\\n\\\\nThe code repository\n may be cited using the following:\\\\n\\\\n> Diana Gergel, Kelly McCusker, Brewster\n Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6:\n (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\\\n\\\\n### ERA5\\\\n\\\\nAdditionally,\n we request you cite the historical dataset used in bias correction and downscaling,\n ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\\\\n\\\\n>\n Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146:\n 1999\\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present.\n Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed\n on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus\n Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4,\n 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n\\\\n###\n GCM-specific citations & licenses\\\\n\\\\nThe CMIP6 simulation data made available\n through the Earth System Grid Federation (ESGF) are subject to Creative Commons\n [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA\n 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate\n Impact Lab has reached out to each of the modeling institutions to request\n waivers from these terms so the outputs of this project may be used with fewer\n restrictions, and has been granted permission to release the data using the\n licenses listed here.\\\\n\\\\n#### Public Domain Datasets\\\\n\\\\nThe following\n bias corrected and downscaled model simulations are available in the public\n domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/).\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\\\n\\\\n*\n **FGOALS-g3**\\\\n\\\\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output\n prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1783\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6\n ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0\n version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version\n 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\\\n\\\\n\\\\n*\n **INM-CM4-8**\\\\n\\\\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8\n model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\\\n\\\\n\\\\n* **INM-CM5-0**\\\\n\\\\n\n \\ License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5\n version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\\\n\\\\n\\\\n####\n CC-BY-4.0\\\\n\\\\nThe following bias corrected and downscaled model simulations\n are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).\n Note that this license requires citation of the source model output (included\n here). Please see https://creativecommons.org/licenses/by/4.0/ for more information.\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\\\n\\\\n*\n **ACCESS-CM2**\\\\n\\\\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler,\n Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell,\n Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire;\n Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman,\n Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen,\n Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans,\n Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;\n Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2\n model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman,\n Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid,\n Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter;\n Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias,\n Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita,\n Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards,\n Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate;\n Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output\n prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2285\\\\n\\\\n\\\\n* **ACCESS-ESM1-5**\\\\n\\\\n\n \\ License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew;\n Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter;\n Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan,\n Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5\n model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman,\n Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan;\n Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell,\n Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared\n for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\\\n\\\\n\\\\n*\n **BCC-CSM2-MR**\\\\n\\\\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen;\n Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang;\n Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*.\n Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang,\n Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei,\n Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318;\n SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\\\n\\\\n\\\\n*\n **CMCC-CM2-SR5**\\\\n\\\\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC\n CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5\n version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\\\n\\\\n\\\\n*\n **CMCC-ESM2**\\\\n\\\\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n, Momme\n **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version\n 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n,\n Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202;\n SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\\\n\\\\n\\\\n*\n **EC-Earth3-AerChem**\\\\n\\\\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*.\n Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\\\n\\\\n\\\\n*\n **EC-Earth3-CC**\\\\n\\\\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium\n EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg-LR**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123;\n SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\\\n\\\\n\\\\n*\n **EC-Earth3**\\\\n\\\\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\\\n\\\\n\\\\n*\n **GFDL-CM4**\\\\n\\\\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh,\n Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh,\n Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne,\n Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen\n M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev,\n Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey;\n Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew;\n Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair;\n Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly,\n P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**.\n *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1402\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei;\n Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta,\n Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth;\n Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies,\n Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay,\n Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi;\n Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.;\n Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova,\n Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4\n model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\\\n\\\\n\\\\n* **GFDL-ESM4**\\\\n\\\\n\n \\ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris;\n McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher;\n Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier,\n Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;\n Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter,\n David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman,\n Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne,\n John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry\n W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael;\n Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4\n model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna;\n Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne,\n John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers,\n Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL\n GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\\\n\\\\n\\\\n*\n **HadGEM3-GC31-LL**\\\\n\\\\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till;\n Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output\n prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.419\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for\n CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908;\n SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\\\n\\\\n\\\\n*\n **MIROC-ES2L**\\\\n\\\\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki,\n Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto,\n Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki,\n Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri,\n Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*.\n Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro;\n Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio;\n Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito,\n Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya,\n Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*.\n Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\\\n\\\\n\\\\n*\n **MIROC6**\\\\n\\\\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**.\n *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**.\n *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-HR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco;\n Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz,\n Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens,\n J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina;\n Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster,\n Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem;\n Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Schupfner, Martin; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann;\n Fr\\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke,\n Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp;\n Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\\xF6rg;\n Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler,\n Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia;\n Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela;\n Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang;\n Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann,\n Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner;\n Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich\n **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*.\n Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-LR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus,\n Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie;\n Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de\n Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song;\n Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast,\n Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas;\n Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke,\n Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco;\n Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler,\n Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten;\n von Storch, Jin-Song; Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin;\n Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger,\n Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh,\n Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz,\n Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von\n Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast,\n Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;\n Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR\n model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\\\n\\\\n\\\\n* **NESM3**\\\\n\\\\n\n \\ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model\n output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2021\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\\\n\\\\n\\\\n*\n **NorESM2-LM**\\\\n\\\\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk\n Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model\n output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.502\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\\\n\\\\n\\\\n*\n **NorESM2-MM**\\\\n\\\\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland,\n \\xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model\n output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.506\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland, \\xD8yvind; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\\\n\\\\n\\\\n*\n **UKESM1-0-LL**\\\\n\\\\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley,\n Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**.\n *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Good, Peter; Sellar, Alistair; Tang, Yongming;\n Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy\n **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726;\n SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\\\n\\\\n*\n **CanESM5**\\\\n\\\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt).\n Note: this dataset was previously licensed\\\\n under CC BY-SA 4.0, but was\n relicensed as CC BY 4.0 in March, 2023.\\\\n\\\\n CMIP Citation:\\\\n\\\\n > Swart,\n Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca,\n John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James\n R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler,\n Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut;\n Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model\n output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1303\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare,\n Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek;\n Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko,\n Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von\n Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma\n CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\\\n\\\\n## Acknowledgements\\\\n\\\\nThis\n work is the result of many years worth of work by members of the [Climate\n Impact Lab](https://impactlab.org), but would not have been possible without\n many contributions from across the wider scientific and computing communities.\\\\n\\\\nSpecifically,\n we would like to acknowledge the World Climate Research Programme's Working\n Group on Coupled Modeling, which is responsible for CMIP, and we would like\n to thank the climate modeling groups for producing and making their model\n output available. We would particularly like to thank the modeling institutions\n whose results are included as an input to this repository (listed above) for\n their contributions to the CMIP6 project and for responding to and granting\n our requests for license waivers.\\\\n\\\\nWe would also like to thank Lamont-Doherty\n Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data)\n (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#))\n and Google Cloud and the Google Public Datasets program for making the [CMIP6\n Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6)\n possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat),\n [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca),\n [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke),\n and [Charles Stern](https://github.com/cisaacstern) for the huge amount of\n work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted,\n analysis-ready zarr stores on Google Cloud.\\\\n\\\\nWe're also grateful to the\n [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors)\n ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in\n particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard),\n and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM\n bias correction method in the xclim python package, supporting our QPLAD implementation\n into the package, and ongoing support in integrating dask into downscaling\n workflows. 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In this dataset, the [Climate Impact Lab](https://impactlab.org)\n provides global, daily minimum and maximum air temperature at the surface\n (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`)\n corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5\n scenarios for 25 global climate models on a 1/4-degree regular global grid.\\\\n\\\\n##\n Accessing the data\\\\n\\\\nGDPCIR data can be accessed on the Microsoft Planetary\n Computer. The dataset is made of of three collections, distinguished by data\n license:\\\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\\\n*\n [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\\\n\\\\nEach\n modeling center with bias corrected and downscaled data in this collection\n falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection)\n to see which model is in each collection, and see the section below on [Citing,\n Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project)\n for citations and additional information about each license.\\\\n\\\\n## Data\n format & contents\\\\n\\\\nThe data is stored as partitioned zarr stores (see\n [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which\n includes thousands of data and metadata files covering the full time span\n of the experiment. Historical zarr stores contain just over 50 GB, while SSP\n zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with\n dimensions time (daily datetime), lat (float latitude), and lon (float longitude).\n The data is chunked at each interval of 365 days and 90 degree interval of\n latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each\n chunk occupying approximately 180MB in memory.\\\\n\\\\nHistorical data is daily,\n excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily,\n excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100,\n depending on data availability in the source GCM.\\\\n\\\\nThe spatial domain\n covers all 0.25-degree grid cells, indexed by the grid center, with grid edges\n on the quarter-degree, using a -180 to 180 longitude convention. Thus, the\n \\u201Clon\\u201D coordinate extends from -179.875 to 179.875, and the \\u201Clat\\u201D\n coordinate extends from -89.875 to 89.875, with intermediate values at each\n 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\\\n\\\\n##\n Available institutions, models, and scenarios by license collection\\\\n\\\\n|\n Modeling institution | Source model | Available experiments\n \\ | License collection |\\\\n| -------------------- | -----------------\n | ------------------------------------------ | ---------------------- |\\\\n|\n CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5\n \\ | Public domain datasets |\\\\n| INM | INM-CM4-8\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets\n |\\\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0,\n and SSP5-8.5 | Public domain datasets |\\\\n| BCC | BCC-CSM2-MR\n \\ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n|\n CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5\n \\ | CC-BY-40 |\\\\n| CMCC | CMCC-ESM2 |\n ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\\\\n|\n CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 |\n CC-BY-40 |\\\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6,\n SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\\\\n| MIROC |\n MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MIROC | MIROC6 | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| MOHC |\n HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MOHC | UKESM1-0-LL | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| MPI-M |\n MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and\n SSP5-8.5 | CC-BY-40 |\\\\n| NCC |\n NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| NCC | NorESM2-MM | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| NOAA-GFDL\n \\ | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 |\n CC-BY-40 |\\\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6,\n SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\\\n| NUIST |\n NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6,\n ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-AerChem | ssp370 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and\n ssp585 | CC-BY-40 |\\\\n| EC-Earth-Consortium\n \\ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40\n \\ |\\\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6,\n ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\\\n| CCCma |\n CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3]\n \\ |\\\\n\\\\n*Notes:*\\\\n\\\\n[^1]: At the time of running, no ssp1-2.6\n precipitation data was available. Therefore, we provide `tasmin` and `tamax`\n for this model and experiment, but not `pr`. All other model/experiment combinations\n in the above table include all three variables.\\\\n\\\\n[^2]: The institution\n which ran MPI-ESM1-2-HR\\u2019s historical (CMIP) simulations is `MPI-M`, while\n the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution\n component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP`\n scenarios.\\\\n\\\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/),\n but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0)\n in March, 2023. \\\\n\\\\n## Project methods\\\\n\\\\nThis project makes use of statistical\n bias correction and downscaling algorithms, which are specifically designed\n to accurately represent changes in the extremes. For this reason, we selected\n Quantile Delta Mapping (QDM), following the method introduced by [Cannon et\n al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific\n trends from the GCM while fitting the full distribution for a given day-of-year\n to a reference dataset (ERA5).\\\\n\\\\nWe then introduce a similar method tailored\n to increase spatial resolution while preserving extreme behavior, Quantile-Preserving\n Localized-Analog Downscaling (QPLAD).\\\\n\\\\nTogether, these methods provide\n a robust means to handle both the central and tail behavior seen in climate\n model output, while aligning the full distribution to a state-of-the-art reanalysis\n dataset and providing the spatial granularity needed to study surface impacts.\\\\n\\\\nFor\n further documentation, see [Global downscaled projections for climate impacts\n research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/)\n (EGUsphere, 2022 [preprint]).\\\\n\\\\n## Citing, licensing, and using data produced\n by this project\\\\n\\\\nProjects making use of the data produced as part of the\n Climate Impact Lab Global Downscaled Projections for Climate Impacts Research\n (CIL GDPCIR) project are requested to cite both this project and the source\n datasets from which these results are derived. Additionally, the use of data\n derived from some GCMs *requires* citations, and some modeling centers impose\n licensing restrictions & requirements on derived works. See each GCM's license\n info in the links below for more information.\\\\n\\\\n### CIL GDPCIR\\\\n\\\\nUsers\n are requested to cite this project in derived works. Our method documentation\n paper may be cited using the following:\\\\n\\\\n> Gergel, D. R., Malevich, S.\n B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp,\n R. E.: Global downscaled projections for climate impacts research (GDPCIR):\n preserving extremes for modeling future climate impacts, EGUsphere [preprint],\n https://doi.org/10.5194/egusphere-2022-1513, 2023. \\\\n\\\\nThe code repository\n may be cited using the following:\\\\n\\\\n> Diana Gergel, Kelly McCusker, Brewster\n Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6:\n (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\\\n\\\\n### ERA5\\\\n\\\\nAdditionally,\n we request you cite the historical dataset used in bias correction and downscaling,\n ERA5. See the [ECMWF guide to citing a dataset on the Climate Data Store](https://confluence.ecmwf.int/display/CKB/How+to+acknowledge+and+cite+a+Climate+Data+Store+%28CDS%29+catalogue+entry+and+the+data+published+as+part+of+it):\\\\n\\\\n>\n Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146:\n 1999\\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present.\n Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed\n on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n>\\\\n>\n Mu\\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus\n Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4,\n 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\\\n\\\\n###\n GCM-specific citations & licenses\\\\n\\\\nThe CMIP6 simulation data made available\n through the Earth System Grid Federation (ESGF) are subject to Creative Commons\n [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA\n 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate\n Impact Lab has reached out to each of the modeling institutions to request\n waivers from these terms so the outputs of this project may be used with fewer\n restrictions, and has been granted permission to release the data using the\n licenses listed here.\\\\n\\\\n#### Public Domain Datasets\\\\n\\\\nThe following\n bias corrected and downscaled model simulations are available in the public\n domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/).\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\\\n\\\\n*\n **FGOALS-g3**\\\\n\\\\n License description: [data_licenses/FGOALS-g3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/FGOALS-g3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output\n prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1783\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6\n ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0\n version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version\n 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\\\n\\\\n\\\\n*\n **INM-CM4-8**\\\\n\\\\n License description: [data_licenses/INM-CM4-8.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM4-8.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8\n model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\\\n\\\\n\\\\n* **INM-CM5-0**\\\\n\\\\n\n \\ License description: [data_licenses/INM-CM5-0.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/INM-CM5-0.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey;\n Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin,\n Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**.\n *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov,\n Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey;\n Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5\n version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\\\n\\\\n\\\\n####\n CC-BY-4.0\\\\n\\\\nThe following bias corrected and downscaled model simulations\n are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).\n Note that this license requires citation of the source model output (included\n here). Please see https://creativecommons.org/licenses/by/4.0/ for more information.\n Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\\\n\\\\n*\n **ACCESS-CM2**\\\\n\\\\n License description: [data_licenses/ACCESS-CM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-CM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler,\n Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell,\n Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire;\n Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman,\n Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen,\n Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans,\n Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon;\n Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2\n model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman,\n Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid,\n Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter;\n Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias,\n Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita,\n Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards,\n Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate;\n Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output\n prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2285\\\\n\\\\n\\\\n* **ACCESS-ESM1-5**\\\\n\\\\n\n \\ License description: [data_licenses/ACCESS-ESM1-5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/ACCESS-ESM1-5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew;\n Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter;\n Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan,\n Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5\n model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman,\n Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan;\n Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell,\n Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared\n for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\\\n\\\\n\\\\n*\n **BCC-CSM2-MR**\\\\n\\\\n License description: [data_licenses/BCC-CSM2-MR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/BCC-CSM2-MR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen;\n Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang;\n Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*.\n Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang,\n Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei,\n Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318;\n SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\\\n\\\\n\\\\n*\n **CMCC-CM2-SR5**\\\\n\\\\n License description: [data_licenses/CMCC-CM2-SR5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-CM2-SR5.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC\n CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5\n model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5\n version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\\\n\\\\n\\\\n*\n **CMCC-ESM2**\\\\n\\\\n License description: [data_licenses/CMCC-ESM2.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CMCC-ESM2.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n, Momme\n **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version\n 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Lovato, Tomas; Peano, Daniele; Butensch\\xF6n,\n Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202;\n SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\\\n\\\\n\\\\n*\n **EC-Earth3-AerChem**\\\\n\\\\n License description: [data_licenses/EC-Earth3-AerChem.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-AerChem.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*.\n Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\\\n\\\\n\\\\n*\n **EC-Earth3-CC**\\\\n\\\\n License description: [data_licenses/EC-Earth3-CC.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-CC.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium\n EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg-LR**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium\n EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2020)**.\n *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123;\n SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\\\n\\\\n\\\\n*\n **EC-Earth3-Veg**\\\\n\\\\n License description: [data_licenses/EC-Earth3-Veg.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3-Veg.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\\\n\\\\n\\\\n*\n **EC-Earth3**\\\\n\\\\n License description: [data_licenses/EC-Earth3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/EC-Earth3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium\n EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\\\n\\\\n\\\\n*\n **GFDL-CM4**\\\\n\\\\n License description: [data_licenses/GFDL-CM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-CM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh,\n Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh,\n Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne,\n Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen\n M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev,\n Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey;\n Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew;\n Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair;\n Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly,\n P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**.\n *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1402\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei;\n Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta,\n Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth;\n Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies,\n Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay,\n Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi;\n Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.;\n Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova,\n Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4\n model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\\\n\\\\n\\\\n* **GFDL-ESM4**\\\\n\\\\n\n \\ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris;\n McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher;\n Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond;\n Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier,\n Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew;\n Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter,\n David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman,\n Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne,\n John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry\n W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael;\n Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4\n model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid\n Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna;\n Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne,\n John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu;\n Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers,\n Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL\n GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\\\n\\\\n\\\\n*\n **HadGEM3-GC31-LL**\\\\n\\\\n License description: [data_licenses/HadGEM3-GC31-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/HadGEM3-GC31-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till;\n Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output\n prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.419\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for\n CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908;\n SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\\\n\\\\n\\\\n*\n **MIROC-ES2L**\\\\n\\\\n License description: [data_licenses/MIROC-ES2L.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC-ES2L.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki,\n Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto,\n Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki,\n Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri,\n Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*.\n Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro;\n Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio;\n Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito,\n Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya,\n Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*.\n Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\\\n\\\\n\\\\n*\n **MIROC6**\\\\n\\\\n License description: [data_licenses/MIROC6.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MIROC6.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**.\n *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\\\n\\\\n ScenarioMIP\n Citation:\\\\n\\\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**.\n *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-HR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-HR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-HR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco;\n Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz,\n Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens,\n J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina;\n Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster,\n Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem;\n Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Schupfner, Martin; Wieners, Karl-Hermann;\n Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann;\n Fr\\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke,\n Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp;\n Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\\xF6rg;\n Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler,\n Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia;\n Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela;\n Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang;\n Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann,\n Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner;\n Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich\n **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*.\n Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\\\n\\\\n\\\\n*\n **MPI-ESM1-2-LR**\\\\n\\\\n License description: [data_licenses/MPI-ESM1-2-LR.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/MPI-ESM1-2-LR.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus,\n Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie;\n Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de\n Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song;\n Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast,\n Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas;\n Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke,\n Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao;\n M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten;\n Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt,\n Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt,\n Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared\n for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Wieners, Karl-Hermann; Giorgetta, Marco;\n Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler,\n Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten;\n von Storch, Jin-Song; Behrens, J\\xF6rg; Brovkin, Victor; Claussen, Martin;\n Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger,\n Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh,\n Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz,\n Uwe; Modali, Kameswarrao; M\\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von\n Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast,\n Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina;\n Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR\n model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System\n Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\\\n\\\\n\\\\n* **NESM3**\\\\n\\\\n\n \\ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model\n output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.2021\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\\\n\\\\n\\\\n*\n **NorESM2-LM**\\\\n\\\\n License description: [data_licenses/NorESM2-LM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-LM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk\n Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model\n output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.502\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Seland, \\xD8yvind; Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\\\n\\\\n\\\\n*\n **NorESM2-MM**\\\\n\\\\n License description: [data_licenses/NorESM2-MM.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NorESM2-MM.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland,\n \\xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard,\n Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger,\n J\\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao;\n Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger\n Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum;\n Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze,\n Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model\n output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.506\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Bentsen, Mats; Olivi\\xE8, Dirk Jan Leo; Seland, \\xD8yvind; Toniazzo, Thomas;\n Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok\n Kumar; He, Yanchun; Kirkev\\xE5g, Alf; Schwinger, J\\xF6rg; Tjiputra, Jerry;\n Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini,\n Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren,\n Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger,\n Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz,\n Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*.\n Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\\\n\\\\n\\\\n*\n **UKESM1-0-LL**\\\\n\\\\n License description: [data_licenses/UKESM1-0-LL.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/UKESM1-0-LL.txt)\\\\n\\\\n\n \\ CMIP Citation:\\\\n\\\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley,\n Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**.\n *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627.\n Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\\\n\\\\n\n \\ ScenarioMIP Citation:\\\\n\\\\n > Good, Peter; Sellar, Alistair; Tang, Yongming;\n Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy\n **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*.\n SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726;\n SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\\\n\\\\n*\n **CanESM5**\\\\n\\\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt).\n Note: this dataset was previously licensed\\\\n under CC BY-SA 4.0, but was\n relicensed as CC BY 4.0 in March, 2023.\\\\n\\\\n CMIP Citation:\\\\n\\\\n > Swart,\n Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca,\n John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James\n R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler,\n Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut;\n Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model\n output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation.\n https://doi.org/10.22033/ESGF/CMIP6.1303\\\\n\\\\n ScenarioMIP Citation:\\\\n\\\\n\n \\ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare,\n Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek;\n Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko,\n Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von\n Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma\n CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth\n System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\\\n\\\\n## Acknowledgements\\\\n\\\\nThis\n work is the result of many years worth of work by members of the [Climate\n Impact Lab](https://impactlab.org), but would not have been possible without\n many contributions from across the wider scientific and computing communities.\\\\n\\\\nSpecifically,\n we would like to acknowledge the World Climate Research Programme's Working\n Group on Coupled Modeling, which is responsible for CMIP, and we would like\n to thank the climate modeling groups for producing and making their model\n output available. We would particularly like to thank the modeling institutions\n whose results are included as an input to this repository (listed above) for\n their contributions to the CMIP6 project and for responding to and granting\n our requests for license waivers.\\\\n\\\\nWe would also like to thank Lamont-Doherty\n Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data)\n (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#))\n and Google Cloud and the Google Public Datasets program for making the [CMIP6\n Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6)\n possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat),\n [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca),\n [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke),\n and [Charles Stern](https://github.com/cisaacstern) for the huge amount of\n work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted,\n analysis-ready zarr stores on Google Cloud.\\\\n\\\\nWe're also grateful to the\n [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors)\n ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in\n particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard),\n and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM\n bias correction method in the xclim python package, supporting our QPLAD implementation\n into the package, and ongoing support in integrating dask into downscaling\n workflows. 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1150 km\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"EARTH SCIENCE\n > OCEANS\\\",\\n \\\"EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE\\\",\\n \\\"Oceans\\\",\\n\n \\ \\\"Ocean Temperature\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n\n \\ \\\"Envisat\\\",\\n \\\"AATSR\\\"\\n ],\\n \\\"created\\\": \\\"2019-05-29T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The L2P product contains full resolution dual-view\n Sea Surface Temperature (SST) values. 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Specifications\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"describedby\\\",\\n \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/AATSR-product-handbook.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"AATSR Product\n Handbook - User Manual\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://earth.esa.int/eogateway/instruments/aatsr/products-information\\\",\\n\n \\ \\\"type\\\": \\\"text/html\\\",\\n \\\"title\\\": \\\"AATSR Product Information\n - Product Information\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/L2P-Product-Description.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"(A)ATSR SST\n L2P/L3U Product User Guide - Product Specifications\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://www.ghrsst.org/about-ghrsst/governance-documents/\\\",\\n\n \\ 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The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR)\n product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected\n product, without correction for any geometric distortions inherent in the\n imaging processes and is not mapped to a cartographic projection. The imagery\n data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification\n by the user. This kind of product is designed for users with advanced image\n processing and geometric correction capabilities. Basic Scene Product\n Components and Format Product Components:\\\\t - Image File (GeoTIFF format)\n - Metadata File (XML format) - Rational Polynomial Coefficients (XML format)\n - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format)\n - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral\n image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green\n I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance:\\\\t\n - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R:\n 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified\n assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and\n geometrically- corrected and is projected to a UTM/WGS84 cartographic map\n projection. The geometric correction uses fine Digital Elevation Models (DEMs)\n with a post spacing of between 30 and 90 metres. Ortho Scene Product Components\n and Format Product Components:\\\\t - Image File (GeoTIFF format) - Metadata\n File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM\n File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands:\n 3-band natural color (red, green, blue) or 4-band multispectral image (blue,\n green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow,\n red, RedEdge, near-infrared) Ground Sampling Distance:\\\\t - Approximate, satellite\n altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove:\n 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho\n Scene product is available in the following: PlanetScope Visual Ortho Scene\n product is orthorectified and color-corrected (using a colour curve) 3-band\n RGB Imagery. This correction attempts to optimise colours as seen by the human\n eye providing images as they would look if viewed from the perspective of\n the satellite PlanetScope Surface Reflectance product is orthorectified,\n 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge,\n NIR Imagery with geometric, radiometric and corrected for surface reflection.\n This data is optimal for value-added image processing such as land cover classifications.\n \\ PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified,\n 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge,\n NIR Imagery with geometric, radiometric and calibrated to top of atmosphere\n radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/\n can be accessed (Planet registration requested) to discover and check the\n data readiness. It is worth to mention that the data distribution is based\n on Data Voucher, corresponding to maximum amount of square kilometers can\n be ordered and downloaded by the project in a maximum period of 15 moths (this\n duration cannot be extended) starting from the project proposal acceptance\n date. Each Date Voucher includes PlanetScope tile view streaming access for\n a total of 20,000 tiles per calendar month during the project period. All\n details about the data provision, data access conditions and quota assignment\n procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf\n available in Resources section.\\\",\\n \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\":\n \\\"PlanetScope Full Archive\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\":\n {\\n \\\"metadata_ogc_17_069r3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/PlanetScope.Full.Archive\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": 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collections for ESA/ESRIN\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/eo:platform/PlanetScope\\\",\\n\n \\ \\\"title\\\": \\\"More collections for PlanetScope platform\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/concepts/instruments/94314806-9c68-521a-87e2-8cd838eb9de0\\\",\\n\n \\ \\\"title\\\": \\\"More collections for PlanetScope Camera instrument\\\"\\n\n \\ }\\n ],\\n \\\"id\\\": \\\"PlanetScope.Full.Archive\\\",\\n \\\"updated\\\":\n \\\"2023-06-13T13:23:41Z\\\",\\n \\\"providers\\\": [\\n {\\n \\\"roles\\\":\n [\\\"producer\\\"],\\n \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"http://www.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\\"PlanetScope\n Camera\\\"],\\n 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SCIENCE > ATMOSPHERE > PRECIPITATION\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"782\n to 785 km\\\",\\n \\\"5 km\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n\n \\ \\\"ERS-1\\\",\\n \\\"ERS-2\\\",\\n \\\"ATSR-1\\\",\\n \\\"ATSR-2\\\"\\n\n \\ ],\\n \\\"created\\\": \\\"2019-05-29T00:00:00.00Z\\\",\\n \\\"description\\\":\n \\\"The Gridded Surface Temperature (GST) Product is the Level 2 full resolution\n geophysical product. 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instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"ERS.AT_NR__2P\\\",\\n \\\"updated\\\": \\\"2023-06-13T13:23:39Z\\\",\\n\n \\ \\\"providers\\\": [\\n {\\n \\\"roles\\\": [\\\"producer\\\"],\\n\n \\ \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"http://www.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\n\n \\ \\\"ATSR-1\\\",\\n \\\"ATSR-2\\\"\\n ],\\n \\\"platform\\\":\n [\\n \\\"ERS-1\\\",\\n \\\"ERS-2\\\"\\n ]\\n }\\n },\\n\n \\ {\\n \\\"extent\\\": {\\n \\\"spatial\\\": {\\\"bbox\\\": [[\\n -180,\\n\n \\ -90,\\n 180,\\n 90\\n ]]},\\n \\\"temporal\\\":\n {\\\"interval\\\": [[\\n \\\"1995-07-01T00:00:00.000Z\\\",\\n \\\"2015-12-31T23:59:59.999Z\\\"\\n\n \\ ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n \\\"sci:doi\\\":\n \\\"10.57780/esa-1268efe\\\",\\n \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"Atmosphere\\\",\\n\n \\ \\\"Atmospheric Chemistry\\\",\\n \\\"Atmospheric Radiation\\\",\\n \\\"Atmospheric\n Temperature\\\",\\n \\\"Climate\\\",\\n \\\"EARTH SCIENCE > ATMOSPHERE\\\",\\n\n \\ \\\"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY\\\",\\n \\\"EARTH\n SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION\\\",\\n \\\"EARTH SCIENCE >\n ATMOSPHERE > ATMOSPHERIC TEMPERATURE\\\",\\n \\\"EARTH SCIENCE > CLIMATE\n INDICATORS\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"GDP 5.0\\\",\\n \\\"782\n to 785 km\\\",\\n \\\"5 km\\\",\\n \\\"Spectrometers\\\",\\n \\\"ERS-2\\\",\\n\n \\ \\\"GOME\\\"\\n ],\\n \\\"created\\\": \\\"2019-05-29T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The GOME Total Column Water Vapour (TCWV) Climate\n product was generated by the Max Planck Institute for Chemistry (MPIC), and\n the German Aerospace Center (DLR) within the ESA GOME-Evolution project. It\n is a Level 3 type product containing homogenized time-series of the global\n distribution of TCWV spanning over more than two decades (1995-2015). The\n data is provided as single netCDF file, containing monthly mean TCWV (units\n kg/m2) with 1-degree resolution, and is based on measurements from the satellite\n instruments ERS-2 GOME, Envisat SCIAMACHY, and MetOp-A GOME-2. Details are\n available in the paper by Beirle et al, 2018,. Please also consult the GOME\n TCWV Product Quality Readme file before using the data. 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\\\"https://emc.spacebel.be/series/concepts/instruments/3a0e9379-9541-569c-8492-3636d0ef2a27\\\",\\n\n \\ \\\"title\\\": \\\"More collections for SeaWinds instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"ADAM.Surface.Reflectance.Database\\\",\\n \\\"updated\\\":\n \\\"2023-06-13T13:23:39Z\\\",\\n \\\"stac_extensions\\\": [\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\"],\\n\n \\ \\\"providers\\\": [\\n {\\n \\\"roles\\\": [\\\"producer\\\"],\\n\n \\ \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"http://www.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\n\n \\ \\\"SeaWiFS\\\",\\n \\\"MODIS\\\",\\n \\\"SeaWinds\\\"\\n ],\\n\n \\ \\\"platform\\\": [\\n \\\"TERRA\\\",\\n \\\"AQUA\\\",\\n \\\"QuikSCAT\\\",\\n\n \\ \\\"OrbView-2\\\"\\n ]\\n }\\n },\\n {\\n \\\"extent\\\":\n 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0.75\n \\xB5m)\\\",\\n \\\"NIR (0.75 - 1.30 \\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"Very High Resolution - VHR (0 - 5m)\\\",\\n \\\"475 km (reference)\\\",\\n\n \\ \\\"25 km\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"PlanetScope\\\",\\n\n \\ \\\"PlanetScope Camera\\\"\\n ],\\n \\\"created\\\": \\\"2019-08-09T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The PlanetScope Level 1B Basic Scene and Level 3B\n Ortho Scene full archive products are available as part of Planet imagery\n offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR)\n product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected\n product, without correction for any geometric distortions inherent in the\n imaging processes and is not mapped to a cartographic projection. The imagery\n data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification\n by the user. This kind of product is designed for users with advanced image\n processing and geometric correction capabilities. Basic Scene Product\n Components and Format Product Components:\\\\t - Image File (GeoTIFF format)\n - Metadata File (XML format) - Rational Polynomial Coefficients (XML format)\n - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format)\n - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral\n image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green\n I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance:\\\\t\n - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R:\n 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified\n assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and\n geometrically- corrected and is projected to a UTM/WGS84 cartographic map\n projection. The geometric correction uses fine Digital Elevation Models (DEMs)\n with a post spacing of between 30 and 90 metres. Ortho Scene Product Components\n and Format Product Components:\\\\t - Image File (GeoTIFF format) - Metadata\n File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM\n File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands:\n 3-band natural color (red, green, blue) or 4-band multispectral image (blue,\n green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow,\n red, RedEdge, near-infrared) Ground Sampling Distance:\\\\t - Approximate, satellite\n altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove:\n 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho\n Scene product is available in the following: PlanetScope Visual Ortho Scene\n product is orthorectified and color-corrected (using a colour curve) 3-band\n RGB Imagery. This correction attempts to optimise colours as seen by the human\n eye providing images as they would look if viewed from the perspective of\n the satellite PlanetScope Surface Reflectance product is orthorectified,\n 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge,\n NIR Imagery with geometric, radiometric and corrected for surface reflection.\n This data is optimal for value-added image processing such as land cover classifications.\n \\ PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified,\n 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge,\n NIR Imagery with geometric, radiometric and calibrated to top of atmosphere\n radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/\n can be accessed (Planet registration requested) to discover and check the\n data readiness. It is worth to mention that the data distribution is based\n on Data Voucher, corresponding to maximum amount of square kilometers can\n be ordered and downloaded by the project in a maximum period of 15 moths (this\n duration cannot be extended) starting from the project proposal acceptance\n date. Each Date Voucher includes PlanetScope tile view streaming access for\n a total of 20,000 tiles per calendar month during the project period. All\n details about the data provision, data access conditions and quota assignment\n procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf\n available in Resources section.\\\",\\n \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\":\n \\\"PlanetScope Full Archive\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\":\n {\\n \\\"metadata_ogc_17_069r3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/PlanetScope.Full.Archive\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": 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wind components. The L2C can therefore be described as an Aeolus-assisted\n horizontal wind vector product. The L2C is a distinct product, however the\n L2C and L2B share a common Earth Explorer file template, with the L2C being\n a superset of the L2B. The L2C consists of extra datasets appended to the\n L2B product with information which are relevant to the data assimilation of\n the L2B winds.\\\",\\n \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\": \\\"Aeolus\n Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction\n assimilation processing\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\":\n {\\n \\\"metadata_ogc_17_069r3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19139%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19139_2\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19139-2%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139-2 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139-2+xml\\\"\\n\n \\ },\\n \\\"metadata_dif_10\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/dif10%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"DIF-10 metadata\\\",\\n \\\"type\\\": \\\"application/dif10+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19115_3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19115-3%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19115-3 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19115-3+xml\\\"\\n\n \\ },\\n \\\"metadata_ogc_17_084r1\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?mode=owc\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-084r1 metadata\\\",\\n \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\\\\\"\\\"\\n\n \\ },\\n \\\"metadata_html\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/html\\\",\\n\n \\ \\\"title\\\": \\\"HTML\\\",\\n \\\"type\\\": \\\"text/html\\\"\\n }\\n\n \\ },\\n \\\"links\\\": [\\n {\\n \\\"rel\\\": \\\"self\\\",\\n \\\"href\\\":\n \\\"https://emc.spacebel.be/collections/L2C_Wind_products\\\",\\n \\\"type\\\":\n \\\"application/json\\\"\\n },\\n {\\n \\\"rel\\\": \\\"root\\\",\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n \\\"type\\\": \\\"application/json\\\",\\n\n \\ \\\"title\\\": \\\"FedEO Clearinghouse\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"parent\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n \\\"title\\\":\n \\\"collections\\\",\\n \\\"type\\\": \\\"application/json\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Level2-Algorithm-Theoretical-Basis-Document.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Aeolus L2B\n Algorithm Theoretical Baseline Document - Level 2B processor algorithm description\\\"\\n\n \\ },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\":\n \\\"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-L2B-2C-Input-Output-DD-ICD.pdf/2321b6a0-27d0-b628-771e-fcca4928bc32\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Aeolus L2B/2C\n Processor Input/Output Data Definitions Interface Control Document - Level\n 2B/2C data definition\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Sensor-and-Product-Description.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Aeolus Sensor\n and Product Description - Aeolus Sensor and Product Description\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://esatellus.service-now.com/csp?id=esa_simple_request\\\",\\n\n \\ \\\"type\\\": \\\"text/html\\\",\\n \\\"title\\\": \\\"Get Help? - ESA\n Earth Observation User Services Portal\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/atom%2Bxml\\\",\\n\n \\ \\\"type\\\": \\\"application/atom+xml\\\",\\n \\\"title\\\": \\\"Atom\n format\\\"\\n },\\n {\\n \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\":\n \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/xml\\\",\\n\n \\ \\\"type\\\": \\\"application/xml\\\",\\n \\\"title\\\": \\\"Dublin Core\n metadata\\\"\\n },\\n {\\n \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\":\n \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/ld%2Bjson;profile=https://schema.org\\\",\\n\n \\ \\\"type\\\": \\\"application/ld+json;profile=\\\\\\\"https://schema.org\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"JSON-LD (schema.org) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\\\",\\n\n \\ \\\"type\\\": \\\"application/ld+json;profile=\\\\\\\"http://data.europa.eu/930/\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"JSON-LD (GeoDCAT-AP) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/rdf%2Bxml;profile=https://schema.org\\\",\\n\n \\ \\\"type\\\": \\\"application/rdf+xml;profile=\\\\\\\"https://schema.org\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"RDF/XML (schema.org) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\\\",\\n\n \\ \\\"type\\\": \\\"application/rdf+xml;profile=\\\\\\\"http://data.europa.eu/930/\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"RDF/XML (GeoDCAT-AP) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/turtle;profile=https://schema.org\\\",\\n\n \\ \\\"type\\\": \\\"text/turtle;profile=\\\\\\\"https://schema.org\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"Turtle (schema.org) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/turtle;profile=http://data.europa.eu/930/\\\",\\n\n \\ \\\"type\\\": \\\"text/turtle;profile=\\\\\\\"http://data.europa.eu/930/\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"Turtle (GeoDCAT-AP) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\\\",\\n\n \\ \\\"title\\\": \\\"More collections for ESA/ESRIN\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/eo:platform/Aeolus\\\",\\n\n \\ \\\"title\\\": \\\"More collections for Aeolus platform\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/concepts/instruments/a032cde6-16e3-5940-988e-f6d79870d14c\\\",\\n\n \\ \\\"title\\\": \\\"More collections for ALADIN instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"L2C_Wind_products\\\",\\n \\\"updated\\\": \\\"2023-06-13T13:23:39Z\\\",\\n\n \\ \\\"providers\\\": [\\n {\\n \\\"roles\\\": [\\\"producer\\\"],\\n\n \\ \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"http://www.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\\"ALADIN\\\"],\\n\n \\ \\\"platform\\\": [\\\"Aeolus\\\"]\\n }\\n },\\n {\\n \\\"extent\\\":\n {\\n \\\"spatial\\\": {\\\"bbox\\\": [[\\n -180,\\n -84,\\n 180,\\n\n \\ 84\\n ]]},\\n \\\"temporal\\\": {\\\"interval\\\": [[\\n \\\"2013-11-13T00:00:00.000Z\\\",\\n\n \\ null\\n ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n\n \\ \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"Planet\\\",\\n \\\"Land\n Surface\\\",\\n \\\"Mapping and Cartography\\\",\\n \\\"Natural Hazards\n and Disaster Risk\\\",\\n \\\"EARTH SCIENCE > LAND SURFACE\\\",\\n \\\"EARTH\n SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS\\\",\\n \\\"VIS (0.40 - 0.75\n \\xB5m)\\\",\\n \\\"NIR (0.75 - 1.30 \\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"Very High Resolution - VHR (0 - 5m)\\\",\\n \\\"475-575 km\\\",\\n\n \\ \\\"8 km at nadir for SkySat-1 to -2, 6.6 km at nadir for SkySat-3 to\n -13\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"SkySat\\\",\\n\n \\ \\\"SkySat Camera\\\"\\n ],\\n \\\"created\\\": \\\"2019-08-09T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The SkySat Level 2B Basic Scene, Level 3B Ortho Scene\n and Level 3B Consolidated full archive and new tasking products are available\n as part of Planet imagery offer. The SkySat Basic Scene product is uncalibrated\n and in a raw digital number format, not corrected for any geometric distortions\n inherent in the imaging process. Rational Polynomial Coefficients (RPCs) is\n provided to enable orthorectification by the user. Basic Scene Product Components\n and Format Processing levels\\\\t/\\\\t- Analytic (unorthorectified, radiometrically\n corrected, multispectral BGRN); - Analytic DN (unorthorectified, multispectral\n BGRN); - Panchromatic DN (unorthorectified, panchromatic)\\\\t// Product Components\n and Format\\\\t/\\\\t- Image File (GeoTIFF format); - Metadata File (JSON format);\n - Rational Polynomial Coefficients (Text File); - UDM File (GeoTIFF format)\\\\t//\n Image configuration\\\\t/\\\\t- 4-band Analytic DN Image (Blue, Green, Red, NIR);\n - 1-band Panchromatic DN Image (Pan)\\\\t// Ground Sampling Distance\\\\t/\\\\t3.7\n m at nadir (average at reference altitude 475 km)\\\\t// Ground Sampling Distance\n (nadir)\\\\t/\\\\t- Panchromatic 0.86m and Multispectral 1.0m for SkySat-1&2;\n - Panchromatic 0.65m and Multispectral 0.8m for SkySat-3 to 13 (0.72 m and\n 1.0m for data acquired before 30/06/2020)\\\\t// Accuracy\\\\t/\\\\t<50 m RMSE\\\\t//\n \\ The SkySat Ortho Scene is sensor- and geometrically-corrected (by using\n DEMs with a post spacing of between 30 and 90 meters) and is projected to\n a cartographic map projection; the accuracy of the product will vary from\n region to region based on available GCPs. Different products are available:\n \\ - The SkySat Visual Ortho Scene product is orthorectified, pansharpened,\n and color-corrected (using a color curve) 3-band RGB Imagery - The SkySat\n Pansharpened Multispectral Scene product is orthorectified, pansharpened 4-band\n BGRN Imagery - The SkySat Analytic DN Ortho Scene product is orthorectified,\n multispectral BGRN, uncalibrated, digital number imagery product. The product\n has been processed to remove distortions caused by terrain; It eliminates\n the perspective effect on the ground (not on buildings), restoring the geometry\n of a vertical shot. Transformation to at-sensor radiance is not included -\n The SkySat Panchromatic DN Ortho Scene product is orthorectified, panchromatic,\n uncalibrated, digital number imagery product. It has a finer GSD than the\n Analytic Product. Transformation to at-sensor radiance is not included. -\n The SkySat Analytic Ortho Scene are calibrated multispectral imagery products\n with radiometric corrections applied to correct for any sensor artifacts and\n transformation to top-of-atmosphere radiance. - The SkySat Consolidated Product\n are Ortho Collect product created by composing ~60 SkySat Ortho Scenes (Visual,\n Pansharpened Multispectral, Analytic DN, Panchromatic DN) along an imaging\n strip into segments. Ortho Scene Product Components and Format \\\\t/\\\\tVisual\n Ortho\\\\t/\\\\tPansharpened Multispectral\\\\t/\\\\tAnalytic DN Ortho\\\\t/\\\\tPanchromatic\n DN Ortho\\\\t/\\\\tAnalytic Ortho\\\\t// Product Components and Format\\\\t/\\\\tImage\n File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients\n (Text File); UDM File (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF); Metadata\n File (JSON format); Rational Polynomial Coefficients (Text File); UDM File\n (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF format); Metadata File (JSON format);\n Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format);\\\\t/\\\\tImage\n File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients\n (Text File); UDM File (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF format);\n Metadata File (JSON format); Rational Polynomial Coefficients (Text File);\n UDM File (GeoTIFF format)\\\\t// Image configuration\\\\t/\\\\t3-band Pansharpened\n Image (PS Red, PS Green, PS Blue)\\\\t/\\\\t4-band Pansharpened Image (PS Blue,\n PS Green, PS Red, PS NIR)\\\\t/\\\\t4-band Analytic DN Image (B, G, R, N)\\\\t/\\\\t1-band\n Panchromatic Image\\\\t/\\\\t4-band Analytic Image (B, G, R, N)\\\\t// Ground Sampling\n Distance\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t//\n Projection\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM\n WGS84\\\\t// Accuracy\\\\t/\\\\t<10 m RMSE\\\\t/\\\\t<10 m RMSE\\\\t/\\\\t<10 m\n RMSE\\\\t/\\\\t<10 m RMSE\\\\t/\\\\tradiometric accuracy: +/- 5% Relative accuracy\n at < 10 degrees off-nadir angle\\\\t// The products are available as part\n of the Planet provision from RapidEye, Skysat and PlanetScope constellations.\n SkySat collection has worldwide coverage: The Planet Explorer Catalogue can\n be accessed (Planet registration requested) to discover and check the data\n readiness. All details about the data provision, data access conditions and\n quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf).\\\",\\n\n \\ \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\": \\\"SkySat Full Archive and\n New Tasking\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\": {\\n \\\"metadata_ogc_17_069r3\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19139%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19139_2\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19139-2%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139-2 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139-2+xml\\\"\\n\n \\ },\\n \\\"metadata_dif_10\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/dif10%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"DIF-10 metadata\\\",\\n \\\"type\\\": \\\"application/dif10+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19115_3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19115-3%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19115-3 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19115-3+xml\\\"\\n\n \\ },\\n \\\"metadata_ogc_17_084r1\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?mode=owc\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-084r1 metadata\\\",\\n \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\\\\\"\\\"\\n\n \\ },\\n \\\"metadata_html\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=text/html\\\",\\n\n \\ \\\"title\\\": \\\"HTML\\\",\\n \\\"type\\\": \\\"text/html\\\"\\n }\\n\n \\ },\\n \\\"links\\\": [\\n {\\n \\\"rel\\\": \\\"self\\\",\\n \\\"href\\\":\n \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking\\\",\\n\n \\ \\\"type\\\": \\\"application/json\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"root\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n \\\"type\\\":\n \\\"application/json\\\",\\n \\\"title\\\": \\\"FedEO Clearinghouse\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"parent\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n\n \\ \\\"title\\\": \\\"collections\\\",\\n \\\"type\\\": \\\"application/json\\\"\\n\n \\ },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\":\n \\\"https://www.planet.com/products/#satellite-imagery\\\",\\n \\\"type\\\":\n \\\"text/html\\\",\\n \\\"title\\\": \\\"Planet products - Web site\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/Planet-combined-imagery-product-specs-2020.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Planet Imagery\n Product Specifications - Product Specifications\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://developers.planet.com/docs/apps/explorer/\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Planet Explorer\n User Guide - User Manual\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/SkySat-tasking-request-form.pdf/075f845e-13bd-36e2-4743-3aeb2053c61c\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"SkySat Tasking\n Request Form - Access guide\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf/91fdeed4-2efe-1bed-8058-1dddb6e14fa6\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Planet Terms\n of Applicability - 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0.75 \\xC2\\xB5m)\\\",\\n \\\"NIR\n (0.75 - 1.30 \\xC2\\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"Medium\n Resolution - MR (20 - 500 m)\\\",\\n \\\"663 km\\\",\\n \\\"625 km\\\",\\n\n \\ \\\"Cameras\\\",\\n \\\"GEOSAT-1\\\",\\n \\\"SLIM6\\\"\\n ],\\n\n \\ \\\"created\\\": \\\"2019-05-22T00:00:00.00Z\\\",\\n \\\"description\\\": \\\"GEOSAT-1\n full archive and new tasking products are available at 22 m resolution in\n two processing levels.\\\\r\\\\rL1R (Basic Geopositioned): All 3 spectral channels\n combined into a band-registered image. 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collections for GEOSAT-1 platform\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/concepts/instruments/5cc6eaab-0ddc-572f-a1b7-afa52ea5333d\\\",\\n\n \\ \\\"title\\\": \\\"More collections for SLIM6 instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"Geosat-1.Full.archive.and.tasking\\\",\\n \\\"updated\\\":\n \\\"2023-06-13T13:23:40Z\\\",\\n \\\"providers\\\": [\\n {\\n \\\"roles\\\":\n [\\\"producer\\\"],\\n \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"https://earth.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\\"SLIM6\\\"],\\n\n \\ \\\"platform\\\": [\\\"GEOSAT-1\\\"]\\n }\\n },\\n {\\n \\\"extent\\\":\n {\\n \\\"spatial\\\": {\\\"bbox\\\": [[\\n -180,\\n -90,\\n 180,\\n\n \\ 90\\n ]]},\\n \\\"temporal\\\": {\\\"interval\\\": [[\\n \\\"2016-06-01T00:00:00.000Z\\\",\\n\n \\ null\\n ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n\n \\ \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"GNSS\\\",\\n \\\"AIS\\\",\\n\n \\ \\\"ADS-B\\\",\\n \\\"Atmosphere\\\",\\n \\\"Ionosphere and Magnetosphere\n Dynamics\\\",\\n \\\"Weather Events\\\",\\n \\\"Oceans\\\",\\n \\\"EARTH\n SCIENCE > ATMOSPHERE\\\",\\n \\\"EARTH SCIENCE > SUN-EARTH INTERACTIONS\n > IONOSPHERE/MAGNETOSPHERE DYNAMICS\\\",\\n \\\"EARTH SCIENCE > ATMOSPHERE\n > WEATHER EVENTS\\\",\\n \\\"EARTH SCIENCE > OCEANS\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"500 km\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"Spire\\\",\\n\n \\ \\\"AIRSAFE\\\",\\n \\\"SENSE\\\",\\n \\\"STRATOS\\\"\\n ],\\n\n \\ \\\"created\\\": \\\"2020-09-04T00:00:00.00Z\\\",\\n \\\"description\\\": \\\"The\n data collected by Spire from it's 110 satellites launched into Low Earth\n Orbit (LEO) has a diverse range of applications, from analysis of global trade\n patterns and commodity flows to aircraft routing to weather forecasting. The\n data also provides interesting research opportunities on topics as varied\n as ocean currents and GNSS-based planetary boundary layer height. The following\n products can be requested: ADS-B Data Stream Global ADS-B satellite data\n observed by Spire satellites and processed through the ground stations network.\n Historical ADS-B data older than 6 months can be delivered as data cuts containing\n CSV file(s) accessible through a Web Service or Cloud storage solutions. Live\n ADS-B data is available through a streaming API, and recent historical data\n can be accessed through a REST API. Data is distributed as a monthly subscription:\n historical data can be requested starting from 3 December 2008, the time period\n for live data starts from a user-defined date and continues for 30 days. AIS\n AIS messages include satellite AIS (S-AIS) as observed by Spire satellites\n and terrestrial AIS (T-AIS) from third party sensor stations (up to 40 million\n messages per day). Historical AIS data are delivered as a cvs file with availability\n back to June 2016 or via Historical API from December 2018; live AIS data\n are pushed to end users via TCP or through Messages API. Data is distributed\n as a monthly subscription, from a user-defined date and continues for a 30\n day period. GNSS-Radio Occultation GNSS Radio Occultation (GNSS-RO) measurements\n are collected globally on a continuous basis, generating profiles of the Earth\\\\u2019s\n atmosphere. Derived Level 1 and Level 2 products include both atmospheric\n and ionospheric products. Historical data for most of the GNSS-RO products\n are available from December 2018 to the present. Near real-time (within 90\n minutes or less latency from collection to delivery) GNSS-RO profiles are\n also available upon request. GNSS Reflectometry GNSS Reflectometry (GNSS-R)\n is a technique to measure Earth\\\\u2019s surface properties using reflections\n of GNSS signals in the form of a bistatic radar. Spire collects two types\n of GNSS-R data: conventional, near-nadir incidence LHCP reflections collected\n by the Spire GNSS-R satellites (e.g., Spire GNSS-R \\\\u201cBatch-1\\\\u201d satellites)\n and grazing angle (i.e., low elevation angle) RHCP reflections collected by\n the Spire GNSS-RO satellites. Derived Level 1 and Level 2 products are available,\n as well as some special Level 0 raw intermediate frequency (IF) data. Historical\n grazing angle GNSS-R data are available from May 2019 to the present, while\n conventional GNSS-R data are available from December 2020 to the present.\n \\ Name: Automatic Identification System (AIS) Description: The automatic identification\n system (AIS) is an automatic tracking system that uses transponders on ships\n and is used by vessel traffic services. Spire data includes satellite AIS\n (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third\n party sensor stations. Data format and content: .parquet.gz files The AIS\n files contain time-series data on received AIS messages, both the raw NMEA\n message and added post-processing data for each message. Application: Supply\n chain analysis, commodity trading, identification of illegal fishing or dark\n targets, ship route and fuel use optimization, analysis of global trade patterns,\n anti-piracy, autonomous vessel software, ocean currents. Name: Automatic\n Dependent Surveillance-Broadcast (ADS-B) Description: Spire AirSafe ADS-B\n products give access to satellite and terrestrial ADS-B data from captured\n aircrafts. Data format and content: .csv.gz files The decompressed csv file\n contains a list of hexadecimal representations of ADS-B messages associated\n with the timestamp they were received on the satellite. Application: Fleet\n management, ICAO regulatory compliance, route optimization, predictive maintenance,\n global airspace, domain awareness. Name: Global Navigation Satellite System\n Radio Occultation (GNSS-RO) Description: GNSS atmospheric radio occultation\n (GNSS-RO) relies on the detection of a change in a radio signal as it passes\n through a planet's atmosphere, i.e. as it is refracted by the atmosphere.\n This data set contains precise orbit determination (POD) solutions, satellite\n attitude information, high-rate occultation observations, excess phase, and\n derived atmospheric dry temperature profiles. Data format and content: podObs*.rnx\n This file contains raw pseudorange, carrier phase, Doppler frequency, and\n signal-to-noise measurements for each observed GPS signal from a single Spire\n satellite which allow to estimate the positions and velocities of each Spire\n satellite and also used to derive ionospheric total electron content data.\n leoOrb*.sp3 This file contains the estimated position, velocity and receiver\n clock error of a given Spire satellite after processing of the POD observation\n file leoAtt*.log It contains 1 Hz rate quaternion information measured from\n a single Spire satellite describing the satellite orientation. opnGns*ro.bin,\n opnGns*rst.bin these files contain raw measurements from the occulting GNSS\n satellite (one for each signal frequency) and raw phase data from one or more\n reference GNSS satellites. atmPhs* The file contains occultation excess phase\n delay. Also contains SNR values, ransmitter and receiver positions and open\n loop model information. atmPrf*.nc The file contains profiles of atmospheric\n dry pressure, dry temperature and neutral refractivity as a function of altitude\n produced from full processing of one occultation event. bfrPrf*.bufr The file\n contains derived profiles of dry pressure, dry temperature, refractivity and\n bending angle for each occultation. Application:\\\\tAtmospheric profiles of\n pressure, dry temperature, bending angle, and refractivity used in numerical\n weather prediction data assimilation and climate change studies. Name: Raw\n IF samples from GNSS-RO satellites Description: Raw intermediate frequency\n (IF) sampled data (I/Q) from the GNSS receiver front-end of GNSS-RO satellites.\n Data format and content: rocRIF*.zip Binary raw IF data and associated ancillary\n data (e.g., POD data) in a zip archive per collection event. Application:\n GNSS-RO studies, GNSS RFI and jamming monitoring, research. Name: Raw IF\n samples from GNSS-R satellites Description: Raw intermediate frequency (IF)\n sampled data (I/Q) from the GNSS receiver front-end of conventional GNSS-R\n satellites. Data format and content: gbrRIF*.zip Binary raw IF data and associated\n ancillary data (e.g., POD data) in a zip archive per collection event. Application:\n GNSS-R studies, GNSS RFI and jamming monitoring, research, etc. Name: Grazing\n angle GNSS-R observations Description: During grazing angle GNSS-R events,\n signal reflection at two frequencies is observed through the limb-facing antenna\n and is trackedusing an open-loop tracking technique thatrelies on a model\n topredict the propagationdelay and Doppler of thereflected signal. Simultaneous\n open-looptracking of the signaldirectly along theline-of-sight from thetransmitter\n to thereceiver is alsoperformed to provideadditional data that maybenecessary\n for signalcalibration. The mainoutput of the open-looptracking are in-phase\n (I)and quadrature (Q)accumulation samples(nominally at 50 Hz),which represent\n the residual Doppler (phase) from the model. Data format and content: grzObs*.nc\n L1A filecontains rawopen loopcarrier phasemeasurementsat 50 Hzsampling forgrazingangleGNSS-Rreflectionscaptured\n in the GNSS-RO RHC Pantennas, (bothdirect andreflectedsignals) on GNSS-RO\n satellites. Application: Sea surface and sea ice height extent, and classification.\n \\ Name: Georeferenced grazing angle GNSS-R observations Description: The low-levelobservations\n of the high-rate grazing angle GNSS-R observationsbut withthegeoreferenced\n bistatic radar parameters of the satellite receiver,specular reflection, and\n GNSS transmitter included. Data format and content: grzRfl*.nc L1B file contains\n the georeferenced grazing angle GNSS-R data collected by Spire GNSS-RO satellites,\n including the low-level observables and bistatic radar geometries (e.g., receiver,\n specular reflection, and the transmitter locations). Application: Sea surface\n and sea ice height extent, and classification Name: GNSS-R calibrated bistatic\n radar reflectivities Description: Higher level product used to derive land-surface\n reflectivity. Data format and content: gbrRfl*.nc L1A along-track calibrated\n relative power between reflected and direct signals (e.g., bistatic radar\n reflectivities) measured by Spire conventional GNSS-R satellites. Application:\n GNSS-R studies, soil moisture, ocean wind, and sea ice applications Name:\n GNSS-R calibrated bistatic radar cross-sections Description: Higher level\n product used to derive ocean surface roughness products. Data format and content:\n gbrRCS*.nc L1B along-track calibrated and normalized bistatic radar cross-sections\n measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies,\n ocean wind and sea ice applications Name: Combined Surface Soil Moisture\n Description: Combined CYGNSS and SMAP soil moisture data are provided as a\n combined surface soil moisture (COMB-SSM) product in two data level formats:\n L2U1 and L3U1. 6 x 6 km grid cell. L-band measurements of surface soil moisture\n benefit from better vegetation penetration in comparison to traditional C-band\n measurements. Data format and content: COMB-SSM.nc This file contains the\n combined data product containing measurements from both CYGNSS and SMAP reported\n on a 6 km global Equi7Grid grid. Application: Agriculture, crop insurance,\n farming solutions, climatology, terrain awareness, peatlands and wetlands\n monitoring etc. Name: Ionosphere total electron content Description: Spire\n routinely collects and processes a large volume of total electron content\n (TEC) data, representing the line-of-sight integration of electron density\n between a Spire satellite and a GNSS satellite. Each file contains line-of-sight\n ionospheric total electron content (TEC) estimates derived for a \\\\u2018single\n viewing arc\\\\u2019 contained in the POD observation file. Viewing arcs are\n at least 10 minutes in duration. Data format and content: podTec*.nc This\n file contains the line-of-sight total electron content with associated orbital\n information. Application: Space weather research, tsunamigenic earthquakes,\n weather applications, space situational awareness (SSA), autonomous vehicles\n etc Name: Ionosphere scintillation Description: The scintillation index for\n each GNSS frequency is computed onboard the spacecraft. This index provides\n a measure of the fluctuations of the GNSS signal over the course of 10 seconds\n caused by propagation of the radio signals through electron density irregularities\n in the ionosphere. After the raw indices are downlinked to the ground, they\n are packaged along with associated metadata such as orbit position to create\n the final scintillation data product. Data format and content: scnLv1*.nc\n This file contains on-board computed scintillation data (S4 only) with associated\n orbital information Application: Space weather research, solar events, TIDs,\n weather applications positioning and navigation, communications etc. Name:\n Electron density profile Description: Electron density profiles are retrieved\n as a function of altitude. Electron density profiles are processed from podTec\n netcdf files, which span a sufficient elevation angle range. A standard Abel\n inversion algorithm is applied to retrieve the profiles. Data format and content:\n ionPrf*.nc This file contains electron density profile retrieved from podTec\n files spanning appropriate elevation angle range Application: Space weather\n research, solar events, TIDs, weather applications positioning and navigation,\n communications. The products are available as part of the Spire provision\n with worldwide coverage. All details about the data provision, data access\n conditions and quota assignment procedure are described in the Terms of Applicability\n (https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639).\\\",\\n\n \\ \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\": \\\"Spire live and historical\n data\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\": {\\n \\\"metadata_ogc_17_069r3\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/Spire.live.and.historical.data\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/vnd.iso.19139%2Bxml\\\",\\n\n 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\\\"Sun-synchronous\\\",\\n\n \\ \\\"Very High Resolution - VHR (0 - 5m)\\\",\\n \\\"High Resolution\n - HR (5 - 20 m)\\\",\\n \\\"832 km for SPOT-1 to 5, 694 km for SPOT-6 and\n 7\\\",\\n \\\"60 km\\\",\\n \\\"Cameras\\\",\\n \\\"SPOT 6\\\",\\n \\\"SPOT\n 7\\\",\\n \\\"NAOMI\\\"\\n ],\\n \\\"created\\\": \\\"2019-07-16T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The SPOT 6 and 7 satellites ensure data continuity\n with the no longer operational SPOT 5 satellite and provide an archive of\n very high resolution optical acquisition as well as the possibility to task\n the satellites for new acquisitions. 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freeboard\\\",\\n \\\"altimeter\\\",\\n \\\"Oceans\\\",\\n\n \\ \\\"EARTH SCIENCE > OCEANS\\\",\\n \\\"Snow and Ice\\\",\\n \\\"EARTH\n SCIENCE > CRYOSPHERE > SNOW/ICE\\\",\\n \\\"EARTH SCIENCE > TERRESTRIAL\n HYDROSPHERE > SNOW/ICE\\\",\\n \\\"Inclined, non-sun-synchronous\\\",\\n \\\"FDM\\\",\\n\n \\ \\\"LRM\\\",\\n \\\"SAR\\\",\\n \\\"SARIn\\\",\\n \\\"GRD\\\",\\n\n \\ \\\"GOP\\\",\\n \\\"IOP\\\",\\n \\\"Radar Altimeters\\\",\\n \\\"CryoSat-2\\\",\\n\n \\ \\\"SIRAL\\\"\\n ],\\n \\\"created\\\": \\\"2019-06-04T00:00:00.000Z\\\",\\n\n \\ \\\"description\\\": \\\"CryoSat's primary payload is the SAR/Interferometric\n Radar Altimeter (SIRAL) (https://earth.esa.int/eogateway/instruments/siral)\n which has extended capabilities to meet the measurement requirements for ice-sheet\n elevation and sea-ice freeboard. CryoSat also carries three star trackers\n for measuring the orientation of the baseline. In addition, a radio receiver\n called Doppler Orbit and Radio Positioning Integration by Satellite (DORIS)\n and a small laser retroreflector ensures that CryoSat's position will be accurately\n tracked. More detailed information on CryoSat instruments is available on\n the CryoSat mission page. The following CryoSat datasets are available and\n distributed to registered users: Level 1B and L2 Ice products: FDM, LRM,\n SAR and SARIn Consolidated Level 2 (GDR): (LRM+SAR+SARIN) consolidated ice\n products over an orbit Intermediate Level 2 Ice products: LRM, SAR and SARIn\n L1b and L2 Ocean Products: GOP and IOP CryoTEMPO EOLIS Point Products CryoTEMPO\n EOLIS Gridded Products Detailed information concerning each of the above\n datasets is available in the CryoSat Products Overview (https://earth.esa.int/eogateway/missions/cryosat/products)\n and in the news item: CryoSat Ocean Products now open to scientific community\n (https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/cryosat/news/-/asset_publisher/47bD/content/cryosat-ocean-products-now-open-to-scientific-community).\n \\ CryoSat Level 1B altimetric products contain time and geo-location information\n as well as SIRAL measurements in engineering units. Calibration corrections\n are included and have been applied to the window delay computations. In Offline\n products, geophysical corrections are computed from Analysis Auxiliary Data\n Files (ADFs), whereas in FDM products corrections are computed for Forecast\n ADFs. All corrections are included in the data products and therefore the\n range can be calculated by taking into account the surface type. The Offline\n Level 2 LRM, SAR and SARIn ice altimetric products are generated 30 days after\n data acquisition and are principally dedicated to glaciologists working on\n sea-ice and land-ice areas. The Level 2 FDM products are near-real time ocean\n products, generated 2-3 hours after data acquisition, and fulfill the needs\n of some ocean operational services. Level 2 products contain the time of measurement,\n the geo-location and the height of the surface. IOP and GOP are outputs of\n the CryoSat Ocean Processor. These products are dedicated to the study of\n ocean surfaces, and provided specifically for the needs of the oceanographic\n community. IOP are generated 2-3 days after data sensing acquisition and use\n the DORIS Preliminary Orbit. GOP are typically generated 30 days after data\n sensing acquisition and use the DORIS Precise Orbit. Geophysical corrections\n are computed from the Analysis ADFs, however following the oceanographic convention\n the corrections are available but not directly applied to the range (as for\n FDM). The CryoSat ThEMatic PrOducts (Cryo-TEMPO) projects aim to deliver\n a new paradigm of simplified, harmonized, and agile CryoSat-2 products, that\n are easily accessible to new communities of non-altimeter experts and end\n users. The Cryo-TEMPO datasets include dedicated products over five thematic\n areas, covering Sea Ice, Land Ice, Polar Ocean, Coastal Ocean and Inland Water,\n together with a novel SWATH product (CryoTEMPO-EOLIS) that exploits CryoSat's\n SARIn mode over ice sheet margins. The standard Cryo-TEMPO products include\n fully-traceable uncertainties and use rapidly evolving, state-of-the-art processing\n dedicated to each thematic area. Throughout the project, the products will\n be constantly evolved, and validated by a group of Thematic Users, thus ensuring\n optimal relevance and impact for the intended target communities. More information\n on the Cryo-TEMPO products can be found on the Project Website (http://cryosat.mssl.ucl.ac.uk/tempo/index.html).\n The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel\n Swath processing technique to deliver increased spatial and temporal coverage\n of time-dependent elevation over land ice, a critical metric for tracking\n ice mass trends in support to a wide variety of end-users. The CryoTEMPO-EOLIS\n swath product exploits CryoSat's SARIn mode and the novel Swath processing\n technique to deliver increased spatial and temporal coverage of time-dependent\n elevation over land ice, a critical metric for tracking ice mass trends in\n support to a wide variety of end-users.The dataset consists of systematic\n reprocessing of the entire CryoSat archive to generate new L2-Swath products,\n increasing data sampling by 1 to 2 orders of magnitude compared with the operational\n L2 ESA product. 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The dataset regularly\n grows as ESA collects new SkySat products. Two different product types are\n offered, Ground Sampling Distance at nadir up to 65 cm for PAN and up to 0.8m\n for MS EO-SIP Product Type Product description Content SSC_DEF_SC Basic and\n Ortho scene * Level 1B 4-bands Analytic /DN Basic scene * Level 1B 4-bands\n Panchromatic /DN Basic scene * Level 1A 1-band Panchromatic DN Pre Sup resolution\n Basic scene * Level 3B 3-bands Visual Ortho Scene * Level 3B 4-bands Pansharpened\n Multispectral Ortho Scene * Level 3B 4-bands Analytic/DN/SR Ortho Scene *\n Level 3B 1-band Panchromatic /DN Ortho Scene SSC_DEF_CO Ortho Collect * Visual\n 3-band Pansharpened Image * Multispectral 4-band Pansharpened Image * Multispectral\n 4-band Analytic/DN/SR Image (B, G, R,N) * 1-band Panchromatic Image The\n Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere\n or for any geometric distortions inherent in the imaging process \\\\u2022Analytic\n - unorthorectified, radiometrically corrected, multispectral BGRN \\\\u2022Analytic\n DN - unorthorectified, multispectral BGRN \\\\u2022Panchromatic - unorthorectified,\n radiometrically corrected, panchromatic (PAN) \\\\u2022Panchromatic DN - unorthorectified,\n panchromatic (PAN) \\\\u2022L1A Panchromatic DN - unorthorectified, pre-super\n resolution, panchromatic (PAN) The Ortho Scene product is sensor and geometrically\n corrected, and is projected to a cartographic map projection. \\\\u2022Visual\n - orthorectified, pansharpened, and color-corrected (using a color curve)\n 3-band RGB Imagery \\\\u2022Pansharpened Multispectral - orthorectified, pansharpened\n 4-band BGRN Imagery \\\\u2022Analytic SR - orthorectified, multispectral BGRN.\n Atmospherically corrected Surface Reflectance product. \\\\u2022Analytic - orthorectified,\n multispectral BGRN. 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km\\\",\\n \\\"Imaging\n Spectrometers/Radiometers\\\",\\n \\\"Landsat-8\\\",\\n \\\"OLI\\\",\\n \\\"TIRS\\\"\\n\n \\ ],\\n \\\"created\\\": \\\"2022-01-14T00:00:00.000Z\\\",\\n \\\"description\\\":\n \\\"This dataset contains the European Coverage of Landsat 8 Collection 2 data,\n both Level 1 and Level 2, since the beginning of the mission. Landsat 8 Collection\n 2 is the result of reprocessing effort on the archive and on fresh products\n with significant improvement with respect to Collection 1 on data quality,\n obtained by means of advancements in data processing, algorithm development.\n The primary characteristic is a relevant improvement in the absolute geolocation\n accuracy (now re-baselined to the European Space Agency Copernicus Sentinel-2\n Global Reference Image, GRI) but includes also updated digital elevation modelling\n sources, improved Radiometric Calibration (even correction for the TIRS striping\n effect), enhanced Quality Assessment Bands, updated and consistent metadata\n files, usage of Cloud Optimized Georeferenced (COG) Tagged Image File Format.\n \\ Landsat 8 level 1 products combine data from the 2 Landsat instruments,\n OLI and TIRS. The level 1 products generated can be either L1TP or L1GT: \\\\u2022\n L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically\n calibrated and orthorectified using ground control points (GCPs) and digital\n elevation model (DEM) data to correct for relief displacement. The highest\n quality Level-1 products suitable for pixel-level time series analysis. GCPs\n used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000)\n data set. \\\\u2022 L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products:\n L1GT data products consist of L0 product data with systematic radiometric,\n geometric and terrain corrections applied and resampled for registration to\n a cartographic projection, referenced to the WGS84, G873, or current version.\n The dissemination server contains three different classes of Level1 products\n \\\\u2022 Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed\n upon downlink but use an initial TIRS line-of-sight model parameters; the\n data is made available in less than 12 hours (4-6 hours typically). Once the\n data have been reprocessed with the refined TIRS parameters, the products\n are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time\n tier (in 14-16 days). \\\\u2022 Tier 1 (T1): Landsat scenes with the highest\n available data quality are placed into Tier 1 and are considered suitable\n for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP)\n corrected data that have well-characterized radiometry and are inter-calibrated\n across the different Landsat instruments. The georegistration of Tier 1 scenes\n is consistent and within prescribed image-to-image tolerances of \\u2266 12-meter\n radial root mean square error (RMSE). \\\\u2022 Tier 2 (T2): Landsat scenes\n not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier\n 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do\n not meet the Tier 1 geometry specification due to less accurate orbital information\n (specific to older Landsat sensors), significant cloud cover, insufficient\n ground control, or other factors. This includes Systematic Terrain (L1GT)\n and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated\n from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith\n Angle constraint and include the required auxiliary data inputs to generate\n a scientifically viable product. The data are available a couple of days after\n the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: \\\\u2022\n L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance\n (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients\n file, and Quality Assessment (QA) Bands. \\\\u2022 L2SR - Level 2 Surface Reflectance\n (L2SR) products: include Surface Reflectance (SR), an angle coefficients file,\n and Quality Assessment (QA) Bands; it is generated if ST could not be generated\n \\ Two different categories of Level 1 products are offered: LC with Optical,\n Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal\n not available). 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0.75\n \\xB5m)\\\",\\n \\\"NIR (0.75 - 1.30 \\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"Very High Resolution - VHR (0 - 5m)\\\",\\n \\\"475 km (reference)\\\",\\n\n \\ \\\"25 km\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"PlanetScope\\\",\\n\n \\ \\\"PlanetScope Camera\\\"\\n ],\\n \\\"created\\\": \\\"2019-08-09T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The PlanetScope Level 1B Basic Scene and Level 3B\n Ortho Scene full archive products are available as part of Planet imagery\n offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR)\n product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected\n product, without correction for any geometric distortions inherent in the\n imaging processes and is not mapped to a cartographic projection. The imagery\n data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification\n by the user. This kind of product is designed for users with advanced image\n processing and geometric correction capabilities. Basic Scene Product\n Components and Format Product Components:\\\\t - Image File (GeoTIFF format)\n - Metadata File (XML format) - Rational Polynomial Coefficients (XML format)\n - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format)\n - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral\n image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green\n I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance:\\\\t\n - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R:\n 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified\n assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and\n geometrically- corrected and is projected to a UTM/WGS84 cartographic map\n projection. The geometric correction uses fine Digital Elevation Models (DEMs)\n with a post spacing of between 30 and 90 metres. Ortho Scene Product Components\n and Format Product Components:\\\\t - Image File (GeoTIFF format) - Metadata\n File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM\n File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands:\n 3-band natural color (red, green, blue) or 4-band multispectral image (blue,\n green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow,\n red, RedEdge, near-infrared) Ground Sampling Distance:\\\\t - Approximate, satellite\n altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove:\n 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho\n Scene product is available in the following: PlanetScope Visual Ortho Scene\n product is orthorectified and color-corrected (using a colour curve) 3-band\n RGB Imagery. This correction attempts to optimise colours as seen by the human\n eye providing images as they would look if viewed from the perspective of\n the satellite PlanetScope Surface Reflectance product is orthorectified,\n 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge,\n NIR Imagery with geometric, radiometric and corrected for surface reflection.\n This data is optimal for value-added image processing such as land cover classifications.\n \\ PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified,\n 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge,\n NIR Imagery with geometric, radiometric and calibrated to top of atmosphere\n radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/\n can be accessed (Planet registration requested) to discover and check the\n data readiness. It is worth to mention that the data distribution is based\n on Data Voucher, corresponding to maximum amount of square kilometers can\n be ordered and downloaded by the project in a maximum period of 15 moths (this\n duration cannot be extended) starting from the project proposal acceptance\n date. Each Date Voucher includes PlanetScope tile view streaming access for\n a total of 20,000 tiles per calendar month during the project period. All\n details about the data provision, data access conditions and quota assignment\n procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf\n available in Resources section.\\\",\\n \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\":\n \\\"PlanetScope Full Archive\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\":\n {\\n \\\"metadata_ogc_17_069r3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/PlanetScope.Full.Archive\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": 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wind components. 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The L2C consists of extra datasets appended to the\n L2B product with information which are relevant to the data assimilation of\n the L2B winds.\\\",\\n \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\": \\\"Aeolus\n Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction\n assimilation processing\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\":\n {\\n \\\"metadata_ogc_17_069r3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19139%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19139_2\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19139-2%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139-2 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139-2+xml\\\"\\n\n \\ },\\n \\\"metadata_dif_10\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/dif10%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"DIF-10 metadata\\\",\\n \\\"type\\\": \\\"application/dif10+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19115_3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19115-3%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19115-3 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19115-3+xml\\\"\\n\n \\ },\\n \\\"metadata_ogc_17_084r1\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?mode=owc\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-084r1 metadata\\\",\\n \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\\\\\"\\\"\\n\n \\ },\\n \\\"metadata_html\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/html\\\",\\n\n \\ \\\"title\\\": \\\"HTML\\\",\\n \\\"type\\\": \\\"text/html\\\"\\n }\\n\n \\ },\\n \\\"links\\\": [\\n {\\n \\\"rel\\\": \\\"self\\\",\\n \\\"href\\\":\n \\\"https://emc.spacebel.be/collections/L2C_Wind_products\\\",\\n \\\"type\\\":\n \\\"application/json\\\"\\n },\\n {\\n \\\"rel\\\": \\\"root\\\",\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n \\\"type\\\": \\\"application/json\\\",\\n\n \\ \\\"title\\\": \\\"FedEO Clearinghouse\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"parent\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n \\\"title\\\":\n \\\"collections\\\",\\n \\\"type\\\": \\\"application/json\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Level2-Algorithm-Theoretical-Basis-Document.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Aeolus L2B\n Algorithm Theoretical Baseline Document - Level 2B processor algorithm description\\\"\\n\n \\ },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\":\n \\\"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-L2B-2C-Input-Output-DD-ICD.pdf/2321b6a0-27d0-b628-771e-fcca4928bc32\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Aeolus L2B/2C\n Processor Input/Output Data Definitions Interface Control Document - Level\n 2B/2C data definition\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Sensor-and-Product-Description.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Aeolus Sensor\n and Product Description - Aeolus Sensor and Product Description\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://esatellus.service-now.com/csp?id=esa_simple_request\\\",\\n\n \\ \\\"type\\\": \\\"text/html\\\",\\n \\\"title\\\": \\\"Get Help? - ESA\n Earth Observation User Services Portal\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/atom%2Bxml\\\",\\n\n \\ \\\"type\\\": \\\"application/atom+xml\\\",\\n \\\"title\\\": \\\"Atom\n format\\\"\\n },\\n {\\n \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\":\n \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/xml\\\",\\n\n \\ \\\"type\\\": \\\"application/xml\\\",\\n \\\"title\\\": \\\"Dublin Core\n metadata\\\"\\n },\\n {\\n \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\":\n \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/ld%2Bjson;profile=https://schema.org\\\",\\n\n \\ \\\"type\\\": \\\"application/ld+json;profile=\\\\\\\"https://schema.org\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"JSON-LD (schema.org) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\\\",\\n\n \\ \\\"type\\\": \\\"application/ld+json;profile=\\\\\\\"http://data.europa.eu/930/\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"JSON-LD (GeoDCAT-AP) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/rdf%2Bxml;profile=https://schema.org\\\",\\n\n \\ \\\"type\\\": \\\"application/rdf+xml;profile=\\\\\\\"https://schema.org\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"RDF/XML (schema.org) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\\\",\\n\n \\ \\\"type\\\": \\\"application/rdf+xml;profile=\\\\\\\"http://data.europa.eu/930/\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"RDF/XML (GeoDCAT-AP) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/turtle;profile=https://schema.org\\\",\\n\n \\ \\\"type\\\": \\\"text/turtle;profile=\\\\\\\"https://schema.org\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"Turtle (schema.org) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/turtle;profile=http://data.europa.eu/930/\\\",\\n\n \\ \\\"type\\\": \\\"text/turtle;profile=\\\\\\\"http://data.europa.eu/930/\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"Turtle (GeoDCAT-AP) metadata\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\\\",\\n\n \\ \\\"title\\\": \\\"More collections for ESA/ESRIN\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/eo:platform/Aeolus\\\",\\n\n \\ \\\"title\\\": \\\"More collections for Aeolus platform\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/concepts/instruments/a032cde6-16e3-5940-988e-f6d79870d14c\\\",\\n\n \\ \\\"title\\\": \\\"More collections for ALADIN instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"L2C_Wind_products\\\",\\n \\\"updated\\\": \\\"2023-06-13T13:23:39Z\\\",\\n\n \\ \\\"providers\\\": [\\n {\\n \\\"roles\\\": [\\\"producer\\\"],\\n\n \\ \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"http://www.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\\"ALADIN\\\"],\\n\n \\ \\\"platform\\\": [\\\"Aeolus\\\"]\\n }\\n },\\n {\\n \\\"extent\\\":\n {\\n \\\"spatial\\\": {\\\"bbox\\\": [[\\n -180,\\n -84,\\n 180,\\n\n \\ 84\\n ]]},\\n \\\"temporal\\\": {\\\"interval\\\": [[\\n \\\"2013-11-13T00:00:00.000Z\\\",\\n\n \\ null\\n ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n\n \\ \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"Planet\\\",\\n \\\"Land\n Surface\\\",\\n \\\"Mapping and Cartography\\\",\\n \\\"Natural Hazards\n and Disaster Risk\\\",\\n \\\"EARTH SCIENCE > LAND SURFACE\\\",\\n \\\"EARTH\n SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS\\\",\\n \\\"VIS (0.40 - 0.75\n \\xB5m)\\\",\\n \\\"NIR (0.75 - 1.30 \\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"Very High Resolution - VHR (0 - 5m)\\\",\\n \\\"475-575 km\\\",\\n\n \\ \\\"8 km at nadir for SkySat-1 to -2, 6.6 km at nadir for SkySat-3 to\n -13\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"SkySat\\\",\\n\n \\ \\\"SkySat Camera\\\"\\n ],\\n \\\"created\\\": \\\"2019-08-09T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The SkySat Level 2B Basic Scene, Level 3B Ortho Scene\n and Level 3B Consolidated full archive and new tasking products are available\n as part of Planet imagery offer. The SkySat Basic Scene product is uncalibrated\n and in a raw digital number format, not corrected for any geometric distortions\n inherent in the imaging process. Rational Polynomial Coefficients (RPCs) is\n provided to enable orthorectification by the user. Basic Scene Product Components\n and Format Processing levels\\\\t/\\\\t- Analytic (unorthorectified, radiometrically\n corrected, multispectral BGRN); - Analytic DN (unorthorectified, multispectral\n BGRN); - Panchromatic DN (unorthorectified, panchromatic)\\\\t// Product Components\n and Format\\\\t/\\\\t- Image File (GeoTIFF format); - Metadata File (JSON format);\n - Rational Polynomial Coefficients (Text File); - UDM File (GeoTIFF format)\\\\t//\n Image configuration\\\\t/\\\\t- 4-band Analytic DN Image (Blue, Green, Red, NIR);\n - 1-band Panchromatic DN Image (Pan)\\\\t// Ground Sampling Distance\\\\t/\\\\t3.7\n m at nadir (average at reference altitude 475 km)\\\\t// Ground Sampling Distance\n (nadir)\\\\t/\\\\t- Panchromatic 0.86m and Multispectral 1.0m for SkySat-1&2;\n - Panchromatic 0.65m and Multispectral 0.8m for SkySat-3 to 13 (0.72 m and\n 1.0m for data acquired before 30/06/2020)\\\\t// Accuracy\\\\t/\\\\t<50 m RMSE\\\\t//\n \\ The SkySat Ortho Scene is sensor- and geometrically-corrected (by using\n DEMs with a post spacing of between 30 and 90 meters) and is projected to\n a cartographic map projection; the accuracy of the product will vary from\n region to region based on available GCPs. Different products are available:\n \\ - The SkySat Visual Ortho Scene product is orthorectified, pansharpened,\n and color-corrected (using a color curve) 3-band RGB Imagery - The SkySat\n Pansharpened Multispectral Scene product is orthorectified, pansharpened 4-band\n BGRN Imagery - The SkySat Analytic DN Ortho Scene product is orthorectified,\n multispectral BGRN, uncalibrated, digital number imagery product. The product\n has been processed to remove distortions caused by terrain; It eliminates\n the perspective effect on the ground (not on buildings), restoring the geometry\n of a vertical shot. Transformation to at-sensor radiance is not included -\n The SkySat Panchromatic DN Ortho Scene product is orthorectified, panchromatic,\n uncalibrated, digital number imagery product. It has a finer GSD than the\n Analytic Product. Transformation to at-sensor radiance is not included. -\n The SkySat Analytic Ortho Scene are calibrated multispectral imagery products\n with radiometric corrections applied to correct for any sensor artifacts and\n transformation to top-of-atmosphere radiance. - The SkySat Consolidated Product\n are Ortho Collect product created by composing ~60 SkySat Ortho Scenes (Visual,\n Pansharpened Multispectral, Analytic DN, Panchromatic DN) along an imaging\n strip into segments. Ortho Scene Product Components and Format \\\\t/\\\\tVisual\n Ortho\\\\t/\\\\tPansharpened Multispectral\\\\t/\\\\tAnalytic DN Ortho\\\\t/\\\\tPanchromatic\n DN Ortho\\\\t/\\\\tAnalytic Ortho\\\\t// Product Components and Format\\\\t/\\\\tImage\n File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients\n (Text File); UDM File (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF); Metadata\n File (JSON format); Rational Polynomial Coefficients (Text File); UDM File\n (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF format); Metadata File (JSON format);\n Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format);\\\\t/\\\\tImage\n File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients\n (Text File); UDM File (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF format);\n Metadata File (JSON format); Rational Polynomial Coefficients (Text File);\n UDM File (GeoTIFF format)\\\\t// Image configuration\\\\t/\\\\t3-band Pansharpened\n Image (PS Red, PS Green, PS Blue)\\\\t/\\\\t4-band Pansharpened Image (PS Blue,\n PS Green, PS Red, PS NIR)\\\\t/\\\\t4-band Analytic DN Image (B, G, R, N)\\\\t/\\\\t1-band\n Panchromatic Image\\\\t/\\\\t4-band Analytic Image (B, G, R, N)\\\\t// Ground Sampling\n Distance\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t//\n Projection\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM\n WGS84\\\\t// Accuracy\\\\t/\\\\t<10 m RMSE\\\\t/\\\\t<10 m RMSE\\\\t/\\\\t<10 m\n RMSE\\\\t/\\\\t<10 m RMSE\\\\t/\\\\tradiometric accuracy: +/- 5% Relative accuracy\n at < 10 degrees off-nadir angle\\\\t// The products are available as part\n of the Planet provision from RapidEye, Skysat and PlanetScope constellations.\n SkySat collection has worldwide coverage: The Planet Explorer Catalogue can\n be accessed (Planet registration requested) to discover and check the data\n readiness. All details about the data provision, data access conditions and\n quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf).\\\",\\n\n \\ \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\": \\\"SkySat Full Archive and\n New Tasking\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\": {\\n \\\"metadata_ogc_17_069r3\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19139%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19139_2\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19139-2%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139-2 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139-2+xml\\\"\\n\n \\ },\\n \\\"metadata_dif_10\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/dif10%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"DIF-10 metadata\\\",\\n \\\"type\\\": \\\"application/dif10+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19115_3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19115-3%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19115-3 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19115-3+xml\\\"\\n\n \\ },\\n \\\"metadata_ogc_17_084r1\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?mode=owc\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-084r1 metadata\\\",\\n \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\\\\\"\\\"\\n\n \\ },\\n \\\"metadata_html\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=text/html\\\",\\n\n \\ \\\"title\\\": \\\"HTML\\\",\\n \\\"type\\\": \\\"text/html\\\"\\n }\\n\n \\ },\\n \\\"links\\\": [\\n {\\n \\\"rel\\\": \\\"self\\\",\\n \\\"href\\\":\n \\\"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking\\\",\\n\n \\ \\\"type\\\": \\\"application/json\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"root\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n \\\"type\\\":\n \\\"application/json\\\",\\n \\\"title\\\": \\\"FedEO Clearinghouse\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"parent\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n\n \\ \\\"title\\\": \\\"collections\\\",\\n \\\"type\\\": \\\"application/json\\\"\\n\n \\ },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\":\n \\\"https://www.planet.com/products/#satellite-imagery\\\",\\n \\\"type\\\":\n \\\"text/html\\\",\\n \\\"title\\\": \\\"Planet products - Web site\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/Planet-combined-imagery-product-specs-2020.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Planet Imagery\n Product Specifications - Product Specifications\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://developers.planet.com/docs/apps/explorer/\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Planet Explorer\n User Guide - User Manual\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/SkySat-tasking-request-form.pdf/075f845e-13bd-36e2-4743-3aeb2053c61c\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"SkySat Tasking\n Request Form - Access guide\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf/91fdeed4-2efe-1bed-8058-1dddb6e14fa6\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"Planet Terms\n of Applicability - 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0.75 \\xC2\\xB5m)\\\",\\n \\\"NIR\n (0.75 - 1.30 \\xC2\\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"Medium\n Resolution - MR (20 - 500 m)\\\",\\n \\\"663 km\\\",\\n \\\"625 km\\\",\\n\n \\ \\\"Cameras\\\",\\n \\\"GEOSAT-1\\\",\\n \\\"SLIM6\\\"\\n ],\\n\n \\ \\\"created\\\": \\\"2019-05-22T00:00:00.00Z\\\",\\n \\\"description\\\": \\\"GEOSAT-1\n full archive and new tasking products are available at 22 m resolution in\n two processing levels.\\\\r\\\\rL1R (Basic Geopositioned): All 3 spectral channels\n combined into a band-registered image. 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collections for GEOSAT-1 platform\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/concepts/instruments/5cc6eaab-0ddc-572f-a1b7-afa52ea5333d\\\",\\n\n \\ \\\"title\\\": \\\"More collections for SLIM6 instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"Geosat-1.Full.archive.and.tasking\\\",\\n \\\"updated\\\":\n \\\"2023-06-13T13:23:40Z\\\",\\n \\\"providers\\\": [\\n {\\n \\\"roles\\\":\n [\\\"producer\\\"],\\n \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"https://earth.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\\"SLIM6\\\"],\\n\n \\ \\\"platform\\\": [\\\"GEOSAT-1\\\"]\\n }\\n },\\n {\\n \\\"extent\\\":\n {\\n \\\"spatial\\\": {\\\"bbox\\\": [[\\n -180,\\n -90,\\n 180,\\n\n \\ 90\\n ]]},\\n \\\"temporal\\\": {\\\"interval\\\": [[\\n \\\"2016-06-01T00:00:00.000Z\\\",\\n\n \\ null\\n ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n\n \\ \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"GNSS\\\",\\n \\\"AIS\\\",\\n\n \\ \\\"ADS-B\\\",\\n \\\"Atmosphere\\\",\\n \\\"Ionosphere and Magnetosphere\n Dynamics\\\",\\n \\\"Weather Events\\\",\\n \\\"Oceans\\\",\\n \\\"EARTH\n SCIENCE > ATMOSPHERE\\\",\\n \\\"EARTH SCIENCE > SUN-EARTH INTERACTIONS\n > IONOSPHERE/MAGNETOSPHERE DYNAMICS\\\",\\n \\\"EARTH SCIENCE > ATMOSPHERE\n > WEATHER EVENTS\\\",\\n \\\"EARTH SCIENCE > OCEANS\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"500 km\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"Spire\\\",\\n\n \\ \\\"AIRSAFE\\\",\\n \\\"SENSE\\\",\\n \\\"STRATOS\\\"\\n ],\\n\n \\ \\\"created\\\": \\\"2020-09-04T00:00:00.00Z\\\",\\n \\\"description\\\": \\\"The\n data collected by Spire from it's 110 satellites launched into Low Earth\n Orbit (LEO) has a diverse range of applications, from analysis of global trade\n patterns and commodity flows to aircraft routing to weather forecasting. The\n data also provides interesting research opportunities on topics as varied\n as ocean currents and GNSS-based planetary boundary layer height. The following\n products can be requested: ADS-B Data Stream Global ADS-B satellite data\n observed by Spire satellites and processed through the ground stations network.\n Historical ADS-B data older than 6 months can be delivered as data cuts containing\n CSV file(s) accessible through a Web Service or Cloud storage solutions. Live\n ADS-B data is available through a streaming API, and recent historical data\n can be accessed through a REST API. Data is distributed as a monthly subscription:\n historical data can be requested starting from 3 December 2008, the time period\n for live data starts from a user-defined date and continues for 30 days. AIS\n AIS messages include satellite AIS (S-AIS) as observed by Spire satellites\n and terrestrial AIS (T-AIS) from third party sensor stations (up to 40 million\n messages per day). Historical AIS data are delivered as a cvs file with availability\n back to June 2016 or via Historical API from December 2018; live AIS data\n are pushed to end users via TCP or through Messages API. Data is distributed\n as a monthly subscription, from a user-defined date and continues for a 30\n day period. GNSS-Radio Occultation GNSS Radio Occultation (GNSS-RO) measurements\n are collected globally on a continuous basis, generating profiles of the Earth\\\\u2019s\n atmosphere. Derived Level 1 and Level 2 products include both atmospheric\n and ionospheric products. Historical data for most of the GNSS-RO products\n are available from December 2018 to the present. Near real-time (within 90\n minutes or less latency from collection to delivery) GNSS-RO profiles are\n also available upon request. GNSS Reflectometry GNSS Reflectometry (GNSS-R)\n is a technique to measure Earth\\\\u2019s surface properties using reflections\n of GNSS signals in the form of a bistatic radar. Spire collects two types\n of GNSS-R data: conventional, near-nadir incidence LHCP reflections collected\n by the Spire GNSS-R satellites (e.g., Spire GNSS-R \\\\u201cBatch-1\\\\u201d satellites)\n and grazing angle (i.e., low elevation angle) RHCP reflections collected by\n the Spire GNSS-RO satellites. Derived Level 1 and Level 2 products are available,\n as well as some special Level 0 raw intermediate frequency (IF) data. Historical\n grazing angle GNSS-R data are available from May 2019 to the present, while\n conventional GNSS-R data are available from December 2020 to the present.\n \\ Name: Automatic Identification System (AIS) Description: The automatic identification\n system (AIS) is an automatic tracking system that uses transponders on ships\n and is used by vessel traffic services. Spire data includes satellite AIS\n (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third\n party sensor stations. Data format and content: .parquet.gz files The AIS\n files contain time-series data on received AIS messages, both the raw NMEA\n message and added post-processing data for each message. Application: Supply\n chain analysis, commodity trading, identification of illegal fishing or dark\n targets, ship route and fuel use optimization, analysis of global trade patterns,\n anti-piracy, autonomous vessel software, ocean currents. Name: Automatic\n Dependent Surveillance-Broadcast (ADS-B) Description: Spire AirSafe ADS-B\n products give access to satellite and terrestrial ADS-B data from captured\n aircrafts. Data format and content: .csv.gz files The decompressed csv file\n contains a list of hexadecimal representations of ADS-B messages associated\n with the timestamp they were received on the satellite. Application: Fleet\n management, ICAO regulatory compliance, route optimization, predictive maintenance,\n global airspace, domain awareness. Name: Global Navigation Satellite System\n Radio Occultation (GNSS-RO) Description: GNSS atmospheric radio occultation\n (GNSS-RO) relies on the detection of a change in a radio signal as it passes\n through a planet's atmosphere, i.e. as it is refracted by the atmosphere.\n This data set contains precise orbit determination (POD) solutions, satellite\n attitude information, high-rate occultation observations, excess phase, and\n derived atmospheric dry temperature profiles. Data format and content: podObs*.rnx\n This file contains raw pseudorange, carrier phase, Doppler frequency, and\n signal-to-noise measurements for each observed GPS signal from a single Spire\n satellite which allow to estimate the positions and velocities of each Spire\n satellite and also used to derive ionospheric total electron content data.\n leoOrb*.sp3 This file contains the estimated position, velocity and receiver\n clock error of a given Spire satellite after processing of the POD observation\n file leoAtt*.log It contains 1 Hz rate quaternion information measured from\n a single Spire satellite describing the satellite orientation. opnGns*ro.bin,\n opnGns*rst.bin these files contain raw measurements from the occulting GNSS\n satellite (one for each signal frequency) and raw phase data from one or more\n reference GNSS satellites. atmPhs* The file contains occultation excess phase\n delay. Also contains SNR values, ransmitter and receiver positions and open\n loop model information. atmPrf*.nc The file contains profiles of atmospheric\n dry pressure, dry temperature and neutral refractivity as a function of altitude\n produced from full processing of one occultation event. bfrPrf*.bufr The file\n contains derived profiles of dry pressure, dry temperature, refractivity and\n bending angle for each occultation. Application:\\\\tAtmospheric profiles of\n pressure, dry temperature, bending angle, and refractivity used in numerical\n weather prediction data assimilation and climate change studies. Name: Raw\n IF samples from GNSS-RO satellites Description: Raw intermediate frequency\n (IF) sampled data (I/Q) from the GNSS receiver front-end of GNSS-RO satellites.\n Data format and content: rocRIF*.zip Binary raw IF data and associated ancillary\n data (e.g., POD data) in a zip archive per collection event. Application:\n GNSS-RO studies, GNSS RFI and jamming monitoring, research. Name: Raw IF\n samples from GNSS-R satellites Description: Raw intermediate frequency (IF)\n sampled data (I/Q) from the GNSS receiver front-end of conventional GNSS-R\n satellites. Data format and content: gbrRIF*.zip Binary raw IF data and associated\n ancillary data (e.g., POD data) in a zip archive per collection event. Application:\n GNSS-R studies, GNSS RFI and jamming monitoring, research, etc. Name: Grazing\n angle GNSS-R observations Description: During grazing angle GNSS-R events,\n signal reflection at two frequencies is observed through the limb-facing antenna\n and is trackedusing an open-loop tracking technique thatrelies on a model\n topredict the propagationdelay and Doppler of thereflected signal. Simultaneous\n open-looptracking of the signaldirectly along theline-of-sight from thetransmitter\n to thereceiver is alsoperformed to provideadditional data that maybenecessary\n for signalcalibration. The mainoutput of the open-looptracking are in-phase\n (I)and quadrature (Q)accumulation samples(nominally at 50 Hz),which represent\n the residual Doppler (phase) from the model. Data format and content: grzObs*.nc\n L1A filecontains rawopen loopcarrier phasemeasurementsat 50 Hzsampling forgrazingangleGNSS-Rreflectionscaptured\n in the GNSS-RO RHC Pantennas, (bothdirect andreflectedsignals) on GNSS-RO\n satellites. Application: Sea surface and sea ice height extent, and classification.\n \\ Name: Georeferenced grazing angle GNSS-R observations Description: The low-levelobservations\n of the high-rate grazing angle GNSS-R observationsbut withthegeoreferenced\n bistatic radar parameters of the satellite receiver,specular reflection, and\n GNSS transmitter included. Data format and content: grzRfl*.nc L1B file contains\n the georeferenced grazing angle GNSS-R data collected by Spire GNSS-RO satellites,\n including the low-level observables and bistatic radar geometries (e.g., receiver,\n specular reflection, and the transmitter locations). Application: Sea surface\n and sea ice height extent, and classification Name: GNSS-R calibrated bistatic\n radar reflectivities Description: Higher level product used to derive land-surface\n reflectivity. Data format and content: gbrRfl*.nc L1A along-track calibrated\n relative power between reflected and direct signals (e.g., bistatic radar\n reflectivities) measured by Spire conventional GNSS-R satellites. Application:\n GNSS-R studies, soil moisture, ocean wind, and sea ice applications Name:\n GNSS-R calibrated bistatic radar cross-sections Description: Higher level\n product used to derive ocean surface roughness products. Data format and content:\n gbrRCS*.nc L1B along-track calibrated and normalized bistatic radar cross-sections\n measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies,\n ocean wind and sea ice applications Name: Combined Surface Soil Moisture\n Description: Combined CYGNSS and SMAP soil moisture data are provided as a\n combined surface soil moisture (COMB-SSM) product in two data level formats:\n L2U1 and L3U1. 6 x 6 km grid cell. L-band measurements of surface soil moisture\n benefit from better vegetation penetration in comparison to traditional C-band\n measurements. Data format and content: COMB-SSM.nc This file contains the\n combined data product containing measurements from both CYGNSS and SMAP reported\n on a 6 km global Equi7Grid grid. Application: Agriculture, crop insurance,\n farming solutions, climatology, terrain awareness, peatlands and wetlands\n monitoring etc. Name: Ionosphere total electron content Description: Spire\n routinely collects and processes a large volume of total electron content\n (TEC) data, representing the line-of-sight integration of electron density\n between a Spire satellite and a GNSS satellite. Each file contains line-of-sight\n ionospheric total electron content (TEC) estimates derived for a \\\\u2018single\n viewing arc\\\\u2019 contained in the POD observation file. Viewing arcs are\n at least 10 minutes in duration. Data format and content: podTec*.nc This\n file contains the line-of-sight total electron content with associated orbital\n information. Application: Space weather research, tsunamigenic earthquakes,\n weather applications, space situational awareness (SSA), autonomous vehicles\n etc Name: Ionosphere scintillation Description: The scintillation index for\n each GNSS frequency is computed onboard the spacecraft. This index provides\n a measure of the fluctuations of the GNSS signal over the course of 10 seconds\n caused by propagation of the radio signals through electron density irregularities\n in the ionosphere. After the raw indices are downlinked to the ground, they\n are packaged along with associated metadata such as orbit position to create\n the final scintillation data product. Data format and content: scnLv1*.nc\n This file contains on-board computed scintillation data (S4 only) with associated\n orbital information Application: Space weather research, solar events, TIDs,\n weather applications positioning and navigation, communications etc. Name:\n Electron density profile Description: Electron density profiles are retrieved\n as a function of altitude. Electron density profiles are processed from podTec\n netcdf files, which span a sufficient elevation angle range. A standard Abel\n inversion algorithm is applied to retrieve the profiles. Data format and content:\n ionPrf*.nc This file contains electron density profile retrieved from podTec\n files spanning appropriate elevation angle range Application: Space weather\n research, solar events, TIDs, weather applications positioning and navigation,\n communications. The products are available as part of the Spire provision\n with worldwide coverage. All details about the data provision, data access\n conditions and quota assignment procedure are described in the Terms of Applicability\n (https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639).\\\",\\n\n \\ \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\": \\\"Spire live and historical\n data\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\": {\\n \\\"metadata_ogc_17_069r3\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/Spire.live.and.historical.data\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/vnd.iso.19139%2Bxml\\\",\\n\n 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\\\"Sun-synchronous\\\",\\n\n \\ \\\"Very High Resolution - VHR (0 - 5m)\\\",\\n \\\"High Resolution\n - HR (5 - 20 m)\\\",\\n \\\"832 km for SPOT-1 to 5, 694 km for SPOT-6 and\n 7\\\",\\n \\\"60 km\\\",\\n \\\"Cameras\\\",\\n \\\"SPOT 6\\\",\\n \\\"SPOT\n 7\\\",\\n \\\"NAOMI\\\"\\n ],\\n \\\"created\\\": \\\"2019-07-16T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The SPOT 6 and 7 satellites ensure data continuity\n with the no longer operational SPOT 5 satellite and provide an archive of\n very high resolution optical acquisition as well as the possibility to task\n the satellites for new acquisitions. 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freeboard\\\",\\n \\\"altimeter\\\",\\n \\\"Oceans\\\",\\n\n \\ \\\"EARTH SCIENCE > OCEANS\\\",\\n \\\"Snow and Ice\\\",\\n \\\"EARTH\n SCIENCE > CRYOSPHERE > SNOW/ICE\\\",\\n \\\"EARTH SCIENCE > TERRESTRIAL\n HYDROSPHERE > SNOW/ICE\\\",\\n \\\"Inclined, non-sun-synchronous\\\",\\n \\\"FDM\\\",\\n\n \\ \\\"LRM\\\",\\n \\\"SAR\\\",\\n \\\"SARIn\\\",\\n \\\"GRD\\\",\\n\n \\ \\\"GOP\\\",\\n \\\"IOP\\\",\\n \\\"Radar Altimeters\\\",\\n \\\"CryoSat-2\\\",\\n\n \\ \\\"SIRAL\\\"\\n ],\\n \\\"created\\\": \\\"2019-06-04T00:00:00.000Z\\\",\\n\n \\ \\\"description\\\": \\\"CryoSat's primary payload is the SAR/Interferometric\n Radar Altimeter (SIRAL) (https://earth.esa.int/eogateway/instruments/siral)\n which has extended capabilities to meet the measurement requirements for ice-sheet\n elevation and sea-ice freeboard. CryoSat also carries three star trackers\n for measuring the orientation of the baseline. In addition, a radio receiver\n called Doppler Orbit and Radio Positioning Integration by Satellite (DORIS)\n and a small laser retroreflector ensures that CryoSat's position will be accurately\n tracked. More detailed information on CryoSat instruments is available on\n the CryoSat mission page. The following CryoSat datasets are available and\n distributed to registered users: Level 1B and L2 Ice products: FDM, LRM,\n SAR and SARIn Consolidated Level 2 (GDR): (LRM+SAR+SARIN) consolidated ice\n products over an orbit Intermediate Level 2 Ice products: LRM, SAR and SARIn\n L1b and L2 Ocean Products: GOP and IOP CryoTEMPO EOLIS Point Products CryoTEMPO\n EOLIS Gridded Products Detailed information concerning each of the above\n datasets is available in the CryoSat Products Overview (https://earth.esa.int/eogateway/missions/cryosat/products)\n and in the news item: CryoSat Ocean Products now open to scientific community\n (https://earth.esa.int/web/guest/missions/esa-operational-eo-missions/cryosat/news/-/asset_publisher/47bD/content/cryosat-ocean-products-now-open-to-scientific-community).\n \\ CryoSat Level 1B altimetric products contain time and geo-location information\n as well as SIRAL measurements in engineering units. Calibration corrections\n are included and have been applied to the window delay computations. In Offline\n products, geophysical corrections are computed from Analysis Auxiliary Data\n Files (ADFs), whereas in FDM products corrections are computed for Forecast\n ADFs. All corrections are included in the data products and therefore the\n range can be calculated by taking into account the surface type. The Offline\n Level 2 LRM, SAR and SARIn ice altimetric products are generated 30 days after\n data acquisition and are principally dedicated to glaciologists working on\n sea-ice and land-ice areas. The Level 2 FDM products are near-real time ocean\n products, generated 2-3 hours after data acquisition, and fulfill the needs\n of some ocean operational services. Level 2 products contain the time of measurement,\n the geo-location and the height of the surface. IOP and GOP are outputs of\n the CryoSat Ocean Processor. These products are dedicated to the study of\n ocean surfaces, and provided specifically for the needs of the oceanographic\n community. IOP are generated 2-3 days after data sensing acquisition and use\n the DORIS Preliminary Orbit. GOP are typically generated 30 days after data\n sensing acquisition and use the DORIS Precise Orbit. Geophysical corrections\n are computed from the Analysis ADFs, however following the oceanographic convention\n the corrections are available but not directly applied to the range (as for\n FDM). The CryoSat ThEMatic PrOducts (Cryo-TEMPO) projects aim to deliver\n a new paradigm of simplified, harmonized, and agile CryoSat-2 products, that\n are easily accessible to new communities of non-altimeter experts and end\n users. The Cryo-TEMPO datasets include dedicated products over five thematic\n areas, covering Sea Ice, Land Ice, Polar Ocean, Coastal Ocean and Inland Water,\n together with a novel SWATH product (CryoTEMPO-EOLIS) that exploits CryoSat's\n SARIn mode over ice sheet margins. The standard Cryo-TEMPO products include\n fully-traceable uncertainties and use rapidly evolving, state-of-the-art processing\n dedicated to each thematic area. Throughout the project, the products will\n be constantly evolved, and validated by a group of Thematic Users, thus ensuring\n optimal relevance and impact for the intended target communities. More information\n on the Cryo-TEMPO products can be found on the Project Website (http://cryosat.mssl.ucl.ac.uk/tempo/index.html).\n The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel\n Swath processing technique to deliver increased spatial and temporal coverage\n of time-dependent elevation over land ice, a critical metric for tracking\n ice mass trends in support to a wide variety of end-users. The CryoTEMPO-EOLIS\n swath product exploits CryoSat's SARIn mode and the novel Swath processing\n technique to deliver increased spatial and temporal coverage of time-dependent\n elevation over land ice, a critical metric for tracking ice mass trends in\n support to a wide variety of end-users.The dataset consists of systematic\n reprocessing of the entire CryoSat archive to generate new L2-Swath products,\n increasing data sampling by 1 to 2 orders of magnitude compared with the operational\n L2 ESA product. 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The dataset regularly\n grows as ESA collects new SkySat products. Two different product types are\n offered, Ground Sampling Distance at nadir up to 65 cm for PAN and up to 0.8m\n for MS EO-SIP Product Type Product description Content SSC_DEF_SC Basic and\n Ortho scene * Level 1B 4-bands Analytic /DN Basic scene * Level 1B 4-bands\n Panchromatic /DN Basic scene * Level 1A 1-band Panchromatic DN Pre Sup resolution\n Basic scene * Level 3B 3-bands Visual Ortho Scene * Level 3B 4-bands Pansharpened\n Multispectral Ortho Scene * Level 3B 4-bands Analytic/DN/SR Ortho Scene *\n Level 3B 1-band Panchromatic /DN Ortho Scene SSC_DEF_CO Ortho Collect * Visual\n 3-band Pansharpened Image * Multispectral 4-band Pansharpened Image * Multispectral\n 4-band Analytic/DN/SR Image (B, G, R,N) * 1-band Panchromatic Image The\n Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere\n or for any geometric distortions inherent in the imaging process \\\\u2022Analytic\n - unorthorectified, radiometrically corrected, multispectral BGRN \\\\u2022Analytic\n DN - unorthorectified, multispectral BGRN \\\\u2022Panchromatic - unorthorectified,\n radiometrically corrected, panchromatic (PAN) \\\\u2022Panchromatic DN - unorthorectified,\n panchromatic (PAN) \\\\u2022L1A Panchromatic DN - unorthorectified, pre-super\n resolution, panchromatic (PAN) The Ortho Scene product is sensor and geometrically\n corrected, and is projected to a cartographic map projection. \\\\u2022Visual\n - orthorectified, pansharpened, and color-corrected (using a color curve)\n 3-band RGB Imagery \\\\u2022Pansharpened Multispectral - orthorectified, pansharpened\n 4-band BGRN Imagery \\\\u2022Analytic SR - orthorectified, multispectral BGRN.\n Atmospherically corrected Surface Reflectance product. \\\\u2022Analytic - orthorectified,\n multispectral BGRN. 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km\\\",\\n \\\"Imaging\n Spectrometers/Radiometers\\\",\\n \\\"Landsat-8\\\",\\n \\\"OLI\\\",\\n \\\"TIRS\\\"\\n\n \\ ],\\n \\\"created\\\": \\\"2022-01-14T00:00:00.000Z\\\",\\n \\\"description\\\":\n \\\"This dataset contains the European Coverage of Landsat 8 Collection 2 data,\n both Level 1 and Level 2, since the beginning of the mission. Landsat 8 Collection\n 2 is the result of reprocessing effort on the archive and on fresh products\n with significant improvement with respect to Collection 1 on data quality,\n obtained by means of advancements in data processing, algorithm development.\n The primary characteristic is a relevant improvement in the absolute geolocation\n accuracy (now re-baselined to the European Space Agency Copernicus Sentinel-2\n Global Reference Image, GRI) but includes also updated digital elevation modelling\n sources, improved Radiometric Calibration (even correction for the TIRS striping\n effect), enhanced Quality Assessment Bands, updated and consistent metadata\n files, usage of Cloud Optimized Georeferenced (COG) Tagged Image File Format.\n \\ Landsat 8 level 1 products combine data from the 2 Landsat instruments,\n OLI and TIRS. The level 1 products generated can be either L1TP or L1GT: \\\\u2022\n L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically\n calibrated and orthorectified using ground control points (GCPs) and digital\n elevation model (DEM) data to correct for relief displacement. The highest\n quality Level-1 products suitable for pixel-level time series analysis. GCPs\n used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000)\n data set. \\\\u2022 L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products:\n L1GT data products consist of L0 product data with systematic radiometric,\n geometric and terrain corrections applied and resampled for registration to\n a cartographic projection, referenced to the WGS84, G873, or current version.\n The dissemination server contains three different classes of Level1 products\n \\\\u2022 Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed\n upon downlink but use an initial TIRS line-of-sight model parameters; the\n data is made available in less than 12 hours (4-6 hours typically). Once the\n data have been reprocessed with the refined TIRS parameters, the products\n are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time\n tier (in 14-16 days). \\\\u2022 Tier 1 (T1): Landsat scenes with the highest\n available data quality are placed into Tier 1 and are considered suitable\n for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP)\n corrected data that have well-characterized radiometry and are inter-calibrated\n across the different Landsat instruments. The georegistration of Tier 1 scenes\n is consistent and within prescribed image-to-image tolerances of \\u2266 12-meter\n radial root mean square error (RMSE). \\\\u2022 Tier 2 (T2): Landsat scenes\n not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier\n 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do\n not meet the Tier 1 geometry specification due to less accurate orbital information\n (specific to older Landsat sensors), significant cloud cover, insufficient\n ground control, or other factors. This includes Systematic Terrain (L1GT)\n and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated\n from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith\n Angle constraint and include the required auxiliary data inputs to generate\n a scientifically viable product. The data are available a couple of days after\n the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: \\\\u2022\n L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance\n (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients\n file, and Quality Assessment (QA) Bands. \\\\u2022 L2SR - Level 2 Surface Reflectance\n (L2SR) products: include Surface Reflectance (SR), an angle coefficients file,\n and Quality Assessment (QA) Bands; it is generated if ST could not be generated\n \\ Two different categories of Level 1 products are offered: LC with Optical,\n Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal\n not available). 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The radiometric accuracy is 0.2 dB and\n auxiliary data include: Orbit state vectors (DORIS, FOS), RA2 and MWR characterisation\n data, Platform attitude, Gain calibration, USO frequency, ECMWF data, time\n relation, leap second, Ionospheric corrections, geoid, mean sea surface, slope\n data, and tide model (ocean, earth, loading, pole). 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\\\"World\\\",\\n \\\"DIF10\\\",\\n \\\"Atmosphere\\\",\\n \\\"EARTH\n SCIENCE > ATMOSPHERE\\\",\\n \\\"Atmospheric Winds\\\",\\n \\\"EARTH SCIENCE\n > ATMOSPHERE > ATMOSPHERIC WINDS\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"MERIS\n RR - 1040 m across track / 1160 m along track\\\",\\n \\\"800 km\\\",\\n \\\"5\n - 1150 km\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"Envisat\\\",\\n\n \\ \\\"MERIS\\\"\\n ],\\n \\\"created\\\": \\\"2019-05-28T00:00:00.000Z\\\",\\n\n \\ \\\"description\\\": \\\"The MERIS Level 1 Reduced Resolution (RR) product\n contains the Top of Atmosphere (TOA) upwelling spectral radiance measures\n at reduced resolution. 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20m\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"swir16\\\",\\\"common_name\\\":\\\"swir16\\\",\\\"description\\\":\\\"SWIR\n 1 (band 11)\\\",\\\"center_wavelength\\\":1.61,\\\"full_width_half_max\\\":0.143}],\\\"gsd\\\":20,\\\"proj:shape\\\":[5490,5490],\\\"proj:transform\\\":[20,0,399960,0,-20,4900020],\\\"raster:bands\\\":[{\\\"nodata\\\":0,\\\"data_type\\\":\\\"uint16\\\",\\\"bits_per_sample\\\":15,\\\"spatial_resolution\\\":20,\\\"scale\\\":0.0001,\\\"offset\\\":0}],\\\"roles\\\":[\\\"data\\\",\\\"reflectance\\\"]},\\\"swir22\\\":{\\\"type\\\":\\\"image/tiff;\n application=geotiff; profile=cloud-optimized\\\",\\\"title\\\":\\\"SWIR 2 (band 12)\n - 20m\\\",\\\"eo:bands\\\":[{\\\"name\\\":\\\"swir22\\\",\\\"common_name\\\":\\\"swir22\\\",\\\"description\\\":\\\"SWIR\n 2 (band 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The level 1 products generated can be either L1TP or L1GT: \\\\u2022\n L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically\n calibrated and orthorectified using ground control points (GCPs) and digital\n elevation model (DEM) data to correct for relief displacement. The highest\n quality Level-1 products suitable for pixel-level time series analysis. GCPs\n used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000)\n data set. \\\\u2022 L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products:\n L1GT data products consist of L0 product data with systematic radiometric,\n geometric and terrain corrections applied and resampled for registration to\n a cartographic projection, referenced to the WGS84, G873, or current version.\n The dissemination server contains three different classes of Level1 products\n \\\\u2022 Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed\n upon downlink but use an initial TIRS line-of-sight model parameters; the\n data is made available in less than 12 hours (4-6 hours typically). Once the\n data have been reprocessed with the refined TIRS parameters, the products\n are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time\n tier (in 14-16 days). \\\\u2022 Tier 1 (T1): Landsat scenes with the highest\n available data quality are placed into Tier 1 and are considered suitable\n for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP)\n corrected data that have well-characterized radiometry and are inter-calibrated\n across the different Landsat instruments. The georegistration of Tier 1 scenes\n is consistent and within prescribed image-to-image tolerances of \\u2266 12-meter\n radial root mean square error (RMSE). \\\\u2022 Tier 2 (T2): Landsat scenes\n not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier\n 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do\n not meet the Tier 1 geometry specification due to less accurate orbital information\n (specific to older Landsat sensors), significant cloud cover, insufficient\n ground control, or other factors. This includes Systematic Terrain (L1GT)\n and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated\n from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith\n Angle constraint and include the required auxiliary data inputs to generate\n a scientifically viable product. The data are available a couple of days after\n the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: \\\\u2022\n L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance\n (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients\n file, and Quality Assessment (QA) Bands. \\\\u2022 L2SR - Level 2 Surface Reflectance\n (L2SR) products: include Surface Reflectance (SR), an angle coefficients file,\n and Quality Assessment (QA) Bands; it is generated if ST could not be generated\n \\ Two different categories of Level 1 products are offered: LC with Optical,\n Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal\n not available). 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The Sentinel-3\n SYN Fire_cci v1.1 pixel product is distributed as 6 continental tiles and\n is based upon surface reflectance data from the OLCI and SLSTR instruments\n (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites.\n This information is complemented by VIIRS thermal information. This product,\n called FireCCIS311 for short, is available for the years 2019 to 2022.The\n FireCCIS311 Pixel product described here includes maps at 0.002777-degree\n (approx. 300m) resolution. Burned area (BA) information includes 3 individual\n files, packed in a compressed tar.gz file: date of BA detection (labelled\n JD), the confidence level (CL, a probability value estimating the confidence\n that a pixel is actually burned), and the land cover (LC) information as defined\n in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product.\n An unpacked version of the data is also available. 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This dataset was produced by the European Space Agency\n (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information\n about the Lakes_cci please visit the project website. This is version 2.1.0\n of the dataset.The six thematic climate variables included in this dataset\n are:\\xE2\\\\u0080\\xA2 Lake Water Level (LWL), derived from satellite altimetry,\n is fundamental to understand the balance between water inputs and water loss\n and their connection with regional and global climate change.\\xE2\\\\u0080\\xA2\n Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution\n spatial extent observed at set time-points, describes the areal extent of\n the water body. This allows the observation of drought in arid environments,\n expansion in high Asia, or impact of large-scale atmospheric oscillations\n on lakes in tropical regions for example. .\\xE2\\\\u0080\\xA2 Lake Surface Water\n temperature (LSWT), derived from optical and thermal satellite observations,\n is correlated with regional air temperatures and is informative about vertical\n mixing regimes, driving biogeochemical cycling and seasonality.\\xE2\\\\u0080\\xA2\n Lake Ice Cover (LIC), determined from optical observations, describes the\n freeze-up in autumn and break-up of ice in spring, which are proxies for gradually\n changing climate patterns and seasonality.\\xE2\\\\u0080\\xA2 Lake Water-Leaving\n Reflectance (LWLR), derived from optical satellite observations, is a direct\n indicator of biogeochemical processes and habitats in the visible part of\n the water column (e.g. seasonal phytoplankton biomass fluctuations), and an\n indicator of the frequency of extreme events (peak terrestrial run-off, changing\n mixing conditions).\\xE2\\\\u0080\\xA2 Lake Ice Thickness (LIT), containing LIT\n information over Great Slave lake from 2002-2022.Data generated in the Lakes_cci\n are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason,\n ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua\n and Metop-A/B.Satellite sensors associated with the thematic climate variables\n are as follows:LWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A,\n Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1\n RA, ERS-2; LWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR,\n Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;LSWT:\n Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR,\n Metop-A/B; LIC: Terra/Aqua MODIS; LWLR: Envisat MERIS, Sentinel-3A OLCI A/B,\n Aqua MODIS;LIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.Detailed\n information about the generation and validation of this dataset is available\n from the Lakes_cci documentation available on the project website and in\n Carrea, L., Cr\\xC3\\xA9taux, JF., Liu, X. et al. 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governments, industries and the wider\n public. A range of scientific disciplines including hydrology, limnology,\n climatology, biogeochemistry and geodesy are interested in distribution and\n \\ functioning of the millions of lakes (from small ponds to inland seas),\n from the local to the global scale. Remote sensing provides an opportunity\n to extend the spatio-temporal scale of lake observation. The five thematic\n climate variables included in this dataset are:\\xE2\\\\u0080\\xA2\\\\tLake Water\n Level (LWL): a proxy fundamental to understand the balance between water inputs\n and water loss and their connection with regional and global climate changes.\\xE2\\\\u0080\\xA2\\\\tLake\n Water Extent (LWE): a proxy for change in glacial regions (lake expansion)\n and drought in many arid environments, water extent relates to local climate\n for the cooling effect that water bodies provide.\\xE2\\\\u0080\\xA2\\\\tLake Surface\n Water temperature (LSWT): correlated with regional air temperatures and a\n proxy for mixing regimes, driving biogeochemical cycling and seasonality.\n \\xE2\\\\u0080\\xA2\\\\tLake Ice Cover (LIC): freeze-up in autumn and advancing\n break-up in spring are proxies for gradually changing climate patterns and\n seasonality. \\xE2\\\\u0080\\xA2\\\\tLake Water-Leaving Reflectance (LWLR): a direct\n indicator of biogeochemical processes and habitats in the visible part of\n the water column (e.g. seasonal phytoplankton biomass fluctuations), and an\n indicator of the frequency of extreme events (peak terrestrial run-off, changing\n mixing conditions).Data generated in the Lakes_cci project are derived from\n data from multiple instruments and multiple satellites including; TOPEX/Poseidon,\n Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop\n and Orbview. 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These SST use the ARC SST retrieval\n and cloud screening which differ from the methods used to produce the Gridded\n Surface Temperature (ATS_NR__2P) products. In addition to SST, the L2P products\n contain the ATSR Saharan Dust Index (ASDI) and the clear-sky probability estimated\n by the ARC cloud detection algorithm. The L2P processor also generates L3U\n products; these are the L2P products averaged onto a regular grid at 0.1 degree\n resolution (they are therefore similar to the AR / Meteo Envisat-format products).\n \\ The L2P and L3U products are provided in NetCDF-4 format following GHRSST\n Data Specifications (GDS) v2. 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The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR)\n product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected\n product, without correction for any geometric distortions inherent in the\n imaging processes and is not mapped to a cartographic projection. The imagery\n data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification\n by the user. This kind of product is designed for users with advanced image\n processing and geometric correction capabilities. Basic Scene Product\n Components and Format Product Components:\\\\t - Image File (GeoTIFF format)\n - Metadata File (XML format) - Rational Polynomial Coefficients (XML format)\n - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format)\n - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral\n image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green\n I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance:\\\\t\n - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R:\n 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified\n assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and\n geometrically- corrected and is projected to a UTM/WGS84 cartographic map\n projection. The geometric correction uses fine Digital Elevation Models (DEMs)\n with a post spacing of between 30 and 90 metres. Ortho Scene Product Components\n and Format Product Components:\\\\t - Image File (GeoTIFF format) - Metadata\n File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM\n File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands:\n 3-band natural color (red, green, blue) or 4-band multispectral image (blue,\n green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow,\n red, RedEdge, near-infrared) Ground Sampling Distance:\\\\t - Approximate, satellite\n altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove:\n 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho\n Scene product is available in the following: PlanetScope Visual Ortho Scene\n product is orthorectified and color-corrected (using a colour curve) 3-band\n RGB Imagery. This correction attempts to optimise colours as seen by the human\n eye providing images as they would look if viewed from the perspective of\n the satellite PlanetScope Surface Reflectance product is orthorectified,\n 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge,\n NIR Imagery with geometric, radiometric and corrected for surface reflection.\n This data is optimal for value-added image processing such as land cover classifications.\n \\ PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified,\n 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge,\n NIR Imagery with geometric, radiometric and calibrated to top of atmosphere\n radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/\n can be accessed (Planet registration requested) to discover and check the\n data readiness. It is worth to mention that the data distribution is based\n on Data Voucher, corresponding to maximum amount of square kilometers can\n be ordered and downloaded by the project in a maximum period of 15 moths (this\n duration cannot be extended) starting from the project proposal acceptance\n date. Each Date Voucher includes PlanetScope tile view streaming access for\n a total of 20,000 tiles per calendar month during the project period. 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instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"ERS.AT_NR__2P\\\",\\n \\\"updated\\\": \\\"2023-06-13T13:23:39Z\\\",\\n\n \\ \\\"providers\\\": [\\n {\\n \\\"roles\\\": [\\\"producer\\\"],\\n\n \\ \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"http://www.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\n\n \\ \\\"ATSR-1\\\",\\n \\\"ATSR-2\\\"\\n ],\\n \\\"platform\\\":\n [\\n \\\"ERS-1\\\",\\n \\\"ERS-2\\\"\\n ]\\n }\\n },\\n\n \\ {\\n \\\"extent\\\": {\\n \\\"spatial\\\": {\\\"bbox\\\": [[\\n -180,\\n\n \\ -90,\\n 180,\\n 90\\n ]]},\\n \\\"temporal\\\":\n {\\\"interval\\\": [[\\n \\\"1995-07-01T00:00:00.000Z\\\",\\n \\\"2015-12-31T23:59:59.999Z\\\"\\n\n \\ ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n \\\"sci:doi\\\":\n \\\"10.57780/esa-1268efe\\\",\\n \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"Atmosphere\\\",\\n\n \\ \\\"Atmospheric Chemistry\\\",\\n \\\"Atmospheric Radiation\\\",\\n \\\"Atmospheric\n Temperature\\\",\\n \\\"Climate\\\",\\n \\\"EARTH SCIENCE > ATMOSPHERE\\\",\\n\n \\ \\\"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY\\\",\\n \\\"EARTH\n SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION\\\",\\n \\\"EARTH SCIENCE >\n ATMOSPHERE > ATMOSPHERIC TEMPERATURE\\\",\\n \\\"EARTH SCIENCE > CLIMATE\n INDICATORS\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"GDP 5.0\\\",\\n \\\"782\n to 785 km\\\",\\n \\\"5 km\\\",\\n \\\"Spectrometers\\\",\\n \\\"ERS-2\\\",\\n\n \\ \\\"GOME\\\"\\n ],\\n \\\"created\\\": \\\"2019-05-29T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The GOME Total Column Water Vapour (TCWV) Climate\n product was generated by the Max Planck Institute for Chemistry (MPIC), and\n the German Aerospace Center (DLR) within the ESA GOME-Evolution project. 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0.4 \\xB5m)\\\",\\n \\\"VIS (0.40 - 0.75 \\xB5m)\\\",\\n \\\"NIR (0.75\n - 1.30 \\xB5m)\\\",\\n \\\"SWIR (1.3 - 3.0 \\xB5m)\\\",\\n \\\"MWIR (3.0\n - 6.0 \\xB5m)\\\",\\n \\\"4.0\\\",\\n \\\"Very Low Resolution - VLR (>\n 1200 m)\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"Scatterometers\\\",\\n\n \\ \\\"TERRA\\\",\\n \\\"AQUA\\\",\\n \\\"QuikSCAT\\\",\\n \\\"OrbView-2\\\",\\n\n \\ \\\"MODIS\\\",\\n \\\"SeaWinds\\\",\\n \\\"SeaWiFS\\\"\\n ],\\n\n \\ \\\"created\\\": \\\"2019-11-21T00:00:00.00Z\\\",\\n \\\"description\\\": \\\"ADAM\n enables generating typical monthly variations of the global Earth surface\n reflectance at 0.1\\xB0 spatial resolution (Plate Carree projection) and over\n the spectral range 240-4000nm. The ADAM product is made of gridded monthly\n mean climatologies over land and ocean surfaces, and of a companion API toolkit\n that enables the calculation of hyperspectral (at 1 nm resolution over the\n whole 240-4000 nm spectral range) and multidirectional reflectances (i.e.\n in any illumination/viewing geometry) depending on user choices. The ADAM\n climatologies that feed the ADAM calculation tools are: For ocean: monthly\n chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is\n used to compute the water column reflectance (which shows large spectral variations\n in the visible, but is insignificant in the near and mid infrared). monthly\n wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate\n the ocean glint reflectance. For land: monthly normalized surface reflectances\n in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain\n of MOD09A1 products (derived from Aqua and Terra observations), on which relies\n the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow)\n reflectance. uncertainty variance-covariance matrix for the 7 spectral bands\n associated to the normalized surface reflectance. For sea-ice: Sea ice pixels\n (masked in the original MOD09A1 products) have been accounted for by a gap-filling\n approach relying on the spatial-temporal distribution of sea ice coverage\n provided by the CryoClim climatology for year 2005.\\\",\\n \\\"type\\\": \\\"Collection\\\",\\n\n \\ \\\"title\\\": \\\"ADAM Surface Reflectance Database v4.0\\\",\\n \\\"license\\\":\n \\\"various\\\",\\n \\\"assets\\\": {\\n \\\"metadata_ogc_17_069r3\\\": {\\n\n \\ \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database?httpAccept=application/vnd.iso.19139%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19139_2\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database?httpAccept=application/vnd.iso.19139-2%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19139-2 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19139-2+xml\\\"\\n\n \\ },\\n \\\"metadata_dif_10\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database?httpAccept=application/dif10%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"DIF-10 metadata\\\",\\n \\\"type\\\": \\\"application/dif10+xml\\\"\\n\n \\ },\\n \\\"metadata_iso_19115_3\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database?httpAccept=application/vnd.iso.19115-3%2Bxml\\\",\\n\n \\ \\\"title\\\": \\\"ISO 19115-3 metadata\\\",\\n \\\"type\\\": \\\"application/vnd.iso.19115-3+xml\\\"\\n\n \\ },\\n \\\"metadata_ogc_17_084r1\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database?mode=owc\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-084r1 metadata\\\",\\n \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\\\\\"\\\"\\n\n \\ },\\n \\\"metadata_html\\\": {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n\n \\ \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database?httpAccept=text/html\\\",\\n\n \\ \\\"title\\\": \\\"HTML\\\",\\n \\\"type\\\": \\\"text/html\\\"\\n }\\n\n \\ },\\n \\\"links\\\": [\\n {\\n \\\"rel\\\": \\\"cite-as\\\",\\n\n \\ \\\"href\\\": \\\"https://doi.org/10.5270/esa-6riial9\\\",\\n \\\"type\\\":\n \\\"text/html\\\",\\n \\\"title\\\": \\\"Landing page\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"self\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database\\\",\\n\n \\ \\\"type\\\": \\\"application/json\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"root\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n \\\"type\\\":\n \\\"application/json\\\",\\n \\\"title\\\": \\\"FedEO Clearinghouse\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"parent\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be\\\",\\n\n \\ \\\"title\\\": \\\"collections\\\",\\n \\\"type\\\": \\\"application/json\\\"\\n\n \\ },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\":\n \\\"https://adam.noveltis.fr/\\\",\\n \\\"type\\\": \\\"text/html\\\",\\n \\\"title\\\":\n \\\"ADAM Database - Software Tool\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"describedby\\\",\\n \\\"href\\\": \\\"https://adam.noveltis.fr/pdfs/NOV-FE-0724-NT-007-v4.2.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"ADAM Algorithm\n Theoretical Basis Document - Technical Note\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"describedby\\\",\\n \\\"href\\\": \\\"https://adam.noveltis.fr/pdfs/NOV-FE-0724-NT-008-v4.0.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"ADAM, description\n of the web interface. Structure of the ADAM input surface database and output\n products - Technical Note\\\"\\n },\\n {\\n \\\"rel\\\": \\\"describedby\\\",\\n\n \\ \\\"href\\\": \\\"https://adam.noveltis.fr/pdfs/NOV-FE-0724-NT-006_v2.1.pdf\\\",\\n\n \\ \\\"type\\\": \\\"application/pdf\\\",\\n \\\"title\\\": \\\"ADAM Final\n Report, Contract Change request 6 - Technical Note\\\"\\n },\\n {\\n\n \\ \\\"rel\\\": \\\"describedby\\\",\\n \\\"href\\\": \\\"https://esatellus.service-now.com/csp?id=esa_simple_request\\\",\\n\n \\ \\\"type\\\": \\\"text/html\\\",\\n \\\"title\\\": \\\"Get Help? - ESA\n Earth Observation User Services Portal\\\"\\n },\\n {\\n \\\"rel\\\":\n \\\"alternate\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/ADAM.Surface.Reflectance.Database?httpAccept=application/atom%2Bxml\\\",\\n\n \\ \\\"type\\\": \\\"application/atom+xml\\\",\\n \\\"title\\\": \\\"Atom\n format\\\"\\n },\\n {\\n \\\"rel\\\": \\\"alternate\\\",\\n \\\"href\\\":\n 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},\\n\n \\ {\\n \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/concepts/instruments/bc5654d6-8472-5509-8aa8-550a078dadd6\\\",\\n\n \\ \\\"title\\\": \\\"More collections for EGG instrument\\\"\\n },\\n\n \\ {\\n \\\"rel\\\": \\\"related\\\",\\n \\\"href\\\": \\\"https://emc.spacebel.be/series/concepts/instruments/03a1a96e-daa1-52e4-ab3f-508cbe9b5cf9\\\",\\n\n \\ \\\"title\\\": \\\"More collections for SSTI instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"GOCE_Level_2\\\",\\n \\\"updated\\\": \\\"2023-06-13T13:23:40Z\\\",\\n\n \\ \\\"stac_extensions\\\": [\\\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\\\"],\\n\n \\ \\\"providers\\\": [\\n {\\n \\\"roles\\\": [\\\"producer\\\"],\\n\n \\ \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"http://www.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\n\n \\ \\\"STR\\\",\\n \\\"EGG\\\",\\n \\\"SSTI\\\"\\n ],\\n \\\"platform\\\":\n [\\\"GOCE\\\"]\\n }\\n },\\n {\\n \\\"extent\\\": {\\n \\\"spatial\\\":\n {\\\"bbox\\\": [[\\n -180,\\n -84,\\n 180,\\n 84\\n\n \\ ]]},\\n \\\"temporal\\\": {\\\"interval\\\": [[\\n \\\"2013-11-13T00:00:00.000Z\\\",\\n\n \\ null\\n ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n\n \\ \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"Planet\\\",\\n \\\"Land\n Surface\\\",\\n \\\"Mapping and Cartography\\\",\\n \\\"Natural Hazards\n and Disaster Risk\\\",\\n \\\"EARTH SCIENCE > LAND SURFACE\\\",\\n \\\"EARTH\n SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS\\\",\\n \\\"VIS (0.40 - 0.75\n \\xB5m)\\\",\\n \\\"NIR (0.75 - 1.30 \\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"Very High Resolution - VHR (0 - 5m)\\\",\\n \\\"475-575 km\\\",\\n\n \\ \\\"8 km at nadir for SkySat-1 to -2, 6.6 km at nadir for SkySat-3 to\n -13\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"SkySat\\\",\\n\n \\ \\\"SkySat Camera\\\"\\n ],\\n \\\"created\\\": \\\"2019-08-09T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The SkySat Level 2B Basic Scene, Level 3B Ortho Scene\n and Level 3B Consolidated full archive and new tasking products are available\n as part of Planet imagery offer. The SkySat Basic Scene product is uncalibrated\n and in a raw digital number format, not corrected for any geometric distortions\n inherent in the imaging process. Rational Polynomial Coefficients (RPCs) is\n provided to enable orthorectification by the user. Basic Scene Product Components\n and Format Processing levels\\\\t/\\\\t- Analytic (unorthorectified, radiometrically\n corrected, multispectral BGRN); - Analytic DN (unorthorectified, multispectral\n BGRN); - Panchromatic DN (unorthorectified, panchromatic)\\\\t// Product Components\n and Format\\\\t/\\\\t- Image File (GeoTIFF format); - Metadata File (JSON format);\n - Rational Polynomial Coefficients (Text File); - UDM File (GeoTIFF format)\\\\t//\n Image configuration\\\\t/\\\\t- 4-band Analytic DN Image (Blue, Green, Red, NIR);\n - 1-band Panchromatic DN Image (Pan)\\\\t// Ground Sampling Distance\\\\t/\\\\t3.7\n m at nadir (average at reference altitude 475 km)\\\\t// Ground Sampling Distance\n (nadir)\\\\t/\\\\t- Panchromatic 0.86m and Multispectral 1.0m for SkySat-1&2;\n - Panchromatic 0.65m and Multispectral 0.8m for SkySat-3 to 13 (0.72 m and\n 1.0m for data acquired before 30/06/2020)\\\\t// Accuracy\\\\t/\\\\t<50 m RMSE\\\\t//\n \\ The SkySat Ortho Scene is sensor- and geometrically-corrected (by using\n DEMs with a post spacing of between 30 and 90 meters) and is projected to\n a cartographic map projection; the accuracy of the product will vary from\n region to region based on available GCPs. Different products are available:\n \\ - The SkySat Visual Ortho Scene product is orthorectified, pansharpened,\n and color-corrected (using a color curve) 3-band RGB Imagery - The SkySat\n Pansharpened Multispectral Scene product is orthorectified, pansharpened 4-band\n BGRN Imagery - The SkySat Analytic DN Ortho Scene product is orthorectified,\n multispectral BGRN, uncalibrated, digital number imagery product. The product\n has been processed to remove distortions caused by terrain; It eliminates\n the perspective effect on the ground (not on buildings), restoring the geometry\n of a vertical shot. Transformation to at-sensor radiance is not included -\n The SkySat Panchromatic DN Ortho Scene product is orthorectified, panchromatic,\n uncalibrated, digital number imagery product. It has a finer GSD than the\n Analytic Product. Transformation to at-sensor radiance is not included. -\n The SkySat Analytic Ortho Scene are calibrated multispectral imagery products\n with radiometric corrections applied to correct for any sensor artifacts and\n transformation to top-of-atmosphere radiance. - The SkySat Consolidated Product\n are Ortho Collect product created by composing ~60 SkySat Ortho Scenes (Visual,\n Pansharpened Multispectral, Analytic DN, Panchromatic DN) along an imaging\n strip into segments. Ortho Scene Product Components and Format \\\\t/\\\\tVisual\n Ortho\\\\t/\\\\tPansharpened Multispectral\\\\t/\\\\tAnalytic DN Ortho\\\\t/\\\\tPanchromatic\n DN Ortho\\\\t/\\\\tAnalytic Ortho\\\\t// Product Components and Format\\\\t/\\\\tImage\n File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients\n (Text File); UDM File (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF); Metadata\n File (JSON format); Rational Polynomial Coefficients (Text File); UDM File\n (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF format); Metadata File (JSON format);\n Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format);\\\\t/\\\\tImage\n File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients\n (Text File); UDM File (GeoTIFF format)\\\\t/\\\\tImage File (GeoTIFF format);\n Metadata File (JSON format); Rational Polynomial Coefficients (Text File);\n UDM File (GeoTIFF format)\\\\t// Image configuration\\\\t/\\\\t3-band Pansharpened\n Image (PS Red, PS Green, PS Blue)\\\\t/\\\\t4-band Pansharpened Image (PS Blue,\n PS Green, PS Red, PS NIR)\\\\t/\\\\t4-band Analytic DN Image (B, G, R, N)\\\\t/\\\\t1-band\n Panchromatic Image\\\\t/\\\\t4-band Analytic Image (B, G, R, N)\\\\t// Ground Sampling\n Distance\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t/\\\\t50 cm\\\\t//\n Projection\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM WGS84\\\\t/\\\\tUTM\n WGS84\\\\t// Accuracy\\\\t/\\\\t<10 m RMSE\\\\t/\\\\t<10 m RMSE\\\\t/\\\\t<10 m\n RMSE\\\\t/\\\\t<10 m RMSE\\\\t/\\\\tradiometric accuracy: +/- 5% Relative accuracy\n at < 10 degrees off-nadir angle\\\\t// The products are available as part\n of the Planet provision from RapidEye, Skysat and PlanetScope constellations.\n SkySat collection has worldwide coverage: The Planet Explorer Catalogue can\n be accessed (Planet registration requested) to discover and check the data\n readiness. 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This FMR follows the first\n Envisat Altimetry reprocessing Version (V2.1) completed in 2012. The GDR\n and S-GDR data products were reprocessed for all cycles from 6 to 113 (May\n 2002 to April 2012) into a homogeneous standard in NetCDF format (close to\n Sentinel-3). The Sensor Data Record (SGDR) Product from RA-2/MWR includes\n the data in the GDR product (https://earth.esa.int/eogateway/catalog/envisat-ra-2-geophysical-data-record-gdr-ra2_gdr__2p-)\n (RA-2 geophysical data, MWR data) and also RA-2 averaged waveforms (18Hz)\n and RA-2 individual waveforms (1800Hz). This product is a continuation of\n ERS RA data. This data product has a coverage of 1 pass, pole-pole, a spatial\n sampling of about 390 m along track and a size of 31 to 40 MB, depending on\n presence of individual waveforms. The radiometric accuracy is 0.2 dB and\n auxiliary data include: Orbit state vectors (DORIS, FOS), RA2 and MWR characterisation\n data, Platform attitude, Gain calibration, USO frequency, ECMWF data, time\n relation, leap second, Ionospheric corrections, geoid, mean sea surface, slope\n data, and tide model (ocean, earth, loading, pole). 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\\\"Vegetation\\\",\\n \\\"Agriculture\\\",\\n \\\"Forestry\\\",\\n\n \\ \\\"EARTH SCIENCE > BIOSPHERE > VEGETATION\\\",\\n \\\"EARTH SCIENCE\n > AGRICULTURE\\\",\\n \\\"EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > TERRESTRIAL\n ECOSYSTEMS > FORESTS\\\",\\n \\\"VIS (0.40 - 0.75 \\xC2\\xB5m)\\\",\\n \\\"NIR\n (0.75 - 1.30 \\xC2\\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"Medium\n Resolution - MR (20 - 500 m)\\\",\\n \\\"663 km\\\",\\n \\\"625 km\\\",\\n\n \\ \\\"Cameras\\\",\\n \\\"GEOSAT-1\\\",\\n \\\"SLIM6\\\"\\n ],\\n\n \\ \\\"created\\\": \\\"2019-05-22T00:00:00.00Z\\\",\\n \\\"description\\\": \\\"GEOSAT-1\n full archive and new tasking products are available at 22 m resolution in\n two processing levels.\\\\r\\\\rL1R (Basic Geopositioned): All 3 spectral channels\n combined into a band-registered image. 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\\\"2016-06-01T00:00:00.000Z\\\",\\n\n \\ null\\n ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n\n \\ \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"GNSS\\\",\\n \\\"AIS\\\",\\n\n \\ \\\"ADS-B\\\",\\n \\\"Atmosphere\\\",\\n \\\"Ionosphere and Magnetosphere\n Dynamics\\\",\\n \\\"Weather Events\\\",\\n \\\"Oceans\\\",\\n \\\"EARTH\n SCIENCE > ATMOSPHERE\\\",\\n \\\"EARTH SCIENCE > SUN-EARTH INTERACTIONS\n > IONOSPHERE/MAGNETOSPHERE DYNAMICS\\\",\\n \\\"EARTH SCIENCE > ATMOSPHERE\n > WEATHER EVENTS\\\",\\n \\\"EARTH SCIENCE > OCEANS\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"500 km\\\",\\n \\\"Imaging Spectrometers/Radiometers\\\",\\n \\\"Spire\\\",\\n\n \\ \\\"AIRSAFE\\\",\\n \\\"SENSE\\\",\\n \\\"STRATOS\\\"\\n ],\\n\n \\ \\\"created\\\": \\\"2020-09-04T00:00:00.00Z\\\",\\n \\\"description\\\": \\\"The\n data collected by Spire from it's 110 satellites launched into Low Earth\n Orbit (LEO) has a diverse range of applications, from analysis of global trade\n patterns and commodity flows to aircraft routing to weather forecasting. The\n data also provides interesting research opportunities on topics as varied\n as ocean currents and GNSS-based planetary boundary layer height. The following\n products can be requested: ADS-B Data Stream Global ADS-B satellite data\n observed by Spire satellites and processed through the ground stations network.\n Historical ADS-B data older than 6 months can be delivered as data cuts containing\n CSV file(s) accessible through a Web Service or Cloud storage solutions. Live\n ADS-B data is available through a streaming API, and recent historical data\n can be accessed through a REST API. Data is distributed as a monthly subscription:\n historical data can be requested starting from 3 December 2008, the time period\n for live data starts from a user-defined date and continues for 30 days. AIS\n AIS messages include satellite AIS (S-AIS) as observed by Spire satellites\n and terrestrial AIS (T-AIS) from third party sensor stations (up to 40 million\n messages per day). Historical AIS data are delivered as a cvs file with availability\n back to June 2016 or via Historical API from December 2018; live AIS data\n are pushed to end users via TCP or through Messages API. Data is distributed\n as a monthly subscription, from a user-defined date and continues for a 30\n day period. GNSS-Radio Occultation GNSS Radio Occultation (GNSS-RO) measurements\n are collected globally on a continuous basis, generating profiles of the Earth\\\\u2019s\n atmosphere. Derived Level 1 and Level 2 products include both atmospheric\n and ionospheric products. Historical data for most of the GNSS-RO products\n are available from December 2018 to the present. Near real-time (within 90\n minutes or less latency from collection to delivery) GNSS-RO profiles are\n also available upon request. GNSS Reflectometry GNSS Reflectometry (GNSS-R)\n is a technique to measure Earth\\\\u2019s surface properties using reflections\n of GNSS signals in the form of a bistatic radar. Spire collects two types\n of GNSS-R data: conventional, near-nadir incidence LHCP reflections collected\n by the Spire GNSS-R satellites (e.g., Spire GNSS-R \\\\u201cBatch-1\\\\u201d satellites)\n and grazing angle (i.e., low elevation angle) RHCP reflections collected by\n the Spire GNSS-RO satellites. Derived Level 1 and Level 2 products are available,\n as well as some special Level 0 raw intermediate frequency (IF) data. Historical\n grazing angle GNSS-R data are available from May 2019 to the present, while\n conventional GNSS-R data are available from December 2020 to the present.\n \\ Name: Automatic Identification System (AIS) Description: The automatic identification\n system (AIS) is an automatic tracking system that uses transponders on ships\n and is used by vessel traffic services. Spire data includes satellite AIS\n (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third\n party sensor stations. Data format and content: .parquet.gz files The AIS\n files contain time-series data on received AIS messages, both the raw NMEA\n message and added post-processing data for each message. Application: Supply\n chain analysis, commodity trading, identification of illegal fishing or dark\n targets, ship route and fuel use optimization, analysis of global trade patterns,\n anti-piracy, autonomous vessel software, ocean currents. Name: Automatic\n Dependent Surveillance-Broadcast (ADS-B) Description: Spire AirSafe ADS-B\n products give access to satellite and terrestrial ADS-B data from captured\n aircrafts. Data format and content: .csv.gz files The decompressed csv file\n contains a list of hexadecimal representations of ADS-B messages associated\n with the timestamp they were received on the satellite. Application: Fleet\n management, ICAO regulatory compliance, route optimization, predictive maintenance,\n global airspace, domain awareness. Name: Global Navigation Satellite System\n Radio Occultation (GNSS-RO) Description: GNSS atmospheric radio occultation\n (GNSS-RO) relies on the detection of a change in a radio signal as it passes\n through a planet's atmosphere, i.e. as it is refracted by the atmosphere.\n This data set contains precise orbit determination (POD) solutions, satellite\n attitude information, high-rate occultation observations, excess phase, and\n derived atmospheric dry temperature profiles. Data format and content: podObs*.rnx\n This file contains raw pseudorange, carrier phase, Doppler frequency, and\n signal-to-noise measurements for each observed GPS signal from a single Spire\n satellite which allow to estimate the positions and velocities of each Spire\n satellite and also used to derive ionospheric total electron content data.\n leoOrb*.sp3 This file contains the estimated position, velocity and receiver\n clock error of a given Spire satellite after processing of the POD observation\n file leoAtt*.log It contains 1 Hz rate quaternion information measured from\n a single Spire satellite describing the satellite orientation. opnGns*ro.bin,\n opnGns*rst.bin these files contain raw measurements from the occulting GNSS\n satellite (one for each signal frequency) and raw phase data from one or more\n reference GNSS satellites. atmPhs* The file contains occultation excess phase\n delay. Also contains SNR values, ransmitter and receiver positions and open\n loop model information. atmPrf*.nc The file contains profiles of atmospheric\n dry pressure, dry temperature and neutral refractivity as a function of altitude\n produced from full processing of one occultation event. bfrPrf*.bufr The file\n contains derived profiles of dry pressure, dry temperature, refractivity and\n bending angle for each occultation. Application:\\\\tAtmospheric profiles of\n pressure, dry temperature, bending angle, and refractivity used in numerical\n weather prediction data assimilation and climate change studies. Name: Raw\n IF samples from GNSS-RO satellites Description: Raw intermediate frequency\n (IF) sampled data (I/Q) from the GNSS receiver front-end of GNSS-RO satellites.\n Data format and content: rocRIF*.zip Binary raw IF data and associated ancillary\n data (e.g., POD data) in a zip archive per collection event. Application:\n GNSS-RO studies, GNSS RFI and jamming monitoring, research. Name: Raw IF\n samples from GNSS-R satellites Description: Raw intermediate frequency (IF)\n sampled data (I/Q) from the GNSS receiver front-end of conventional GNSS-R\n satellites. Data format and content: gbrRIF*.zip Binary raw IF data and associated\n ancillary data (e.g., POD data) in a zip archive per collection event. Application:\n GNSS-R studies, GNSS RFI and jamming monitoring, research, etc. Name: Grazing\n angle GNSS-R observations Description: During grazing angle GNSS-R events,\n signal reflection at two frequencies is observed through the limb-facing antenna\n and is trackedusing an open-loop tracking technique thatrelies on a model\n topredict the propagationdelay and Doppler of thereflected signal. Simultaneous\n open-looptracking of the signaldirectly along theline-of-sight from thetransmitter\n to thereceiver is alsoperformed to provideadditional data that maybenecessary\n for signalcalibration. The mainoutput of the open-looptracking are in-phase\n (I)and quadrature (Q)accumulation samples(nominally at 50 Hz),which represent\n the residual Doppler (phase) from the model. Data format and content: grzObs*.nc\n L1A filecontains rawopen loopcarrier phasemeasurementsat 50 Hzsampling forgrazingangleGNSS-Rreflectionscaptured\n in the GNSS-RO RHC Pantennas, (bothdirect andreflectedsignals) on GNSS-RO\n satellites. Application: Sea surface and sea ice height extent, and classification.\n \\ Name: Georeferenced grazing angle GNSS-R observations Description: The low-levelobservations\n of the high-rate grazing angle GNSS-R observationsbut withthegeoreferenced\n bistatic radar parameters of the satellite receiver,specular reflection, and\n GNSS transmitter included. Data format and content: grzRfl*.nc L1B file contains\n the georeferenced grazing angle GNSS-R data collected by Spire GNSS-RO satellites,\n including the low-level observables and bistatic radar geometries (e.g., receiver,\n specular reflection, and the transmitter locations). Application: Sea surface\n and sea ice height extent, and classification Name: GNSS-R calibrated bistatic\n radar reflectivities Description: Higher level product used to derive land-surface\n reflectivity. Data format and content: gbrRfl*.nc L1A along-track calibrated\n relative power between reflected and direct signals (e.g., bistatic radar\n reflectivities) measured by Spire conventional GNSS-R satellites. Application:\n GNSS-R studies, soil moisture, ocean wind, and sea ice applications Name:\n GNSS-R calibrated bistatic radar cross-sections Description: Higher level\n product used to derive ocean surface roughness products. Data format and content:\n gbrRCS*.nc L1B along-track calibrated and normalized bistatic radar cross-sections\n measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies,\n ocean wind and sea ice applications Name: Combined Surface Soil Moisture\n Description: Combined CYGNSS and SMAP soil moisture data are provided as a\n combined surface soil moisture (COMB-SSM) product in two data level formats:\n L2U1 and L3U1. 6 x 6 km grid cell. L-band measurements of surface soil moisture\n benefit from better vegetation penetration in comparison to traditional C-band\n measurements. Data format and content: COMB-SSM.nc This file contains the\n combined data product containing measurements from both CYGNSS and SMAP reported\n on a 6 km global Equi7Grid grid. Application: Agriculture, crop insurance,\n farming solutions, climatology, terrain awareness, peatlands and wetlands\n monitoring etc. Name: Ionosphere total electron content Description: Spire\n routinely collects and processes a large volume of total electron content\n (TEC) data, representing the line-of-sight integration of electron density\n between a Spire satellite and a GNSS satellite. Each file contains line-of-sight\n ionospheric total electron content (TEC) estimates derived for a \\\\u2018single\n viewing arc\\\\u2019 contained in the POD observation file. Viewing arcs are\n at least 10 minutes in duration. Data format and content: podTec*.nc This\n file contains the line-of-sight total electron content with associated orbital\n information. Application: Space weather research, tsunamigenic earthquakes,\n weather applications, space situational awareness (SSA), autonomous vehicles\n etc Name: Ionosphere scintillation Description: The scintillation index for\n each GNSS frequency is computed onboard the spacecraft. This index provides\n a measure of the fluctuations of the GNSS signal over the course of 10 seconds\n caused by propagation of the radio signals through electron density irregularities\n in the ionosphere. After the raw indices are downlinked to the ground, they\n are packaged along with associated metadata such as orbit position to create\n the final scintillation data product. Data format and content: scnLv1*.nc\n This file contains on-board computed scintillation data (S4 only) with associated\n orbital information Application: Space weather research, solar events, TIDs,\n weather applications positioning and navigation, communications etc. Name:\n Electron density profile Description: Electron density profiles are retrieved\n as a function of altitude. Electron density profiles are processed from podTec\n netcdf files, which span a sufficient elevation angle range. A standard Abel\n inversion algorithm is applied to retrieve the profiles. Data format and content:\n ionPrf*.nc This file contains electron density profile retrieved from podTec\n files spanning appropriate elevation angle range Application: Space weather\n research, solar events, TIDs, weather applications positioning and navigation,\n communications. The products are available as part of the Spire provision\n with worldwide coverage. All details about the data provision, data access\n conditions and quota assignment procedure are described in the Terms of Applicability\n (https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639).\\\",\\n\n \\ \\\"type\\\": \\\"Collection\\\",\\n \\\"title\\\": \\\"Spire live and historical\n data\\\",\\n \\\"license\\\": \\\"various\\\",\\n \\\"assets\\\": {\\n \\\"metadata_ogc_17_069r3\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/Spire.live.and.historical.data\\\",\\n\n \\ \\\"type\\\": \\\"application/geo+json;profile=\\\\\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\\\\\"\\\",\\n\n \\ \\\"title\\\": \\\"OGC 17-069r3 metadata\\\"\\n },\\n \\\"metadata_iso_19139\\\":\n {\\n \\\"roles\\\": [\\\"metadata\\\"],\\n \\\"href\\\": \\\"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/vnd.iso.19139%2Bxml\\\",\\n\n 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\\\"https://emc.spacebel.be/series/concepts/instruments/11f58510-1770-53a7-9b13-5a5564b4e9c2\\\",\\n\n \\ \\\"title\\\": \\\"More collections for AIRSAFE instrument\\\"\\n }\\n\n \\ ],\\n \\\"id\\\": \\\"Spire.live.and.historical.data\\\",\\n \\\"updated\\\":\n \\\"2023-06-13T13:23:42Z\\\",\\n \\\"providers\\\": [\\n {\\n \\\"roles\\\":\n [\\\"producer\\\"],\\n \\\"name\\\": \\\"ESA/ESRIN\\\",\\n \\\"url\\\": \\\"http://www.esa.int\\\"\\n\n \\ },\\n {\\n \\\"roles\\\": [\\\"host\\\"],\\n \\\"name\\\":\n \\\"FedEO Clearinghouse\\\",\\n \\\"url\\\": \\\"https://emc.spacebel.be/readme.html\\\"\\n\n \\ }\\n ],\\n \\\"summaries\\\": {\\n \\\"instruments\\\": [\\n\n \\ \\\"STRATOS\\\",\\n \\\"SENSE\\\",\\n \\\"AIRSAFE\\\"\\n ],\\n\n \\ \\\"platform\\\": [\\\"Spire\\\"]\\n }\\n },\\n {\\n \\\"extent\\\":\n {\\n \\\"spatial\\\": {\\\"bbox\\\": [[\\n -180,\\n -90,\\n 180,\\n\n \\ 90\\n ]]},\\n \\\"temporal\\\": {\\\"interval\\\": [[\\n \\\"2012-10-01T00:00:00.000Z\\\",\\n\n \\ null\\n ]]}\\n },\\n \\\"stac_version\\\": \\\"1.0.0\\\",\\n\n \\ \\\"keywords\\\": [\\n \\\"World\\\",\\n \\\"Multispectral\\\",\\n \\\"Panchromatic\\\",\\n\n \\ \\\"Pansharpened\\\",\\n \\\"Pleiades\\\",\\n \\\"Agriculture\\\",\\n\n \\ \\\"Afforestation/Reforestation\\\",\\n \\\"Crops and Yields\\\",\\n\n \\ \\\"Forest Fires\\\",\\n \\\"Landscape\\\",\\n \\\"Topography\\\",\\n\n \\ \\\"Weather Events\\\",\\n \\\"EARTH SCIENCE > AGRICULTURE\\\",\\n \\\"EARTH\n SCIENCE > AGRICULTURE > FOREST SCIENCE > AFFORESTATION/REFORESTATION\\\",\\n\n \\ \\\"EARTH SCIENCE > AGRICULTURE > AGRICULTURAL PLANT SCIENCE > CROP/PLANT\n YIELDS\\\",\\n \\\"EARTH SCIENCE > AGRICULTURE > FOREST SCIENCE > FOREST\n FIRE SCIENCE\\\",\\n \\\"EARTH SCIENCE > LAND SURFACE > LANDSCAPE\\\",\\n \\\"EARTH\n SCIENCE > LAND SURFACE > TOPOGRAPHY\\\",\\n \\\"EARTH SCIENCE > ATMOSPHERE\n > WEATHER EVENTS\\\",\\n \\\"VIS (0.40 - 0.75 \\xB5m)\\\",\\n \\\"NIR (0.75\n - 1.30 \\xB5m)\\\",\\n \\\"SWIR (1.3 - 3.0 \\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n\n \\ \\\"Very High Resolution - VHR (0 - 5m)\\\",\\n \\\"High Resolution\n - HR (5 - 20 m)\\\",\\n \\\"832 km for SPOT-1 to 5, 694 km for SPOT-6 and\n 7\\\",\\n \\\"60 km\\\",\\n \\\"Cameras\\\",\\n \\\"SPOT 6\\\",\\n \\\"SPOT\n 7\\\",\\n \\\"NAOMI\\\"\\n ],\\n \\\"created\\\": \\\"2019-07-16T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The SPOT 6 and 7 satellites ensure data continuity\n with the no longer operational SPOT 5 satellite and provide an archive of\n very high resolution optical acquisition as well as the possibility to task\n the satellites for new acquisitions. 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0.4 \\xB5m)\\\",\\n \\\"Sun-synchronous\\\",\\n \\\"Horizontal\n resolution at measurement scale: 3 km; Horizontal resolution at observation\n scale for Rayleigh/Mie: 87/10 km\\\",\\n \\\"250-2000 m vertical resolution\\\",\\n\n \\ \\\"ALD_U_N_1B\\\",\\n \\\"320 km\\\",\\n \\\"Lidar/Laser Sounders\\\",\\n\n \\ \\\"Aeolus\\\",\\n \\\"ALADIN\\\"\\n ],\\n \\\"created\\\": \\\"2020-04-30T00:00:00.00Z\\\",\\n\n \\ \\\"description\\\": \\\"The Level 1B wind product of the Aeolus mission contains\n the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh\n and Mie receivers, which are generated in Near Real Time. 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\"https://emc.spacebel.be?httpAccept=application/ld%2Bjson\",\n \"type\": \"application/ld+json\",\n \"title\": \"Landing Page in JSON-LD media type.\"\n },\n {\n \"method\": \"GET\",\n \"rel\": \"search\",\n \"href\": \"https://emc.spacebel.be/search\",\n \"type\": \"application/geo+json\",\n \"title\": \"STAC Search\"\n },\n {\n \"method\": \"POST\",\n \"rel\": \"search\",\n \"href\": \"https://emc.spacebel.be/search\",\n \"type\": \"application/geo+json\",\n \"title\": \"STAC Search\"\n },\n {\n \"rel\": \"child\",\n \"href\": \"https://emc.spacebel.be/series\",\n \"type\": \"application/json\",\n \"title\": \"Collections\"\n },\n {\n \"rel\": \"child\",\n \"href\": \"https://emc.spacebel.be/services\",\n \"type\": \"application/json\",\n \"title\": \"Services\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\"\n }\n ],\n \"id\": \"fedeo\",\n \"conformsTo\": [\n \"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core\",\n \"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30\",\n \"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson\",\n \"http://www.opengis.net/spec/ogcapi_common-2/1.0/conf/collections\",\n \"http://www.opengis.net/spec/ogcapi-common-2/1.0/conf/simple-query\",\n \"http://www.opengis.net/spec/ogcapi-records-1/1.0/req/cql-filter\",\n \"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson\",\n \"http://www.opengis.net/spec/ogcapi-features-3/1.0/conf/features-filter\",\n \"https://api.stacspec.org/v1.0.0-rc.2/core\",\n \"https://api.stacspec.org/v1.0.0-rc.2/stac-search\",\n \"https://api.stacspec.org/v1.0.0-rc.2/stac-response\",\n \"https://api.stacspec.org/v1.0.0-rc.2/collection-search\",\n \"https://api.stacspec.org/v1.0.0-rc.2/collection-search#filter\",\n \"https://api.stacspec.org/v1.0.0-rc.1/collection-search#free-text\",\n \"https://api.stacspec.org/v1.0.0-rc.1/collection-search#sort\",\n \"https://api.stacspec.org/v1.0.0-rc.2/item-search\",\n \"https://api.stacspec.org/v1.0.0-rc.2/item-search#filter\",\n \"http://www.opengis.net/spec/cql2/1.0/conf/cql2-text\",\n \"http://www.opengis.net/spec/cql2/1.0/conf/basic-cql2\",\n \"https://api.stacspec.org/v1.0.0/item-search#sort\",\n \"https://api.stacspec.org/v1.0.0/ogcapi-features#sort\"\n ],\n \"title\": \"FedEO Clearinghouse\",\n \"type\": \"Catalog\"\n}" }, { "path": "tests/data/invalid-collection.json", "content": "{\n \"id\": \"planet\",\n \"title\": \"Planet\",\n \"extent\": {},\n \"license\": \"proprietary\",\n \"description\": \"Planet\",\n \"stac_version\": \"1.0.0\",\n \"item_type_accessor\": \"pl:item_type\",\n \"links\": [\n {\n \"rel\": \"items\",\n \"type\": \"application/geo+json\",\n \"href\": \"https://csdap.earthdata.nasa.gov/stac/collections/planet/items\"\n },\n {\n \"rel\": \"parent\",\n \"type\": \"application/json\",\n \"href\": \"https://csdap.earthdata.nasa.gov/stac/\"\n },\n {\n \"rel\": \"root\",\n \"type\": \"application/json\",\n \"href\": \"https://csdap.earthdata.nasa.gov/stac/\"\n },\n {\n \"rel\": \"self\",\n \"type\": \"application/json\",\n \"href\": \"https://csdap.earthdata.nasa.gov/stac/collections/planet\"\n },\n {\n \"rel\": \"http://www.opengis.net/def/rel/ogc/1.0/queryables\",\n \"type\": \"application/schema+json\",\n \"title\": \"Queryables\",\n \"href\": \"https://csdap.earthdata.nasa.gov/stac/collections/planet/queryables\"\n }\n ]\n}" }, { "path": "tests/data/netherlands_aoi.json", "content": "{\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 6.42425537109375,\n 53.174765470134616\n ],\n [\n 7.344360351562499,\n 53.174765470134616\n ],\n [\n 7.344360351562499,\n 53.67393435835391\n ],\n [\n 6.42425537109375,\n 53.67393435835391\n ],\n [\n 6.42425537109375,\n 53.174765470134616\n ]\n ]\n ]\n}\n" }, { "path": "tests/data/planetary-computer-aster-l1t-collection.json", "content": "{\n \"id\": \"aster-l1t\",\n \"type\": \"Collection\",\n \"links\": [\n {\n \"rel\": \"items\",\n \"type\": \"application/geo+json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t/items\"\n },\n {\n \"rel\": \"parent\",\n \"type\": \"application/json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/\"\n },\n {\n \"rel\": \"root\",\n \"type\": \"application/json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/\"\n },\n {\n \"rel\": \"self\",\n \"type\": \"application/json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t\"\n },\n {\n \"rel\": \"license\",\n \"href\": \"https://www.usgs.gov/core-science-systems/hdds/data-policy\",\n \"title\": \"Public Domain\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://planetarycomputer.microsoft.com/dataset/aster-l1t\",\n \"title\": \"Human readable dataset overview and reference\",\n \"type\": \"text/html\"\n }\n ],\n \"title\": \"ASTER L1T\",\n \"assets\": {\n \"thumbnail\": {\n \"href\": \"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/aster.png\",\n \"type\": \"image/png\",\n \"roles\": [\n \"thumbnail\"\n ],\n \"title\": \"ASTER L1T\"\n }\n },\n \"extent\": {\n \"spatial\": {\n \"bbox\": [\n [\n -180,\n -90,\n 180,\n 90\n ]\n ]\n },\n \"temporal\": {\n \"interval\": [\n [\n \"2000-03-04T12:00:00.000000Z\",\n \"2006-12-31T12:00:00.000000Z\"\n ]\n ]\n }\n },\n \"license\": \"proprietary\",\n \"keywords\": [\n \"ASTER\",\n \"USGS\",\n \"NASA\",\n \"Satellite\",\n \"Global\"\n ],\n \"providers\": [\n {\n \"url\": \"https://terra.nasa.gov/about/terra-instruments/aster\",\n \"name\": \"NASA\",\n \"roles\": [\n \"producer\",\n \"licensor\"\n ]\n },\n {\n \"url\": \"https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/aster-overview/\",\n \"name\": \"USGS\",\n \"roles\": [\n \"processor\",\n \"producer\",\n \"licensor\"\n ]\n },\n {\n \"url\": \"https://planetarycomputer.microsoft.com\",\n \"name\": \"Microsoft\",\n \"roles\": [\n \"host\",\n \"processor\"\n ]\n }\n ],\n \"summaries\": {\n \"gsd\": [\n 15,\n 30,\n 90\n ],\n \"eo:bands\": [\n {\n \"gsd\": 15,\n \"name\": \"VNIR_Band1\",\n \"common_name\": \"green\",\n \"description\": \"visible yellow/green\",\n \"center_wavelength\": 0.56,\n \"full_width_half_max\": 0.08\n },\n {\n \"gsd\": 15,\n \"name\": \"VNIR_Band2\",\n \"common_name\": \"red\",\n \"description\": \"visible red\",\n \"center_wavelength\": 0.66,\n \"full_width_half_max\": 0.06\n },\n {\n \"gsd\": 15,\n \"name\": \"VNIR_Band3N\",\n \"common_name\": \"nir08\",\n \"description\": \"near infrared\",\n \"center_wavelength\": 0.82,\n \"full_width_half_max\": 0.08\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band4\",\n \"common_name\": \"swir16\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 1.65,\n \"full_width_half_max\": 0.1\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band5\",\n \"common_name\": \"swir22\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.165,\n \"full_width_half_max\": 0.04\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band6\",\n \"common_name\": \"swir22\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.205,\n \"full_width_half_max\": 0.04\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band7\",\n \"common_name\": \"swir22\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.26,\n \"full_width_half_max\": 0.05\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band8\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.339,\n \"full_width_half_max\": 0.07\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band9\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.395,\n \"full_width_half_max\": 0.07\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band10\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 8.3,\n \"full_width_half_max\": 0.35\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band11\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 8.65,\n \"full_width_half_max\": 0.35\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band12\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 9.11,\n \"full_width_half_max\": 0.35\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band13\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 10.6,\n \"full_width_half_max\": 0.7\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band14\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 11.3,\n \"full_width_half_max\": 0.7\n }\n ],\n \"platform\": [\n \"terra\"\n ],\n \"instruments\": [\n \"aster\"\n ]\n },\n \"description\": \"The [ASTER](https://terra.nasa.gov/about/terra-instruments/aster) instrument, launched on-board NASA's [Terra](https://terra.nasa.gov/) satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. ASTER images provide information about land surface temperature, color, elevation, and mineral composition.\\\\n\\\\nThis dataset represents ASTER [L1T](https://lpdaac.usgs.gov/products/ast_l1tv003/) data from 2000-2006. L1T images have been terrain-corrected and rotated to a north-up UTM projection. Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\\\\n\",\n \"item_assets\": {\n \"TIR\": {\n \"roles\": [\n \"data\"\n ],\n \"title\": \"TIR Swath data\",\n \"eo:bands\": [\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band10\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 8.3,\n \"full_width_half_max\": 0.35\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band11\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 8.65,\n \"full_width_half_max\": 0.35\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band12\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 9.11,\n \"full_width_half_max\": 0.35\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band13\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 10.6,\n \"full_width_half_max\": 0.7\n },\n {\n \"gsd\": 90,\n \"name\": \"TIR_Band14\",\n \"common_name\": \"lwir\",\n \"description\": \"thermal infrared\",\n \"center_wavelength\": 11.3,\n \"full_width_half_max\": 0.7\n }\n ]\n },\n \"xml\": {\n \"type\": \"application/xml\",\n \"roles\": [\n \"metadata\"\n ],\n \"title\": \"XML metadata\"\n },\n \"SWIR\": {\n \"type\": \"image/tiff; application=geotiff; profile=cloud-optimized\",\n \"roles\": [\n \"data\"\n ],\n \"title\": \"SWIR Swath data\",\n \"eo:bands\": [\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band4\",\n \"common_name\": \"swir\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 1.65,\n \"full_width_half_max\": 0.1\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band5\",\n \"common_name\": \"swir\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.165,\n \"full_width_half_max\": 0.04\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band6\",\n \"common_name\": \"swir\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.205,\n \"full_width_half_max\": 0.04\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band7\",\n \"common_name\": \"swir\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.26,\n \"full_width_half_max\": 0.05\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band8\",\n \"common_name\": \"swir\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.339,\n \"full_width_half_max\": 0.07\n },\n {\n \"gsd\": 30,\n \"name\": \"SWIR_Band9\",\n \"common_name\": \"swir\",\n \"description\": \"short-wave infrared\",\n \"center_wavelength\": 2.395,\n \"full_width_half_max\": 0.07\n }\n ]\n },\n \"VNIR\": {\n \"type\": \"image/tiff; application=geotiff; profile=cloud-optimized\",\n \"roles\": [\n \"data\"\n ],\n \"title\": \"VNIR Swath data\",\n \"eo:bands\": [\n {\n \"gsd\": 15,\n \"name\": \"VNIR_Band1\",\n \"common_name\": \"yellow/green\",\n \"description\": \"visible yellow/green\",\n \"center_wavelength\": 0.56,\n \"full_width_half_max\": 0.08\n },\n {\n \"gsd\": 15,\n \"name\": \"VNIR_Band2\",\n \"common_name\": \"red\",\n \"description\": \"visible red\",\n \"center_wavelength\": 0.66,\n \"full_width_half_max\": 0.06\n },\n {\n \"gsd\": 15,\n \"name\": \"VNIR_Band3N\",\n \"common_name\": \"near infrared\",\n \"description\": \"near infrared\",\n \"center_wavelength\": 0.82,\n \"full_width_half_max\": 0.08\n }\n ]\n },\n \"qa-txt\": {\n \"type\": \"text/plain\",\n \"roles\": [\n \"metadata\"\n ],\n \"title\": \"QA browse file\",\n \"description\": \"Geometric quality assessment report.\"\n },\n \"qa-browse\": {\n \"type\": \"image/jpeg\",\n \"roles\": [\n \"thumbnail\"\n ],\n \"title\": \"QA browse file\",\n \"description\": \"Single-band black and white reduced resolution browse overlaid with red, green, and blue (RGB) markers for GCPs used during the geometric verification quality check.\"\n },\n \"tir-browse\": {\n \"type\": \"image/jpeg\",\n \"roles\": [\n \"thumbnail\"\n ],\n \"title\": \"Standalone reduced resolution TIR\"\n },\n \"vnir-browse\": {\n \"type\": \"image/jpeg\",\n \"roles\": [\n \"thumbnail\"\n ],\n \"title\": \"VNIR browse file\",\n \"description\": \"Standalone reduced resolution VNIR\"\n }\n },\n \"stac_version\": \"1.0.0\",\n \"msft:container\": \"aster\",\n \"stac_extensions\": [\n \"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"\n ],\n \"msft:storage_account\": \"astersa\",\n \"msft:short_description\": \"The ASTER instrument, launched on-board NASA's Terra satellite in 1999, provides multispectral images of the Earth at 15m-90m resolution. This dataset contains ASTER data from 2000-2006.\"\n}\n" }, { "path": "tests/data/planetary-computer-collection.json", "content": "{\n \"id\": \"gap\",\n \"type\": \"Collection\",\n \"links\": [\n {\n \"rel\": \"items\",\n \"type\": \"application/geo+json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap/items\"\n },\n {\n \"rel\": \"parent\",\n \"type\": \"application/json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/\"\n },\n {\n \"rel\": \"root\",\n \"type\": \"application/json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/\"\n },\n {\n \"rel\": \"self\",\n \"type\": \"application/json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gap\"\n },\n {\n \"rel\": \"license\",\n \"href\": \"https://www.usgs.gov/core-science-systems/hdds/data-policy\",\n \"title\": \"public domain\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://planetarycomputer.microsoft.com/dataset/gap\",\n \"title\": \"Human readable dataset overview and reference\",\n \"type\": \"text/html\"\n }\n ],\n \"title\": \"USGS Gap Land Cover\",\n \"assets\": {\n \"thumbnail\": {\n \"href\": \"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gap.png\",\n \"type\": \"image/png\",\n \"roles\": [\n \"data\"\n ],\n \"title\": \"USGS GAP\"\n },\n \"original-data-ak\": {\n \"href\": \"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_ak.zip\",\n \"type\": \"application/zip\",\n \"roles\": [\n \"data\"\n ],\n \"title\": \"Alaska source data\",\n \"description\": \"Original source data for Alaska\"\n },\n \"original-data-hi\": {\n \"href\": \"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_hi.zip\",\n \"type\": \"application/zip\",\n \"roles\": [\n \"data\"\n ],\n \"title\": \"Hawaii source data\",\n \"description\": \"Original source data for Hawaii\"\n },\n \"original-data-pr\": {\n \"href\": \"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/pr_landcover.zip\",\n \"type\": \"application/zip\",\n \"roles\": [\n \"data\"\n ],\n \"title\": \"Puerto Rico source data\",\n \"description\": \"Original source data for Puerto Rico\"\n },\n \"original-data-conus\": {\n \"href\": \"https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gap_landfire_nationalterrestrialecosystems2011.zip\",\n \"type\": \"application/zip\",\n \"roles\": [\n \"data\"\n ],\n \"title\": \"CONUS source data\",\n \"description\": \"Original source data for the continental United States (CONUS)\"\n }\n },\n \"extent\": {\n \"spatial\": {\n \"bbox\": [\n [\n -127.9710481801793,\n 22.797789263564383,\n -65.26634281147894,\n 51.64692620669362\n ],\n [\n -178.13166387448902,\n 49.09079265233118,\n 179.87849702345594,\n 71.43382483774205\n ],\n [\n -160.26640694607218,\n 18.851824447510786,\n -154.66974350173518,\n 22.295114188194738\n ],\n [\n -67.9573345827195,\n 17.874066536543,\n -65.21836408976736,\n 18.5296513469496\n ]\n ]\n },\n \"temporal\": {\n \"interval\": [\n [\n \"1999-01-01T00:00:00Z\",\n \"2011-12-31T00:00:00Z\"\n ]\n ]\n }\n },\n \"license\": \"proprietary\",\n \"keywords\": [\n \"USGS\",\n \"GAP\",\n \"LANDFIRE\",\n \"Land Cover\",\n \"United States\"\n ],\n \"providers\": [\n {\n \"url\": \"https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover\",\n \"name\": \"USGS\",\n \"roles\": [\n \"processor\",\n \"producer\",\n \"licensor\"\n ]\n },\n {\n \"url\": \"https://planetarycomputer.microsoft.com\",\n \"name\": \"Microsoft\",\n \"roles\": [\n \"host\"\n ]\n }\n ],\n \"summaries\": {\n \"gsd\": [\n 30\n ],\n \"label:classes\": [\n {\n \"classes\": [\n \"0\",\n \"South Florida Bayhead Swamp\",\n \"South Florida Cypress Dome\",\n \"South Florida Dwarf Cypress Savanna\",\n \"South Florida Mangrove Swamp\",\n \"South Florida Hardwood Hammock\",\n \"Southeast Florida Coastal Strand and Maritime Hammock\",\n \"Southwest Florida Coastal Strand and Maritime Hammock\",\n \"South Florida Pine Rockland\",\n \"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Open Understory\",\n \"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory\",\n \"Atlantic Coastal Plain Upland Longleaf Pine Woodland\",\n \"Atlantic Coastal Plain Xeric River Dune\",\n \"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Open Understory Modifier\",\n \"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Scrub/Shrub Modifier\",\n \"Florida Longleaf Pine Sandhill - Scrub/Shrub Understory Modifier\",\n \"Florida Longleaf Pine Sandhill- Open Understory Modifier\",\n \"West Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland\",\n \"Atlantic Coastal Plain Central Maritime Forest\",\n \"Atlantic Coastal Plain Southern Maritime Forest\",\n \"Central and South Texas Coastal Fringe Forest and Woodland\",\n \"East Gulf Coastal Plain Limestone Forest\",\n \"East Gulf Coastal Plain Maritime Forest\",\n \"East Gulf Coastal Plain Southern Loess Bluff Forest\",\n \"East Gulf Coastal Plain Southern Mesic Slope Forest\",\n \"Mississippi Delta Maritime Forest\",\n \"Southern Coastal Plain Dry Upland Hardwood Forest\",\n \"Southern Coastal Plain Oak Dome and Hammock\",\n \"West Gulf Coastal Plain Chenier and Upper Texas Coastal Fringe Forest and Woodland\",\n \"West Gulf Coastal Plain Mesic Hardwood Forest\",\n \"East-Central Texas Plains Pine Forest and Woodland\",\n \"West Gulf Coastal Plain Pine-Hardwood Forest\",\n \"West Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest and Woodland\",\n \"Atlantic Coastal Plain Fall-Line Sandhills Longleaf Pine Woodland - Loblolly Modifier\",\n \"Deciduous Plantations\",\n \"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Loblolly Modifier\",\n \"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood Modifier\",\n \"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Offsite Hardwood Modifier\",\n \"Evergreen Plantation or Managed Pine\",\n \"California Central Valley Mixed Oak Savanna\",\n \"California Coastal Closed-Cone Conifer Forest and Woodland\",\n \"California Coastal Live Oak Woodland and Savanna\",\n \"California Lower Montane Blue Oak-Foothill Pine Woodland and Savanna\",\n \"Central and Southern California Mixed Evergreen Woodland\",\n \"Mediterranean California Lower Montane Black Oak-Conifer Forest and Woodland\",\n \"Southern California Oak Woodland and Savanna\",\n \"Madrean Encinal\",\n \"Madrean Pinyon-Juniper Woodland\",\n \"Madrean Pine-Oak Forest and Woodland\",\n \"Madrean Upper Montane Conifer-Oak Forest and Woodland\",\n \"Edwards Plateau Dry-Mesic Slope Forest and Woodland\",\n \"Edwards Plateau Limestone Savanna and Woodland\",\n \"Edwards Plateau Mesic Canyon\",\n \"Llano Uplift Acidic Forest, Woodland and Glade\",\n \"East Cascades Oak-Ponderosa Pine Forest and Woodland\",\n \"Mediterranean California Mixed Evergreen Forest\",\n \"Mediterranean California Mixed Oak Woodland\",\n \"North Pacific Dry Douglas-fir-(Madrone) Forest and Woodland\",\n \"North Pacific Oak Woodland\",\n \"Edwards Plateau Limestone Shrubland\",\n \"Allegheny-Cumberland Dry Oak Forest and Woodland - Hardwood\",\n \"Allegheny-Cumberland Dry Oak Forest and Woodland - Pine Modifier\",\n \"Central and Southern Appalachian Montane Oak Forest\",\n \"Central and Southern Appalachian Northern Hardwood Forest\",\n \"Central Appalachian Oak and Pine Forest\",\n \"Crosstimbers Oak Forest and Woodland\",\n \"East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Woodland Modifier\",\n \"East Gulf Coastal Plain Northern Dry Upland Hardwood Forest\",\n \"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Hardwood Modifier\",\n \"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Juniper Modifier\",\n \"East-Central Texas Plains Post Oak Savanna and Woodland\",\n \"Lower Mississippi River Dune Woodland and Forest\",\n \"Mississippi River Alluvial Plain Dry-Mesic Loess Slope Forest\",\n \"North-Central Interior Dry Oak Forest and Woodland\",\n \"North-Central Interior Dry-Mesic Oak Forest and Woodland\",\n \"Northeastern Interior Dry Oak Forest - Mixed Modifier\",\n \"Northeastern Interior Dry Oak Forest - Virginia/Pitch Pine Modifier\",\n \"Northeastern Interior Dry Oak Forest-Hardwood Modifier\",\n \"Northeastern Interior Dry-Mesic Oak Forest\",\n \"Northern Atlantic Coastal Plain Dry Hardwood Forest\",\n \"Crowley's Ridge Sand Forest\",\n \"Ouachita Montane Oak Forest\",\n \"Ozark-Ouachita Dry Oak Woodland\",\n \"Ozark-Ouachita Dry-Mesic Oak Forest\",\n \"Southern and Central Appalachian Oak Forest\",\n \"Southern and Central Appalachian Oak Forest - Xeric\",\n \"Southern Interior Low Plateau Dry-Mesic Oak Forest\",\n \"Southern Ridge and Valley Dry Calcareous Forest\",\n \"Southern Ridge and Valley Dry Calcareous Forest - Pine modifier\",\n \"East Gulf Coastal Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier\",\n \"Managed Tree Plantation\",\n \"Ruderal forest\",\n \"Southern Piedmont Dry Oak-(Pine) Forest - Loblolly Pine Modifier\",\n \"Acadian Low-Elevation Spruce-Fir-Hardwood Forest\",\n \"Acadian-Appalachian Montane Spruce-Fir Forest\",\n \"Appalachian Hemlock-Hardwood Forest\",\n \"Central and Southern Appalachian Spruce-Fir Forest\",\n \"0\",\n \"Laurentian-Acadian Northern Hardwoods Forest\",\n \"Laurentian-Acadian Northern Pine-(Oak) Forest\",\n \"Laurentian-Acadian Pine-Hemlock-Hardwood Forest\",\n \"Paleozoic Plateau Bluff and Talus\",\n \"Southern Appalachian Northern Hardwood Forest\",\n \"Atlantic Coastal Plain Dry and Dry-Mesic Oak Forest\",\n \"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Offsite Hardwood\",\n \"East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Hardwood Modifier\",\n \"East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Mixed Modifier\",\n \"Ozark-Ouachita Shortleaf Pine-Bluestem Woodland\",\n \"Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland\",\n \"Southeastern Interior Longleaf Pine Woodland\",\n \"Southern Appalachian Low Mountain Pine Forest\",\n \"Southern Piedmont Dry Oak-(Pine) Forest\",\n \"Southern Piedmont Dry Oak-(Pine) Forest - Hardwood Modifier\",\n \"Southern Piedmont Dry Oak-(Pine) Forest - Mixed Modifier\",\n \"Southern Piedmont Dry Oak-Heath Forest - Mixed Modifier\",\n \"Eastern Great Plains Tallgrass Aspen Parkland\",\n \"Northwestern Great Plains Aspen Forest and Parkland\",\n \"Northwestern Great Plains Shrubland\",\n \"Western Great Plains Dry Bur Oak Forest and Woodland\",\n \"Western Great Plains Wooded Draw and Ravine\",\n \"Southern Atlantic Coastal Plain Mesic Hardwood Forest\",\n \"East Gulf Coastal Plain Northern Loess Bluff Forest\",\n \"East Gulf Coastal Plain Northern Mesic Hardwood Forest\",\n \"North-Central Interior Beech-Maple Forest\",\n \"North-Central Interior Maple-Basswood Forest\",\n \"Ozark-Ouachita Mesic Hardwood Forest\",\n \"South-Central Interior Mesophytic Forest\",\n \"Southern and Central Appalachian Cove Forest\",\n \"Crowley's Ridge Mesic Loess Slope Forest\",\n \"Southern Piedmont Mesic Forest\",\n \"Appalachian Shale Barrens\",\n \"Atlantic Coastal Plain Northern Maritime Forest\",\n \"Laurentian Pine-Oak Barrens\",\n \"Northeastern Interior Pine Barrens\",\n \"Northern Atlantic Coastal Plain Pitch Pine Barrens\",\n \"Southern Appalachian Montane Pine Forest and Woodland\",\n \"East Cascades Mesic Montane Mixed-Conifer Forest and Woodland\",\n \"Middle Rocky Mountain Montane Douglas-fir Forest and Woodland\",\n \"Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest\",\n \"Northern Rocky Mountain Foothill Conifer Wooded Steppe\",\n \"Northern Rocky Mountain Mesic Montane Mixed Conifer Forest\",\n \"Northern Rocky Mountain Ponderosa Pine Woodland and Savanna\",\n \"Northern Rocky Mountain Western Larch Savanna\",\n \"Northwestern Great Plains - Black Hills Ponderosa Pine Woodland and Savanna\",\n \"Rocky Mountain Foothill Limber Pine-Juniper Woodland\",\n \"Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland\",\n \"Inter-Mountain Basins Subalpine Limber-Bristlecone Pine Woodland\",\n \"Northern Rocky Mountain Subalpine Woodland and Parkland\",\n \"Rocky Mountain Aspen Forest and Woodland\",\n \"Rocky Mountain Lodgepole Pine Forest\",\n \"Rocky Mountain Poor-Site Lodgepole Pine Forest\",\n \"Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland\",\n \"Rocky Mountain Subalpine Mesic Spruce-Fir Forest and Woodland\",\n \"Rocky Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland\",\n \"Rocky Mountain Bigtooth Maple Ravine Woodland\",\n \"Southern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland\",\n \"Southern Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland\",\n \"Southern Rocky Mountain Ponderosa Pine Savanna\",\n \"Southern Rocky Mountain Ponderosa Pine Woodland\",\n \"California Montane Jeffrey Pine-(Ponderosa Pine) Woodland\",\n \"Klamath-Siskiyou Lower Montane Serpentine Mixed Conifer Woodland\",\n \"Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland\",\n \"Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland\",\n \"Mediterranean California Mesic Mixed Conifer Forest and Woodland\",\n \"Sierran-Intermontane Desert Western White Pine-White Fir Woodland\",\n \"California Coastal Redwood Forest\",\n \"North Pacific Broadleaf Landslide Forest and Shrubland\",\n \"North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest\",\n \"North Pacific Hypermaritime Sitka Spruce Forest\",\n \"North Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest\",\n \"North Pacific Lowland Mixed Hardwood-Conifer Forest and Woodland\",\n \"North Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest\",\n \"North Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest\",\n \"North Pacific Mesic Western Hemlock-Silver Fir Forest\",\n \"North Pacific Wooded Volcanic Flowage\",\n \"Mediterranean California Red Fir Forest\",\n \"Mediterranean California Subalpine Woodland\",\n \"North Pacific Maritime Mesic Subalpine Parkland\",\n \"North Pacific Mountain Hemlock Forest\",\n \"Northern California Mesic Subalpine Woodland\",\n \"Northern Pacific Mesic Subalpine Woodland\",\n \"Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland\",\n \"Columbia Plateau Western Juniper Woodland and Savanna\",\n \"Great Basin Pinyon-Juniper Woodland\",\n \"Inter-Mountain Basins Curl-leaf Mountain Mahogany Woodland and Shrubland\",\n \"Inter-Mountain Basins Juniper Savanna\",\n \"Colorado Plateau Pinyon-Juniper Shrubland\",\n \"Colorado Plateau Pinyon-Juniper Woodland\",\n \"Southern Rocky Mountain Juniper Woodland and Savanna\",\n \"Southern Rocky Mountain Pinyon-Juniper Woodland\",\n \"Northwestern Great Plains Floodplain\",\n \"Northwestern Great Plains Riparian\",\n \"Western Great Plains Floodplain\",\n \"Western Great Plains Floodplain Systems\",\n \"Western Great Plains Riparian Woodland and Shrubland\",\n \"Central Appalachian Floodplain - Forest Modifier\",\n \"Central Appalachian Riparian - Forest Modifier\",\n \"Central Interior and Appalachian Floodplain Systems\",\n \"Central Interior and Appalachian Riparian Systems\",\n \"Laurentian-Acadian Floodplain Systems\",\n \"Ozark-Ouachita Riparian\",\n \"South-Central Interior Large Floodplain\",\n \"South-Central Interior Large Floodplain - Forest Modifier\",\n \"South-Central Interior Small Stream and Riparian\",\n \"North-Central Interior and Appalachian Rich Swamp\",\n \"0\",\n \"0\",\n \"Laurentian-Acadian Swamp Systems\",\n \"North-Central Interior Wet Flatwoods\",\n \"0\",\n \"South-Central Interior / Upper Coastal Plain Wet Flatwoods\",\n \"0\",\n \"Southern Piedmont/Ridge and Valley Upland Depression Swamp\",\n \"Atlantic Coastal Plain Blackwater Stream Floodplain Forest - Forest Modifier\",\n \"Atlantic Coastal Plain Brownwater Stream Floodplain Forest\",\n \"Atlantic Coastal Plain Northern Tidal Wooded Swamp\",\n \"Atlantic Coastal Plain Small Blackwater River Floodplain Forest\",\n \"Atlantic Coastal Plain Small Brownwater River Floodplain Forest\",\n \"Atlantic Coastal Plain Southern Tidal Wooded Swamp\",\n \"East Gulf Coastal Plain Large River Floodplain Forest - Forest Modifier\",\n \"East Gulf Coastal Plain Small Stream and River Floodplain Forest\",\n \"East Gulf Coastal Plain Tidal Wooded Swamp\",\n \"0\",\n \"Southeastern Great Plains Riparian Forest\",\n \"Southeastern Great Plains Floodplain Forest\",\n \"Mississippi River Bottomland Depression\",\n \"Mississippi River Floodplain and Riparian Forest\",\n \"Mississippi River Low Floodplain (Bottomland) Forest\",\n \"Mississippi River Riparian Forest\",\n \"Red River Large Floodplain Forest\",\n \"Southern Coastal Plain Blackwater River Floodplain Forest\",\n \"Southern Piedmont Large Floodplain Forest - Forest Modifier\",\n \"Southern Piedmont Small Floodplain and Riparian Forest\",\n \"West Gulf Coastal Plain Large River Floodplain Forest\",\n \"West Gulf Coastal Plain Near-Coast Large River Swamp\",\n \"West Gulf Coastal Plain Small Stream and River Forest\",\n \"Atlantic Coastal Plain Streamhead Seepage Swamp - Pocosin - and Baygall\",\n \"Gulf and Atlantic Coastal Plain Swamp Systems\",\n \"Southern Coastal Plain Hydric Hammock\",\n \"Southern Coastal Plain Seepage Swamp and Baygall\",\n \"West Gulf Coastal Plain Seepage Swamp and Baygall\",\n \"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Taxodium/Nyssa Modifier\",\n \"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Oak Dominated Modifier\",\n \"East Gulf Coastal Plain Southern Loblolly-Hardwood Flatwoods\",\n \"Lower Mississippi River Bottomland Depressions - Forest Modifier\",\n \"Lower Mississippi River Flatwoods\",\n \"Northern Atlantic Coastal Plain Basin Swamp and Wet Hardwood Forest\",\n \"Southern Coastal Plain Nonriverine Basin Swamp\",\n \"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier\",\n \"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier\",\n \"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier\",\n \"West Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods\",\n \"West Gulf Coastal Plain Pine-Hardwood Flatwoods\",\n \"Edwards Plateau Riparian\",\n \"Atlantic Coastal Plain Clay-Based Carolina Bay Forested Wetland\",\n \"Atlantic Coastal Plain Clay-Based Carolina Bay Herbaceous Wetland\",\n \"Atlantic Coastal Plain Southern Wet Pine Savanna and Flatwoods\",\n \"Central Atlantic Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\",\n \"Central Florida Pine Flatwoods\",\n \"East Gulf Coastal Plain Near-Coast Pine Flatwoods\",\n \"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Open Understory Modifier\",\n \"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Scrub/Shrub Understory Modifier\",\n \"South Florida Pine Flatwoods\",\n \"Southern Coastal Plain Nonriverine Cypress Dome\",\n \"West Gulf Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\",\n \"Columbia Basin Foothill Riparian Woodland and Shrubland\",\n \"Great Basin Foothill and Lower Montane Riparian Woodland and Shrubland\",\n \"0\",\n \"Northern Rocky Mountain Conifer Swamp\",\n \"Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland\",\n \"Rocky Mountain Lower Montane Riparian Woodland and Shrubland\",\n \"Rocky Mountain Montane Riparian Systems\",\n \"Rocky Mountain Subalpine-Montane Riparian Woodland\",\n \"North Pacific Hardwood-Conifer Swamp\",\n \"North Pacific Lowland Riparian Forest and Shrubland\",\n \"North Pacific Montane Riparian Woodland and Shrubland\",\n \"North Pacific Shrub Swamp\",\n \"California Central Valley Riparian Woodland and Shrubland\",\n \"Mediterranean California Foothill and Lower Montane Riparian Woodland\",\n \"Mediterranean California Serpentine Foothill and Lower Montane Riparian Woodland and Seep\",\n \"North American Warm Desert Lower Montane Riparian Woodland and Shrubland\",\n \"North American Warm Desert Riparian Systems\",\n \"North American Warm Desert Riparian Woodland and Shrubland\",\n \"Tamaulipan Floodplain\",\n \"Tamaulipan Riparian Systems\",\n \"Boreal Aspen-Birch Forest\",\n \"Boreal Jack Pine-Black Spruce Forest\",\n \"Boreal White Spruce-Fir-Hardwood Forest\",\n \"Boreal-Laurentian Conifer Acidic Swamp and Treed Poor Fen\",\n \"Eastern Boreal Floodplain\",\n \"South Florida Shell Hash Beach\",\n \"Southeast Florida Beach\",\n \"Southwest Florida Beach\",\n \"South Florida Everglades Sawgrass Marsh\",\n \"South Florida Freshwater Slough and Gator Hole\",\n \"South Florida Wet Marl Prairie\",\n \"California Maritime Chaparral\",\n \"California Mesic Chaparral\",\n \"California Xeric Serpentine Chaparral\",\n \"Klamath-Siskiyou Xeromorphic Serpentine Savanna and Chaparral\",\n \"Mediterranean California Mesic Serpentine Woodland and Chaparral\",\n \"Northern and Central California Dry-Mesic Chaparral\",\n \"Southern California Dry-Mesic Chaparral\",\n \"Southern California Coastal Scrub\",\n \"California Central Valley and Southern Coastal Grassland\",\n \"California Mesic Serpentine Grassland\",\n \"Columbia Basin Foothill and Canyon Dry Grassland\",\n \"Columbia Basin Palouse Prairie\",\n \"North Pacific Alpine and Subalpine Dry Grassland\",\n \"North Pacific Montane Grassland\",\n \"North Pacific Montane Shrubland\",\n \"Northern Rocky Mountain Lower Montane, Foothill and Valley Grassland\",\n \"Northern Rocky Mountain Montane-Foothill Deciduous Shrubland\",\n \"Northern Rocky Mountain Subalpine Deciduous Shrubland\",\n \"Northern Rocky Mountain Subalpine-Upper Montane Grassland\",\n \"Southern Rocky Mountain Montane-Subalpine Grassland\",\n \"Rocky Mountain Gambel Oak-Mixed Montane Shrubland\",\n \"Rocky Mountain Lower Montane-Foothill Shrubland\",\n \"California Northern Coastal Grassland\",\n \"North Pacific Herbaceous Bald and Bluff\",\n \"North Pacific Hypermaritime Shrub and Herbaceous Headland\",\n \"Willamette Valley Upland Prairie and Savanna\",\n \"Mediterranean California Subalpine Meadow\",\n \"Rocky Mountain Subalpine-Montane Mesic Meadow\",\n \"Central Mixedgrass Prairie\",\n \"Northwestern Great Plains Mixedgrass Prairie\",\n \"Western Great Plains Foothill and Piedmont Grassland\",\n \"Western Great Plains Tallgrass Prairie\",\n \"Western Great Plains Sand Prairie\",\n \"Western Great Plains Sandhill Steppe\",\n \"Western Great Plains Mesquite Woodland and Shrubland\",\n \"Western Great Plains Shortgrass Prairie\",\n \"Arkansas Valley Prairie and Woodland\",\n \"Central Tallgrass Prairie\",\n \"North-Central Interior Oak Savanna\",\n \"North-Central Interior Sand and Gravel Tallgrass Prairie\",\n \"North-Central Oak Barrens\",\n \"Northern Tallgrass Prairie\",\n \"Southeastern Great Plains Tallgrass Prairie\",\n \"Texas Blackland Tallgrass Prairie\",\n \"Texas-Louisiana Coastal Prairie\",\n \"Central Appalachian Pine-Oak Rocky Woodland\",\n \"Southern Appalachian Grass and Shrub Bald\",\n \"Southern Appalachian Grass and Shrub Bald - Herbaceous Modifier\",\n \"Southern Appalachian Grass and Shrub Bald - Shrub Modifier\",\n \"Central Appalachian Alkaline Glade and Woodland\",\n \"Central Interior Highlands Calcareous Glade and Barrens\",\n \"Central Interior Highlands Dry Acidic Glade and Barrens\",\n \"Cumberland Sandstone Glade and Barrens\",\n \"Great Lakes Alvar\",\n \"Nashville Basin Limestone Glade\",\n \"Southern Ridge and Valley / Cumberland Dry Calcareous Forest\",\n \"Southern Piedmont Glade and Barrens\",\n \"East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Herbaceous Modifier\",\n \"East Gulf Coastal Plain Jackson Prairie and Woodland\",\n \"Eastern Highland Rim Prairie and Barrens - Dry Modifier\",\n \"Coahuilan Chaparral\",\n \"Madrean Oriental Chaparral\",\n \"Mogollon Chaparral\",\n \"Sonora-Mojave Semi-Desert Chaparral\",\n \"California Montane Woodland and Chaparral\",\n \"Great Basin Semi-Desert Chaparral\",\n \"Florida Dry Prairie\",\n \"Florida Peninsula Inland Scrub\",\n \"West Gulf Coastal Plain Catahoula Barrens\",\n \"West Gulf Coastal Plain Nepheline Syenite Glade\",\n \"East Gulf Coastal Plain Jackson Plain Dry Flatwoods - Open Understory Modifier\",\n \"West Gulf Coastal Plain Northern Calcareous Prairie\",\n \"West Gulf Coastal Plain Southern Calcareous Prairie\",\n \"Acadian-Appalachian Subalpine Woodland and Heath-Krummholz\",\n \"Atlantic and Gulf Coastal Plain Interdunal Wetland\",\n \"Atlantic Coastal Plain Southern Dune and Maritime Grassland\",\n \"Central and Upper Texas Coast Dune and Coastal Grassland\",\n \"East Gulf Coastal Plain Dune and Coastal Grassland\",\n \"Great Lakes Dune\",\n \"Northern Atlantic Coastal Plain Dune and Swale\",\n \"Northern Atlantic Coastal Plain Heathland and Grassland\",\n \"South Texas Dune and Coastal Grassland\",\n \"South Texas Sand Sheet Grassland\",\n \"Southwest Florida Dune and Coastal Grassland\",\n \"North Pacific Coastal Cliff and Bluff\",\n \"North Pacific Maritime Coastal Sand Dune and Strand\",\n \"Northern California Coastal Scrub\",\n \"Mediterranean California Coastal Bluff\",\n \"Mediterranean California Northern Coastal Dune\",\n \"Mediterranean California Southern Coastal Dune\",\n \"Atlantic Coastal Plain Northern Sandy Beach\",\n \"Atlantic Coastal Plain Sea Island Beach\",\n \"Atlantic Coastal Plain Southern Beach\",\n \"Florida Panhandle Beach Vegetation\",\n \"Louisiana Beach\",\n \"Northern Atlantic Coastal Plain Sandy Beach\",\n \"Texas Coastal Bend Beach\",\n \"Upper Texas Coast Beach\",\n \"0\",\n \"Mediterranean California Serpentine Fen\",\n \"Mediterranean California Subalpine-Montane Fen\",\n \"North Pacific Bog and Fen\",\n \"Rocky Mountain Subalpine-Montane Fen\",\n \"Atlantic Coastal Plain Peatland Pocosin\",\n \"Southern and Central Appalachian Bog and Fen\",\n \"Atlantic Coastal Plain Central Fresh-Oligohaline Tidal Marsh\",\n \"Atlantic Coastal Plain Embayed Region Tidal Freshwater Marsh\",\n \"Atlantic Coastal Plain Northern Fresh and Oligohaline Tidal Marsh\",\n \"Florida Big Bend Fresh-Oligohaline Tidal Marsh\",\n \"Atlantic Coastal Plain Depression Pondshore\",\n \"Atlantic Coastal Plain Large Natural Lakeshore\",\n \"Central Florida Herbaceous Pondshore\",\n \"Central Florida Herbaceous Seep\",\n \"East Gulf Coastal Plain Savanna and Wet Prairie\",\n \"East Gulf Coastal Plain Depression Pondshore\",\n \"Floridian Highlands Freshwater Marsh\",\n \"Southern Coastal Plain Herbaceous Seepage Bog\",\n \"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier\",\n \"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier\",\n \"Texas-Louisiana Coastal Prairie Slough\",\n \"Central Interior and Appalachian Shrub-Herbaceous Wetland Systems\",\n \"Great Lakes Coastal Marsh Systems\",\n \"0\",\n \"0\",\n \"Laurentian-Acadian Shrub-Herbaceous Wetland Systems\",\n \"0\",\n \"Eastern Great Plains Wet Meadow, Prairie and Marsh\",\n \"Great Lakes Wet-Mesic Lakeplain Prairie\",\n \"Great Plains Prairie Pothole\",\n \"Western Great Plains Closed Depression Wetland\",\n \"Western Great Plains Depressional Wetland Systems\",\n \"Western Great Plains Open Freshwater Depression Wetland\",\n \"Cumberland Riverscour\",\n \"Inter-Mountain Basins Interdunal Swale Wetland\",\n \"North Pacific Avalanche Chute Shrubland\",\n \"North Pacific Intertidal Freshwater Wetland\",\n \"Temperate Pacific Freshwater Emergent Marsh\",\n \"Temperate Pacific Freshwater Mudflat\",\n \"Columbia Plateau Vernal Pool\",\n \"Northern California Claypan Vernal Pool\",\n \"Northern Rocky Mountain Wooded Vernal Pool\",\n \"Columbia Plateau Silver Sagebrush Seasonally Flooded Shrub-Steppe\",\n \"Rocky Mountain Alpine-Montane Wet Meadow\",\n \"Rocky Mountain Subalpine-Montane Riparian Shrubland\",\n \"Temperate Pacific Montane Wet Meadow\",\n \"Willamette Valley Wet Prairie\",\n \"Chihuahuan-Sonoran Desert Bottomland and Swale Grassland\",\n \"North American Arid West Emergent Marsh\",\n \"North American Warm Desert Riparian Mesquite Bosque\",\n \"Western Great Plains Saline Depression Wetland\",\n \"Acadian Salt Marsh and Estuary Systems\",\n \"Atlantic Coastal Plain Central Salt and Brackish Tidal Marsh\",\n \"Atlantic Coastal Plain Embayed Region Tidal Salt and Brackish Marsh\",\n \"Atlantic Coastal Plain Indian River Lagoon Tidal Marsh\",\n \"Atlantic Coastal Plain Northern Tidal Salt Marsh\",\n \"Florida Big Bend Salt-Brackish Tidal Marsh\",\n \"Gulf and Atlantic Coastal Plain Tidal Marsh Systems\",\n \"Mississippi Sound Salt and Brackish Tidal Marsh\",\n \"Texas Saline Coastal Prairie\",\n \"Temperate Pacific Tidal Salt and Brackish Marsh\",\n \"Inter-Mountain Basins Alkaline Closed Depression\",\n \"Inter-Mountain Basins Greasewood Flat\",\n \"Inter-Mountain Basins Playa\",\n \"North American Warm Desert Playa\",\n \"Apacherian-Chihuahuan Mesquite Upland Scrub\",\n \"Apacherian-Chihuahuan Semi-Desert Grassland and Steppe\",\n \"Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub\",\n \"Chihuahuan Gypsophilous Grassland and Steppe\",\n \"Chihuahuan Loamy Plains Desert Grassland\",\n \"Chihuahuan Mixed Desert and Thorn Scrub\",\n \"Chihuahuan Sandy Plains Semi-Desert Grassland\",\n \"Chihuahuan Stabilized Coppice Dune and Sand Flat Scrub\",\n \"Chihuahuan Succulent Desert Scrub\",\n \"Madrean Juniper Savanna\",\n \"Mojave Mid-Elevation Mixed Desert Scrub\",\n \"North American Warm Desert Active and Stabilized Dune\",\n \"Sonora-Mojave Creosotebush-White Bursage Desert Scrub\",\n \"Sonoran Mid-Elevation Desert Scrub\",\n \"Sonoran Paloverde-Mixed Cacti Desert Scrub\",\n \"Chihuahuan Mixed Salt Desert Scrub\",\n \"Sonora-Mojave Mixed Salt Desert Scrub\",\n \"North American Warm Desert Wash\",\n \"South Texas Lomas\",\n \"Tamaulipan Calcareous Thornscrub\",\n \"Tamaulipan Clay Grassland\",\n \"Tamaulipan Mesquite Upland Scrub\",\n \"Tamaulipan Mixed Deciduous Thornscrub\",\n \"Tamaulipan Savanna Grassland\",\n \"Inter-Mountain Basins Mat Saltbush Shrubland\",\n \"Inter-Mountain Basins Mixed Salt Desert Scrub\",\n \"Inter-Mountain Basins Wash\",\n \"Columbia Plateau Steppe and Grassland\",\n \"Great Basin Xeric Mixed Sagebrush Shrubland\",\n \"Inter-Mountain Basins Big Sagebrush Shrubland\",\n \"Inter-Mountain Basins Big Sagebrush Steppe\",\n \"Inter-Mountain Basins Montane Sagebrush Steppe\",\n \"Colorado Plateau Mixed Low Sagebrush Shrubland\",\n \"Columbia Plateau Low Sagebrush Steppe\",\n \"Columbia Plateau Scabland Shrubland\",\n \"Wyoming Basins Dwarf Sagebrush Shrubland and Steppe\",\n \"Colorado Plateau Blackbrush-Mormon-tea Shrubland\",\n \"Inter-Mountain Basins Semi-Desert Grassland\",\n \"Inter-Mountain Basins Semi-Desert Shrub Steppe\",\n \"Southern Colorado Plateau Sand Shrubland\",\n \"Acadian-Appalachian Alpine Tundra\",\n \"Rocky Mountain Alpine Dwarf-Shrubland\",\n \"Rocky Mountain Alpine Fell-Field\",\n \"Rocky Mountain Alpine Turf\",\n \"Mediterranean California Alpine Dry Tundra\",\n \"Mediterranean California Alpine Fell-Field\",\n \"North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow\",\n \"Rocky Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit\",\n \"Temperate Pacific Intertidal Mudflat\",\n \"Mediterranean California Eelgrass Bed\",\n \"North Pacific Maritime Eelgrass Bed\",\n \"South-Central Interior Large Floodplain - Herbaceous Modifier\",\n \"East Gulf Coastal Plain Large River Floodplain Forest - Herbaceous Modifier\",\n \"Temperate Pacific Freshwater Aquatic Bed\",\n \"Central California Coast Ranges Cliff and Canyon\",\n \"Mediterranean California Serpentine Barrens\",\n \"Southern California Coast Ranges Cliff and Canyon\",\n \"Central Interior Acidic Cliff and Talus\",\n \"Central Interior Calcareous Cliff and Talus\",\n \"East Gulf Coastal Plain Dry Chalk Bluff\",\n \"North-Central Appalachian Acidic Cliff and Talus\",\n \"North-Central Appalachian Circumneutral Cliff and Talus\",\n \"Southern Appalachian Montane Cliff\",\n \"Southern Interior Acid Cliff\",\n \"Southern Interior Calcareous Cliff\",\n \"Southern Piedmont Cliff\",\n \"Southern Appalachian Granitic Dome\",\n \"Southern Appalachian Rocky Summit\",\n \"Southern Piedmont Granite Flatrock\",\n \"Rocky Mountain Cliff, Canyon and Massive Bedrock\",\n \"Klamath-Siskiyou Cliff and Outcrop\",\n \"North Pacific Montane Massive Bedrock, Cliff and Talus\",\n \"North Pacific Serpentine Barren\",\n \"North Pacific Active Volcanic Rock and Cinder Land\",\n \"Sierra Nevada Cliff and Canyon\",\n \"Western Great Plains Badland\",\n \"Southwestern Great Plains Canyon\",\n \"Western Great Plains Cliff and Outcrop\",\n \"North American Warm Desert Badland\",\n \"North American Warm Desert Bedrock Cliff and Outcrop\",\n \"North American Warm Desert Pavement\",\n \"North American Warm Desert Volcanic Rockland\",\n \"Colorado Plateau Mixed Bedrock Canyon and Tableland\",\n \"Columbia Plateau Ash and Tuff Badland\",\n \"Geysers and Hot Springs\",\n \"Inter-Mountain Basins Active and Stabilized Dune\",\n \"Inter-Mountain Basins Cliff and Canyon\",\n \"Inter-Mountain Basins Shale Badland\",\n \"Inter-Mountain Basins Volcanic Rock and Cinder Land\",\n \"Rocky Mountain Alpine Bedrock and Scree\",\n \"Mediterranean California Alpine Bedrock and Scree\",\n \"North Pacific Alpine and Subalpine Bedrock and Scree\",\n \"Unconsolidated Shore\",\n \"Undifferentiated Barren Land\",\n \"North American Alpine Ice Field\",\n \"Orchards Vineyards and Other High Structure Agriculture\",\n \"Cultivated Cropland\",\n \"Pasture/Hay\",\n \"Introduced Upland Vegetation - Annual Grassland\",\n \"Introduced Upland Vegetation - Perennial Grassland and Forbland\",\n \"Modified/Managed Southern Tall Grassland\",\n \"Introduced Upland Vegetation - Shrub\",\n \"Introduced Riparian and Wetland Vegetation\",\n \"Introduced Upland Vegetation - Treed\",\n \"0\",\n \"Disturbed, Non-specific\",\n \"Recently Logged Areas\",\n \"Harvested Forest - Grass/Forb Regeneration\",\n \"Harvested Forest-Shrub Regeneration\",\n \"Harvested Forest - Northwestern Conifer Regeneration\",\n \"Recently Burned\",\n \"Recently burned grassland\",\n \"Recently burned shrubland\",\n \"Recently burned forest\",\n \"Disturbed/Successional - Grass/Forb Regeneration\",\n \"Disturbed/Successional - Shrub Regeneration\",\n \"Disturbed/Successional - Recently Chained Pinyon-Juniper\",\n \"Open Water (Aquaculture)\",\n \"Open Water (Brackish/Salt)\",\n \"Open Water (Fresh)\",\n \"Quarries, Mines, Gravel Pits and Oil Wells\",\n \"Developed, Open Space\",\n \"Developed, Low Intensity\",\n \"Developed, Medium Intensity\",\n \"Developed, High Intensity\"\n ]\n }\n ]\n },\n \"description\": \"The USGS GAP/LANDFIRE National Terrestrial Ecosystems data, based on the NatureServe Ecological Systems Classification, are the foundation of the most detailed, consistent map of vegetation available for the United States. These data facilitate planning and management for biological diversity on a regional and national scale.\\\\n\\\\nThis dataset includes the land cover component of the GAP Analyais project.\\\\n\\\\n\",\n \"item_assets\": {\n \"data\": {\n \"type\": \"image/tiff; application=geotiff; profile=cloud-optimized\",\n \"roles\": [\n \"data\"\n ],\n \"title\": \"GeoTIFF data\"\n }\n },\n \"stac_version\": \"1.0.0\",\n \"msft:container\": \"usgs-gap\",\n \"stac_extensions\": [\n \"https://stac-extensions.github.io/label/v1.0.0/schema.json\",\n \"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"\n ],\n \"msft:storage_account\": \"ai4edataeuwest\",\n \"msft:short_description\": \"US-wide land cover information for 2011\"\n}\n" }, { "path": "tests/data/planetary-computer-root.json", "content": "{\n \"id\": \"microsoft-pc\",\n \"description\": \"Searchable spatiotemporal metadata describing Earth science datasets hosted by the Microsoft Planetary Computer\",\n \"title\": \"Microsoft Planetary Computer STAC API\",\n \"stac_version\": \"1.0.0-beta.2\",\n \"links\": [\n {\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/\",\n \"rel\": \"self\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"data\",\n \"type\": \"application/json\",\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections\"\n },\n {\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/docs\",\n \"rel\": \"docs\",\n \"type\": \"text/html\",\n \"title\": \"OpenAPI docs\"\n },\n {\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/conformance\",\n \"rel\": \"conformance\",\n \"type\": \"application/json\",\n \"title\": \"STAC/WFS3 conformance classes implemented by this server\"\n },\n {\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/search\",\n \"rel\": \"search\",\n \"type\": \"application/geo+json\",\n \"title\": \"STAC search\"\n },\n {\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t\",\n \"rel\": \"child\",\n \"type\": \"application/json\",\n \"title\": \"ASTER L1T\"\n },\n {\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-8-c2-l2\",\n \"rel\": \"child\",\n \"type\": \"application/json\",\n \"title\": \"Landsat 8 Collection 2 Level-2\"\n },\n {\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-2-l2a\",\n \"rel\": \"child\",\n \"type\": \"application/json\",\n \"title\": \"Sentinel-2 Level-2A\"\n },\n {\n \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip\",\n \"rel\": \"child\",\n \"type\": \"application/json\",\n \"title\": \"NAIP: National Agriculture Imagery Program\"\n }\n ],\n \"conformsTo\": [\n \"https://api.stacspec.org/v1.0.0-beta.1/core\",\n \"https://api.stacspec.org/v1.0.0-beta.1/collections\",\n \"https://api.stacspec.org/v1.0.0-beta.1/item-search\",\n \"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core\",\n \"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30\",\n \"http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson\"\n ]\n}" }, { "path": "tests/data/sample-item-collection.json", "content": "{\n \"type\": \"FeatureCollection\",\n \"stac_version\": \"1.0.0-rc.4\",\n \"numberMatched\": 324132,\n \"numberReturned\": 10,\n \"features\": [\n {\n \"type\": \"Feature\",\n \"id\": \"G1994512890-LPCLOUD\",\n \"stac_version\": \"1.0.0-rc.4\",\n \"stac_extensions\": [\n \"https://stac-extensions.github.io/eo/v1.1.0/schema.json\"\n ],\n \"collection\": \"HLSL30.v1.5\",\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -177.0002548,\n -45.2413329\n ],\n [\n -176.7533258,\n -45.2410666\n ],\n [\n -176.4223072,\n -44.2514167\n ],\n [\n -177.0002505,\n -44.2528774\n ],\n [\n -177.0002548,\n -45.2413329\n ]\n ]\n ]\n },\n \"bbox\": [\n -177.000255,\n -45.241333,\n -176.422307,\n -44.251417\n ],\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/HLSL30.v1.5/items/G1994512890-LPCLOUD\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/HLSL30.v1.5\"\n },\n {\n \"rel\": \"collection\",\n \"href\": \"https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/HLSL30.v1.5\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"https://cmr.earthdata.nasa.gov/stac/\"\n },\n {\n \"rel\": \"provider\",\n \"href\": \"https://cmr.earthdata.nasa.gov/stac/LPCLOUD\"\n },\n {\n \"rel\": \"via\",\n \"href\": \"https://cmr.earthdata.nasa.gov/search/concepts/G1994512890-LPCLOUD.json\"\n },\n {\n \"rel\": \"via\",\n \"href\": \"https://cmr.earthdata.nasa.gov/search/concepts/G1994512890-LPCLOUD.umm_json\"\n }\n ],\n \"properties\": {\n \"datetime\": \"2021-01-01T21:31:13.552Z\",\n \"start_datetime\": \"2021-01-01T21:31:13.552Z\",\n \"end_datetime\": \"2021-01-01T21:31:13.552Z\",\n 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\"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T01GEL.2021001T213113.v1.5.SAA.tif\"\n },\n \"VAA\": {\n \"title\": \"Download HLS.L30.T01GEL.2021001T213113.v1.5.VAA.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T01GEL.2021001T213113.v1.5.VAA.tif\"\n },\n \"B07\": {\n \"title\": \"Download HLS.L30.T01GEL.2021001T213113.v1.5.B07.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T01GEL.2021001T213113.v1.5.B07.tif\"\n },\n \"VZA\": {\n \"title\": \"Download HLS.L30.T01GEL.2021001T213113.v1.5.VZA.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T01GEL.2021001T213113.v1.5.VZA.tif\"\n },\n \"B03\": {\n \"title\": \"Download HLS.L30.T01GEL.2021001T213113.v1.5.B03.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T01GEL.2021001T213113.v1.5.B03.tif\"\n },\n \"B04\": {\n \"title\": \"Download 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HLS.L30.T60MUD.2021014T222708.v1.5.SZA.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.SZA.tif\"\n },\n \"B04\": {\n \"title\": \"Download HLS.L30.T60MUD.2021014T222708.v1.5.B04.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B04.tif\"\n },\n \"B01\": {\n \"title\": \"Download HLS.L30.T60MUD.2021014T222708.v1.5.B01.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B01.tif\"\n },\n \"B05\": {\n \"title\": \"Download HLS.L30.T60MUD.2021014T222708.v1.5.B05.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B05.tif\"\n },\n \"SAA\": {\n \"title\": \"Download HLS.L30.T60MUD.2021014T222708.v1.5.SAA.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.SAA.tif\"\n },\n \"B11\": {\n \"title\": \"Download HLS.L30.T60MUD.2021014T222708.v1.5.B11.tif\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B11.tif\"\n },\n \"browse\": {\n \"title\": \"Download HLS.L30.T60MUD.2021014T222708.v1.5.jpg\",\n \"href\": \"https://lpdaac.earthdata.nasa.gov/lp-prod-public/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.jpg\",\n \"type\": \"image/jpeg\"\n },\n \"metadata\": {\n \"href\": \"https://cmr.earthdata.nasa.gov/search/concepts/G1994873826-LPCLOUD.xml\",\n \"type\": \"application/xml\"\n }\n }\n }\n ],\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/HLSL30.v1.5/items\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"https://cmr.earthdata.nasa.gov/stac/\"\n },\n {\n \"rel\": \"next\",\n \"method\": \"GET\",\n \"href\": \"https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/HLSL30.v1.5/items?page=2\"\n }\n ],\n \"context\": {\n \"returned\": 10,\n \"limit\": 1000000,\n \"matched\": 324132\n }\n }" }, { "path": "tests/data/sample-item.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.0.0\",\n \"id\": \"CS3-20160503_132131_05\",\n \"properties\": {\n \"datetime\": \"2016-05-03T13:22:30.040000Z\",\n \"title\": \"A CS3 item\",\n \"license\": \"PDDL-1.0\",\n \"providers\": [\n {\n \"name\": \"CoolSat\",\n \"roles\": [\n \"producer\",\n \"licensor\"\n ],\n \"url\": \"https://cool-sat.com/\"\n }\n ]\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.308150179,\n 37.488035566\n ],\n [\n -122.597502109,\n 37.538869539\n ],\n [\n -122.576687533,\n 37.613537207\n ],\n [\n -122.2880486,\n 37.562818007\n ],\n [\n -122.308150179,\n 37.488035566\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"collection\",\n \"href\": \"https://raw.githubusercontent.com/radiantearth/stac-spec/v0.8.1/collection-spec/examples/sentinel2.json\"\n }\n ],\n \"assets\": {\n \"analytic\": {\n \"href\": \"http://cool-sat.com/catalog/CS3-20160503_132130_04/analytic.tif\",\n \"title\": \"4-Band Analytic\",\n \"product\": \"http://cool-sat.com/catalog/products/analytic.json\",\n \"type\": \"image/tiff; application=geotiff; profile=cloud-optimized\",\n \"roles\": [\n \"data\",\n \"analytic\"\n ]\n },\n \"thumbnail\": {\n \"href\": \"http://cool-sat.com/catalog/CS3-20160503_132130_04/thumbnail.png\",\n \"title\": \"Thumbnail\",\n \"type\": \"image/png\",\n \"roles\": [\n \"thumbnail\"\n ]\n }\n },\n \"bbox\": [\n -122.59750209,\n 37.48803556,\n -122.2880486,\n 37.613537207\n ],\n \"stac_extensions\": [],\n \"collection\": \"CS3\"\n }" }, { "path": "tests/data/test-case-1/catalog.json", "content": "{\n \"type\": \"Catalog\",\n \"id\": \"test\",\n \"stac_version\": \"1.1.0\",\n \"description\": \"test catalog\",\n \"links\": [\n {\n \"rel\": \"child\",\n \"href\": \"./country-1/catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"child\",\n \"href\": \"./country-2/catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"./catalog.json\",\n \"type\": \"application/json\"\n }\n ],\n \"stac_extensions\": []\n}" }, { "path": "tests/data/test-case-1/country-1/area-1-1/area-1-1-imagery/area-1-1-imagery.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.1.0\",\n \"id\": \"area-1-1-imagery\",\n \"properties\": {\n \"datetime\": \"2019-10-04T18:55:37Z\"\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.5048828125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 3.8916575492899987\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n }\n ],\n \"assets\": {\n \"ortho\": {\n \"href\": \"http://example.com/area-1-1_ortho.tif\",\n \"type\": \"image/vnd.stac.geotiff\"\n },\n \"dsm\": {\n \"href\": \"http://example.com/area-1-1_dsm.tif\",\n \"type\": \"image/vnd.stac.geotiff\"\n }\n },\n \"bbox\": [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 3.8916575492899987\n ],\n \"stac_extensions\": [],\n \"collection\": \"area-1-1\"\n}" }, { "path": "tests/data/test-case-1/country-1/area-1-1/area-1-1-labels/area-1-1-labels.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.1.0\",\n \"id\": \"area-1-1-labels\",\n \"properties\": {\n \"datetime\": \"2019-10-04T18:55:37Z\",\n \"label:description\": \"labels for area-1-1\",\n \"label:type\": \"vector\",\n \"label:properties\": [\n \"label\"\n ],\n \"label:classes\": [\n {\n \"name\": \"label\",\n \"classes\": [\n \"one\",\n \"two\"\n ]\n }\n ],\n \"label:tasks\": [\n \"classification\"\n ],\n \"label:methods\": [\n \"manual\"\n ]\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.5048828125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 3.8916575492899987\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"source\",\n \"href\": \"../area-1-1-imagery/area-1-1-imagery.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n }\n ],\n \"assets\": {\n \"labels\": {\n \"href\": \"http://example.com/area-1-1-labels.geojson\",\n \"type\": \"application/geo+json\"\n }\n },\n \"bbox\": [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 3.8916575492899987\n ],\n \"stac_extensions\": [\n \"https://stac-extensions.github.io/label/v1.0.1/schema.json\"\n ],\n \"collection\": \"area-1-1\"\n}" }, { "path": "tests/data/test-case-1/country-1/area-1-1/collection.json", "content": "{\n \"type\": \"Collection\",\n \"id\": \"area-1-1\",\n \"stac_version\": \"1.1.0\",\n \"description\": \"test collection country-1\",\n \"links\": [\n {\n \"rel\": \"item\",\n \"href\": \"./area-1-1-imagery/area-1-1-imagery.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"item\",\n \"href\": \"./area-1-1-labels/area-1-1-labels.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../catalog.json\",\n \"type\": \"application/json\"\n }\n ],\n \"stac_extensions\": [],\n \"extent\": {\n \"spatial\": {\n \"bbox\": [\n [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 4.275202171119132\n ]\n ]\n },\n \"temporal\": {\n \"interval\": [\n [\n \"2019-10-04T18:55:37Z\",\n null\n ]\n ]\n }\n },\n \"license\": \"other\"\n}" }, { "path": "tests/data/test-case-1/country-1/area-1-2/area-1-2-imagery/area-1-2-imagery.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.1.0\",\n \"id\": \"area-1-2-imagery\",\n \"properties\": {\n \"datetime\": \"2019-10-04T18:55:37Z\"\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.5048828125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 3.8916575492899987\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n }\n ],\n \"assets\": {\n \"ortho\": {\n \"href\": \"http://example.com/area-1-2_ortho.tif\",\n \"type\": \"image/vnd.stac.geotiff\"\n },\n \"dsm\": {\n \"href\": \"http://example.com/area-1-2_dsm.tif\",\n \"type\": \"image/vnd.stac.geotiff\"\n }\n },\n \"bbox\": [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 3.8916575492899987\n ],\n \"stac_extensions\": [],\n \"collection\": \"area-1-2\"\n}" }, { "path": "tests/data/test-case-1/country-1/area-1-2/area-1-2-labels/area-1-2-labels.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.1.0\",\n \"id\": \"area-1-2-labels\",\n \"properties\": {\n \"datetime\": \"2019-10-04T18:55:37Z\",\n \"label:description\": \"labels for area-1-2\",\n \"label:type\": \"vector\",\n \"label:properties\": [\n \"label\"\n ],\n \"label:classes\": [\n {\n \"name\": \"label\",\n \"classes\": [\n \"one\",\n \"two\"\n ]\n }\n ],\n \"label:tasks\": [\n \"classification\"\n ],\n \"label:methods\": [\n \"manual\"\n ]\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.5048828125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 3.8916575492899987\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"source\",\n \"href\": \"../area-1-2-imagery/area-1-2-imagery.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n }\n ],\n \"assets\": {\n \"labels\": {\n \"href\": \"http://example.com/area-1-2-labels.geojson\",\n \"type\": \"application/geo+json\"\n }\n },\n \"bbox\": [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 3.8916575492899987\n ],\n \"stac_extensions\": [\n \"https://stac-extensions.github.io/label/v1.0.1/schema.json\"\n ],\n \"collection\": \"area-1-2\"\n}" }, { "path": "tests/data/test-case-1/country-1/area-1-2/collection.json", "content": "{\n \"type\": \"Collection\",\n \"id\": \"area-1-2\",\n \"stac_version\": \"1.1.0\",\n \"description\": \"test collection country-1\",\n \"links\": [\n {\n \"rel\": \"item\",\n \"href\": \"./area-1-2-imagery/area-1-2-imagery.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"item\",\n \"href\": \"./area-1-2-labels/area-1-2-labels.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../catalog.json\",\n \"type\": \"application/json\"\n }\n ],\n \"stac_extensions\": [],\n \"extent\": {\n \"spatial\": {\n \"bbox\": [\n [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 4.275202171119132\n ]\n ]\n },\n \"temporal\": {\n \"interval\": [\n [\n \"2019-10-04T18:55:37Z\",\n null\n ]\n ]\n }\n },\n \"license\": \"other\"\n}" }, { "path": "tests/data/test-case-1/country-1/catalog.json", "content": "{\n \"type\": \"Catalog\",\n \"id\": \"country-1\",\n \"stac_version\": \"1.1.0\",\n \"description\": \"test catalog country-1\",\n \"links\": [\n {\n \"rel\": \"child\",\n \"href\": \"./area-1-1/collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"child\",\n \"href\": \"./area-1-2/collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../catalog.json\",\n \"type\": \"application/json\"\n }\n ],\n \"stac_extensions\": []\n}" }, { "path": "tests/data/test-case-1/country-2/area-2-1/area-2-1-imagery/area-2-1-imagery.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.1.0\",\n \"id\": \"area-2-1-imagery\",\n \"properties\": {\n \"datetime\": \"2019-10-04T18:55:37Z\"\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.5048828125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 3.8916575492899987\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n }\n ],\n \"assets\": {\n \"ortho\": {\n \"href\": \"http://example.com/area-2-1_ortho.tif\",\n \"type\": \"image/vnd.stac.geotiff\"\n },\n \"dsm\": {\n \"href\": \"http://example.com/area-2-1_dsm.tif\",\n \"type\": \"image/vnd.stac.geotiff\"\n }\n },\n \"bbox\": [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 3.8916575492899987\n ],\n \"stac_extensions\": [],\n \"collection\": \"area-2-1\"\n}" }, { "path": "tests/data/test-case-1/country-2/area-2-1/area-2-1-labels/area-2-1-labels.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.1.0\",\n \"id\": \"area-2-1-labels\",\n \"properties\": {\n \"datetime\": \"2019-10-04T18:55:37Z\",\n \"label:description\": \"labels for area-2-1\",\n \"label:type\": \"vector\",\n \"label:properties\": [\n \"label\"\n ],\n \"label:classes\": [\n {\n \"name\": \"label\",\n \"classes\": [\n \"one\",\n \"two\"\n ]\n }\n ],\n \"label:tasks\": [\n \"classification\"\n ],\n \"label:methods\": [\n \"manual\"\n ]\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.5048828125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 3.8916575492899987\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"source\",\n \"href\": \"../area-2-1-imagery/area-2-1-imagery.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n }\n ],\n \"assets\": {\n \"labels\": {\n \"href\": \"http://example.com/area-2-1-labels.geojson\",\n \"type\": \"application/geo+json\"\n }\n },\n \"bbox\": [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 3.8916575492899987\n ],\n \"stac_extensions\": [\n \"https://stac-extensions.github.io/label/v1.0.1/schema.json\"\n ],\n \"collection\": \"area-2-1\"\n}" }, { "path": "tests/data/test-case-1/country-2/area-2-1/collection.json", "content": "{\n \"type\": \"Collection\",\n \"id\": \"area-2-1\",\n \"stac_version\": \"1.1.0\",\n \"description\": \"test collection country-2\",\n \"links\": [\n {\n \"rel\": \"item\",\n \"href\": \"./area-2-1-imagery/area-2-1-imagery.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"item\",\n \"href\": \"./area-2-1-labels/area-2-1-labels.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../catalog.json\",\n \"type\": \"application/json\"\n }\n ],\n \"stac_extensions\": [],\n \"extent\": {\n \"spatial\": {\n \"bbox\": [\n [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 4.275202171119132\n ]\n ]\n },\n \"temporal\": {\n \"interval\": [\n [\n \"2019-10-04T18:55:37Z\",\n null\n ]\n ]\n }\n },\n \"license\": \"other\"\n}" }, { "path": "tests/data/test-case-1/country-2/area-2-2/area-2-2-imagery/area-2-2-imagery.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.1.0\",\n \"id\": \"area-2-2-imagery\",\n \"properties\": {\n \"datetime\": \"2019-10-04T18:55:37Z\"\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.5048828125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 3.8916575492899987\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n }\n ],\n \"assets\": {\n \"ortho\": {\n \"href\": \"http://example.com/area-2-2_ortho.tif\",\n \"type\": \"image/vnd.stac.geotiff\"\n },\n \"dsm\": {\n \"href\": \"http://example.com/area-2-2_dsm.tif\",\n \"type\": \"image/vnd.stac.geotiff\"\n }\n },\n \"bbox\": [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 3.8916575492899987\n ],\n \"stac_extensions\": [],\n \"collection\": \"area-2-2\"\n}" }, { "path": "tests/data/test-case-1/country-2/area-2-2/area-2-2-labels/area-2-2-labels.json", "content": "{\n \"type\": \"Feature\",\n \"stac_version\": \"1.1.0\",\n \"id\": \"area-2-2-labels\",\n \"properties\": {\n \"datetime\": \"2019-10-04T18:55:37Z\",\n \"label:description\": \"labels for area-2-2\",\n \"label:type\": \"vector\",\n \"label:properties\": [\n \"label\"\n ],\n \"label:classes\": [\n {\n \"name\": \"label\",\n \"classes\": [\n \"one\",\n \"two\"\n ]\n }\n ],\n \"label:tasks\": [\n \"classification\"\n ],\n \"label:methods\": [\n \"manual\"\n ]\n },\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -2.5048828125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 3.8916575492899987\n ],\n [\n -1.9610595703125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 4.275202171119132\n ],\n [\n -2.5048828125,\n 3.8916575492899987\n ]\n ]\n ]\n },\n \"links\": [\n {\n \"rel\": \"source\",\n \"href\": \"../area-2-2-imagery/area-2-2-imagery.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\",\n \"type\": \"application/json\"\n }\n ],\n \"assets\": {\n \"labels\": {\n \"href\": \"http://example.com/area-2-2-labels.geojson\",\n \"type\": \"application/geo+json\"\n }\n },\n \"bbox\": [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 3.8916575492899987\n ],\n \"stac_extensions\": [\n \"https://stac-extensions.github.io/label/v1.0.1/schema.json\"\n ],\n \"collection\": \"area-2-2\"\n}" }, { "path": "tests/data/test-case-1/country-2/area-2-2/collection.json", "content": "{\n \"type\": \"Collection\",\n \"id\": \"area-2-2\",\n \"stac_version\": \"1.1.0\",\n \"description\": \"test collection country-2\",\n \"links\": [\n {\n \"rel\": \"item\",\n \"href\": \"./area-2-2-imagery/area-2-2-imagery.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"item\",\n \"href\": \"./area-2-2-labels/area-2-2-labels.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../catalog.json\",\n \"type\": \"application/json\"\n }\n ],\n \"stac_extensions\": [],\n \"extent\": {\n \"spatial\": {\n \"bbox\": [\n [\n -2.5048828125,\n 3.8916575492899987,\n -1.9610595703125,\n 4.275202171119132\n ]\n ]\n },\n \"temporal\": {\n \"interval\": [\n [\n \"2019-10-04T18:55:37Z\",\n null\n ]\n ]\n }\n },\n \"license\": \"other\"\n}" }, { "path": "tests/data/test-case-1/country-2/catalog.json", "content": "{\n \"type\": \"Catalog\",\n \"id\": \"country-2\",\n \"stac_version\": \"1.1.0\",\n \"description\": \"test catalog country-2\",\n \"links\": [\n {\n \"rel\": \"child\",\n \"href\": \"./area-2-1/collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"child\",\n \"href\": \"./area-2-2/collection.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../catalog.json\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../catalog.json\",\n \"type\": \"application/json\"\n }\n ],\n \"stac_extensions\": []\n}" }, { "path": "tests/helpers.py", "content": "import json\nfrom pathlib import Path\nfrom typing import Any\n\nTEST_DATA = Path(__file__).parent / \"data\"\n\nSTAC_URLS = {\n \"PLANETARY-COMPUTER\": \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n \"EARTH-SEARCH\": \"https://earth-search.aws.element84.com/v1\",\n \"MLHUB\": \"https://api.radiant.earth/mlhub/v1\",\n \"SPACEBEL\": \"https://emc.spacebel.be\",\n}\n\n\ndef read_data_file(file_name: str, mode: str = \"r\", parse_json: bool = False) -> Any:\n file_path = TEST_DATA / file_name\n with file_path.open(mode=mode) as src:\n if parse_json:\n return json.load(src)\n else:\n return src.read()\n" }, { "path": "tests/test_base_search.py", "content": "import json\nfrom collections.abc import Iterator\nfrom datetime import datetime\nfrom typing import Any\n\nimport pytest\nfrom dateutil.tz import gettz, tzutc\n\nfrom pystac_client import Client\nfrom pystac_client.item_search import BaseSearch\n\nfrom .helpers import STAC_URLS, read_data_file\n\nSEARCH_URL = f\"{STAC_URLS['PLANETARY-COMPUTER']}/search\"\nINTERSECTS_EXAMPLE = {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [-73.21, 43.99],\n [-73.21, 44.05],\n [-73.12, 44.05],\n [-73.12, 43.99],\n [-73.21, 43.99],\n ]\n ],\n}\n\nITEM_EXAMPLE: dict[str, Any] = {\"collections\": \"io-lulc\", \"ids\": \"60U-2020\"}\n\n\nclass TestBaseSearchParams:\n @pytest.fixture(scope=\"function\")\n def sample_client(self) -> Client:\n api_content = read_data_file(\"planetary-computer-root.json\", parse_json=True)\n return Client.from_dict(api_content)\n\n def test_tuple_bbox(self) -> None:\n # Tuple input\n search = BaseSearch(url=SEARCH_URL, bbox=(-104.5, 44.0, -104.0, 45.0))\n assert search.get_parameters()[\"bbox\"] == (-104.5, 44.0, -104.0, 45.0)\n\n def test_list_bbox(self) -> None:\n # List input\n search = BaseSearch(url=SEARCH_URL, bbox=[-104.5, 44.0, -104.0, 45.0])\n assert search.get_parameters()[\"bbox\"] == (-104.5, 44.0, -104.0, 45.0)\n\n def test_string_bbox(self) -> None:\n # String Input\n search = BaseSearch(url=SEARCH_URL, bbox=\"-104.5,44.0,-104.0,45.0\")\n assert search.get_parameters()[\"bbox\"] == (-104.5, 44.0, -104.0, 45.0)\n\n def test_generator_bbox(self) -> None:\n # Generator Input\n def bboxer() -> Iterator[float]:\n yield from [-104.5, 44.0, -104.0, 45.0]\n\n search = BaseSearch(url=SEARCH_URL, bbox=bboxer())\n assert search.get_parameters()[\"bbox\"] == (-104.5, 44.0, -104.0, 45.0)\n\n def test_url_with_parameters(self) -> None:\n # Single timestamp input\n search = BaseSearch(\n url=SEARCH_URL,\n datetime=\"2020-02-01T00:00:00Z\",\n bbox=[-104.5, 44.0, -104.0, 45.0],\n )\n assert \"bbox=-104.5%2C44.0%2C-104.0%2C45.0\" in search.url_with_parameters()\n\n # Motivating example: https://github.com/stac-utils/pystac-client/issues/299\n search = BaseSearch(\n url=\"https://planetarycomputer.microsoft.com/api/stac/v1/search\",\n collections=[\"cop-dem-glo-30\"],\n bbox=[88.214, 27.927, 88.302, 28.034],\n )\n assert (\n search.url_with_parameters()\n == \"https://planetarycomputer.microsoft.com/api/stac/v1/search?\"\n \"bbox=88.214%2C27.927%2C88.302%2C28.034&collections=cop-dem-glo-30\"\n )\n\n def test_single_string_datetime(self) -> None:\n # Single timestamp input\n search = BaseSearch(url=SEARCH_URL, datetime=\"2020-02-01T00:00:00Z\")\n assert search.get_parameters()[\"datetime\"] == \"2020-02-01T00:00:00Z\"\n\n def test_range_string_datetime(self) -> None:\n # Timestamp range input\n search = BaseSearch(\n url=SEARCH_URL, datetime=\"2020-02-01T00:00:00Z/2020-02-02T00:00:00Z\"\n )\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2020-02-01T00:00:00Z/2020-02-02T00:00:00Z\"\n )\n\n def test_list_of_strings_datetime(self) -> None:\n # Timestamp list input\n search = BaseSearch(\n url=SEARCH_URL, datetime=[\"2020-02-01T00:00:00Z\", \"2020-02-02T00:00:00Z\"]\n )\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2020-02-01T00:00:00Z/2020-02-02T00:00:00Z\"\n )\n\n def test_open_range_string_datetime(self) -> None:\n # Open timestamp range input\n search = BaseSearch(url=SEARCH_URL, datetime=\"2020-02-01T00:00:00Z/..\")\n assert search.get_parameters()[\"datetime\"] == \"2020-02-01T00:00:00Z/..\"\n\n def test_single_datetime_object(self) -> None:\n start = datetime(2020, 2, 1, 0, 0, 0, tzinfo=tzutc())\n\n # Single datetime input\n search = BaseSearch(url=SEARCH_URL, datetime=start)\n assert search.get_parameters()[\"datetime\"] == \"2020-02-01T00:00:00Z\"\n\n def test_list_of_datetimes(self) -> None:\n start = datetime(2020, 2, 1, 0, 0, 0, tzinfo=tzutc())\n end = datetime(2020, 2, 2, 0, 0, 0, tzinfo=tzutc())\n\n # Datetime range input\n search = BaseSearch(url=SEARCH_URL, datetime=[start, end])\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2020-02-01T00:00:00Z/2020-02-02T00:00:00Z\"\n )\n\n def test_open_list_of_datetimes(self) -> None:\n start = datetime(2020, 2, 1, 0, 0, 0, tzinfo=tzutc())\n\n # Open datetime range input\n search = BaseSearch(url=SEARCH_URL, datetime=(start, None))\n assert search.get_parameters()[\"datetime\"] == \"2020-02-01T00:00:00Z/..\"\n\n def test_localized_datetime_converted_to_utc(self) -> None:\n # Localized datetime input (should be converted to UTC)\n start_localized = datetime(2020, 2, 1, 0, 0, 0, tzinfo=gettz(\"US/Eastern\"))\n search = BaseSearch(url=SEARCH_URL, datetime=start_localized)\n assert search.get_parameters()[\"datetime\"] == \"2020-02-01T05:00:00Z\"\n\n def test_single_year(self) -> None:\n search = BaseSearch(url=SEARCH_URL, datetime=\"2020\")\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2020-01-01T00:00:00Z/2020-12-31T23:59:59Z\"\n )\n\n def test_range_of_years(self) -> None:\n search = BaseSearch(url=SEARCH_URL, datetime=\"2019/2020\")\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2019-01-01T00:00:00Z/2020-12-31T23:59:59Z\"\n )\n\n def test_single_month(self) -> None:\n search = BaseSearch(url=SEARCH_URL, datetime=\"2020-06\")\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2020-06-01T00:00:00Z/2020-06-30T23:59:59Z\"\n )\n\n def test_range_of_months(self) -> None:\n search = BaseSearch(url=SEARCH_URL, datetime=\"2020-04/2020-06\")\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2020-04-01T00:00:00Z/2020-06-30T23:59:59Z\"\n )\n\n def test_single_date(self) -> None:\n search = BaseSearch(url=SEARCH_URL, datetime=\"2020-06-10\")\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2020-06-10T00:00:00Z/2020-06-10T23:59:59Z\"\n )\n\n def test_range_of_dates(self) -> None:\n search = BaseSearch(url=SEARCH_URL, datetime=\"2020-06-10/2020-06-20\")\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2020-06-10T00:00:00Z/2020-06-20T23:59:59Z\"\n )\n\n def test_mixed_simple_date_strings(self) -> None:\n search = BaseSearch(url=SEARCH_URL, datetime=\"2019/2020-06-10\")\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2019-01-01T00:00:00Z/2020-06-10T23:59:59Z\"\n )\n\n def test_time(self) -> None:\n search = BaseSearch(\n url=SEARCH_URL, datetime=\"2019-01-01T00:00:00Z/2019-01-01T00:12:00\"\n )\n assert (\n search.get_parameters()[\"datetime\"]\n == \"2019-01-01T00:00:00Z/2019-01-01T00:12:00Z\"\n )\n\n def test_many_datetimes(self) -> None:\n datetimes = [\n \"1985-04-12T23:20:50.52Z\",\n \"1996-12-19T16:39:57-08:00\",\n \"1990-12-31T23:59:60Z\",\n \"1990-12-31T15:59:60-08:00\",\n \"1937-01-01T12:00:27.87+01:00\",\n \"1985-04-12T23:20:50.52Z\",\n \"1937-01-01T12:00:27.8710+01:00\",\n \"1937-01-01T12:00:27.8+01:00\",\n \"1937-01-01T12:00:27.8Z\",\n \"1985-04-12t23:20:50.5202020z\",\n \"2020-07-23T00:00:00Z\",\n \"2020-07-23T00:00:00.0Z\",\n \"2020-07-23T00:00:00.01Z\",\n \"2020-07-23T00:00:00.012Z\",\n \"2020-07-23T00:00:00.0123Z\",\n \"2020-07-23T00:00:00.01234Z\",\n \"2020-07-23T00:00:00.012345Z\",\n \"2020-07-23T00:00:00.000Z\",\n \"2020-07-23T00:00:00.000+03:00\",\n \"2020-07-23T00:00:00+03:00\",\n \"2020-07-23T00:00:00.000+03:00\",\n \"2020-07-23T00:00:00.000z\",\n \"/2023-01-01T00:00:00Z\",\n \"2023-01-01T00:00:00Z/\",\n ]\n for date_time in datetimes:\n BaseSearch(url=SEARCH_URL, datetime=date_time)\n\n def test_three_datetimes(self) -> None:\n start = datetime(2020, 2, 1, 0, 0, 0, tzinfo=tzutc())\n middle = datetime(2020, 2, 2, 0, 0, 0, tzinfo=tzutc())\n end = datetime(2020, 2, 3, 0, 0, 0, tzinfo=tzutc())\n\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, datetime=[start, middle, end])\n\n def test_double_open_ended_interval(self) -> None:\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, datetime=[None, None])\n\n def test_datetime_list_of_one_none(self) -> None:\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, datetime=[None])\n\n def test_poorly_formed_datetimes(self) -> None:\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, datetime=\"2020-7/2020-8\")\n\n def test_single_collection_string(self) -> None:\n # Single ID string\n search = BaseSearch(url=SEARCH_URL, collections=\"naip\")\n assert search.get_parameters()[\"collections\"] == (\"naip\",)\n\n def test_multiple_collection_string(self) -> None:\n # Comma-separated ID string\n search = BaseSearch(url=SEARCH_URL, collections=\"naip,landsat8_l1tp\")\n assert search.get_parameters()[\"collections\"] == (\"naip\", \"landsat8_l1tp\")\n\n def test_list_of_collection_strings(self) -> None:\n # List of ID strings\n search = BaseSearch(url=SEARCH_URL, collections=[\"naip\", \"landsat8_l1tp\"])\n assert search.get_parameters()[\"collections\"] == (\"naip\", \"landsat8_l1tp\")\n\n def test_generator_of_collection_strings(self) -> None:\n # Generator of ID strings\n def collectioner() -> Iterator[str]:\n yield from [\"naip\", \"landsat8_l1tp\"]\n\n search = BaseSearch(url=SEARCH_URL, collections=collectioner())\n assert search.get_parameters()[\"collections\"] == (\"naip\", \"landsat8_l1tp\")\n\n def test_single_id_string(self) -> None:\n # Single ID\n search = BaseSearch(url=SEARCH_URL, ids=\"m_3510836_se_12_060_20180508_20190331\")\n assert search.get_parameters()[\"ids\"] == (\n \"m_3510836_se_12_060_20180508_20190331\",\n )\n\n def test_multiple_id_string(self) -> None:\n # Comma-separated ID string\n search = BaseSearch(\n url=SEARCH_URL,\n ids=\"m_3510836_se_12_060_20180508_20190331,m_3510840_se_12_060_20180504_20190331\",\n )\n assert search.get_parameters()[\"ids\"] == (\n \"m_3510836_se_12_060_20180508_20190331\",\n \"m_3510840_se_12_060_20180504_20190331\",\n )\n\n def test_list_of_id_strings(self) -> None:\n # List of IDs\n search = BaseSearch(\n url=SEARCH_URL,\n ids=[\n \"m_3510836_se_12_060_20180508_20190331\",\n \"m_3510840_se_12_060_20180504_20190331\",\n ],\n )\n assert search.get_parameters()[\"ids\"] == (\n \"m_3510836_se_12_060_20180508_20190331\",\n \"m_3510840_se_12_060_20180504_20190331\",\n )\n\n def test_generator_of_id_string(self) -> None:\n # Generator of IDs\n def ids() -> Iterator[str]:\n yield from [\n \"m_3510836_se_12_060_20180508_20190331\",\n \"m_3510840_se_12_060_20180504_20190331\",\n ]\n\n search = BaseSearch(url=SEARCH_URL, ids=ids())\n assert search.get_parameters()[\"ids\"] == (\n \"m_3510836_se_12_060_20180508_20190331\",\n \"m_3510840_se_12_060_20180504_20190331\",\n )\n\n def test_intersects_dict(self) -> None:\n # Dict input\n search = BaseSearch(url=SEARCH_URL, intersects=INTERSECTS_EXAMPLE)\n assert search.get_parameters()[\"intersects\"] == INTERSECTS_EXAMPLE\n\n def test_intersects_json_string(self) -> None:\n # JSON string input\n search = BaseSearch(url=SEARCH_URL, intersects=json.dumps(INTERSECTS_EXAMPLE))\n assert search.get_parameters()[\"intersects\"] == INTERSECTS_EXAMPLE\n\n def test_intersects_non_geo_interface_object(self) -> None:\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, intersects=object()) # type: ignore\n\n def test_filter_lang_default_for_method_despite_filter_as_dict(self) -> None:\n search = BaseSearch(url=SEARCH_URL, method=\"GET\", filter={})\n assert search.get_parameters()[\"filter-lang\"] == \"cql2-text\"\n\n def test_filter_lang_default_for_method_despite_filter_as_str(self) -> None:\n search = BaseSearch(url=SEARCH_URL, method=\"POST\", filter=\"\")\n assert search.get_parameters()[\"filter-lang\"] == \"cql2-json\"\n\n def test_filter_lang_cql2_text(self) -> None:\n # Use specified filter_lang\n search = BaseSearch(url=SEARCH_URL, filter_lang=\"cql2-text\", filter={})\n assert search.get_parameters()[\"filter-lang\"] == \"cql2-text\"\n\n def test_filter_lang_cql2_json(self) -> None:\n # Use specified filter_lang\n search = BaseSearch(url=SEARCH_URL, filter_lang=\"cql2-json\", filter=\"\")\n assert search.get_parameters()[\"filter-lang\"] == \"cql2-json\"\n\n def test_filter_lang_without_filter(self) -> None:\n # No filter provided\n search = BaseSearch(url=SEARCH_URL)\n assert \"filter-lang\" not in search.get_parameters()\n\n def test_filter_conversion_to_cql2_json(self) -> None:\n search = BaseSearch(url=SEARCH_URL, method=\"POST\", filter=\"eo:cloud_cover<=10\")\n assert search.get_parameters()[\"filter-lang\"] == \"cql2-json\"\n assert search.get_parameters()[\"filter\"] == {\n \"args\": [{\"property\": \"eo:cloud_cover\"}, 10],\n \"op\": \"<=\",\n }\n\n def test_filter_conversion_to_cql2_text(self) -> None:\n search = BaseSearch(\n url=SEARCH_URL,\n method=\"GET\",\n filter={\"op\": \"<=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 10]},\n )\n assert search.get_parameters()[\"filter-lang\"] == \"cql2-text\"\n assert search.get_parameters()[\"filter\"] == '(\"eo:cloud_cover\" <= 10)'\n\n def test_filter_conversion_does_not_happen_if_filter_lang_specified_json(\n self,\n ) -> None:\n search = BaseSearch(\n url=SEARCH_URL,\n method=\"GET\",\n filter={\"op\": \"<=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 10]},\n filter_lang=\"cql2-json\",\n )\n # assert search.get_parameters()[\"filter-lang\"] == \"cql2-json\"\n assert (\n search.get_parameters()[\"filter\"]\n == '{\"op\": \"<=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 10]}'\n )\n\n def test_filter_conversion_does_not_happen_if_filter_lang_specified_text(\n self,\n ) -> None:\n search = BaseSearch(\n url=SEARCH_URL,\n method=\"POST\",\n filter=\"eo:cloud_cover<=10\",\n filter_lang=\"cql2-text\",\n )\n # note that this is likely to fail when it hits the server\n assert search.get_parameters()[\"filter-lang\"] == \"cql2-text\"\n assert search.get_parameters()[\"filter\"] == \"eo:cloud_cover<=10\"\n\n def test_sortby(self) -> None:\n search = BaseSearch(url=SEARCH_URL, sortby=\"properties.datetime\")\n assert search.get_parameters()[\"sortby\"] == [\n {\"direction\": \"asc\", \"field\": \"properties.datetime\"}\n ]\n\n search = BaseSearch(url=SEARCH_URL, sortby=\"+properties.datetime\")\n assert search.get_parameters()[\"sortby\"] == [\n {\"direction\": \"asc\", \"field\": \"properties.datetime\"}\n ]\n\n search = BaseSearch(url=SEARCH_URL, sortby=\"-properties.datetime\")\n assert search.get_parameters()[\"sortby\"] == [\n {\"direction\": \"desc\", \"field\": \"properties.datetime\"}\n ]\n\n search = BaseSearch(\n url=SEARCH_URL, sortby=\"-properties.datetime,+id,collection\"\n )\n assert search.get_parameters()[\"sortby\"] == [\n {\"direction\": \"desc\", \"field\": \"properties.datetime\"},\n {\"direction\": \"asc\", \"field\": \"id\"},\n {\"direction\": \"asc\", \"field\": \"collection\"},\n ]\n\n search = BaseSearch(\n url=SEARCH_URL,\n sortby=[\n {\"direction\": \"desc\", \"field\": \"properties.datetime\"},\n {\"direction\": \"asc\", \"field\": \"id\"},\n {\"direction\": \"asc\", \"field\": \"collection\"},\n ],\n )\n assert search.get_parameters()[\"sortby\"] == [\n {\"direction\": \"desc\", \"field\": \"properties.datetime\"},\n {\"direction\": \"asc\", \"field\": \"id\"},\n {\"direction\": \"asc\", \"field\": \"collection\"},\n ]\n\n search = BaseSearch(\n url=SEARCH_URL, sortby=[\"-properties.datetime\", \"id\", \"collection\"]\n )\n assert search.get_parameters()[\"sortby\"] == [\n {\"direction\": \"desc\", \"field\": \"properties.datetime\"},\n {\"direction\": \"asc\", \"field\": \"id\"},\n {\"direction\": \"asc\", \"field\": \"collection\"},\n ]\n\n search = BaseSearch(\n url=SEARCH_URL,\n method=\"GET\",\n sortby=[\"-properties.datetime\", \"id\", \"collection\"],\n )\n assert (\n search.get_parameters()[\"sortby\"] == \"-properties.datetime,+id,+collection\"\n )\n\n search = BaseSearch(\n url=SEARCH_URL, method=\"GET\", sortby=\"-properties.datetime,id,collection\"\n )\n assert (\n search.get_parameters()[\"sortby\"] == \"-properties.datetime,+id,+collection\"\n )\n\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, sortby=1) # type: ignore\n\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, sortby=[1]) # type: ignore\n\n def test_fields(self) -> None:\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, fields=1) # type: ignore\n\n with pytest.raises(Exception):\n BaseSearch(url=SEARCH_URL, fields=[1]) # type: ignore\n\n search = BaseSearch(url=SEARCH_URL, fields=\"id,collection,+foo,-bar\")\n assert search.get_parameters()[\"fields\"] == {\n \"exclude\": [\"bar\"],\n \"include\": [\"id\", \"collection\", \"foo\"],\n }\n\n search = BaseSearch(url=SEARCH_URL, fields=[\"id\", \"collection\", \"+foo\", \"-bar\"])\n assert search.get_parameters()[\"fields\"] == {\n \"exclude\": [\"bar\"],\n \"include\": [\"id\", \"collection\", \"foo\"],\n }\n\n search = BaseSearch(\n url=SEARCH_URL,\n fields={\"exclude\": [\"bar\"], \"include\": [\"id\", \"collection\"]},\n )\n assert search.get_parameters()[\"fields\"] == {\n \"exclude\": [\"bar\"],\n \"include\": [\"id\", \"collection\"],\n }\n\n search = BaseSearch(\n url=SEARCH_URL, method=\"GET\", fields=\"id,collection,+foo,-bar\"\n )\n assert search.get_parameters()[\"fields\"] == \"+id,+collection,+foo,-bar\"\n\n search = BaseSearch(\n url=SEARCH_URL, method=\"GET\", fields=[\"id\", \"collection\", \"+foo\", \"-bar\"]\n )\n assert search.get_parameters()[\"fields\"] == \"+id,+collection,+foo,-bar\"\n\n search = BaseSearch(\n url=SEARCH_URL,\n method=\"GET\",\n fields={\"exclude\": [\"bar\"], \"include\": [\"id\", \"collection\"]},\n )\n assert search.get_parameters()[\"fields\"] == \"+id,+collection,-bar\"\n" }, { "path": "tests/test_cli.py", "content": "import json\nimport tempfile\n\nimport pytest\nfrom pytest_console_scripts import ScriptRunner\n\nimport pystac_client.cli\nfrom tests.helpers import STAC_URLS, TEST_DATA\n\nFILTER_JSON = {\"op\": \"lte\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 40]}\n\n\n# We want to ensure that the CLI is handling these warnings properly\n@pytest.mark.filterwarnings(\"ignore::pystac_client.warnings.PystacClientWarning\")\nclass TestCLISearch:\n @pytest.mark.vcr\n def test_item_search(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"PLANETARY-COMPUTER\"],\n \"-c\",\n \"naip\",\n \"--max-items\",\n \"20\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success\n\n @pytest.mark.vcr\n def test_filter(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"EARTH-SEARCH\"],\n \"--filter\",\n json.dumps(FILTER_JSON),\n \"--max-items\",\n \"20\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success, result.stderr\n assert result.stdout\n\n @pytest.mark.vcr\n @pytest.mark.parametrize(\"filename\", [\"netherlands_aoi.json\", \"sample-item.json\"])\n def test_intersects(self, script_runner: ScriptRunner, filename: str) -> None:\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"PLANETARY-COMPUTER\"],\n \"--collections\",\n \"landsat-8-c2-l2\",\n \"--intersects\",\n str(TEST_DATA / filename),\n \"--max-items\",\n \"10\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success, result.stderr\n assert result.stdout\n\n @pytest.mark.vcr\n def test_intersects_despite_warning(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"PLANETARY-COMPUTER\"],\n \"--collections\",\n \"landsat-8-c2-l2\",\n \"--intersects\",\n str(TEST_DATA / \"sample-item-collection.json\"),\n \"--max-items\",\n \"10\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success\n assert result.stdout\n assert \"input to intersects is a FeatureCollection\" in result.stderr\n\n @pytest.mark.parametrize(\n \"headers,good_header_count\",\n [\n ([\"kick=flip\", \"home=run\"], 2),\n ([\"mad=pow\"], 1),\n ([\"=no-var\"], 0),\n ([\"no-val=\"], 0),\n ([\"good=header\", \"bad-header\"], 1),\n ([\"header=value-with-three-=-signs-=\", \"plain=jane\"], 2),\n ],\n )\n def test_headers(self, headers: list[str], good_header_count: int) -> None:\n args = [\n \"search\",\n STAC_URLS[\"PLANETARY-COMPUTER\"],\n \"-c\",\n \"naip\",\n \"--max-items\",\n \"20\",\n \"--headers\",\n ] + headers\n pargs = pystac_client.cli.parse_args(args)\n assert len(pargs[\"headers\"]) == good_header_count\n\n def test_no_arguments(self, script_runner: ScriptRunner) -> None:\n args = [\"stac-client\"]\n result = script_runner.run(*args, print_result=False)\n assert not result.success\n assert result.returncode == 1\n\n @pytest.mark.vcr\n def test_non_conformant_raises_by_default(\n self, script_runner: ScriptRunner\n ) -> None:\n args = [\n \"stac-client\",\n \"search\",\n \"https://earth-search.aws.element84.com/v0\",\n \"-c\",\n \"sentinel-s2-l2a-cogs\",\n \"--matched\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success is False\n assert \"Server does not conform to ITEM_SEARCH\" in result.stderr\n assert result.returncode == 1\n\n @pytest.mark.vcr\n @pytest.mark.parametrize(\"warning_flag\", [\"--error\", \"--error=no-conforms-to\"])\n def test_non_conformant_raises_if_warning_set_to_error(\n self, script_runner: ScriptRunner, warning_flag: str\n ) -> None:\n args = [\n \"stac-client\",\n \"search\",\n \"https://earth-search.aws.element84.com/v0\",\n \"-c\",\n \"sentinel-s2-l2a-cogs\",\n warning_flag,\n \"--matched\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success is False\n assert \"Server does not advertise any conformance classes\" in result.stderr\n assert result.returncode == 1\n\n @pytest.mark.vcr\n def test_non_conformant_can_be_fixed(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"search\",\n \"https://earth-search.aws.element84.com/v0\",\n \"-c\",\n \"sentinel-s2-l2a-cogs\",\n \"--add-conforms-to=ITEM_SEARCH\",\n \"--matched\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success\n\n @pytest.mark.vcr\n @pytest.mark.parametrize(\"warning_flag\", [\"--ignore\", \"--ignore=no-conforms-to\"])\n def test_non_conformant_can_be_ignored(\n self, script_runner: ScriptRunner, warning_flag: str\n ) -> None:\n args = [\n \"stac-client\",\n \"search\",\n \"https://earth-search.aws.element84.com/v0\",\n \"-c\",\n \"sentinel-s2-l2a-cogs\",\n warning_flag,\n \"--add-conforms-to=ITEM_SEARCH\",\n \"--matched\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success\n\n @pytest.mark.vcr\n @pytest.mark.parametrize(\n \"conforms_to_flag\", [\"--clear-conforms-to\", \"--remove-conforms-to=ITEM_SEARCH\"]\n )\n def test_altering_conforms_to(\n self, script_runner: ScriptRunner, conforms_to_flag: str\n ) -> None:\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"EARTH-SEARCH\"],\n conforms_to_flag,\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success is False\n assert \"Server does not conform to ITEM_SEARCH\" in result.stderr\n assert result.returncode == 1\n\n @pytest.mark.vcr\n @pytest.mark.filterwarnings(\"ignore::Warning\")\n def test_matched_not_available(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"PLANETARY-COMPUTER\"],\n \"-c\",\n \"naip\",\n \"--matched\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success is False\n assert \"'matched' is not supported for this catalog\" in result.stderr\n assert result.returncode == 1\n\n @pytest.mark.vcr\n def test_matched(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"EARTH-SEARCH\"],\n \"-c\",\n \"cop-dem-glo-30\",\n \"--max-items\",\n \"1\",\n \"--matched\",\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success\n assert result.stdout[0].isdigit(), \"Output does not start with a number\"\n\n @pytest.mark.vcr\n def test_fields(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"EARTH-SEARCH\"],\n \"-c\",\n \"cop-dem-glo-30\",\n \"--max-items\",\n \"1\",\n \"--fields=-geometry,-assets\",\n ]\n result = script_runner.run(args, print_result=True)\n assert result.success\n assert \"assets\" not in result.stdout\n assert \"geometry\" not in result.stdout\n\n @pytest.mark.vcr\n def test_save(self, script_runner: ScriptRunner) -> None:\n with tempfile.NamedTemporaryFile() as fp:\n path = fp.name\n args = [\n \"stac-client\",\n \"search\",\n STAC_URLS[\"EARTH-SEARCH\"],\n \"-c\",\n \"cop-dem-glo-30\",\n \"--max-items\",\n \"1\",\n \"--save\",\n path,\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success\n\n with open(path) as f:\n output = json.loads(f.read())\n\n assert \"features\" in output\n assert len(output[\"features\"]) == 1\n\n\n@pytest.mark.filterwarnings(\"ignore::pystac_client.warnings.PystacClientWarning\")\nclass TestCLICollections:\n @pytest.mark.vcr\n def test_collections(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"collections\",\n STAC_URLS[\"EARTH-SEARCH\"],\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success\n assert result.stdout.startswith('[{\"type\": \"Collection\"')\n\n @pytest.mark.vcr\n def test_collection_search(self, script_runner: ScriptRunner) -> None:\n args = [\n \"stac-client\",\n \"collections\",\n STAC_URLS[\"EARTH-SEARCH\"],\n \"--q\",\n \"sentinel\",\n ]\n with pytest.warns(UserWarning, match=\"COLLECTION_SEARCH\"):\n result = script_runner.run(args, print_result=False)\n\n assert result.success\n\n collections = json.loads(result.stdout)\n assert len(collections) == 5\n\n @pytest.mark.vcr\n def test_save(self, script_runner: ScriptRunner) -> None:\n with tempfile.NamedTemporaryFile() as fp:\n path = fp.name\n args = [\n \"stac-client\",\n \"collections\",\n STAC_URLS[\"EARTH-SEARCH\"],\n \"--save\",\n path,\n ]\n result = script_runner.run(args, print_result=False)\n assert result.success\n\n with open(path) as f:\n output = json.loads(f.read())\n\n assert isinstance(output, list)\n assert len(output) == 9, \"earth-search does not have 9 collections\"\n assert all(c[\"type\"] == \"Collection\" for c in output)\n" }, { "path": "tests/test_client.py", "content": "import json\nimport os.path\nimport warnings\nfrom datetime import datetime\nfrom tempfile import TemporaryDirectory\nfrom typing import Any\nfrom urllib.parse import parse_qs, urlsplit\n\nimport pystac\nimport pytest\nfrom dateutil.tz import tzutc\nfrom pystac import MediaType\nfrom requests_mock import Mocker\n\nfrom pystac_client import Client, CollectionClient\nfrom pystac_client._utils import Modifiable\nfrom pystac_client.conformance import ConformanceClasses\nfrom pystac_client.errors import ClientTypeError, IgnoredResultWarning\nfrom pystac_client.exceptions import APIError\nfrom pystac_client.stac_api_io import StacApiIO\nfrom pystac_client.warnings import (\n DoesNotConformTo,\n FallbackToPystac,\n MissingLink,\n NoConformsTo,\n strict,\n)\n\nfrom .helpers import STAC_URLS, TEST_DATA, read_data_file\n\n\nclass TestAPI:\n @pytest.mark.vcr\n def test_instance(self) -> None:\n api = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n\n # An API instance is also a Catalog instance\n assert isinstance(api, pystac.Catalog)\n\n assert str(api) == \"\"\n\n @pytest.mark.vcr\n def test_links(self) -> None:\n api = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n\n # Should be able to get collections via links as with a typical PySTAC Catalog\n collection_links = api.get_links(\"child\")\n assert len(collection_links) > 0\n\n collections = list(api.get_collections())\n assert len(collection_links) == len(collections)\n\n first_child_link = api.get_single_link(\"child\")\n assert first_child_link is not None\n first_collection = first_child_link.resolve_stac_object(root=api).target\n assert isinstance(first_collection, pystac.Collection)\n\n @pytest.mark.vcr\n def test_from_file(self) -> None:\n api = Client.from_file(STAC_URLS[\"PLANETARY-COMPUTER\"])\n\n assert api.title == \"Microsoft Planetary Computer STAC API\"\n\n def test_invalid_url(self) -> None:\n with pytest.raises(TypeError):\n Client.open() # type: ignore[call-arg]\n\n def test_get_collections_with_conformance(self, requests_mock: Mocker) -> None:\n \"\"\"Checks that the \"data\" endpoint is used if the API published the\n STAC API Collections conformance class.\"\"\"\n pc_root_text = read_data_file(\"planetary-computer-root.json\")\n pc_collection_dict = read_data_file(\n \"planetary-computer-aster-l1t-collection.json\", parse_json=True\n )\n\n # Mock the root catalog\n requests_mock.get(\n STAC_URLS[\"PLANETARY-COMPUTER\"], status_code=200, text=pc_root_text\n )\n api = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n\n assert api.conforms_to(ConformanceClasses.COLLECTIONS)\n\n # Get & mock the collections (rel type \"data\") link\n collections_link = api.get_single_link(\"data\")\n assert collections_link is not None\n requests_mock.get(\n collections_link.href,\n status_code=200,\n json={\"collections\": [pc_collection_dict], \"links\": []},\n )\n _ = next(api.get_collections())\n\n history = requests_mock.request_history\n assert len(history) == 2\n assert history[1].url == collections_link.href\n\n def test_get_collections_single_slash(self, requests_mock: Mocker) -> None:\n pc_root_text = read_data_file(\"planetary-computer-root.json\")\n root_url = \"http://pystac-client.test/\"\n requests_mock.get(root_url, status_code=200, text=pc_root_text)\n api = Client.open(root_url)\n pc_collection_dict = read_data_file(\n \"planetary-computer-aster-l1t-collection.json\", parse_json=True\n )\n requests_mock.get(\n f\"{root_url}collections\", # note the lack of the slash\n status_code=200,\n json={\"collections\": [pc_collection_dict], \"links\": []},\n )\n api.remove_links(\"data\")\n with pytest.warns(MissingLink, match=\"No link with rel='data'\"):\n _ = next(api.get_collections())\n history = requests_mock.request_history\n assert len(history) == 2\n assert history[1].url == f\"{root_url}collections\"\n\n def test_keep_trailing_slash_on_root(self, requests_mock: Mocker) -> None:\n pc_root_text = read_data_file(\"planetary-computer-root.json\")\n root_url = \"http://pystac-client.test/\"\n requests_mock.get(root_url, status_code=200, text=pc_root_text)\n client = Client.open(root_url)\n self_href = client.get_self_href()\n assert self_href\n assert self_href.endswith(\"/\")\n\n def test_fall_back_to_data_link_for_collections(\n self, requests_mock: Mocker\n ) -> None:\n pc_root_text = read_data_file(\"planetary-computer-root.json\")\n root_url = \"http://pystac-client.test/\"\n requests_mock.get(root_url, status_code=200, text=pc_root_text)\n api = Client.open(root_url)\n api.set_self_href(None)\n pc_collection_dict = read_data_file(\n \"planetary-computer-aster-l1t-collection.json\", parse_json=True\n )\n requests_mock.get(\n # the href of the data link\n \"https://planetarycomputer.microsoft.com/api/stac/v1/collections\",\n status_code=200,\n json={\"collections\": [pc_collection_dict], \"links\": []},\n )\n _ = next(api.get_collections())\n history = requests_mock.request_history\n assert len(history) == 2\n assert (\n history[1].url\n == \"https://planetarycomputer.microsoft.com/api/stac/v1/collections\"\n )\n\n def test_build_absolute_href_from_data_link(self, requests_mock: Mocker) -> None:\n pc_root = read_data_file(\"planetary-computer-root.json\", parse_json=True)\n assert isinstance(pc_root, dict)\n for link in pc_root[\"links\"]:\n if link[\"rel\"] == \"data\":\n link[\"href\"] = \"./collections\"\n root_url = \"http://pystac-client.test/\"\n requests_mock.get(root_url, status_code=200, text=json.dumps(pc_root))\n api = Client.open(root_url)\n api.set_self_href(None)\n api.add_link(\n pystac.Link(\n rel=\"self\",\n target=\"https://planetarycomputer.microsoft.com/api/stac/v1/\",\n )\n )\n pc_collection_dict = read_data_file(\n \"planetary-computer-aster-l1t-collection.json\", parse_json=True\n )\n requests_mock.get(\n # the href of the data link\n \"https://planetarycomputer.microsoft.com/api/stac/v1/collections\",\n status_code=200,\n json={\"collections\": [pc_collection_dict], \"links\": []},\n )\n _ = next(api.get_collections())\n history = requests_mock.request_history\n assert len(history) == 2\n assert (\n history[1].url\n == \"https://planetarycomputer.microsoft.com/api/stac/v1/collections\"\n )\n\n def test_error_if_no_self_href_or_data_link(self, requests_mock: Mocker) -> None:\n pc_root = read_data_file(\"planetary-computer-root.json\", parse_json=True)\n assert isinstance(pc_root, dict)\n pc_root[\"links\"] = [link for link in pc_root[\"links\"] if link[\"rel\"] != \"data\"]\n root_url = \"http://pystac-client.test/\"\n requests_mock.get(root_url, status_code=200, text=json.dumps(pc_root))\n api = Client.open(root_url)\n api.set_self_href(None)\n with pytest.warns(MissingLink, match=\"No link with rel='data'\"):\n with pytest.raises(ValueError, match=\"does not have a self_href set\"):\n _ = api.get_collection(\"an-id\")\n\n def test_custom_request_parameters(self, requests_mock: Mocker) -> None:\n pc_root_text = read_data_file(\"planetary-computer-root.json\")\n pc_collection_dict = read_data_file(\n \"planetary-computer-collection.json\", parse_json=True\n )\n\n requests_mock.get(\n STAC_URLS[\"PLANETARY-COMPUTER\"], status_code=200, text=pc_root_text\n )\n\n init_qp_name = \"my-param\"\n init_qp_value = \"some-value\"\n\n api = Client.open(\n STAC_URLS[\"PLANETARY-COMPUTER\"], parameters={init_qp_name: init_qp_value}\n )\n\n # Ensure that the Client will use the /collections endpoint and not fall back\n # to traversing child links.\n assert api.conforms_to(ConformanceClasses.COLLECTIONS)\n\n # Get the /collections endpoint\n collections_link = api.get_single_link(\"data\")\n assert collections_link is not None\n\n # Mock the request\n requests_mock.get(\n collections_link.href,\n status_code=200,\n json={\"collections\": [pc_collection_dict], \"links\": []},\n )\n\n # Make the collections request\n _ = next(api.get_collections())\n\n history = requests_mock.request_history\n assert len(history) == 2\n\n actual_qs = urlsplit(history[1].url).query\n actual_qp = parse_qs(actual_qs)\n\n # Check that the param from the init method is present\n assert init_qp_name in actual_qp\n assert len(actual_qp[init_qp_name]) == 1\n assert actual_qp[init_qp_name][0] == init_qp_value\n\n def test_custom_query_params_get_collections_propagation(\n self, requests_mock: Mocker\n ) -> None:\n \"\"\"Checks that query params passed to the init method are added to requests for\n CollectionClients fetched from\n the /collections endpoint.\"\"\"\n pc_root_text = read_data_file(\"planetary-computer-root.json\")\n pc_collection_dict = read_data_file(\n \"planetary-computer-collection.json\", parse_json=True\n )\n\n requests_mock.get(\n STAC_URLS[\"PLANETARY-COMPUTER\"], status_code=200, text=pc_root_text\n )\n\n init_qp_name = \"my-param\"\n init_qp_value = \"some-value\"\n\n client = Client.open(\n STAC_URLS[\"PLANETARY-COMPUTER\"], parameters={init_qp_name: init_qp_value}\n )\n\n # Get the /collections endpoint\n collections_link = client.get_single_link(\"data\")\n assert collections_link is not None\n\n # Mock the request\n requests_mock.get(\n collections_link.href,\n status_code=200,\n json={\"collections\": [pc_collection_dict], \"links\": []},\n )\n\n # Make the collections request\n collection = next(client.get_collections())\n\n # Mock the items endpoint\n items_link = collection.get_single_link(\"items\")\n assert items_link is not None\n requests_mock.get(\n items_link.href,\n status_code=200,\n json={\n \"type\": \"FeatureCollection\",\n \"stac_version\": \"1.0.0\",\n \"features\": [],\n \"links\": [],\n },\n )\n\n # Make the items request\n _ = list(collection.get_items())\n\n history = requests_mock.request_history\n assert len(history) == 3\n\n actual_qs = urlsplit(history[2].url).query\n actual_qp = parse_qs(actual_qs)\n\n # Check that the query param from the root Client is present\n assert init_qp_name in actual_qp\n assert len(actual_qp[init_qp_name]) == 1\n assert actual_qp[init_qp_name][0] == init_qp_value\n\n def test_custom_query_params_get_collection_propagation(\n self, requests_mock: Mocker\n ) -> None:\n \"\"\"Checks that query params passed to the init method are added to\n requests for CollectionClients fetched from the /collections endpoint.\"\"\"\n pc_root_text = read_data_file(\"planetary-computer-root.json\")\n pc_collection_dict = read_data_file(\n \"planetary-computer-collection.json\", parse_json=True\n )\n assert isinstance(pc_collection_dict, dict)\n pc_collection_id = pc_collection_dict[\"id\"]\n\n requests_mock.get(\n STAC_URLS[\"PLANETARY-COMPUTER\"], status_code=200, text=pc_root_text\n )\n\n init_qp_name = \"my-param\"\n init_qp_value = \"some-value\"\n\n client = Client.open(\n STAC_URLS[\"PLANETARY-COMPUTER\"], parameters={init_qp_name: init_qp_value}\n )\n\n # Get the /collections endpoint\n collections_link = client.get_single_link(\"data\")\n assert collections_link is not None\n collection_href = collections_link.href + \"/\" + pc_collection_id\n\n # Mock the request\n requests_mock.get(collection_href, status_code=200, json=pc_collection_dict)\n\n # Make the collections request\n collection = client.get_collection(pc_collection_id)\n assert collection is not None\n\n # Mock the items endpoint\n items_link = collection.get_single_link(\"items\")\n assert items_link is not None\n requests_mock.get(\n items_link.href,\n status_code=200,\n json={\n \"type\": \"FeatureCollection\",\n \"stac_version\": \"1.0.0\",\n \"features\": [],\n \"links\": [],\n },\n )\n\n # Make the items request\n _ = list(collection.get_items())\n\n history = requests_mock.request_history\n assert len(history) == 3\n\n actual_qs = urlsplit(history[2].url).query\n actual_qp = parse_qs(actual_qs)\n\n # Check that the query param from the root Client is present\n assert init_qp_name in actual_qp\n assert len(actual_qp[init_qp_name]) == 1\n assert actual_qp[init_qp_name][0] == init_qp_value\n\n def test_get_collections_without_conformance_fallsback_to_pystac(\n self, requests_mock: Mocker\n ) -> None:\n pc_root_dict = read_data_file(\"planetary-computer-root.json\", parse_json=True)\n pc_collection_dict = read_data_file(\n \"planetary-computer-aster-l1t-collection.json\", parse_json=True\n )\n\n # Remove the collections conformance class\n pc_root_dict[\"conformsTo\"].remove(\n \"https://api.stacspec.org/v1.0.0-beta.1/collections\"\n )\n\n # Remove all child links except for the collection that we are mocking\n pc_collection_href = next(\n link[\"href\"]\n for link in pc_collection_dict[\"links\"]\n if link[\"rel\"] == \"self\"\n )\n pc_root_dict[\"links\"] = [\n link\n for link in pc_root_dict[\"links\"]\n if link[\"rel\"] != \"child\" or link[\"href\"] == pc_collection_href\n ]\n\n # Mock the root catalog\n requests_mock.get(\n STAC_URLS[\"PLANETARY-COMPUTER\"], status_code=200, json=pc_root_dict\n )\n api = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n\n assert api.has_conforms_to()\n assert not api.conforms_to(ConformanceClasses.COLLECTIONS)\n assert not api.conforms_to(ConformanceClasses.FEATURES)\n\n # Mock the collection\n requests_mock.get(pc_collection_href, status_code=200, json=pc_collection_dict)\n\n with pytest.warns(FallbackToPystac):\n with pytest.warns(DoesNotConformTo, match=\"COLLECTIONS, FEATURES\"):\n _ = next(api.get_collections())\n\n history = requests_mock.request_history\n assert len(history) == 2\n\n def test_opening_a_collection(self) -> None:\n path = str(TEST_DATA / \"planetary-computer-aster-l1t-collection.json\")\n with pytest.raises(ClientTypeError):\n Client.open(path)\n\n def test_headers_with_custom_stac_io(self, requests_mock: Mocker) -> None:\n pc_root_dict = read_data_file(\"planetary-computer-root.json\", parse_json=True)\n requests_mock.get(\n STAC_URLS[\"PLANETARY-COMPUTER\"],\n status_code=200,\n json=pc_root_dict,\n request_headers={\"ski\": \"pow\", \"shred\": \"gnar\"},\n )\n stac_io = StacApiIO(headers={\"ski\": \"pow\"})\n _ = Client.open(\n STAC_URLS[\"PLANETARY-COMPUTER\"], headers={\"shred\": \"gnar\"}, stac_io=stac_io\n )\n\n\nclass TestAPISearch:\n @pytest.fixture(scope=\"function\")\n def api(self) -> Client:\n return Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n\n def test_search_conformance_error(self, api: Client) -> None:\n # Remove item search conformance\n api.remove_conforms_to(\"ITEM_SEARCH\")\n\n with strict():\n with pytest.raises(DoesNotConformTo, match=\"ITEM_SEARCH\"):\n api.search(limit=10, max_items=10, collections=\"mr-peebles\")\n\n def test_no_search_link(self, api: Client) -> None:\n # Remove the search link\n api.remove_links(\"search\")\n\n with strict():\n with pytest.raises(\n MissingLink,\n match=\"No link with rel='search' could be found on this Client\",\n ):\n api.search(limit=10, max_items=10, collections=\"naip\")\n\n def test_no_conforms_to(self) -> None:\n with open(str(TEST_DATA / \"planetary-computer-root.json\")) as f:\n data = json.load(f)\n del data[\"conformsTo\"]\n with TemporaryDirectory() as temporary_directory:\n path = os.path.join(temporary_directory, \"catalog.json\")\n with open(path, \"w\") as f:\n json.dump(data, f)\n api = Client.from_file(path)\n\n with strict():\n with pytest.raises(DoesNotConformTo, match=\"ITEM_SEARCH\"):\n api.search(limit=10, max_items=10, collections=\"naip\")\n\n def test_search(self, api: Client) -> None:\n results = api.search(\n bbox=[-73.21, 43.99, -73.12, 44.05],\n collections=\"naip\",\n limit=10,\n max_items=20,\n datetime=[datetime(2020, 1, 1, 0, 0, 0, tzinfo=tzutc()), None],\n )\n\n assert results._parameters == {\n \"bbox\": (-73.21, 43.99, -73.12, 44.05),\n \"collections\": (\"naip\",),\n \"limit\": 10,\n \"datetime\": \"2020-01-01T00:00:00Z/..\",\n }\n\n def test_json_search_link(self, api: Client) -> None:\n search_link = api.get_single_link(rel=\"search\")\n assert search_link\n api.remove_links(rel=\"search\")\n search_link.media_type = MediaType.JSON\n api.add_link(search_link)\n api.search(limit=1, max_items=1, collections=\"naip\")\n\n @pytest.mark.vcr\n def test_search_max_items_unlimited_default(self, api: Client) -> None:\n search = api.search(\n bbox=[-73.21, 43.99, -73.12, 45.05],\n collections=\"naip\",\n datetime=\"2014-01-01/2020-12-31\",\n )\n items = list(search.items())\n assert len(items) > 100\n\n\nclass TestAPICollectionSearch:\n @pytest.fixture(scope=\"function\")\n def api(self) -> Client:\n return Client.from_file(str(TEST_DATA / \"fedeo_clearinghouse.json\"))\n\n def test_search_conformance_error(self, api: Client) -> None:\n # Remove collection search conformance\n api.remove_conforms_to(\"COLLECTION_SEARCH\")\n api.remove_conforms_to(\"COLLECTIONS\")\n\n with strict():\n with pytest.raises(DoesNotConformTo, match=\"COLLECTION_SEARCH\"):\n api.collection_search(limit=10, max_collections=10, q=\"test\")\n\n def test_search_conformance_warning(self) -> None:\n api = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n\n # Remove collection search conformance just in case...\n api.remove_conforms_to(\"COLLECTION_SEARCH\")\n\n with strict():\n with pytest.warns(UserWarning, match=\"COLLECTION_SEARCH\"):\n api.collection_search(limit=10, max_collections=10, q=\"test\")\n\n @pytest.mark.vcr\n def test_search(self, api: Client) -> None:\n results = api.collection_search(\n bbox=[-73.21, 43.99, -73.12, 44.05],\n limit=10,\n datetime=[datetime(2020, 1, 1, 0, 0, 0, tzinfo=tzutc()), None],\n )\n\n assert results._parameters == {\n \"bbox\": (-73.21, 43.99, -73.12, 44.05),\n \"limit\": 10,\n \"datetime\": \"2020-01-01T00:00:00Z/..\",\n }\n\n\nclass MySign:\n def __init__(self) -> None:\n self.call_count = 0\n\n def __call__(self, x: Modifiable) -> None:\n self.call_count += 1\n\n\nclass TestSigning:\n @pytest.mark.vcr\n def test_signing(self) -> None:\n sign = MySign()\n # sign is callable, but mypy keeps trying to interpret it as a \"MySign\" object.\n client = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"], modifier=sign)\n assert client.modifier is sign\n\n collection = client.get_collection(\"cil-gdpcir-cc0\")\n assert collection\n assert isinstance(collection, CollectionClient)\n assert collection.modifier is sign\n assert sign.call_count == 1\n\n collection.get_item(\"cil-gdpcir-INM-INM-CM5-0-ssp585-r1i1p1f1-day\")\n assert sign.call_count == 2\n\n next(client.get_collections())\n assert sign.call_count == 3\n\n search = client.search(collections=[\"sentinel-2-l2a\"], max_items=10)\n next(search.items_as_dicts())\n assert sign.call_count == 4\n\n next(search.items())\n assert sign.call_count == 5\n\n next(search.pages_as_dicts())\n assert sign.call_count == 6\n\n next(search.pages())\n assert sign.call_count == 7\n\n search.item_collection_as_dict()\n assert sign.call_count == 8\n\n search.item_collection()\n assert sign.call_count == 9\n\n @pytest.mark.vcr\n def test_sign_with_return_warns(self) -> None:\n def modifier_ok(x: Any) -> Any:\n return x\n\n def modifier_bad(x: Any) -> Any:\n return 0\n\n client = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"], modifier=modifier_ok)\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n client.get_collection(\"sentinel-2-l2a\")\n\n client = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"], modifier=modifier_bad)\n with pytest.warns(IgnoredResultWarning):\n client.get_collection(\"sentinel-2-l2a\")\n\n\nclass TestQueryables:\n @pytest.mark.vcr\n def test_get_queryables_collections(self) -> None:\n api = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n with pytest.warns(MissingLink, match=\"queryables\"):\n col = api.get_collection(\"3dep-seamless\")\n assert isinstance(col, CollectionClient)\n tdep_seamless_props = col.get_queryables()[\"properties\"]\n col = api.get_collection(\"fia\")\n assert isinstance(col, CollectionClient)\n fia_props = col.get_queryables()[\"properties\"]\n result = api.get_merged_queryables([\"fia\", \"3dep-seamless\"])\n assert \"properties\" in result\n assert \"id\" in result[\"properties\"]\n assert set(fia_props.keys()).issubset(result[\"properties\"])\n assert set(tdep_seamless_props.keys()).issubset(result[\"properties\"])\n\n def test_get_queryables_errors(self, requests_mock: Mocker) -> None:\n pc_root_text = read_data_file(\"planetary-computer-root.json\")\n root_url = \"http://pystac-client.test/\"\n requests_mock.get(root_url, status_code=200, text=pc_root_text)\n api = Client.open(root_url)\n with pytest.raises(DoesNotConformTo, match=\"FILTER\"):\n api.get_queryables()\n\n assert api._stac_io is not None\n api.add_conforms_to(\"FILTER\")\n self_href = api.get_self_href()\n api.set_self_href(None)\n with pytest.warns(MissingLink, match=\"queryables\"):\n with pytest.raises(ValueError, match=\"does not have a self_href set\"):\n api.get_queryables()\n\n api.set_self_href(self_href)\n api._stac_io = None\n with pytest.warns(MissingLink, match=\"queryables\"):\n with pytest.raises(APIError, match=\"API access is not properly configured\"):\n api.get_queryables()\n\n\nclass TestConformsTo:\n def test_ignore_conformance_is_deprecated_and_noop(self) -> None:\n with pytest.warns(\n FutureWarning, match=\"`ignore_conformance` option is deprecated\"\n ):\n client = Client.open(\n str(TEST_DATA / \"planetary-computer-root.json\"),\n ignore_conformance=True,\n )\n assert client.has_conforms_to()\n assert client.conforms_to(ConformanceClasses.CORE)\n\n def test_set_conforms_to_using_list_of_uris(self) -> None:\n client = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n client.set_conforms_to([\"https://api.stacspec.org/v1.0.0-rc.2/core\"])\n\n assert client.conforms_to(ConformanceClasses.CORE)\n\n def test_add_and_remove_conforms_to_by_string(self) -> None:\n client = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n\n client.remove_conforms_to(\"core\")\n assert not client.conforms_to(ConformanceClasses.CORE)\n\n client.add_conforms_to(\"core\")\n assert client.conforms_to(\"CORE\")\n\n def test_clear_all_conforms_to(self) -> None:\n client = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n client.clear_conforms_to()\n assert not client.has_conforms_to()\n\n def test_empty_conforms_to(self) -> None:\n client = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n client.set_conforms_to([])\n assert client.has_conforms_to(), \"The conformsTo field should still exist\"\n\n assert not client.conforms_to(ConformanceClasses.CORE)\n assert not client.conforms_to(ConformanceClasses.ITEM_SEARCH)\n\n def test_no_conforms_to_falls_back_to_pystac(self) -> None:\n client = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n client.clear_conforms_to()\n\n with strict():\n with pytest.raises(FallbackToPystac):\n next(client.get_collections())\n\n @pytest.mark.vcr\n def test_changing_conforms_to_changes_behavior(self) -> None:\n with pytest.warns(NoConformsTo):\n client = Client.open(\"https://earth-search.aws.element84.com/v0\")\n\n with pytest.warns(FallbackToPystac):\n next(client.get_collections())\n\n client.add_conforms_to(\"COLLECTIONS\")\n\n with pytest.warns(MissingLink, match=\"rel='data'\"):\n next(client.get_collections())\n\n\n@pytest.mark.vcr\ndef test_collections_are_clients() -> None:\n # https://github.com/stac-utils/pystac-client/issues/548\n catalog = Client.open(\n \"https://planetarycomputer.microsoft.com/api/stac/v1/\",\n )\n collection = catalog.get_collection(\"cil-gdpcir-cc-by\")\n assert collection is not None\n item = collection.get_item(\"cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day\")\n assert item is not None\n item.get_collection()\n\n\n@pytest.mark.vcr\ndef test_get_items_without_ids() -> None:\n client = Client.open(\n \"https://stac.eoapi.dev\",\n )\n next(client.get_items())\n\n\n@pytest.mark.vcr\ndef test_non_recursion_on_fallback() -> None:\n path = \"https://raw.githubusercontent.com/stac-utils/pystac/v1.9.0/docs/example-catalog/catalog.json\"\n catalog = Client.from_file(path)\n with pytest.warns(FallbackToPystac):\n [i for i in catalog.get_items()]\n\n\n@pytest.mark.vcr\ndef test_fallback_strategy() -> None:\n \"\"\"Make sure links get recreated correctly using APILayoutStrategy.\"\"\"\n\n client = Client.open(\n \"https://planetarycomputer.microsoft.com/api/stac/v1/\",\n )\n col = client.get_collection(\"landsat-c2-l2\")\n assert col is not None\n\n item = next(col.get_items())\n\n item_href = item.self_href\n col_href = col.self_href\n root_href = client.self_href\n\n col.links = []\n item.links = []\n\n client.add_child(col)\n col.add_item(item)\n\n assert col.self_href == col_href\n\n assert (col_parent := col.get_single_link(\"parent\"))\n assert col_parent.href == root_href\n\n assert (col_root := col.get_single_link(\"root\"))\n assert col_root.href == root_href\n\n assert item.self_href == item_href\n\n assert (item_col := item.get_single_link(\"collection\"))\n assert item_col.href == col_href\n\n assert (item_parent := item.get_single_link(\"parent\"))\n assert item_parent.href == col_href\n\n assert (item_root := item.get_single_link(\"root\"))\n assert item_root.href == root_href\n\n\ndef test_get_collection_fallback_strategy_for_static_catalogs() -> None:\n client = Client.from_file(\n str(TEST_DATA / \"test-case-1\" / \"country-1\" / \"catalog.json\")\n )\n\n with pytest.warns(FallbackToPystac):\n area_1_1 = client.get_collection(\"area-1-1\")\n assert area_1_1 is not None\n\n with pytest.warns(FallbackToPystac):\n area_1_2 = client.get_collection(\"area-1-2\")\n assert area_1_2 is not None\n\n assert area_1_1.id == \"area-1-1\"\n assert area_1_2.id == \"area-1-2\"\n\n\ndef test_get_collection_for_static_catalogs_returns_none_if_not_found() -> None:\n client = Client.from_file(str(TEST_DATA / \"test-case-1\" / \"catalog.json\"))\n with pytest.warns(FallbackToPystac):\n collection = client.get_collection(\"country-1\")\n assert collection is None\n\n\ndef test_get_collection_raises_if_collection_id_is_empty() -> None:\n client = Client.from_file(str(TEST_DATA / \"test-case-1\" / \"catalog.json\"))\n with pytest.raises(ValueError, match=\"A `collection_id` must be provided\"):\n client.get_collection(\"\")\n\n with pytest.raises(ValueError, match=\"A `collection_id` must be provided\"):\n client.get_collection(None)\n\n\n@pytest.mark.vcr\ndef test_get_collection_returns_none_if_not_found() -> None:\n client = Client.open(STAC_URLS[\"EARTH-SEARCH\"])\n collection = client.get_collection(\"foo\")\n assert collection is None\n\n\n@pytest.mark.vcr\ndef test_query_string_in_collections_url() -> None:\n # https://github.com/stac-utils/pystac-client/issues/745\n client = Client.open(\"https://paituli.csc.fi/geoserver/ogc/stac/v1\")\n client.get_collection(\"sentinel2-l2a\")\n" }, { "path": "tests/test_collection_client.py", "content": "import pytest\n\nfrom pystac_client import CollectionClient\nfrom pystac_client.client import Client\nfrom pystac_client.warnings import FallbackToPystac, MissingLink\n\nfrom .helpers import STAC_URLS\n\n\nclass TestCollectionClient:\n @pytest.mark.vcr\n def test_instance(self) -> None:\n client = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n collection = client.get_collection(\"aster-l1t\")\n\n assert isinstance(collection, CollectionClient)\n assert str(collection) == \"\"\n\n @pytest.mark.vcr\n def test_get_items(self) -> None:\n client = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n collection = client.get_collection(\"aster-l1t\")\n assert collection is not None\n for item in collection.get_items():\n assert item.collection_id == collection.id\n return\n\n @pytest.mark.vcr\n def test_get_items_with_ids(self) -> None:\n client = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n collection = client.get_collection(\"aster-l1t\")\n ids = [\n \"AST_L1T_00312272006020322_20150518201805\",\n \"AST_L1T_00312272006020313_20150518201753\",\n \"AST_L1T_00312272006020304_20150518201753\",\n ]\n assert collection is not None\n for item in collection.get_items(*ids):\n assert item.collection_id == collection.id\n assert item.id in ids\n\n @pytest.mark.vcr\n def test_get_item(self) -> None:\n client = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n collection = client.get_collection(\"aster-l1t\")\n assert collection is not None\n item = collection.get_item(\"AST_L1T_00312272006020322_20150518201805\")\n assert item\n assert item.id == \"AST_L1T_00312272006020322_20150518201805\"\n\n item = collection.get_item(\"for-sure-not-a-real-id\")\n assert item is None\n\n @pytest.mark.vcr\n def test_get_item_with_item_search(self) -> None:\n client = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n collection = client.get_collection(\"aster-l1t\")\n assert collection is not None\n\n client.set_conforms_to(\n [\n \"https://api.stacspec.org/v1.0.0-rc.2/core\",\n \"https://api.stacspec.org/v1.0.0-rc.2/item-search\",\n ]\n )\n\n item = collection.get_item(\"AST_L1T_00312272006020322_20150518201805\")\n assert item\n assert item.id == \"AST_L1T_00312272006020322_20150518201805\"\n\n item = collection.get_item(\"for-sure-not-a-real-id\")\n assert item is None\n with pytest.warns(FallbackToPystac):\n item = collection.get_item(\n \"AST_L1T_00312272006020322_20150518201805\", recursive=True\n )\n assert item\n assert item.id == \"AST_L1T_00312272006020322_20150518201805\"\n\n @pytest.mark.vcr\n def test_get_queryables(self) -> None:\n api = Client.open(STAC_URLS[\"PLANETARY-COMPUTER\"])\n collection_client = api.get_collection(\"landsat-c2-l2\")\n assert collection_client is not None\n assert isinstance(collection_client, CollectionClient)\n with pytest.warns(MissingLink, match=\"/queryables\"):\n result = collection_client.get_queryables()\n assert \"instrument\" in result[\"properties\"]\n assert \"landsat:scene_id\" in result[\"properties\"]\n" }, { "path": "tests/test_collection_search.py", "content": "import json\nfrom datetime import datetime\nfrom math import ceil\nfrom typing import Any\n\nimport pystac\nimport pytest\nimport requests\nfrom dateutil.tz import tzutc\nfrom pytest_benchmark.fixture import BenchmarkFixture\n\nfrom pystac_client.client import Client\nfrom pystac_client.collection_search import (\n CollectionSearch,\n bboxes_overlap,\n collection_matches,\n temporal_intervals_overlap,\n)\nfrom pystac_client.warnings import PystacClientWarning\n\nfrom .helpers import STAC_URLS, read_data_file\n\nCOLLECTION_SEARCH_URL = f\"{STAC_URLS['SPACEBEL']}/collections\"\n\nCOLLECTION_EXAMPLE: dict[str, Any] = {\n \"id\": \"F-TCC\",\n \"q\": '\"cloudless mosaics\"',\n \"datetime\": \"2021-09-15T00:00:00Z\",\n}\n\n\nclass TestCollectionPerformance:\n @pytest.fixture(scope=\"function\")\n def single_href(self) -> str:\n collection_href = COLLECTION_SEARCH_URL + f\"/{COLLECTION_EXAMPLE['id']}\"\n return collection_href\n\n def test_requests(self, benchmark: BenchmarkFixture, single_href: str) -> None:\n response = benchmark(requests.get, single_href)\n assert response.status_code == 200\n\n assert response.json()[\"id\"] == COLLECTION_EXAMPLE[\"id\"]\n\n def test_single_collection(\n self, benchmark: BenchmarkFixture, single_href: str\n ) -> None:\n collection = benchmark(pystac.Collection.from_file, single_href)\n\n assert collection.id == COLLECTION_EXAMPLE[\"id\"]\n\n def test_single_collection_search(\n self, benchmark: BenchmarkFixture, single_href: str\n ) -> None:\n search = CollectionSearch(url=COLLECTION_SEARCH_URL, **COLLECTION_EXAMPLE)\n\n collection_list = benchmark(search.collection_list())\n\n assert len(collection_list) == 1\n assert collection_list[0].id == COLLECTION_EXAMPLE[\"id\"]\n\n\nclass TestCollectionSearch:\n @pytest.fixture(scope=\"function\")\n def fedeo_api(self) -> Client:\n api_content = read_data_file(\"fedeo_clearinghouse.json\", parse_json=True)\n return Client.from_dict(api_content)\n\n def test_method(self) -> None:\n # Default method should be POST...\n search = CollectionSearch(url=COLLECTION_SEARCH_URL)\n assert search.method == \"GET\"\n\n def test_method_params(self) -> None:\n params_in: dict[str, Any] = {\n \"bbox\": (-72, 41, -71, 42),\n \"datetime\": \"2021-01-01T00:00:00Z/2021-12-31T23:59:59Z\",\n \"q\": \"vegetation AND biomass\",\n }\n # For POST this is pass through\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=True,\n **params_in,\n )\n params = search.get_parameters()\n assert params == search.get_parameters()\n\n # For GET requests, parameters are in query string and must be serialized\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=True,\n **params_in,\n )\n params = search.get_parameters()\n assert search.method == \"GET\"\n assert all(key in params for key in params_in)\n assert all(isinstance(params[key], str) for key in params_in)\n\n @pytest.mark.vcr\n def test_q_results(self) -> None:\n search_string = \"monthly cloudless mosaics\"\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=True,\n q=f'\"{search_string}\"',\n max_collections=20,\n limit=10,\n )\n results = search.collection_list()\n\n assert all(isinstance(collection, pystac.Collection) for collection in results)\n assert all(search_string in collection.description for collection in results)\n\n assert search.matched() == 1\n\n @pytest.mark.vcr\n def test_datetime_results(self) -> None:\n min_datetime = datetime(2024, 1, 1, 0, 0, 1, tzinfo=tzutc())\n max_datetime = datetime(2024, 1, 1, 0, 0, 10, tzinfo=tzutc())\n datetime_ = \"/\".join(\n ts.isoformat().replace(\"+00:00\", \"Z\") for ts in [min_datetime, max_datetime]\n )\n\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=True,\n datetime=datetime_,\n max_collections=20,\n )\n\n for collection in search.collections():\n for temporal_intervals in collection.extent.temporal.intervals:\n assert temporal_intervals_overlap(\n (temporal_intervals[0], temporal_intervals[1]),\n (min_datetime, max_datetime),\n )\n\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=True,\n datetime=(min_datetime, max_datetime),\n max_collections=20,\n )\n\n new_results = search.collections()\n for collection in new_results:\n for temporal_intervals in collection.extent.temporal.intervals:\n assert temporal_intervals_overlap(\n (temporal_intervals[0], temporal_intervals[1]),\n (min_datetime, max_datetime),\n )\n\n @pytest.mark.vcr\n def test_bbox_results(self) -> None:\n bbox = (-159.893, 21.843, -159.176, 22.266)\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=True,\n bbox=bbox,\n limit=10,\n max_collections=10,\n )\n\n for collection in search.collections():\n for _bbox in collection.extent.spatial.bboxes:\n assert bboxes_overlap(bbox, tuple(float(coord) for coord in _bbox))\n\n @pytest.mark.vcr\n def test_result_paging(self) -> None:\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=True,\n q=\"sentinel\",\n limit=10,\n max_collections=20,\n )\n\n # Check that the current page changes on the ItemSearch instance when a new page\n # is requested\n pages = list(search.pages())\n\n assert pages[0] != pages[1]\n\n @pytest.mark.vcr\n def test_matched(self) -> None:\n q = \"sentinel\"\n search = CollectionSearch(\n url=f\"{STAC_URLS['EARTH-SEARCH']}/collections\",\n collection_search_extension_enabled=False,\n collection_search_free_text_enabled=False,\n q=q,\n limit=4,\n )\n\n assert search.matched() == 5\n\n @pytest.mark.vcr\n def test_enabled_but_client_side_q(self) -> None:\n q = \"sentinel\"\n limit = 5\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=False,\n limit=limit,\n max_collections=limit,\n q=q,\n )\n\n collection_list = search.collection_list()\n assert len(collection_list) <= limit\n collection_list_dict = search.collection_list_as_dict()\n assert len(collection_list_dict[\"collections\"]) == len(collection_list)\n\n for collection in search.collections():\n text_fields = []\n if collection.description:\n text_fields.append(collection.description)\n if collection.title:\n text_fields.append(collection.title)\n if collection.keywords:\n text_fields.extend(collection.keywords)\n\n assert any(q in text_field.lower() for text_field in text_fields), (\n f\"{collection.id} failed check\"\n )\n\n @pytest.mark.vcr\n def test_client_side_q(self) -> None:\n q = \"sentinel\"\n limit = 10\n search = CollectionSearch(\n url=f\"{STAC_URLS['EARTH-SEARCH']}/collections\",\n collection_search_extension_enabled=False,\n collection_search_free_text_enabled=False,\n limit=limit,\n max_collections=limit,\n q=q,\n )\n\n collection_list = search.collection_list()\n assert len(collection_list) <= limit\n\n for collection in search.collections():\n text_fields = []\n if collection.description:\n text_fields.append(collection.description)\n if collection.title:\n text_fields.append(collection.title)\n if collection.keywords:\n text_fields.extend(collection.keywords)\n\n assert any(q in text_field.lower() for text_field in text_fields), (\n f\"{collection.id} failed check\"\n )\n\n @pytest.mark.vcr\n def test_client_side_bbox(self) -> None:\n bbox = (60, 0, 70, 10)\n limit = 10\n search = CollectionSearch(\n url=f\"{STAC_URLS['EARTH-SEARCH']}/collections\",\n collection_search_extension_enabled=False,\n collection_search_free_text_enabled=False,\n limit=limit,\n max_collections=limit,\n bbox=bbox,\n )\n\n collection_list = search.collection_list()\n assert len(collection_list) <= limit\n\n for collection in search.collections():\n assert any(\n bboxes_overlap(bbox, tuple(float(coord) for coord in collection_bbox))\n for collection_bbox in collection.extent.spatial.bboxes\n ), f\"{collection.id} failed check\"\n\n @pytest.mark.vcr\n def test_client_side_datetime(self) -> None:\n _datetime_interval = (\"2024-09-15T00:00:00+00:00\", \"2024-09-16T00:00:00+00:00\")\n limit = 10\n search = CollectionSearch(\n url=f\"{STAC_URLS['EARTH-SEARCH']}/collections\",\n collection_search_extension_enabled=False,\n collection_search_free_text_enabled=False,\n limit=limit,\n max_collections=limit,\n datetime=\"/\".join(_datetime_interval),\n )\n\n collection_list = search.collection_list()\n assert len(collection_list) <= limit\n\n temporal_interval = (\n datetime.fromisoformat(_datetime_interval[0]),\n datetime.fromisoformat(_datetime_interval[1]),\n )\n for collection in search.collections():\n assert any(\n temporal_intervals_overlap(\n temporal_interval,\n (collection_temporal_interval[0], collection_temporal_interval[1]),\n )\n for collection_temporal_interval in collection.extent.temporal.intervals\n ), f\"{collection.id} failed check\"\n\n def test_client_side_extra_args(self) -> None:\n with pytest.raises(ValueError):\n CollectionSearch(\n url=f\"{STAC_URLS['EARTH-SEARCH']}/collections\",\n collection_search_extension_enabled=False,\n collection_search_free_text_enabled=False,\n filter=\"title LIKE '%nope%'\",\n )\n\n @pytest.mark.vcr\n def test_result_paging_max_collections(self) -> None:\n max_collections = 15\n limit = 10\n search = CollectionSearch(\n url=COLLECTION_SEARCH_URL,\n collection_search_extension_enabled=True,\n collection_search_free_text_enabled=True,\n bbox=(-92.379, 46.662, -91.788, 46.919),\n limit=limit,\n max_collections=max_collections,\n )\n num_pages = 0\n collections = list()\n for page in search.pages_as_dicts():\n num_pages += 1\n collections.extend(page[\"collections\"])\n assert num_pages == ceil(max_collections / limit)\n assert len(collections) == max_collections\n\n\ndef test_bboxes_overlap() -> None:\n assert bboxes_overlap(\n (0, 0, 2, 2),\n (1, 1, 3, 3),\n )\n\n assert not bboxes_overlap(\n (0, 0, 2, 2),\n (3, 3, 4, 4),\n )\n\n\ndef test_temporal_intervals_overlap() -> None:\n assert temporal_intervals_overlap(\n (datetime(2024, 9, 1, tzinfo=tzutc()), datetime(2024, 9, 2, tzinfo=tzutc())),\n (\n datetime(2024, 9, 1, 12, tzinfo=tzutc()),\n datetime(2024, 9, 2, 12, tzinfo=tzutc()),\n ),\n )\n assert temporal_intervals_overlap(\n (datetime(2024, 9, 1, tzinfo=tzutc()), None),\n (\n datetime(2024, 9, 1, 12, tzinfo=tzutc()),\n datetime(2024, 9, 2, 12, tzinfo=tzutc()),\n ),\n )\n assert temporal_intervals_overlap(\n (None, None),\n (\n datetime(2024, 9, 1, 12, tzinfo=tzutc()),\n datetime(2024, 9, 2, 12, tzinfo=tzutc()),\n ),\n )\n assert not temporal_intervals_overlap(\n (datetime(2023, 9, 1, tzinfo=tzutc()), datetime(2023, 9, 2, tzinfo=tzutc())),\n (\n datetime(2024, 9, 1, 12, tzinfo=tzutc()),\n datetime(2024, 9, 2, 12, tzinfo=tzutc()),\n ),\n )\n assert not temporal_intervals_overlap(\n (datetime(2023, 9, 1, tzinfo=tzutc()), datetime(2023, 9, 2, tzinfo=tzutc())),\n (\n datetime(2024, 9, 1, 12, tzinfo=tzutc()),\n None,\n ),\n )\n\n\ndef test_invalid_collection() -> None:\n # https://github.com/stac-utils/pystac-client/issues/786\n data = json.loads(read_data_file(\"invalid-collection.json\"))\n with pytest.warns(PystacClientWarning):\n collection_matches(data)\n" }, { "path": "tests/test_conformance.py", "content": "import pytest\n\nfrom pystac_client.conformance import ConformanceClasses\n\n\nclass TestConformanceClasses:\n def test_get_by_name_raises_for_invalid_names(self) -> None:\n \"\"\"Test get_by_name raises ValueError for invalid conformance class names.\"\"\"\n with pytest.raises(ValueError, match=\"Invalid conformance class 'invalid'\"):\n ConformanceClasses.get_by_name(\"invalid\")\n\n with pytest.raises(ValueError, match=\"Invalid conformance class 'nonexistent'\"):\n ConformanceClasses.get_by_name(\"nonexistent\")\n\n with pytest.raises(ValueError, match=\"Invalid conformance class ''\"):\n ConformanceClasses.get_by_name(\"\")\n\n def test_get_by_name_valid(self) -> None:\n \"\"\"Test get_by_name with valid conformance class names.\"\"\"\n assert ConformanceClasses.get_by_name(\"core\") == ConformanceClasses.CORE\n assert ConformanceClasses.get_by_name(\"CORE\") == ConformanceClasses.CORE\n\n def test_valid_uri_property(self) -> None:\n \"\"\"Test valid_uri property returns correct URI pattern.\"\"\"\n assert (\n ConformanceClasses.CORE.valid_uri == \"https://api.stacspec.org/v1.0.*/core\"\n )\n\n def test_pattern_property(self) -> None:\n \"\"\"Test pattern property returns compiled regex.\"\"\"\n core_pattern = ConformanceClasses.CORE.pattern\n assert core_pattern.match(\"https://api.stacspec.org/v1.0.0/core\")\n" }, { "path": "tests/test_free_text.py", "content": "from pystac_client.free_text import (\n sqlite_text_search,\n)\n\n\ndef test_sqlite_single_term() -> None:\n query = \"sentinel\"\n assert sqlite_text_search(query, {\"description\": \"The sentinel node was true\"})\n assert not sqlite_text_search(query, {\"description\": \"No match here\"})\n\n\ndef test_sqlite_special_characters() -> None:\n query = \"sentinel-2\"\n assert sqlite_text_search(query, {\"description\": \"The sentinel-2 node was true\"})\n assert not sqlite_text_search(query, {\"description\": \"No, just sentinel-1 here\"})\n\n query = \"sentinel+2\"\n assert sqlite_text_search(query, {\"description\": \"The sentinel+2 node was true\"})\n assert not sqlite_text_search(query, {\"description\": \"No, just sentinel+1 here\"})\n\n query = \"sentinel@2\"\n assert sqlite_text_search(query, {\"description\": \"The sentinel@2 node was true\"})\n assert not sqlite_text_search(query, {\"description\": \"No, just sentinel@1 here\"})\n\n\ndef test_sqlite_exact_phrase() -> None:\n query = '\"climate model\"'\n assert sqlite_text_search(query, {\"description\": \"The climate model is impressive\"})\n assert not sqlite_text_search(\n query, {\"description\": \"This model is for climate modeling\"}\n )\n\n # an exact phrase with a comma inside\n query = '\"models, etc\"'\n assert sqlite_text_search(\n query, {\"description\": \"Produced with equations, and models, etc.\"}\n )\n assert not sqlite_text_search(query, {\"description\": \"Produced with models\"})\n\n\ndef test_sqlite_and_terms_default() -> None:\n query = \"climate model\"\n assert sqlite_text_search(\n query,\n {\n \"description\": \"Climate change is a significant challenge\",\n \"keywords\": \"model, prediction\",\n },\n )\n assert sqlite_text_search(\n query, {\"description\": \"The model was developed using climate observation data\"}\n )\n assert not sqlite_text_search(query, {\"description\": \"This is an advanced model\"})\n assert not sqlite_text_search(query, {\"description\": \"No relevant terms here\"})\n\n\ndef test_sqlite_or_terms_explicit() -> None:\n query = \"climate OR model\"\n assert sqlite_text_search(query, {\"description\": \"Climate discussion\"})\n assert sqlite_text_search(query, {\"description\": \"FPGA model creation\"})\n assert not sqlite_text_search(query, {\"description\": \"No matching term here\"})\n\n\ndef test_sqlite_or_terms_commas() -> None:\n query = \"climate,model\"\n assert sqlite_text_search(query, {\"description\": \"Climate change is here\"})\n assert sqlite_text_search(query, {\"description\": \"They built a model train\"})\n assert sqlite_text_search(query, {\"description\": \"It's a climate model!\"})\n assert not sqlite_text_search(\n query, {\"description\": \"It's a mathematical equation\"}\n )\n\n\ndef test_sqlite_and_terms() -> None:\n query = \"climate AND model\"\n assert sqlite_text_search(query, {\"description\": \"The climate model is impressive\"})\n assert not sqlite_text_search(\n query, {\"description\": \"This climate change discussion\"}\n )\n assert not sqlite_text_search(query, {\"description\": \"Advanced model system\"})\n\n\ndef test_sqlite_parentheses_grouping() -> None:\n query = \"(quick OR brown) AND fox\"\n assert sqlite_text_search(query, {\"description\": \"The quick brown fox\"})\n assert sqlite_text_search(query, {\"description\": \"A quick fox jumps\"})\n assert sqlite_text_search(query, {\"description\": \"brown bear and a fox\"})\n assert not sqlite_text_search(query, {\"description\": \"The fox is clever\"})\n\n query = \"(quick AND brown) OR (fast AND red)\"\n assert sqlite_text_search(query, {\"description\": \"quick brown fox\"})\n assert sqlite_text_search(query, {\"description\": \"fast red car\"})\n assert not sqlite_text_search(query, {\"description\": \"quick red car\"})\n\n\ndef test_sqlite_inclusions_exclusions() -> None:\n query = \"quick +brown -fox\"\n assert sqlite_text_search(query, {\"description\": \"The quick brown bear\"})\n assert not sqlite_text_search(query, {\"description\": \"The quick fox\"})\n assert not sqlite_text_search(query, {\"description\": \"The quickest\"})\n assert sqlite_text_search(query, {\"description\": \"A quick light brown jumper\"})\n\n\ndef test_sqlite_partial_match() -> None:\n query = \"climat\"\n assert not sqlite_text_search(query, {\"description\": \"climatology\"})\n assert not sqlite_text_search(query, {\"description\": \"climate\"})\n assert not sqlite_text_search(query, {\"description\": \"climbing\"})\n" }, { "path": "tests/test_item_search.py", "content": "import operator\nimport urllib.parse\nfrom datetime import datetime, timedelta\nfrom typing import Any\n\nimport pystac\nimport pytest\nimport requests\nfrom dateutil.tz import tzutc\nfrom pytest_benchmark.fixture import BenchmarkFixture\nfrom requests_mock import Mocker\n\nfrom pystac_client import Client\nfrom pystac_client.item_search import ItemSearch\n\nfrom .helpers import STAC_URLS, read_data_file\n\nSEARCH_URL = f\"{STAC_URLS['PLANETARY-COMPUTER']}/search\"\nINTERSECTS_EXAMPLE = {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [-73.21, 43.99],\n [-73.21, 44.05],\n [-73.12, 44.05],\n [-73.12, 43.99],\n [-73.21, 43.99],\n ]\n ],\n}\n\nITEM_EXAMPLE: dict[str, Any] = {\"collections\": \"io-lulc\", \"ids\": \"60U-2020\"}\n\n\nclass TestItemPerformance:\n @pytest.fixture(scope=\"function\")\n def single_href(self) -> str:\n item_href = (\n \"https://planetarycomputer.microsoft.com/api/stac/v1/collections/\"\n f\"{ITEM_EXAMPLE['collections']}/items/{ITEM_EXAMPLE['ids']}\"\n )\n return item_href\n\n def test_requests(self, benchmark: BenchmarkFixture, single_href: str) -> None:\n response = benchmark(requests.get, single_href)\n assert response.status_code == 200\n\n assert response.json()[\"id\"] == ITEM_EXAMPLE[\"ids\"]\n\n def test_single_item(self, benchmark: BenchmarkFixture, single_href: str) -> None:\n item = benchmark(pystac.Item.from_file, single_href)\n\n assert item.id == ITEM_EXAMPLE[\"ids\"]\n\n def test_single_item_search(\n self, benchmark: BenchmarkFixture, single_href: str\n ) -> None:\n search = ItemSearch(url=SEARCH_URL, **ITEM_EXAMPLE)\n\n item_collection = benchmark(search.item_collection())\n\n assert len(item_collection.items) == 1\n assert item_collection.items[0].id == ITEM_EXAMPLE[\"ids\"]\n\n\nclass TestItemSearch:\n @pytest.fixture(scope=\"function\")\n def astraea_api(self) -> Client:\n api_content = read_data_file(\"astraea_api.json\", parse_json=True)\n return Client.from_dict(api_content)\n\n def test_method(self) -> None:\n # Default method should be POST...\n search = ItemSearch(url=SEARCH_URL)\n assert search.method == \"POST\"\n\n # \"method\" argument should take precedence over presence of \"intersects\"\n search = ItemSearch(url=SEARCH_URL, method=\"GET\", intersects=INTERSECTS_EXAMPLE)\n assert search.method == \"GET\"\n\n def test_method_params(self) -> None:\n params_in: dict[str, Any] = {\n \"bbox\": (-72, 41, -71, 42),\n \"ids\": (\n \"idone\",\n \"idtwo\",\n ),\n \"collections\": (\"collectionone\",),\n \"intersects\": INTERSECTS_EXAMPLE,\n }\n # For POST this is pass through\n search = ItemSearch(url=SEARCH_URL, **params_in)\n params = search.get_parameters()\n assert params == search.get_parameters()\n\n # For GET requests, parameters are in query string and must be serialized\n search = ItemSearch(url=SEARCH_URL, method=\"GET\", **params_in)\n params = search.get_parameters()\n assert all(key in params for key in params_in)\n assert all(isinstance(params[key], str) for key in params_in)\n\n @pytest.mark.vcr\n def test_results(self) -> None:\n search = ItemSearch(\n url=SEARCH_URL,\n collections=\"naip\",\n max_items=20,\n limit=10,\n )\n results = search.items()\n\n assert all(isinstance(item, pystac.Item) for item in results)\n\n @pytest.mark.vcr\n def test_ids_results(self) -> None:\n ids = [\n \"S2B_MSIL2A_20210610T115639_N0212_R066_T33XXG_20210613T185024.SAFE\",\n \"fl_m_2608004_nw_17_060_20191215_20200113\",\n ]\n search = ItemSearch(\n url=SEARCH_URL,\n collections=[\"naip\"],\n ids=ids,\n )\n results = list(search.items())\n\n assert len(results) == 1\n assert all(item.id in ids for item in results)\n\n @pytest.mark.vcr\n def test_datetime_results(self) -> None:\n min_datetime = datetime(2019, 1, 1, 0, 0, 1, tzinfo=tzutc())\n max_datetime = datetime(2019, 1, 1, 0, 0, 10, tzinfo=tzutc())\n datetime_ = \"2019-01-01T00:00:01Z/2019-01-01T00:00:10Z\"\n search = ItemSearch(\n url=SEARCH_URL,\n datetime=datetime_,\n collections=[\"sentinel-2-l2a\"],\n max_items=20,\n )\n for item in search.items():\n assert item.datetime is not None\n assert (\n min_datetime <= item.datetime <= (max_datetime + timedelta(seconds=1))\n )\n search = ItemSearch(\n url=SEARCH_URL,\n datetime=(min_datetime, max_datetime),\n collections=[\"sentinel-2-l2a\"],\n max_items=20,\n )\n new_results = search.items()\n for item in new_results:\n assert item.datetime is not None\n assert (\n min_datetime <= item.datetime <= (max_datetime + timedelta(seconds=1))\n )\n\n @pytest.mark.vcr\n def test_intersects_results(self) -> None:\n # GeoJSON-like dict\n intersects_dict = {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [-73.21, 43.99],\n [-73.21, 44.05],\n [-73.12, 44.05],\n [-73.12, 43.99],\n [-73.21, 43.99],\n ]\n ],\n }\n search = ItemSearch(\n url=SEARCH_URL, intersects=intersects_dict, collections=\"naip\"\n )\n results = list(search.items())\n assert len(results) == 42\n\n # Geo-interface object\n class MockGeoObject:\n @property\n def __geo_interface__(self) -> dict[str, Any]:\n return intersects_dict\n\n intersects_obj = MockGeoObject()\n search = ItemSearch(\n url=SEARCH_URL, intersects=intersects_obj, collections=\"naip\"\n )\n new_results = search.items()\n assert all(isinstance(item, pystac.Item) for item in new_results)\n\n def test_get_with_query(self, requests_mock: Mocker) -> None:\n requests_mock.get(\n (\n f\"{SEARCH_URL}?query=%7B%22eo%3Acloud_cover%22%3A%7B%22gte%22%3A0%2C%22lte%22%3A10%7D%7D\"\n ),\n status_code=200,\n json={\"features\": [{\"foo\": \"bar\"}], \"links\": []},\n )\n items = list(\n ItemSearch(\n url=SEARCH_URL,\n method=\"GET\",\n query={\"eo:cloud_cover\": {\"gte\": 0, \"lte\": 10}},\n ).items_as_dicts()\n )\n assert len(items) == 1\n\n @pytest.mark.vcr\n def test_result_paging(self) -> None:\n search = ItemSearch(\n url=SEARCH_URL,\n bbox=(-73.21, 43.99, -73.12, 44.05),\n collections=\"naip\",\n limit=10,\n max_items=20,\n )\n\n # Check that the current page changes on the ItemSearch instance when a new page\n # is requested\n pages = list(search.pages())\n\n assert pages[0] != pages[1]\n assert pages[0].items != pages[1].items\n\n @pytest.mark.vcr\n def test_result_paging_max_items(self) -> None:\n search = ItemSearch(\n url=SEARCH_URL,\n collections=\"naip\",\n limit=10,\n max_items=25,\n )\n num_pages = 0\n items = list()\n for page in search.pages_as_dicts():\n num_pages += 1\n items.extend(page[\"features\"])\n assert num_pages == 3\n assert len(items) == 25\n\n @pytest.mark.vcr\n def test_item_collection(self) -> None:\n search = ItemSearch(\n url=SEARCH_URL,\n bbox=(-73.21, 43.99, -73.12, 44.05),\n collections=\"naip\",\n limit=10,\n max_items=20,\n )\n item_collection = search.item_collection()\n assert isinstance(item_collection, pystac.ItemCollection)\n assert len(item_collection) == 20\n\n @pytest.mark.vcr\n @pytest.mark.parametrize(\n \"method, alternative, is_sequence, is_pystac\",\n [\n (\"get_item_collections\", \"pages\", True, True),\n (\"item_collections\", \"pages\", True, True),\n (\"get_items\", \"items\", True, True),\n (\"get_all_items\", \"item_collection\", False, True),\n (\"get_all_items_as_dict\", \"item_collection_as_dict\", False, False),\n ],\n )\n def test_deprecations(\n self, method: str, alternative: str, is_sequence: bool, is_pystac: bool\n ) -> None:\n search = ItemSearch(\n url=SEARCH_URL,\n bbox=(-73.21, 43.99, -73.12, 44.05),\n collections=\"naip\",\n limit=10,\n max_items=20,\n )\n\n with pytest.warns(FutureWarning, match=method):\n result = operator.methodcaller(method)(search)\n\n expected = operator.methodcaller(alternative)(search)\n\n if is_sequence:\n result = list(result)\n expected = list(expected)\n if is_pystac:\n result = [x.to_dict() for x in result]\n expected = [x.to_dict() for x in expected]\n else:\n if is_pystac:\n result = result.to_dict()\n expected = expected.to_dict()\n\n assert result == expected\n\n @pytest.mark.vcr\n def test_items_as_dicts(self) -> None:\n search = ItemSearch(\n url=SEARCH_URL,\n bbox=(-73.21, 43.99, -73.12, 44.05),\n collections=\"naip\",\n limit=10,\n max_items=20,\n )\n assert len(list(search.items_as_dicts())) == 20\n\n\nclass TestItemSearchQuery:\n @pytest.mark.vcr\n def test_query_shortcut_syntax(self) -> None:\n search = ItemSearch(\n url=SEARCH_URL,\n collections=\"naip\",\n bbox=(-73.21, 43.99, -73.12, 44.05),\n query=[\"gsd=0.6\"],\n max_items=1,\n )\n items1 = list(search.items())\n\n search = ItemSearch(\n url=SEARCH_URL,\n collections=\"naip\",\n bbox=(-73.21, 43.99, -73.12, 44.05),\n query={\"gsd\": {\"eq\": 0.6}},\n max_items=1,\n )\n items2 = list(search.items())\n\n assert len(items1) == 1\n assert len(items2) == 1\n assert items1[0].id == items2[0].id\n\n @pytest.mark.vcr\n def test_query_json_syntax(self) -> None:\n # with a list of json strs (format of CLI argument to ItemSearch)\n search = ItemSearch(\n url=SEARCH_URL,\n collections=\"naip\",\n bbox=(-73.21, 43.99, -73.12, 44.05),\n query=['{\"gsd\": { \"gte\": 0, \"lte\": 1 }}'],\n max_items=1,\n )\n item1 = list(search.items())[0]\n assert item1.properties[\"gsd\"] <= 1\n\n # with a single dict\n search = ItemSearch(\n url=SEARCH_URL,\n collections=\"naip\",\n bbox=(-73.21, 43.99, -73.12, 44.05),\n query={\"gsd\": {\"gte\": 0, \"lte\": 1}},\n max_items=1,\n )\n item2 = list(search.items())[0]\n assert item2.properties[\"gsd\"] <= 1\n\n assert item1.id == item2.id\n\n\ndef test_query_json_syntax() -> None:\n item_search = ItemSearch(\"\")\n assert item_search._format_query(\n ['{\"eo:cloud_cover\": { \"gte\": 0, \"lte\": 1 }}']\n ) == {\"eo:cloud_cover\": {\"gte\": 0, \"lte\": 1}}\n assert item_search._format_query({\"eo:cloud_cover\": {\"gte\": 0, \"lte\": 1}}) == {\n \"eo:cloud_cover\": {\"gte\": 0, \"lte\": 1}\n }\n assert item_search._format_query([\"eo:cloud_cover<=1\"]) == {\n \"eo:cloud_cover\": {\"lte\": \"1\"}\n }\n assert item_search._format_query([\"eo:cloud_cover<=1\", \"eo:cloud_cover>0\"]) == {\n \"eo:cloud_cover\": {\"lte\": \"1\", \"gt\": \"0\"}\n }\n\n\ndef test_url_with_query_parameter() -> None:\n # https://github.com/stac-utils/pystac-client/issues/522\n search = ItemSearch(\n url=\"http://pystac-client.test\", query={\"eo:cloud_cover\": {\"lt\": 42}}\n )\n url = urllib.parse.urlparse(search.url_with_parameters())\n query = urllib.parse.parse_qs(url.query)\n assert query[\"query\"] == [r'{\"eo:cloud_cover\":{\"lt\":42}}']\n\n\n@pytest.mark.vcr\ndef test_multiple_collections() -> None:\n search = ItemSearch(\n url=\"https://earth-search.aws.element84.com/v1/search\",\n collections=[\"sentinel-2-l2a\", \"landsat-c2-l2\"],\n intersects={\"type\": \"Point\", \"coordinates\": [-105.1019, 40.1672]},\n datetime=\"2023-10-08\",\n )\n collections = {item.collection_id for item in search.items()}\n assert collections == {\"sentinel-2-l2a\", \"landsat-c2-l2\"}\n\n\ndef test_naive_datetime() -> None:\n search = ItemSearch(\n url=\"https://earth-search.aws.element84.com/v1/search\",\n datetime=datetime(2024, 5, 14, 4, 25, 42, tzinfo=None),\n method=\"POST\",\n )\n assert search.get_parameters()[\"datetime\"] == \"2024-05-14T04:25:42Z\"\n\n\n@pytest.mark.vcr\ndef test_fields() -> None:\n search = ItemSearch(\n url=\"https://earth-search.aws.element84.com/v1/search\",\n collections=[\"sentinel-2-c1-l2a\"],\n intersects={\"type\": \"Point\", \"coordinates\": [-105.1019, 40.1672]},\n max_items=1,\n fields=[\"-geometry\", \"-assets\", \"-links\"],\n )\n item = next(search.items_as_dicts())\n assert \"geometry\" not in item\n assert \"assets\" not in item\n assert \"links\" not in item\n\n\ndef test_feature() -> None:\n search = ItemSearch(\n url=\"https://earth-search.aws.element84.com/v1/search\",\n intersects={\n \"type\": \"Feature\",\n \"geometry\": {\"type\": \"Point\", \"coordinates\": [-105.1019, 40.1672]},\n },\n )\n assert search.get_parameters()[\"intersects\"] == {\n \"type\": \"Point\",\n \"coordinates\": [-105.1019, 40.1672],\n }\n" }, { "path": "tests/test_stac_api_io.py", "content": "import typing\nfrom pathlib import Path\nfrom urllib.parse import parse_qs, urlsplit\n\nimport pystac\nimport pytest\nfrom pytest import MonkeyPatch\nfrom requests_mock.mocker import Mocker\n\nfrom pystac_client.exceptions import APIError\nfrom pystac_client.stac_api_io import StacApiIO\n\nfrom .helpers import STAC_URLS\n\n\nclass TestSTAC_IOOverride:\n @pytest.mark.vcr\n def test_request_input(self) -> None:\n stac_api_io = StacApiIO()\n response = stac_api_io.read_text(STAC_URLS[\"PLANETARY-COMPUTER\"])\n assert isinstance(response, str)\n\n @pytest.mark.vcr\n def test_str_input(self) -> None:\n stac_api_io = StacApiIO()\n response = stac_api_io.read_text(STAC_URLS[\"PLANETARY-COMPUTER\"])\n\n assert isinstance(response, str)\n\n @pytest.mark.vcr\n def test_http_error(self) -> None:\n stac_api_io = StacApiIO()\n # Attempt to access an authenticated endpoint\n with pytest.raises(APIError) as excinfo:\n stac_api_io.read_text(f\"{STAC_URLS['MLHUB']}/search\")\n\n assert isinstance(excinfo.value, APIError)\n\n def test_local_file(self, tmp_path: Path) -> None:\n stac_api_io = StacApiIO()\n test_file = tmp_path / \"test.txt\"\n with test_file.open(\"w\") as dst:\n dst.write(\"Hi there!\")\n\n response = stac_api_io.read_text(str(test_file))\n\n assert response == \"Hi there!\"\n\n def test_conformance_deprecated(self) -> None:\n with pytest.warns(FutureWarning, match=\"`conformance` option is deprecated\"):\n stac_api_io = StacApiIO(conformance=[])\n assert not hasattr(stac_api_io, \"conformance\")\n\n def test_custom_headers(self, requests_mock: Mocker) -> None:\n \"\"\"Checks that headers passed to the init method are added to requests.\"\"\"\n header_name = \"x-my-header\"\n header_value = \"Some Value\"\n url = \"https://some-url.com/some-file.json\"\n stac_api_io = StacApiIO(headers={header_name: header_value})\n\n requests_mock.get(url, status_code=200, json={})\n\n stac_api_io.read_json(url)\n\n history = requests_mock.request_history\n assert len(history) == 1\n assert header_name in history[0].headers\n assert history[0].headers[header_name] == header_value\n\n def test_modifier(self, requests_mock: Mocker) -> None:\n \"\"\"Verify the modifier is correctly called with a returned object.\"\"\"\n header_name = \"x-my-header\"\n header_value = \"Some Value\"\n url = \"https://some-url.com/some-file.json\"\n\n def custom_modifier(request: typing.Any) -> typing.Any | None:\n request.headers[\"x-pirate-name\"] = \"yellowbeard\"\n return request\n\n stac_api_io = StacApiIO(\n headers={header_name: header_value}, request_modifier=custom_modifier\n )\n\n requests_mock.get(url, status_code=200, json={})\n\n stac_api_io.read_json(url)\n\n history = requests_mock.request_history\n assert len(history) == 1\n assert header_name in history[0].headers\n assert history[0].headers[\"x-pirate-name\"] == \"yellowbeard\"\n\n def test_modifier_noreturn(self, requests_mock: Mocker) -> None:\n \"\"\"Verify the modifier is correctly called when None is returned.\"\"\"\n header_name = \"x-my-header\"\n header_value = \"Some Value\"\n url = \"https://some-url.com/some-file.json\"\n\n def custom_modifier(request: typing.Any) -> typing.Any | None:\n request.headers[\"x-pirate-name\"] = \"yellowbeard\"\n return None\n\n stac_api_io = StacApiIO(\n headers={header_name: header_value}, request_modifier=custom_modifier\n )\n\n requests_mock.get(url, status_code=200, json={})\n\n stac_api_io.read_json(url)\n\n history = requests_mock.request_history\n assert len(history) == 1\n assert header_name in history[0].headers\n assert history[0].headers[\"x-pirate-name\"] == \"yellowbeard\"\n\n def test_custom_query_params(self, requests_mock: Mocker) -> None:\n \"\"\"Checks that query params passed to the init method are added to requests.\"\"\"\n init_qp_name = \"my-param\"\n init_qp_value = \"something\"\n url = \"https://some-url.com/some-file.json\"\n stac_api_io = StacApiIO(parameters={init_qp_name: init_qp_value})\n\n request_qp_name = \"another-param\"\n request_qp_value = \"another_value\"\n requests_mock.get(url, status_code=200, json={})\n\n stac_api_io.read_json(url, parameters={request_qp_name: request_qp_value})\n\n history = requests_mock.request_history\n assert len(history) == 1\n\n actual_qs = urlsplit(history[0].url).query\n actual_qp = parse_qs(actual_qs)\n\n # Check that the param from the init method is present\n assert init_qp_name in actual_qp\n assert len(actual_qp[init_qp_name]) == 1\n assert actual_qp[init_qp_name][0] == init_qp_value\n\n # Check that the param from the request is present\n assert request_qp_name in actual_qp\n assert len(actual_qp[request_qp_name]) == 1\n assert actual_qp[request_qp_name][0] == request_qp_value\n\n def test_write(self, tmp_path: Path) -> None:\n stac_api_io = StacApiIO()\n test_file = tmp_path / \"test.txt\"\n stac_api_io.write_text_to_href(str(test_file), \"Hi there!\")\n with open(test_file) as file:\n data = file.read()\n assert data == \"Hi there!\"\n\n @pytest.mark.parametrize(\n (\"attribute\", \"endpoint\"),\n ((\"features\", \"search\"), (\"collections\", \"collections\")),\n )\n def test_stop_on_empty_page(\n self, requests_mock: Mocker, attribute: str, endpoint: str\n ) -> None:\n url = f\"https://pystac-client.test/{endpoint}\"\n requests_mock.get(\n url,\n status_code=200,\n json={\n attribute: [{\"foo\": \"bar\"}],\n \"links\": [\n {\n \"rel\": \"next\",\n \"href\": url + \"?token=baz\",\n }\n ],\n },\n )\n requests_mock.get(\n url + \"?token=baz\",\n status_code=200,\n json={\n attribute: [],\n \"links\": [\n {\n \"rel\": \"next\",\n \"href\": url + \"?token=bam\",\n }\n ],\n },\n )\n requests_mock.get(\n url + \"?token=bam\",\n status_code=500,\n )\n stac_api_io = StacApiIO()\n pages = list(stac_api_io.get_pages(url))\n assert len(pages) == 1\n assert pages[0][attribute][0][\"foo\"] == \"bar\"\n\n @pytest.mark.parametrize(\n (\"attribute\", \"endpoint\"),\n ((\"features\", \"search\"), (\"collections\", \"collections\")),\n )\n def test_stop_on_attributeless_page(\n self, requests_mock: Mocker, attribute: str, endpoint: str\n ) -> None:\n url = f\"https://pystac-client.test/{endpoint}\"\n requests_mock.get(\n url,\n status_code=200,\n json={\n attribute: [{\"foo\": \"bar\"}],\n \"links\": [\n {\n \"rel\": \"next\",\n \"href\": url + \"?token=baz\",\n }\n ],\n },\n )\n requests_mock.get(\n url + \"?token=baz\",\n status_code=200,\n json={\n \"links\": [\n {\n \"rel\": \"next\",\n \"href\": url + \"?token=bam\",\n }\n ],\n },\n )\n requests_mock.get(\n url + \"?token=bam\",\n status_code=500,\n )\n stac_api_io = StacApiIO()\n pages = list(stac_api_io.get_pages(url))\n assert len(pages) == 1\n assert pages[0][attribute][0][\"foo\"] == \"bar\"\n\n @pytest.mark.parametrize(\n (\"attribute\", \"endpoint\"),\n ((\"features\", \"search\"), (\"collections\", \"collections\")),\n )\n def test_stop_on_first_empty_page(\n self, requests_mock: Mocker, attribute: str, endpoint: str\n ) -> None:\n url = f\"https://pystac-client.test/{endpoint}\"\n requests_mock.get(\n url,\n status_code=200,\n json={\n attribute: [],\n \"links\": [\n {\n \"rel\": \"next\",\n \"href\": url + \"?token=bam\",\n }\n ],\n },\n )\n requests_mock.get(url + \"?token=bam\", status_code=500)\n stac_api_io = StacApiIO()\n pages = list(stac_api_io.get_pages(url))\n assert len(pages) == 0\n\n @pytest.mark.vcr\n def test_timeout_smoke_test(self) -> None:\n # Testing timeout behavior is hard, so we just have a simple smoke test to make\n # sure that providing a timeout doesn't break anything.\n stac_api_io = StacApiIO(timeout=42)\n response = stac_api_io.read_text(STAC_URLS[\"PLANETARY-COMPUTER\"])\n assert isinstance(response, str)\n\n @pytest.mark.parametrize(\"name\", (\"REQUESTS_CA_BUNDLE\", \"CURL_CA_BUNDLE\"))\n def test_respect_env_for_certs(self, monkeypatch: MonkeyPatch, name: str) -> None:\n monkeypatch.setenv(name, \"/not/a/real/file\")\n stac_api_io = StacApiIO()\n with pytest.raises(APIError):\n stac_api_io.request(\"https://earth-search.aws.element84.com/v1/\")\n\n\n@pytest.mark.vcr\ndef test_stac_io_in_pystac() -> None:\n # https://github.com/stac-utils/pystac-client/issues/706\n collection = pystac.read_file(\n href=\"tests/data/planetary-computer-collection.json\",\n stac_io=StacApiIO(timeout=42),\n )\n root = collection.get_root()\n assert root\n stac_io = root._stac_io\n assert isinstance(stac_io, StacApiIO)\n assert stac_io.timeout == 42\n" }, { "path": "tests/test_warnings.py", "content": "import pytest\n\nfrom pystac_client import Client\nfrom pystac_client.warnings import DoesNotConformTo, ignore, strict\n\nfrom .helpers import TEST_DATA\n\n\nclass TestWarningContextManagers:\n def test_ignore_context_manager(self) -> None:\n \"\"\"Test that ignore() context manager suppresses warnings.\"\"\"\n api = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n api.remove_conforms_to(\"COLLECTION_SEARCH\")\n\n with ignore():\n api.collection_search(limit=10, max_collections=10, q=\"test\")\n\n def test_strict_context_manager(self) -> None:\n \"\"\"Test that strict() context manager converts warnings to exceptions.\"\"\"\n api = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n api.remove_conforms_to(\"COLLECTION_SEARCH\")\n\n with strict():\n with pytest.raises(DoesNotConformTo, match=\"COLLECTION_SEARCH\"):\n api.collection_search(limit=10, max_collections=10, q=\"test\")\n\n def test_ignore_context_manager_cleanup(self) -> None:\n \"\"\"Test that ignore() properly restores warning filters after exit.\"\"\"\n api = Client.from_file(str(TEST_DATA / \"planetary-computer-root.json\"))\n api.remove_conforms_to(\"COLLECTION_SEARCH\")\n\n with ignore():\n api.collection_search(limit=10, max_collections=10, q=\"test\")\n\n with strict():\n with pytest.raises(DoesNotConformTo, match=\"COLLECTION_SEARCH\"):\n api.collection_search(limit=10, max_collections=10, q=\"test\")\n" } ]