Repository: stac-utils/pystac-client Branch: main Commit: 07582d0a303b Files: 168 Total size: 45.2 MB Directory structure: gitextract_yi101d5s/ ├── .adr-dir ├── .codespellignore ├── .gitattributes ├── .github/ │ ├── dependabot.yml │ ├── pull_request_template.md │ └── workflows/ │ ├── continuous-integration.yml │ └── release.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yml ├── CHANGELOG.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── RELEASING.md ├── docs/ │ ├── _static/ │ │ └── custom.css │ ├── api.rst │ ├── conf.py │ ├── contributing.rst │ ├── design/ │ │ ├── 0001-record-architecture-decisions.md │ │ ├── 0002-choose-stac-library.md │ │ └── design_decisions.rst │ ├── index.rst │ ├── quickstart.rst │ ├── tutorials/ │ │ ├── aoi-coverage.ipynb │ │ ├── authentication.md │ │ ├── cql2-filter.ipynb │ │ ├── item-search-intersects.ipynb │ │ ├── pystac-client-introduction.ipynb │ │ └── stac-metadata-viz.ipynb │ ├── tutorials.rst │ └── usage.rst ├── pyproject.toml ├── pystac_client/ │ ├── __init__.py │ ├── _utils.py │ ├── cli.py │ ├── client.py │ ├── collection_client.py │ ├── collection_search.py │ ├── conformance.py │ ├── errors.py │ ├── exceptions.py │ ├── free_text.py │ ├── item_search.py │ ├── mixins.py │ ├── py.typed │ ├── stac_api_io.py │ ├── version.py │ └── warnings.py ├── scripts/ │ ├── build-docs │ ├── format │ ├── lint │ └── test └── tests/ ├── __init__.py ├── cassettes/ │ ├── test_cli/ │ │ ├── TestCLICollections.test_collection_search[inprocess].yaml │ │ ├── TestCLICollections.test_collections[inprocess].yaml │ │ ├── TestCLICollections.test_save[inprocess].yaml │ │ ├── TestCLISearch.test_altering_conforms_to[inprocess---clear-conforms-to].yaml │ │ ├── TestCLISearch.test_altering_conforms_to[inprocess---remove-conforms-to=ITEM_SEARCH].yaml │ │ ├── TestCLISearch.test_fields[inprocess].yaml │ │ ├── TestCLISearch.test_filter[inprocess].yaml │ │ ├── TestCLISearch.test_intersects[inprocess-netherlands_aoi.json].yaml │ │ ├── TestCLISearch.test_intersects[inprocess-sample-item.json].yaml │ │ ├── TestCLISearch.test_intersects_despite_warning[inprocess].yaml │ │ ├── TestCLISearch.test_item_search[inprocess].yaml │ │ ├── TestCLISearch.test_matched[inprocess].yaml │ │ ├── TestCLISearch.test_matched_not_available[inprocess].yaml │ │ ├── TestCLISearch.test_non_conformant_can_be_fixed[inprocess].yaml │ │ ├── TestCLISearch.test_non_conformant_can_be_ignored[inprocess---ignore=no-conforms-to].yaml │ │ ├── TestCLISearch.test_non_conformant_can_be_ignored[inprocess---ignore].yaml │ │ ├── TestCLISearch.test_non_conformant_raises_by_default[inprocess].yaml │ │ ├── TestCLISearch.test_non_conformant_raises_if_warning_set_to_error[inprocess---error=no-conforms-to].yaml │ │ ├── TestCLISearch.test_non_conformant_raises_if_warning_set_to_error[inprocess---error].yaml │ │ └── TestCLISearch.test_save[inprocess].yaml │ ├── test_client/ │ │ ├── TestAPI.test_collections_fallback.yaml │ │ ├── TestAPI.test_environment_variable.yaml │ │ ├── TestAPI.test_from_file.yaml │ │ ├── TestAPI.test_instance.yaml │ │ ├── TestAPI.test_links.yaml │ │ ├── TestAPICollectionSearch.test_search.yaml │ │ ├── TestAPISearch.test_search_max_items_unlimited_default.yaml │ │ ├── TestConformsTo.test_changing_conforms_to_changes_behavior.yaml │ │ ├── TestQueryables.test_get_queryables.yaml │ │ ├── TestQueryables.test_get_queryables_collections.yaml │ │ ├── TestSigning.test_sign_with_return_warns.yaml │ │ ├── TestSigning.test_signing.yaml │ │ ├── test_collections_are_clients.yaml │ │ ├── test_fallback_strategy.yaml │ │ ├── test_get_collection_returns_none_if_not_found.yaml │ │ ├── test_get_items_without_ids.yaml │ │ ├── test_non_recursion_on_fallback.yaml │ │ └── test_query_string_in_collections_url.yaml │ ├── test_collection_client/ │ │ ├── TestCollectionClient.test_get_item.yaml │ │ ├── TestCollectionClient.test_get_item_with_item_search.yaml │ │ ├── TestCollectionClient.test_get_items.yaml │ │ ├── TestCollectionClient.test_get_items_with_ids.yaml │ │ ├── TestCollectionClient.test_get_queryables.yaml │ │ └── TestCollectionClient.test_instance.yaml │ ├── test_collection_search/ │ │ ├── TestCollectionSearch.test_bbox_results.yaml │ │ ├── TestCollectionSearch.test_client_side_bbox.yaml │ │ ├── TestCollectionSearch.test_client_side_datetime.yaml │ │ ├── TestCollectionSearch.test_client_side_q.yaml │ │ ├── TestCollectionSearch.test_datetime_results.yaml │ │ ├── TestCollectionSearch.test_enabled_but_client_side_q.yaml │ │ ├── TestCollectionSearch.test_matched.yaml │ │ ├── TestCollectionSearch.test_q_results.yaml │ │ ├── TestCollectionSearch.test_result_paging.yaml │ │ └── TestCollectionSearch.test_result_paging_max_collections.yaml │ ├── test_item_search/ │ │ ├── TestItemSearch.test_datetime_results.yaml │ │ ├── TestItemSearch.test_deprecations[get_all_items-item_collection-False-True].yaml │ │ ├── TestItemSearch.test_deprecations[get_all_items_as_dict-item_collection_as_dict-False-False].yaml │ │ ├── TestItemSearch.test_deprecations[get_item_collections-pages-True-True].yaml │ │ ├── TestItemSearch.test_deprecations[get_items-items-True-True].yaml │ │ ├── TestItemSearch.test_deprecations[item_collections-pages-True-True].yaml │ │ ├── TestItemSearch.test_get_all_items.yaml │ │ ├── TestItemSearch.test_get_all_items_deprecated.yaml │ │ ├── TestItemSearch.test_ids_results.yaml │ │ ├── TestItemSearch.test_intersects_results.yaml │ │ ├── TestItemSearch.test_item_collection.yaml │ │ ├── TestItemSearch.test_items_as_dicts.yaml │ │ ├── TestItemSearch.test_result_paging.yaml │ │ ├── TestItemSearch.test_result_paging_max_items.yaml │ │ ├── TestItemSearch.test_results.yaml │ │ ├── TestItemSearchParams.test_collection_object.yaml │ │ ├── TestItemSearchParams.test_mixed_collection_object_and_string.yaml │ │ ├── TestItemSearchQuery.test_query_json_syntax.yaml │ │ ├── TestItemSearchQuery.test_query_shortcut_syntax.yaml │ │ ├── test_fields.yaml │ │ └── test_multiple_collections.yaml │ └── test_stac_api_io/ │ ├── TestSTAC_IOOverride.test_request_input.yaml │ ├── TestSTAC_IOOverride.test_str_input.yaml │ ├── TestSTAC_IOOverride.test_timeout_smoke_test.yaml │ └── test_stac_io_in_pystac.yaml ├── data/ │ ├── astraea_api.json │ ├── fedeo_clearinghouse.json │ ├── invalid-collection.json │ ├── netherlands_aoi.json │ ├── planetary-computer-aster-l1t-collection.json │ ├── planetary-computer-collection.json │ ├── planetary-computer-root.json │ ├── sample-item-collection.json │ ├── sample-item.json │ └── test-case-1/ │ ├── catalog.json │ ├── country-1/ │ │ ├── area-1-1/ │ │ │ ├── area-1-1-imagery/ │ │ │ │ └── area-1-1-imagery.json │ │ │ ├── area-1-1-labels/ │ │ │ │ └── area-1-1-labels.json │ │ │ └── collection.json │ │ ├── area-1-2/ │ │ │ ├── area-1-2-imagery/ │ │ │ │ └── area-1-2-imagery.json │ │ │ ├── area-1-2-labels/ │ │ │ │ └── area-1-2-labels.json │ │ │ └── collection.json │ │ └── catalog.json │ └── country-2/ │ ├── area-2-1/ │ │ ├── area-2-1-imagery/ │ │ │ └── area-2-1-imagery.json │ │ ├── area-2-1-labels/ │ │ │ └── area-2-1-labels.json │ │ └── collection.json │ ├── area-2-2/ │ │ ├── area-2-2-imagery/ │ │ │ └── area-2-2-imagery.json │ │ ├── area-2-2-labels/ │ │ │ └── area-2-2-labels.json │ │ └── collection.json │ └── catalog.json ├── helpers.py ├── test_base_search.py ├── test_cli.py ├── test_client.py ├── test_collection_client.py ├── test_collection_search.py ├── test_conformance.py ├── test_free_text.py ├── test_item_search.py ├── test_stac_api_io.py └── test_warnings.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .adr-dir ================================================ docs/source/design ================================================ FILE: .codespellignore ================================================ filetest ================================================ FILE: .gitattributes ================================================ tests/cassettes/**/*.yaml linguist-generated=true ================================================ FILE: .github/dependabot.yml ================================================ version: 2 updates: - package-ecosystem: github-actions directory: "/" schedule: interval: weekly - package-ecosystem: pip directory: "/.github/workflows" schedule: interval: weekly - package-ecosystem: pip directory: "." schedule: interval: daily versioning-strategy: increase-if-necessary ================================================ FILE: .github/pull_request_template.md ================================================ **Related Issue(s):** - # **Description:** **PR Checklist:** - [ ] Code is formatted - [ ] Tests pass - [ ] Changes are added to CHANGELOG.md ================================================ FILE: .github/workflows/continuous-integration.yml ================================================ name: CI on: push: branches: - main pull_request: concurrency: group: ${{ github.ref }} cancel-in-progress: true jobs: build: name: build runs-on: ${{ matrix.os }} strategy: matrix: python-version: - "3.10" - "3.11" - "3.12" os: - ubuntu-latest - windows-latest - macos-latest steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0 with: python-version: ${{ matrix.python-version }} - name: Sync run: uv sync - name: Run pre-commit run: uv run pre-commit run --all-files - name: Run pytest run: uv run pytest -Werror -s --block-network --cov pystac_client --cov-report term-missing - name: Run coverage run: uv run coverage xml - uses: codecov/codecov-action@57e3a136b779b570ffcdbf80b3bdc90e7fab3de2 # v6 with: token: ${{ secrets.CODECOV_TOKEN }} files: ./coverage.xml fail_ci_if_error: false min-versions: name: min-versions runs-on: ubuntu-latest steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0 with: activate-environment: true python-version: "3.10" - name: Install with min requirements run: uv sync --no-dev --resolution=lowest-direct - name: Run smoke test run: stac-client search https://landsatlook.usgs.gov/stac-server -c landsat-c2l2-sr --max-items 1 pre-release: name: pre-release runs-on: ubuntu-latest steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0 - name: Sync run: uv sync - name: Install any pre-releases of pystac run: uv pip install -U --pre pystac - name: Run pytest run: uv run pytest -Werror -s --block-network upstream: name: upstream runs-on: ubuntu-latest if: github.event_name != 'pull_request' steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0 - name: Sync run: uv sync - name: Install pystac from main run: uv pip install --force-reinstall git+https://github.com/stac-utils/pystac.git - name: Run pytest run: uv run pytest -Werror -s --block-network docs: name: docs runs-on: ubuntu-latest defaults: run: shell: bash -el {0} steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0 - name: Install pandoc run: sudo apt-get install -y pandoc - name: Sync run: uv sync --group docs - name: Build docs run: ./scripts/build-docs ================================================ FILE: .github/workflows/release.yml ================================================ name: Release on: push: tags: - "*" jobs: release: name: release runs-on: ubuntu-latest steps: - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6 - name: Set up Python 3.11 uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6 with: python-version: "3.11" - name: Install release dependencies run: | python -m pip install --upgrade pip pip install build twine - name: Build and publish package env: TWINE_USERNAME: ${{ secrets.PYPI_STACUTILS_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_STACUTILS_PASSWORD }} run: | python -m build twine upload dist/* ================================================ FILE: .gitignore ================================================ .python-version **/*.private* # Ignore everything JetBrains .idea # Created by .ignore support plugin (hsz.mobi) ### VisualStudio template ## Ignore Visual Studio temporary files, build results, and ## files generated by popular Visual Studio add-ons. ## ## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore # User-specific files *.rsuser *.suo *.user *.userosscache *.sln.docstates # User-specific files (MonoDevelop/Xamarin Studio) *.userprefs # Mono 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python_version == "3.7" - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.15.0 hooks: - id: mypy files: ".*\\.py$" additional_dependencies: - pystac - pytest-vcr - types-requests - types-python-dateutil ================================================ FILE: .readthedocs.yml ================================================ version: 2 build: os: ubuntu-24.04 tools: python: "3.12" jobs: pre_create_environment: - asdf plugin add uv - asdf install uv latest - asdf global uv latest create_environment: - uv venv "${READTHEDOCS_VIRTUALENV_PATH}" install: - UV_PROJECT_ENVIRONMENT="${READTHEDOCS_VIRTUALENV_PATH}" uv sync --group docs --link-mode=copy sphinx: configuration: docs/conf.py fail_on_warning: false formats: - pdf - htmlzip ================================================ FILE: CHANGELOG.md ================================================ # Changelog All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html). ## [Unreleased] ### Added - Add comprehensive test coverage for warning context managers (`ignore()` and `strict()`) ([#832](https://github.com/stac-utils/pystac-client/pull/832)) - Moved -Werror to pyproject.toml ([#841](https://github.com/stac-utils/pystac-client/pull/841)) ### Changed - Make `get_collection` raise if `collection_id` is empty ([#809](https://github.com/stac-utils/pystac-client/pull/809)) - Update `get_collection` to return None if no collection exists ([#861](https://github.com/stac-utils/pystac-client/pull/861)) ### Added - Add comprehensive test coverage for ConformanceClasses enum ([#834](https://github.com/stac-utils/pystac-client/pull/834)) ### Documentation - Update contributing guide to consistently use `uv` workflow ([#822](https://github.com/stac-utils/pystac-client/pull/822)) - Fix format script to use `ruff-format` instead of deprecated `black` hook ([#822](https://github.com/stac-utils/pystac-client/pull/822)) - 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)) ## [v0.9.0] - 2025-07-17 ### Added - Coerce cql2 style to match HTTP method using `cql2` library ([#804](https://github.com/stac-utils/pystac-client/pull/804)) ### Fixed - Fix usage documentation of `ItemSearch` ([#790](https://github.com/stac-utils/pystac-client/pull/790)) - Fix fields argument to CLI ([#797](https://github.com/stac-utils/pystac-client/pull/797)) - Clarify recursive behaviour of the `get_items` method in the method docstring ([#800](https://github.com/stac-utils/pystac-client/pull/800)) ## [v0.8.6] - 2025-02-11 ### Changed - Use [uv](https://docs.astral.sh/uv/) for development ([#784](https://github.com/stac-utils/pystac-client/pull/784)) - Updated to Python 3.10 syntax with **pyupgrade** ([#783](https://github.com/stac-utils/pystac-client/pull/783/)) ### Fixed - `Client.get_collection` for static catalogs ([#782](https://github.com/stac-utils/pystac-client/pull/782)) - Permissive collection reading ([#787](https://github.com/stac-utils/pystac-client/pull/787)) ## [v0.8.5] - 2024-10-23 ### Fixed - Use urljoin to build hrefs ([#746](https://github.com/stac-utils/pystac-client/pull/746)) ## [v0.8.4] - 2024-10-16 ### Added - Support for collection search via `CollectionSearch` class and associated client methods ([#735](https://github.com/stac-utils/pystac-client/pull/735)) ### Removed - Python 3.9 support ([#724](https://github.com/stac-utils/pystac-client/pull/724)) ## [v0.8.3] - 2024-07-01 ### Fixed - Actually set `timeout` when initializing `StacApiIO` ([#709](https://github.com/stac-utils/pystac-client/pull/709)) - Set `_stac_io` on `CollectionClient` when reading ([#709](https://github.com/stac-utils/pystac-client/pull/709)) ## [v0.8.2] - 2024-05-30 ### Added - Support for "Feature" type `intersects` dictionaries ([#696](https://github.com/stac-utils/pystac-client/pull/696)) ## [v0.8.1] - 2024-05-23 ### Fixed - Use singular `include` and `exclude` Field extension key names ([#690](https://github.com/stac-utils/pystac-client/pull/690)) ## [v0.8.0] - 2024-05-17 ### Fixed - Update `requires-python` in metadata to require >= 3.9 ([#684](https://github.com/stac-utils/pystac-client/pull/684)) - Interpret naive datetimes as UTC ([#686](https://github.com/stac-utils/pystac-client/pull/686)) ## [v0.7.7] - 2024-04-19 ### Changed - Updated to **pystac** v1.10.0 ([#661](https://github.com/stac-utils/pystac-client/pull/661)) - Use [uv](https://github.com/astral-sh/uv) for CI ([#663](https://github.com/stac-utils/pystac-client/pull/663)) - use `APILayoutStrategy` as fallback strategy ([#666](https://github.com/stac-utils/pystac-client/pull/666)) - Updated grammar in README ([#676](https://github.com/stac-utils/pystac-client/pull/676)) ### Fixed - Respect the `REQUESTS_CA_BUNDLE` and `CURL_CA_BUNDLE` environment variables when sending requests ([#669](https://github.com/stac-utils/pystac-client/pull/669)) ## [v0.7.6] - 2024-02-28 ### Fixed - Recursion error in `get_items` ([#608](https://github.com/stac-utils/pystac-client/pull/608)) - Fixes inconsistent behavior in `CollectionClient.get_items` when Item IDs are supplied ([#639](https://github.com/stac-utils/pystac-client/pull/639)) - Logic determining if `context.matched` in `ItemSearch.matched` response ([#646](https://github.com/stac-utils/pystac-client/pull/646)) ### Removed - Passing `-q` to the CLI rather than `--query` ([#614](https://github.com/stac-utils/pystac-client/pull/614)) ## [v0.7.5] - 2023-09-05 ### Fixed - `--matched` flag in CLI ([#588](https://github.com/stac-utils/pystac-client/pull/588)) ## [v0.7.4] - 2023-09-05 ### Changed - Don't provide a `limit` by default, trust the server to know its limits ([#584](https://github.com/stac-utils/pystac-client/pull/584)) ## [v0.7.3] - 2023-08-21 ### Changed - Raise more informative errors from CLI ([#554](https://github.com/stac-utils/pystac-client/pull/554)) - Add `Retry` type hint to `StacApiIO` ([#577](https://github.com/stac-utils/pystac-client/pull/577)) ### Fixed - 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)) ## [v0.7.2] - 2023-06-23 ### Fixed - Remove troublesome assertion ([#549](https://github.com/stac-utils/pystac-client/pull/549)) ## [v0.7.1] - 2023-06-13 ### Fixed - Remove unnecessary `typing_extensions` import ([#541](https://github.com/stac-utils/pystac-client/pull/541)) ## [v0.7.0] - 2023-06-12 ### Added - 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)) - Support for fetching catalog queryables ([#477](https://github.com/stac-utils/pystac-client/pull/477)) - PySTAC Client specific warnings ([#480](https://github.com/stac-utils/pystac-client/pull/480)) - Support for fetching and merging a selection of queryables ([#511](https://github.com/stac-utils/pystac-client/pull/511)) - Better error messages for the CLI ([#531](https://github.com/stac-utils/pystac-client/pull/531)) - `Modifiable` to our public API ([#534](https://github.com/stac-utils/pystac-client/pull/534)) - `max_retries` parameter to `StacApiIO` ([#532](https://github.com/stac-utils/pystac-client/pull/532)) ### Changed - Switched to Ruff from isort/flake8 ([#457](https://github.com/stac-utils/pystac-client/pull/457)) - 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)) - Consolidate contributing docs into one place ([#478](https://github.com/stac-utils/pystac-client/issues/478)) - Use `pyproject.toml` instead of `setup.py` ([#501](https://github.com/stac-utils/pystac-client/pull/501)) - Enable Ruff import sorting ([#518](https://github.com/stac-utils/pystac-client/pull/518)) ### Fixed - `query` parameter in GET requests ([#362](https://github.com/stac-utils/pystac-client/pull/362)) - Double encoding of `intersects` parameter in GET requests ([#362](https://github.com/stac-utils/pystac-client/pull/362)) - Fix geometry instantiation in item-search-intersects.ipynb ([#484](https://github.com/stac-utils/pystac-client/pull/484)) - Three tests that were false positives due to out-of-date cassettes ([#491](https://github.com/stac-utils/pystac-client/pull/491)) - Max items checks when paging ([#492](https://github.com/stac-utils/pystac-client/pull/492)) - `ItemSearch.url_with_parameters` no longer unquotes the url ([#530](https://github.com/stac-utils/pystac-client/pull/530)) ### Removed - `pystac_client.conformance.CONFORMANCE_URIS` dictionary ([#480](https://github.com/stac-utils/pystac-client/pull/480)) ## [v0.6.1] - 2023-03-14 ### Changed - Bumped PySTAC dependency to >= 1.7.0 ([#449](https://github.com/stac-utils/pystac-client/pull/449)) ### Fixed - Fix parse fail when header has multiple '=' characters ([#440](https://github.com/stac-utils/pystac-client/pull/440)) - `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))) ## [v0.6.0] - 2023-01-27 ### Added - Python 3.11 support ([#347](https://github.com/stac-utils/pystac-client/pull/347)) - `request_modifier` to `StacApiIO` to allow for additional authentication mechanisms (e.g. AWS SigV4) ([#372](https://github.com/stac-utils/pystac-client/pull/372)) - _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)) - CI checks for Windows and MacOS ([#378](https://github.com/stac-utils/pystac-client/pull/378)) - 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)) ### Fixed - Stop iteration on an empty page ([#338](https://github.com/stac-utils/pystac-client/pull/338)) - Some mishandled cases for datetime intervals ([#363](https://github.com/stac-utils/pystac-client/pull/363)) - 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)) - Parse datetimes more strictly ([#364](https://github.com/stac-utils/pystac-client/pull/364)) ### Removed - Python 3.7 support ([#347](https://github.com/stac-utils/pystac-client/pull/347)) ## [v0.5.1] - 2022-09-19 ### Added - Added `ItemSearch.url_with_parameters` to get a formatted search url ([#304](https://github.com/stac-utils/pystac-client/issues/304)) ### Fixed - Fix variable name in quickstart example ([#316](https://github.com/stac-utils/pystac-client/pull/316)) - `StacApiIO.write_text_to_href` ([#312](https://github.com/stac-utils/pystac-client/pull/312)) - Removed mention of STAC_URL in `Client.open` docstring ([#317](https://github.com/stac-utils/pystac-client/pull/317)) ## [v0.5.0] - 2022-08-19 ### Added - Added a new keyword `modifier` to various constructors like `Client.open()` ([#259](https://github.com/stac-utils/pystac-client/issues/259)) ### Fixed - 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)) - Added `ItemSearch.pages`, `ItemSearch.pages_as_dicts`, `ItemSearch.item_collection`, and `ItemSearch.item_collection_as_dict` as replacements for various deprecated methods ([#237](https://github.com/stac-utils/pystac-client/issues/237)) - 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)) ### Deprecated - `ItemSearch.item_collections` has been deprecated in favor of `ItemSearch.pages`. ([#237](https://github.com/stac-utils/pystac-client/issues/237)) ## [v0.4.0] - 2022-06-08 ### Added - Significantly improved type hints - lru_cache to several methods ([#167](https://github.com/stac-utils/pystac-client/pull/167)) - Direct item GET via ogcapi-features, if conformant ([#166](https://github.com/stac-utils/pystac-client/pull/166)) - `py.typed` for downstream type checking ([#163](https://github.com/stac-utils/pystac-client/pull/163)) - 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)) ### Changed - Item Search no longer defaults to returning an unlimited number of result Items from its "items" methods. The `max_items` parameter now defaults to 100 instead of None. Since the `limit` parameter also defaults to 100, in an ideal situation, only one request will be made to the server to retrieve all 100 items. Both of these parameters can be carefully adjusted upwards to align with the server's capabilities and the expected number of search results. ([#208](https://github.com/stac-utils/pystac-client/pull/208)) - Better error message when trying to search a non-item-search-conforming catalog ([#164](https://github.com/stac-utils/pystac-client/pull/164)) - Search `filter-lang` defaults to `cql2-json` instead of `cql-json` ([#169](https://github.com/stac-utils/pystac-client/pull/169)) - 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)) - 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)) - Better error message when trying to open a Collection with `Client.open` ([#222](https://github.com/stac-utils/pystac-client/pull/222)) ### Deprecated - Item Search methods `get_items()` and `get_item_collections()` have been renamed to `items()` and `item_collections()`. The original methods are now deprecated and may be removed as early as v0.5.0. ([#206](https://github.com/stac-utils/pystac-client/pull/206)) - Item Search methods `get_all_items()` and `get_all_items_as_dict()` are now deprecated, and may be removed as early as v0.5.0. These have been deprecated because they have the potential to perform a large number of requests to the server and instantiate a large number of objects in memory. To a user, this is only visible as a large delay in the method call and/or the exhaustion of all available memory. The iterator methods `items()` or `item_collections()` should be used instead. ([#206](https://github.com/stac-utils/pystac-client/pull/206)) - 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)) ## Removed - Client parameter `require_geojson_link` has been removed. ([#165](https://github.com/stac-utils/pystac-client/pull/165)) ### Fixed - Search query parameter now has correct typing and handles Query Extension JSON format. ([#220](https://github.com/stac-utils/pystac-client/pull/220)) - 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)) - 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)) - Use pytest configuration to skip benchmarks by default (instead of a `skip` mark). ([#168](https://github.com/stac-utils/pystac-client/pull/168)) - Methods retrieving collections incorrectly checked the existence of the OAFeat OpenAPI 3.0 conformance class () instead of the `STAC API - Collections` () or `STAC API - Features` () conformance classes. ([#223](https://github.com/stac-utils/pystac-client/pull/223)) ## [v0.3.5] - 2022-05-26 ### Fixed - 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)) ## [v0.3.4] - 2022-05-18 ### Changed - 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)) ## [v0.3.3] - 2022-04-28 ### Added - 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)) - 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)) ### Changed - Relaxed upper bound on PySTAC dependency ([#144](https://github.com/stac-utils/pystac-client/pull/144)) - Bumped PySTAC dependency to >= 1.4.0 ([#147](https://github.com/stac-utils/pystac-client/pull/147)) ## [v0.3.2] - 2022-01-11 ### Added - `Client.search` accepts an optional `filter_lang` argument for `filter` requests ([#128](https://github.com/stac-utils/pystac-client/pull/128)) - Added support for filtering the search endpoint using the `media_type` in `Client.open` ([#142](https://github.com/stac-utils/pystac-client/pull/142)) ### Fixed - Values from `parameters` and `headers` arguments to `Client.open` and `Client.from_file` are now also used in requests made from `CollectionClient` instances fetched from the same API (([#126](https://github.com/stac-utils/pystac-client/pull/126))) - The tests folder is no longer installed as a package. ## [v0.3.1] - 2021-11-17 ### Added - Adds `--block-network` option to all test commands to ensure no network requests are made during unit tests ([#119](https://github.com/stac-utils/pystac-client/pull/119)) - `parameters` argument to `StacApiIO`, `Client.open`, and `Client.from_file` to allow query string parameters to be passed to all requests ([#118](https://github.com/stac-utils/pystac-client/pull/118)) ### Changed - Update min PySTAC version to 1.2 - Default page size limit set to 100 rather than relying on the server default - Fetch single collection directly from endpoint in API rather than iterating through children [Issue #114](https://github.com/stac-utils/pystac-client/issues/114) ### Fixed - `Client.get_collections` raised an exception when API did not publish `/collections` conformance class instead of falling back to using child links ([#120](https://github.com/stac-utils/pystac-client/pull/120)) ## [v0.3.0] - 2021-09-28 ### Added - Jupyter Notebook tutorials - Basic CQL-JSON filtering ([#100](https://github.com/stac-utils/pystac-client/pull/100)) ### Changed - Improved performance when constructing `pystac.ItemCollection` objects. - Relax `requests` dependency ([#87](https://github.com/stac-utils/pystac-client/pull/87)) - Use regular expressions for checking conformance classes ([#97](https://github.com/stac-utils/pystac-client/pull/97)) - Reorganized documentation, updated all docs ### Fixed - `ItemSearch` now correctly handles times without a timezone specifier ([#92](https://github.com/stac-utils/pystac-client/issues/92)) - 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)) - Documentation lints ([#108](https://github.com/stac-utils/pystac-client/pull/108)) ## [v0.2.0] - 2021-08-04 ### Added - `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)) - New Search.get_pages() iterator function to retrieve pages as raw JSON, not as ItemCollections - `StacApiIO` class added, subclass from PySTAC `StacIO`. A `StacApiIO` instance is used for all IO for a Client instance, and all requests are in a single HTTP session, handle pagination and respects conformance - `conformance.CONFORMANCE_CLASSES` dictionary added containing all STAC API Capabilities from stac-api-spec - `collections` subcommand to CLI, for saving all Collections in catalog as JSON - `Client.get_collections` overrides Catalog to use /collections endpoint if API conforms - `Client.get_collection()` for getting specific collection - `Client.get_items` and `Client.get_all_items` override Catalog functions to use search endpoint instead of traversing catalog ### Changed - Update to use PySTAC 1.1.0 - IO changed to use PySTAC's new StacIO base class. - `Search.item_collections()` renamed to `Search.get_item_collections()` - `Search.item()` renamed to `Search.get_items()` - Conformance is checked by each individual function that requires a particular conformance - STAC API testing URLs changed to updated APIs - `ItemSearch.get_pages()` function moved to StacApiIO class for general use - Logging is now enabled in the CLI in all cases. If data are being printed to stdout, logging goes to stderr. ([#79](https://github.com/stac-utils/pystac-client/pull/79)) - Improved logging for GET requests (prints encoded URL) ### Removed - `get_pages` and `simple_stac_resolver` functions from `pystac_client.stac_io` (The new StacApiIO class understands `Link` objects) - `Client.search()` no longer accepts a `next_resolver` argument - pystac.extensions modules, which were based on PySTAC's previous extension implementation, replaced in 1.0.0 - `stac_api_object.StacApiObjectMixin`, replaced with conformance checking in `StacApiIO` - PySTAC Collection objects can no longer be passed in as `collections` arguments to the `ItemSearch` class (just pass ids) - `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` - `Search.item_collections()` - `Search.items()` - STAC_URL environment variable in Client.open(). url parameter in Client is now required - STAC_URL environment variable in CLI. CLI now has a required positional argument for the URL ### Fixed - Running `stac-client` with no arguments no longer raises a confusing exception ([#52](https://github.com/stac-utils/pystac-client/pull/52)) - `Client.get_collections_list` ([#44](https://github.com/stac-utils/pystac-client/issues/44)) - The regular expression used for datetime parsing ([#59](https://github.com/stac-utils/pystac-client/pull/59)) - `Client.from_file` now works as expected, using `Client.open` is not required, although it will fetch STAC_URL from an envvar ## [v0.1.1] - 2021-04-16 ### Added - `ItemSearch.items_as_collection` ([#37](https://github.com/stac-utils/pystac-client/pull/37)) - Documentation [published on ReadTheDocs](https://pystac-client.readthedocs.io/en/latest/) ([#46](https://github.com/stac-utils/pystac-client/pull/46)) ### Changed - CLI: pass in headers as list of KEY=VALUE pairs ### Fixed - Include headers in STAC_IO ([#38](https://github.com/stac-utils/pystac-client/pull/38)) - README updated to reflect actual CLI behavior ## [v0.1.0] - 2021-04-14 Initial release. [Unreleased]: https://github.com/stac-utils/pystac-client/compare/v0.9.0...main [v0.9.0]: https://github.com/stac-utils/pystac-client/compare/v0.8.6...v0.9.0 [v0.8.6]: https://github.com/stac-utils/pystac-client/compare/v0.8.5...v0.8.6 [v0.8.5]: https://github.com/stac-utils/pystac-client/compare/v0.8.4...v0.8.5 [v0.8.4]: https://github.com/stac-utils/pystac-client/compare/v0.8.3...v0.8.4 [v0.8.3]: https://github.com/stac-utils/pystac-client/compare/v0.8.2...v0.8.3 [v0.8.2]: https://github.com/stac-utils/pystac-client/compare/v0.8.1...v0.8.2 [v0.8.1]: https://github.com/stac-utils/pystac-client/compare/v0.8.0...v0.8.1 [v0.8.0]: https://github.com/stac-utils/pystac-client/compare/v0.7.7...v0.8.0 [v0.7.7]: https://github.com/stac-utils/pystac-client/compare/v0.7.6...v0.7.7 [v0.7.6]: https://github.com/stac-utils/pystac-client/compare/v0.7.5...v0.7.6 [v0.7.5]: https://github.com/stac-utils/pystac-client/compare/v0.7.4...v0.7.5 [v0.7.4]: https://github.com/stac-utils/pystac-client/compare/v0.7.3...v0.7.4 [v0.7.3]: https://github.com/stac-utils/pystac-client/compare/v0.7.2...v0.7.3 [v0.7.2]: https://github.com/stac-utils/pystac-client/compare/v0.7.1...v0.7.2 [v0.7.1]: https://github.com/stac-utils/pystac-client/compare/v0.7.0...v0.7.1 [v0.7.0]: https://github.com/stac-utils/pystac-client/compare/v0.6.1...v0.7.0 [v0.6.1]: https://github.com/stac-utils/pystac-client/compare/v0.6.0...v0.6.1 [v0.6.0]: https://github.com/stac-utils/pystac-client/compare/v0.5.1...v0.6.0 [v0.5.1]: https://github.com/stac-utils/pystac-client/compare/v0.5.0...v0.5.1 [v0.5.0]: https://github.com/stac-utils/pystac-client/compare/v0.4.0...v0.5.0 [v0.4.0]: https://github.com/stac-utils/pystac-client/compare/v0.3.5...v0.4.0 [v0.3.5]: https://github.com/stac-utils/pystac-client/compare/v0.3.4...v0.3.5 [v0.3.4]: https://github.com/stac-utils/pystac-client/compare/v0.3.3...v0.3.4 [v0.3.3]: https://github.com/stac-utils/pystac-client/compare/v0.3.2...v0.3.3 [v0.3.2]: https://github.com/stac-utils/pystac-client/compare/v0.3.1...v0.3.2 [v0.3.1]: https://github.com/stac-utils/pystac-client/compare/v0.3.0...v0.3.1 [v0.3.0]: https://github.com/stac-utils/pystac-client/compare/v0.2.0...v0.3.0 [v0.2.0]: https://github.com/stac-utils/pystac-client/compare/v0.1.1...v0.2.0 [v0.1.1]: https://github.com/stac-utils/pystac-client/compare/v0.1.0...v0.1.1 [v0.1.0]: https://github.com/stac-utils/pystac-client/tree/v0.1.0 ================================================ FILE: LICENSE ================================================ Copyright 2021 Jon Duckworth Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: MANIFEST.in ================================================ global-include *.typed ================================================ FILE: README.md ================================================ # pystac-client [![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) [![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) [![PyPI version](https://badge.fury.io/py/pystac-client.svg)](https://badge.fury.io/py/pystac-client) [![Documentation](https://readthedocs.org/projects/pystac-client/badge/?version=stable)](https://pystac-client.readthedocs.io) [![codecov](https://codecov.io/gh/stac-utils/pystac-client/branch/main/graph/badge.svg)](https://codecov.io/gh/stac-utils/pystac-client) A Python client for working with [STAC](https://stacspec.org/) APIs. ## Installation PySTAC Client is published to PyPi as [pystac-client](https://pypi.org/project/pystac-client/). The only direct Python dependencies of **pystac-client** are [PySTAC](https://pystac.readthedocs.io), [requests](https://docs.python-requests.org), and [dateutil](https://dateutil.readthedocs.io). To install with pip, run: ```shell python -m pip install pystac-client ``` ## Documentation See the [documentation page](https://pystac-client.readthedocs.io/en/latest/) for the latest docs. ## Development See the [contributing page](https://pystac-client.readthedocs.io/en/latest/contributing.html) for the latest development instructions. ================================================ FILE: RELEASING.md ================================================ # Releasing 1. Determine the next version. We follow [semantic versioning](https://semver.org/). 2. Create a release branch named `release/vX.Y.Z`, where `X.Y.Z` is the next version. 3. Update [version.py](pystac_client/version.py) with the new version. 4. Update all cassettes: `pytest --record-mode rewrite` 5. Update the pre-commit hook versions: `pre-commit autoupdate` 6. Update [CHANGELOG.md](CHANGELOG.md). 1. Add a new header under "Unreleased" with the new version and the date, e.g. `## [vX.Y.Z] - YYYY-MM-DD`. 2. Audit the changelog section to ensure all changes are captured. 3. Add a link reference for the new version after the Unreleased link reference at the bottom of the file. Follow the format from the previous version links. 7. If necessary, update the versions table in [the documentation](docs/index.rst) 8. (optional) Build the package locally and inspect its contents: `pip install build && python -m build` 9. 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`). 10. After pull request merge, create an annotated tag for your version, e.g. `git tag -a vX.Y.Z`. 11. Push the tag. This will trigger [the Github release workflow](.github/workflows/release.yml) and publish to PyPI. 12. [Create a new release on Github](https://github.com/stac-utils/pystac-client/releases/new) pointing to the new tag. ================================================ FILE: docs/_static/custom.css ================================================ .no-sidebar { display: none; } .card-text a { font-weight: 600; color: rgb(255, 26, 26); } .card-header p { font-size: large; font-weight: 600; } .navbar-brand img { height: 50px; } .navbar-brand { height: 75px; } ================================================ FILE: docs/api.rst ================================================ API Reference ============= This section is autogenerated from in-line code documentation. It is mostly useful as a reference for the various classes, methods, and other objects in the library, but is not intended to function as a starting point for working with ``pystac_client``. Client ------ Client is the base PySTAC-Client that inherits from :class:`Catalog `. In addition to the PySTAC functionality, Client allows opening of API URLs, understanding of conformance, and support for searching and paging through results. .. autoclass:: pystac_client.Client :members: :undoc-members: :show-inheritance: Collection Client ----------------- Client is the a PySTAC-Client that inherits from :class:`Collection `. In addition to the PySTAC functionality, CollectionClient allows opening of API URLs, and iterating through items at a search endpoint, if supported. .. autoclass:: pystac_client.CollectionClient :members: :undoc-members: Collection Search ----------------- The `CollectionSearch` class represents a search of collections in a STAC API. .. autoclass:: pystac_client.CollectionSearch :members: :undoc-members: :member-order: bysource Item Search ----------- The `ItemSearch` class represents a search of a STAC API. .. autoclass:: pystac_client.ItemSearch :members: :undoc-members: :member-order: bysource STAC API IO ----------- The StacApiIO class inherits from the :class:`Collection ` class and allows for reading over http, such as with REST APIs. .. autoclass:: pystac_client.stac_api_io.StacApiIO :members: :undoc-members: :show-inheritance: Conformance ----------- .. automodule:: pystac_client.conformance :members: :undoc-members: :show-inheritance: Exceptions ---------- .. automodule:: pystac_client.exceptions :members: :undoc-members: :show-inheritance: Warnings -------- .. automodule:: pystac_client.warnings :members: :undoc-members: :show-inheritance: ================================================ FILE: docs/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html import re import subprocess import sys from pathlib import Path # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # sys.path.insert(0, str(Path(__file__).parent.parent.parent.resolve())) from pystac_client import __version__ # noqa: E402 git_branch = ( subprocess.check_output(["git", "rev-parse", "--abbrev-ref", "HEAD"]) .decode("utf-8") .strip() ) # -- Project information ----------------------------------------------------- project = "pystac-client" copyright = "2021, Jon Duckworth" author = "Matthew Hanson, Jon Duckworth" github_user = "stac-utils" github_repo = "pystac-client" package_description = "A Python client for the STAC and STAC-API specs" # The full version, including alpha/beta/rc tags version = re.fullmatch(r"^(\d+\.\d+\.\d).*$", __version__).group(1) # type: ignore release = __version__ # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinx.ext.extlinks", "sphinxcontrib.fulltoc", "nbsphinx", "myst_parser", ] extlinks = { "tutorial": ( "https://github.com/stac-utils/pystac-client/tree/{}/docs/tutorials/%s".format( git_branch ), "tutorial", ) } nbsphinx_allow_errors = False # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. source_suffix = [".rst", "*.md", "*.ipynb"] exclude_patterns = ["build/*"] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = { "icon_links": [ { "name": "GitHub", "url": "https://github.com/stac-utils/pystac-client", "icon": "fab fa-github-square", }, { "name": "Gitter", "url": "https://gitter.im/SpatioTemporal-Asset-Catalog/" "python?utm_source=share-link&utm_medium=link&utm_campaign=share-link", "icon": "fab fa-gitter", }, ], "external_links": [ {"name": "STAC Spec", "url": "https://github.com/radiantearth/stac-spec"} ], "navigation_with_keys": False, # "navbar_end": ["navbar-icon-links.html", "search-field.html"] } html_logo = "_static/STAC-03.png" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path: list[str] = ["_static"] # -- Options for intersphinx extension --------------------------------------- intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "requests": ("https://requests.readthedocs.io/en/latest", None), "pystac": ("https://pystac.readthedocs.io/en/latest", None), "dateutil": ("https://dateutil.readthedocs.io/en/stable/", None), } # -- Options for autodoc extension ------------------------------------------- autodoc_typehints = "none" ================================================ FILE: docs/contributing.rst ================================================ Contributing ============ A list of issues and ongoing work is available on the PySTAC Client `issues page `_. If you want to contribute code, the best way is to coordinate with the core developers via an issue or pull request conversation. Development installation ^^^^^^^^^^^^^^^^^^^^^^^^ Fork PySTAC Client into your GitHub account. Clone the repo, install `uv `_ then install the package in editable mode with development dependencies: .. code-block:: bash $ git clone git@github.com:your_user_name/pystac-client.git $ cd pystac-client $ uv pip install -e . --group dev $ uv run pre-commit install Testing ^^^^^^^ tl;dr: Run ``./scripts/test`` to run all tests and linters. PySTAC Client runs tests using `pytest `_. You can find unit tests in the ``tests/`` directory. To run the tests and generate the coverage report: .. code-block:: bash $ uv run pytest -v -s --block-network --cov pystac_client --cov-report term-missing The PySTAC Client tests use `vcrpy `_ to mock API calls with "pre-recorded" API responses. When adding new tests use the ``@pytest.mark.vcr`` decorator function to indicate ``vcrpy`` should be used. Record the new responses and commit them to the repository. .. code-block:: bash $ uv run pytest -v -s --record-mode new_episodes $ git add $ git commit -a -m 'new test episodes' To update PySTAC Client to use future versions of STAC API, the existing recorded API responses should be "re-recorded": .. code-block:: bash $ uv run pytest -v -s --record-mode rewrite --block-network $ git commit -a -m 'updated test episodes' Code quality checks ^^^^^^^^^^^^^^^^^^^ `pre-commit `_ is used to ensure a standard set of formatting and linting is run before every commit. These hooks should be installed with: .. code-block:: bash $ uv run pre-commit install These can then be run independent of a commit with: .. code-block:: bash $ uv run pre-commit run --all-files PySTAC Client uses - `ruff `_ for Python code formatting and style checks - `codespell `_ to check code for common misspellings - `doc8 `_ for style checking on RST files in the docs - `mypy `_ for Python type annotation checks Once installed you can bypass pre-commit by adding the ``--no-verify`` (or ``-n``) flag to Git commit commands, as in ``git commit --no-verify``. Pull Requests ^^^^^^^^^^^^^ To make Pull Requests to PySTAC Client, the code must pass linting, formatting, and code tests. To run the entire suit of checks and tests that will be run the GitHub Action Pipeline, use the ``test`` script. .. code-block:: bash $ uv run scripts/test If automatic formatting is desired (incorrect formatting will cause the GitHub Action to fail), use the format script (which uses ruff) and commit the resulting files: .. code-block:: bash $ uv run scripts/format $ git commit -a -m 'formatting updates' To build the documentation, `install Pandoc `_, install the Python documentation requirements via uv, then use the ``build-docs`` script: .. code-block:: bash $ uv pip install -e . --group docs $ uv run scripts/build-docs CHANGELOG ^^^^^^^^^ PySTAC Client maintains a `changelog `_ to track changes between releases. All Pull Requests should make a changelog entry unless the change is trivial (e.g. fixing typos) or is entirely invisible to users who may be upgrading versions (e.g. an improvement to the CI system). For changelog entries, please link to the PR of that change. This needs to happen in a few steps: - Make a Pull Request (see above) to PySTAC Client with your changes - Record the link to the Pull Request - Push an additional commit to your branch with the changelog entry with the link to the Pull Request. For more information on changelogs and how to write a good entry, see `keep a changelog `_. Benchmark ^^^^^^^^^ By default, PySTAC Client benchmarks are skipped during test runs. To run the benchmarks, use the ``--benchmark-only`` flag: .. code-block:: bash $ uv run pytest --benchmark-only ============================= test session starts ============================== platform darwin -- Python 3.9.13, pytest-6.2.4, py-1.10.0, pluggy-0.13.1 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) rootdir: /Users/gadomski/Code/pystac-client, configfile: pytest.ini 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 collected 75 items tests/test_cli.py ss [ 2%] tests/test_client.py ssssssssssssssss [ 24%] tests/test_collection_client.py ss [ 26%] tests/test_item_search.py ...sssssssssssssssssssssssssssssssssssssssssss [ 88%] s [ 89%] tests/test_stac_api_io.py ssssssss [100%] --------------------------------------------------------------------------------------- benchmark: 3 tests -------------------------------------------------------------------------------------- Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 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 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 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 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Legend: Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile. OPS: Operations Per Second, computed as 1 / Mean ======================== 3 passed, 72 skipped in 11.86s ======================== For more information on running and comparing benchmarks, see the `pytest-benchmark documentation `_. ================================================ FILE: docs/design/0001-record-architecture-decisions.md ================================================ # 1. Record architecture decisions Date: 2021-03-01 ## Status Accepted ## Context We need to record the architectural decisions made on this project. ## Decision We will use Architecture Decision Records, as [described by Michael Nygard](http://thinkrelevance.com/blog/2011/11/15/documenting-architecture-decisions). ## Consequences See Michael Nygard's article, linked above. For a lightweight ADR toolset, see Nat Pryce's [adr-tools](https://github.com/npryce/adr-tools). ================================================ FILE: docs/design/0002-choose-stac-library.md ================================================ # 2. Choose STAC library Date: 2021-03-01 ## Status Accepted ## Context We would like to use an existing Python library for working with STAC objects as Python objects. Ideally this library will meet the following requirements: * **Actively maintained** to ensure that it is up-to-date with the latest STAC spec * **Well-documented** to reduce the documentation burden for this library * **Easily extensible** to allow us to take advantage of existing STAC object functionality The 2 most obvious choices for Python STAC clients are [`PySTAC`](https://github.com/stac-utils/pystac) and [`sat-stac`](https://github.com/sat-utils/sat-stac). ### `PySTAC` `PySTAC` is an actively maintained STAC client for Python 3. Its last release was less than 2 months ago and its last commit was less than 2 weeks ago. It has had 15 contributors within the last year and it supports the latest release candidate for the STAC spec (as well as previous releases). `PySTAC` hosts [documentation on ReadTheDocs](https://pystac.readthedocs.io/en/latest/) that includes a description of programming concepts related to the library, quickstart instructions, tutorials, and an API reference. `PySTAC` supports both reading and writing of STAC objects. Extending `PySTAC` classes through inheritance is made a bit more difficult by the fact that some class methods (like ``from_dict``) have references to specific classes (like ``Catalog``) rather than using the ``cls`` argument. This will require us to overwrite some of these methods to implement inheritance. It has robust support for traversing catalogs using links. There is no existing support for the STAC API spec in `PySTAC` or any related tooling. ### `sat-stac` `sat-stac` is an actively maintained STAC client for Python 3. Its last release was less than 2 months ago and its last commit was at that same time. It has had 2 contributors within the last year and it supports the latest release candidate for the STAC spec (as well as previous releases). `sat-stac` has installation documented in the GitHub repo's main README and has 2 tutorials in the form of Jupyter Notebooks in that repo. `sat-stac` supports reading STAC catalogs (support for writing STAC catalogs was removed in v0.4.0). There is also an existing library for working with STAC API - Item Search results called [sat-search](https://github.com/sat-utils/sat-search) that uses `sat-stac` as its backend. ## Decision We will use `PySTAC` as our "backend" for working with STAC objects in Python. While both libraries are well-maintained, `PySTAC` has more thorough documentation and seems to be more widely supported through community contribution. ## Consequences We will need to create our own implementation of a STAC API - Item Search client since there is not existing tooling based on `PySTAC`. We will be able to point to the hosted documentation for `PySTAC` to guide users through existing functionality for navigating STAC Catalogs. Special care should be taken to ensure that we do not break any of `PySTAC`'s functionality through inheritance. ================================================ FILE: docs/design/design_decisions.rst ================================================ Design Decisions ================ `Architectural Design Records (ADRs) `__ for major design decisions related to the library. In general, this library makes an attempt to follow the design patterns laid out in the PySTAC library. .. toctree:: :glob: :maxdepth: 1 * ================================================ FILE: docs/index.rst ================================================ PySTAC Client Documentation =========================== PySTAC Client is a Python package for working with `STAC `__ Catalogs and `STAC APIs `__. PySTAC Client builds upon PySTAC through higher-level functionality and ability to access STAC API search endpoints. STAC Versions ============= +---------------+---------------+-----------------------------+ | pystac-client | STAC spec | STAC API Spec | +===============+===============+=============================+ | 0.9.x | 1.0.x - 1.1.x | 1.0.0-beta.1 - 1.0.0 | +---------------+---------------+-----------------------------+ | 0.8.x | 1.0.x | 1.0.0-beta.1 - 1.0.0 | +---------------+---------------+-----------------------------+ | 0.7.x | 1.0.x | 1.0.0-beta.1 - 1.0.0 | +---------------+---------------+-----------------------------+ | 0.6.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-rc.2 | +---------------+---------------+-----------------------------+ | 0.5.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-rc.1 | +---------------+---------------+-----------------------------+ | 0.4.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-rc.1 | +---------------+---------------+-----------------------------+ | 0.3.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-beta.4 | +---------------+---------------+-----------------------------+ | 0.2.x | 1.0.x | 1.0.0-beta.1 - 1.0.0-beta.2 | +---------------+---------------+-----------------------------+ Installation ------------ ``pystac-client`` requires `Python >=3.10 `__. To install with pip, run: .. code-block:: console $ python -m pip install pystac-client This will install the dependencies :doc:`pystac `, :doc:`python-dateutil `, and :doc:`requests `. Acknowledgements ---------------- This package builds upon the great work of the PySTAC library for working with STAC objects in Python. It also uses concepts from the `sat-search `__ library for working with STAC API search endpoints. Table of Contents ----------------- .. toctree:: :maxdepth: 2 quickstart usage api tutorials contributing design/design_decisions ================================================ FILE: docs/quickstart.rst ================================================ Quickstart ---------- PySTAC Client can be used as either a Command Line Interface (CLI) or a Python library. CLI ~~~ Use the CLI to quickly perform Item or Collection searches and output or save the results. The ``--matched`` switch performs a search with limit=1 so does not get any Items, but gets the total number of matches which will be output to the screen (if supported by the STAC API). .. code-block:: console $ stac-client search https://earth-search.aws.element84.com/v1 -c sentinel-2-l2a --bbox -72.5 40.5 -72 41 --matched 3141 items matched The ``--matched`` flag can also be used for collection search to get the total number of collections that match your search terms. .. code-block:: console $ stac-client collections https://emc.spacebel.be --q sentinel-2 --matched 76 collections matched If the same URL is to be used over and over, define an environment variable to be used in the CLI call: .. code-block:: console $ export STAC_API_URL=https://earth-search.aws.element84.com/v1 $ 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 48 items matched Without the ``--matched`` switch, all items will be fetched, paginating if necessary. If the ``--max-items`` switch is provided it will stop paging once that many items has been retrieved. It then prints all items to stdout as an ItemCollection. This can be useful to pipe output to another process such as `stac-terminal `__, `geojsonio-cli `__, or `jq `__. .. code-block:: console $ 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 .. figure:: images/stacterm-cal.png :alt: If the ``--save`` switch is provided instead, the results will not be output to stdout, but instead will be saved to the specified file. .. code-block:: console $ 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 If the Catalog supports the `Query extension `__, any Item property can also be included in the search. Rather than requiring the JSON syntax the Query extension uses, pystac-client uses a simpler syntax that it will translate to the JSON equivalent. Note however that when the simple syntax is used it sends all property values to the server as strings, except for ``gsd`` which it casts to ``float``. This means that if there are extensions in use with numeric properties these will be sent as strings. Some servers may automatically cast this to the appropriate data type, others may not. The query filter will also accept complete JSON as per the specification. :: where operator is one of `>=`, `<=`, `>`, `<`, `=` Examples: eo:cloud_cover<10 created=2021-01-06 view:sun_elevation<20 Any number of properties can be included, and each can be included more than once to use additional operators. .. code-block:: console $ 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 10 items matched .. code-block:: console $ 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 4 items matched Collection searches can also use multiple filters like this example search for collections that include the term ``"biomass"`` and have a spatial extent that intersects Scandinavia. .. code-block:: console $ stac-client collections https://emc.spacebel.be --q biomass --bbox 0.09 54.72 33.31 71.36 --matched 43 items matched Since most STAC APIs have not yet implemented the `collection search extension `__, ``pystac-client`` will perform a limited client-side filter on the full list of collections using only the ``bbox``, ``datetime``, and ``q`` (free-text search) parameters. In the case that the STAC API does not support collection search, a warning will be displayed to inform you that the filter is being applied client-side. Python ~~~~~~ First, create a Client instance configured to use a specific STAC API by the root URL of that API. For this example, we will use `Earth Search `__. .. code-block:: python from pystac_client import Client client = Client.open("https://earth-search.aws.element84.com/v1") Create an Item Search instance that represents a search to run. This does not actually run a search yet -- that does not happen until a method is called that requires data from the STAC API. .. code-block:: python search = client.search( max_items=10, collections=["sentinel-2-c1-l2a"], bbox=[-72.5,40.5,-72,41] ) Calling ``matched()`` will send a request to the STAC API and retrieve a single item and metadata about how many Items match the search criteria. .. code-block:: python print(f"{search.matched()} items found") The ``items()`` iterator method can be used to iterate through all resulting items. This iterator hides the pagination behavior that the may occur if there are sufficient results. Be careful with this method -- you could end up iterating over the entire catalog if ``max_items`` is not set! .. code-block:: python for item in search.items(): print(item.id) Use ``item_collection()`` to convert all Items from a search into a single `PySTAC ItemCollection `__. The ``ItemCollection`` can then be saved as a GeoJSON FeatureCollection. This requires retrieving all of the results from the search, so it may take some time to retrieve all the paginated responses. .. code-block:: python item_collection = search.item_collection() item_collection.save_object("my_itemcollection.json") Some STAC APIs also implement the Collection Search Extension. Earth Search does not, so we use the ORNL_CLOUD CMR-STAC Catalog instead: .. code-block:: python client = Client.open("https://cmr.earthdata.nasa.gov/stac/ORNL_CLOUD") collection_search = client.collection_search( q="rain", ) print(f"{collection_search.matched()} collections found") The ``collections()`` iterator method can be used to iterate through all resulting collections. .. code-block:: python for collection in collection_search.collections(): print(collection.id) ================================================ FILE: docs/tutorials/aoi-coverage.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "e06a27bf", "metadata": {}, "source": [ "# Calculating Coverage Percentage of the AOI by an Item\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." ] }, { "cell_type": "code", "execution_count": null, "id": "b65de617", "metadata": {}, "outputs": [], "source": [ "from typing import Any, Dict\n", "\n", "import matplotlib.pyplot as plt\n", "from pystac.item import Item\n", "from shapely.geometry import shape\n", "\n", "from pystac_client import Client" ] }, { "cell_type": "code", "execution_count": null, "id": "98942e75", "metadata": {}, "outputs": [], "source": [ "def intersection_percent(item: Item, aoi: Dict[str, Any]) -> float:\n", " \"\"\"The percentage that the Item's geometry intersects the AOI. An Item that\n", " completely covers the AOI has a value of 100.\n", " \"\"\"\n", " geom_item = shape(item.geometry)\n", " geom_aoi = shape(aoi)\n", "\n", " intersected_geom = geom_aoi.intersection(geom_item)\n", "\n", " intersection_percent = (intersected_geom.area * 100) / geom_aoi.area\n", "\n", " return intersection_percent\n", "\n", "\n", "# STAC API root URL\n", "URL = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", "\n", "# geometry of the AOI to search over\n", "intersects_geometry = {\n", " \"type\": \"Polygon\",\n", " \"coordinates\": [\n", " [\n", " [-73.21, 43.99],\n", " [-73.21, 47.05],\n", " [-70.12, 47.05],\n", " [-70.12, 43.99],\n", " [-73.21, 43.99],\n", " ]\n", " ],\n", "}\n", "\n", "# Create a Client and an ItemSearch representing our search\n", "# No search operations will be performed until we call the items() method\n", "client = Client.open(URL)\n", "item_search = client.search(\n", " collections=[\"sentinel-2-l2a\"], intersects=intersects_geometry, max_items=100\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "0b0883fe", "metadata": {}, "outputs": [], "source": [ "print(\n", " [\n", " f\"{intersection_percent(item, intersects_geometry):.2f}\"\n", " for item in item_search.items()\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "03254847", "metadata": {}, "outputs": [], "source": [ "# create a generator that filters to only those Items that intersect more than 5%\n", "items_gt_5_percent = (\n", " i for i in item_search.items() if intersection_percent(i, intersects_geometry) > 5\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "e1de7a09", "metadata": {}, "outputs": [], "source": [ "# Render the AOI and Item results\n", "# The green shape is the AOI\n", "# The blue shapes are the Item geometries\n", "# If there are no blue shapes, adjust the intersection percent filter above\n", "# until there are\n", "\n", "cm = plt.get_cmap(\"RdBu\")\n", "fig, axs = plt.subplots()\n", "axs.set_aspect(\"equal\", \"datalim\")\n", "\n", "for item in items_gt_5_percent:\n", " xs, ys = shape(item.geometry).exterior.xy\n", " axs.fill(xs, ys, alpha=0.5, fc=\"b\", ec=\"none\")\n", "\n", "geom_intersects = shape(intersects_geometry)\n", "xs, ys = geom_intersects.exterior.xy\n", "axs.fill(xs, ys, alpha=0.5, fc=\"g\", ec=\"none\")\n", "\n", "plt.show()" ] } ], "metadata": { "interpreter": { "hash": "6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea" }, "kernelspec": { "display_name": "Python 3.9.11 ('.venv': venv)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.11" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/tutorials/authentication.md ================================================ # Authentication While 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. ## Basic auth Pystac-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. ```python import base64 import pystac_client # encode credentials user_name = "yellowbeard" password = "yaarg" userpass = f"{user_name}:{password}" b64_userpass = base64.b64encode(userpass.encode()).decode() # create the client client = pystac_client.Client.open( url="https://planetarycomputer.microsoft.com/api/stac/v1", headers={ 'Authorization': f"Basic {b64_userpass}" } ) ``` ## Token auth Providing 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. ## AWS SigV4 Accessing 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. Pystac-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. The code cell below demonstrates this, using the `boto3` module. ```python import boto3 import botocore.auth import botocore.awsrequest import pystac_client import requests # Details regarding the private stac api region = "us-east-1" service_name = "execute-api" endpoint_id = "xxxxxxxx" deployment_stage = "dev" stac_api_url = f"https://{endpoint_id}.{service_name}.{region}.amazonaws.com/{deployment_stage}" # load AWS credentials credentials = boto3.Session(region_name=region).get_credentials() signer = botocore.auth.SigV4Auth(credentials, service_name, region) def sign_request(request: requests.Request) -> requests.Request: """Sign the request using AWS SigV4. Args: request (requests.Request): The fully populated request to sign. Returns: requests.Request: The provided request object, with auth header added. """ aws_request = botocore.awsrequest.AWSRequest( method=request.method, url=request.url, params=request.params, data=request.data, headers=request.headers ) signer.add_auth(aws_request) request.headers = aws_request.headers return request # create the client client = pystac_client.Client.open( url=stac_api_url, request_modifier=sign_request ) ``` ================================================ FILE: docs/tutorials/cql2-filter.ipynb ================================================ { "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "e06a27bf", "metadata": {}, "source": [ "# CQL2 Filtering\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", "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." ] }, { "cell_type": "code", "execution_count": null, "id": "b65de617", "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "import geopandas as gpd\n", "import pandas as pd\n", "\n", "from pystac_client import Client" ] }, { "cell_type": "markdown", "id": "c8ac88bb", "metadata": {}, "source": [ "The first step as always with pystac-client is opening the catalog:" ] }, { "cell_type": "code", "execution_count": null, "id": "98942e75", "metadata": {}, "outputs": [], "source": [ "# STAC API root URL\n", "URL = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", "\n", "catalog = Client.open(URL)" ] }, { "cell_type": "markdown", "id": "1e16077c", "metadata": {}, "source": [ "## Initial Search Parameters\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`)." ] }, { "cell_type": "code", "execution_count": null, "id": "5e961981", "metadata": {}, "outputs": [], "source": [ "# AOI around Delfzijl, in the north of The Netherlands\n", "geom = {\n", " \"type\": \"Polygon\",\n", " \"coordinates\": [\n", " [\n", " [6.42425537109375, 53.174765470134616],\n", " [7.344360351562499, 53.174765470134616],\n", " [7.344360351562499, 53.67393435835391],\n", " [6.42425537109375, 53.67393435835391],\n", " [6.42425537109375, 53.174765470134616],\n", " ]\n", " ],\n", "}\n", "\n", "params = {\n", " \"max_items\": 100,\n", " \"collections\": \"landsat-8-c2-l2\",\n", " \"intersects\": geom,\n", " \"datetime\": \"2018-01-01/2020-12-31\",\n", "}" ] }, { "cell_type": "markdown", "id": "d6f1dd5f", "metadata": {}, "source": [ "## Using Filters\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", "Remember that in this whole notebook we are only looking at STAC metadata, there is no part where we are reading the data itself." ] }, { "cell_type": "code", "execution_count": null, "id": "8b26e89b", "metadata": {}, "outputs": [], "source": [ "def search_and_plot(filter):\n", " search = catalog.search(**params, filter=filter)\n", "\n", " gdf = gpd.GeoDataFrame.from_features(search.item_collection_as_dict())\n", " gdf[\"datetime\"] = pd.to_datetime(gdf[\"datetime\"])\n", " print(f\"Found {len(gdf)} items\")\n", "\n", " gdf.plot.line(x=\"datetime\", y=\"eo:cloud_cover\", title=json.dumps(filter))" ] }, { "cell_type": "markdown", "id": "11afcc19", "metadata": {}, "source": [ "We can test out the function by passing an empty dict to do no filtering at all." ] }, { "cell_type": "code", "execution_count": null, "id": "b6293c11", "metadata": {}, "outputs": [], "source": [ "search_and_plot({})" ] }, { "cell_type": "markdown", "id": "44d3bc04", "metadata": {}, "source": [ "## CQL2 Filters\n", "\n", "We will use `eo:cloud_cover` as an example and filter for all the STAC Items where `eo:cloud_cover <= 10%`." ] }, { "cell_type": "code", "execution_count": null, "id": "dfc0e759", "metadata": {}, "outputs": [], "source": [ "filter = {\"op\": \"<=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 10]}\n", "\n", "search_and_plot(filter)" ] }, { "cell_type": "markdown", "id": "75e835f1", "metadata": {}, "source": [ "Next let's look for all the STAC Items where `eo:cloud_cover >= 80%`." ] }, { "cell_type": "code", "execution_count": null, "id": "9c2f9ca1", "metadata": {}, "outputs": [], "source": [ "filter = {\"op\": \">=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 80]}\n", "\n", "search_and_plot(filter)" ] }, { "cell_type": "markdown", "id": "0ad984bf", "metadata": {}, "source": [ "We can combine multiple CQL2 statements to express more complicated logic:" ] }, { "cell_type": "code", "execution_count": null, "id": "109f673c", "metadata": {}, "outputs": [], "source": [ "filter = {\n", " \"op\": \"and\",\n", " \"args\": [\n", " {\"op\": \"<=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 60]},\n", " {\"op\": \">=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 40]},\n", " ],\n", "}\n", "\n", "search_and_plot(filter)" ] }, { "cell_type": "markdown", "id": "617c7416", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": null, "id": "7b0dc965", "metadata": {}, "outputs": [], "source": [ "filter = {\n", " \"op\": \"and\",\n", " \"args\": [\n", " {\"op\": \"s_intersects\", \"args\": [{\"property\": \"geometry\"}, geom]},\n", " {\"op\": \">=\", \"args\": [{\"property\": \"datetime\"}, \"2018-01-01\"]},\n", " {\"op\": \"<=\", \"args\": [{\"property\": \"datetime\"}, \"2020-12-31\"]},\n", " {\"op\": \"<=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 60]},\n", " {\"op\": \">=\", \"args\": [{\"property\": \"eo:cloud_cover\"}, 40]},\n", " ],\n", "}\n", "search = catalog.search(max_items=100, collections=\"landsat-8-c2-l2\", filter=filter)\n", "\n", "print(f\"Found {len(search.item_collection())} items\")" ] }, { "cell_type": "markdown", "id": "56503c7b", "metadata": {}, "source": [ "### CQL2 Text\n", "\n", "The examples above all use CQL2-json but pystac-client also supports passing `filter` as CQL2 text.\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." ] }, { "cell_type": "code", "execution_count": null, "id": "5e8f62f5", "metadata": {}, "outputs": [], "source": [ "search = catalog.search(**params, method=\"GET\", filter=\"eo:cloud_cover<=10\")\n", "\n", "print(f\"Found {len(search.item_collection())} items\")" ] }, { "cell_type": "markdown", "id": "9b865c1f", "metadata": {}, "source": [ "Just like CQL2 json, CQL2 text statements can be combined to express more complex logic:" ] }, { "cell_type": "code", "execution_count": null, "id": "c06f40cf", "metadata": {}, "outputs": [], "source": [ "search = catalog.search(\n", " **params, method=\"GET\", filter=\"eo:cloud_cover<=60 and eo:cloud_cover>=40\"\n", ")\n", "\n", "print(f\"Found {len(search.item_collection())} items\")" ] }, { "cell_type": "markdown", "id": "35cbf612", "metadata": {}, "source": [ "## Queryables\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", "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." ] }, { "cell_type": "code", "execution_count": null, "id": "90f1cc6d", "metadata": {}, "outputs": [], "source": [ "collection = catalog.get_collection(\"landsat-c2-l2\")\n", "queryables = collection.get_queryables()\n", "\n", "list(queryables[\"properties\"].keys())" ] }, { "cell_type": "markdown", "id": "c407ffec", "metadata": {}, "source": [ "## Read More\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", "- 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/)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/tutorials/item-search-intersects.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "e06a27bf", "metadata": {}, "source": [ "# Item Search with Intersects\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)." ] }, { "cell_type": "markdown", "id": "1e16077c", "metadata": {}, "source": [ "# Client\n", "\n", "We first connect to an API by retrieving the root catalog, or landing page, of the API with the `Client.open` function." ] }, { "cell_type": "code", "execution_count": null, "id": "98942e75", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "from typing import Any, Dict\n", "\n", "from pystac_client import Client\n", "\n", "# STAC API root URL\n", "URL = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", "\n", "client = Client.open(URL)" ] }, { "cell_type": "markdown", "id": "62e26114", "metadata": {}, "source": [ "# Item Search\n", "\n", "When the Catalog is a STAC API, we have the ability to search for items based on spatio-temporal properties.\n", "\n", "The STAC API endpoint `/search` accepts a parameter `intersects` that is a GeoJSON Geometry representing the AOI of the search.\n", "\n", "The `search` method of the pystac_client `Client` class accepts several different types of objects:\n", "\n", "1. a string representing a GeoJSON geometry\n", "2. a dictionary representing a GeoJSON geometry\n", "3. any object that implements a ``__geo_interface__`` property, [an informal specification](https://gist.github.com/sgillies/2217756) \n", " supported by several libraries for generating a GeoJSON representation from an object. Several prominent libraries support this\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)" ] }, { "cell_type": "markdown", "id": "0d27fabf", "metadata": {}, "source": [ "## Item Search with AOI as a Dictionary" ] }, { "cell_type": "code", "execution_count": null, "id": "d8af6334", "metadata": { "tags": [] }, "outputs": [], "source": [ "# AOI around Delfzijl, in northern Netherlands\n", "aoi_as_dict: Dict[str, Any] = {\n", " \"type\": \"Polygon\",\n", " \"coordinates\": [[[6, 53], [7, 53], [7, 54], [6, 54], [6, 53]]],\n", "}\n", "\n", "search = client.search(\n", " max_items=25,\n", " collections=\"aster-l1t\",\n", " intersects=aoi_as_dict,\n", ")\n", "\n", "print(f\"AOI as dictionary, found {len(list(search.items()))} items\")" ] }, { "cell_type": "markdown", "id": "9da4956b", "metadata": {}, "source": [ "## Item Search with AOI as a String" ] }, { "cell_type": "code", "execution_count": null, "id": "9a7c9336", "metadata": { "tags": [] }, "outputs": [], "source": [ "aoi_as_str: str = json.dumps(aoi_as_dict)\n", "\n", "search = client.search(\n", " max_items=25,\n", " collections=\"aster-l1t\",\n", " intersects=aoi_as_str,\n", ")\n", "\n", "print(f\"AOI as string, found {len(list(search.items()))} items\")" ] }, { "cell_type": "markdown", "id": "f22ffb5a", "metadata": {}, "source": [ "## Item Search with AOI as a Shapely Geometry Object" ] }, { "cell_type": "code", "execution_count": null, "id": "a44598ef", "metadata": { "tags": [] }, "outputs": [], "source": [ "import shapely.geometry\n", "\n", "aoi_as_shapely_shape = shapely.geometry.shape(aoi_as_dict)\n", "\n", "search = client.search(\n", " max_items=25,\n", " collections=\"aster-l1t\",\n", " intersects=aoi_as_shapely_shape,\n", ")\n", "\n", "print(\n", " \"AOI as Shapely Geometry object from shape(), \"\n", " f\"found {len(list(search.items()))} items\"\n", ")\n", "\n", "aoi_as_shapely_polygon = shapely.geometry.Polygon(aoi_as_dict[\"coordinates\"][0])\n", "\n", "search = client.search(\n", " max_items=25,\n", " collections=\"aster-l1t\",\n", " intersects=aoi_as_shapely_polygon,\n", ")\n", "\n", "print(\n", " \"AOI as Shapely Geometry object with Polygon, \"\n", " f\"found {len(list(search.items()))} items\"\n", ")" ] }, { "cell_type": "markdown", "id": "8f236254", "metadata": {}, "source": [ "## Item Search with AOI as a \"geojson\" library object" ] }, { "cell_type": "code", "execution_count": null, "id": "84ce4395", "metadata": { "tags": [] }, "outputs": [], "source": [ "import geojson\n", "\n", "aoi_as_geojson_polygon = geojson.Polygon(aoi_as_dict[\"coordinates\"])\n", "\n", "search = client.search(\n", " max_items=25,\n", " collections=\"aster-l1t\",\n", " intersects=aoi_as_geojson_polygon,\n", ")\n", "\n", "print(f\"AOI as geojson Polygon, found {len(list(search.items()))} items\")" ] }, { "cell_type": "markdown", "id": "3fe9c255", "metadata": {}, "source": [ "## Item Search with AOI as a pygeoif Object" ] }, { "cell_type": "code", "execution_count": null, "id": "a27dcb72", "metadata": { "tags": [] }, "outputs": [], "source": [ "import pygeoif\n", "\n", "aoi_as_pygeoif_polygon = pygeoif.geometry.Polygon(aoi_as_dict[\"coordinates\"][0])\n", "\n", "search = client.search(\n", " max_items=25,\n", " collections=\"aster-l1t\",\n", " intersects=aoi_as_pygeoif_polygon,\n", ")\n", "\n", "print(f\"AOI as pygeoif Polygon, found {len(list(search.items()))} items\")" ] } ], "metadata": { "interpreter": { "hash": "6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/tutorials/pystac-client-introduction.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "e06a27bf", "metadata": {}, "source": [ "# PySTAC-Client Introduction\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." ] }, { "cell_type": "code", "execution_count": null, "id": "b65de617", "metadata": {}, "outputs": [], "source": [ "# set pystac_client logger to DEBUG to see API calls\n", "import logging\n", "\n", "from pystac_client import Client\n", "\n", "logging.basicConfig()\n", "logger = logging.getLogger(\"pystac_client\")\n", "logger.setLevel(logging.DEBUG)" ] }, { "cell_type": "markdown", "id": "1e16077c", "metadata": {}, "source": [ "# Client\n", "\n", "We first connect to an API by retrieving the root catalog, or landing page, of the API with the `Client.open` function." ] }, { "cell_type": "code", "execution_count": null, "id": "98942e75", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# STAC API root URL\n", "URL = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", "\n", "# custom headers\n", "headers = []\n", "\n", "cat = Client.open(URL, headers=headers)\n", "cat" ] }, { "cell_type": "markdown", "id": "f6efbb86", "metadata": {}, "source": [ "# CollectionClient\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." ] }, { "cell_type": "code", "execution_count": null, "id": "bb7693fb", "metadata": {}, "outputs": [], "source": [ "for collection in cat.get_collections():\n", " print(collection)" ] }, { "cell_type": "markdown", "id": "ebab2724-cab3-4fba-b25b-fdfb4e537014", "metadata": {}, "source": [ "# Collection Search\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." ] }, { "cell_type": "code", "execution_count": null, "id": "a23a53ec-5b5f-421d-9f0e-01dbde8c3697", "metadata": {}, "outputs": [], "source": [ "collection_search = cat.collection_search(\n", " q=\"ASTER\",\n", ")" ] }, { "cell_type": "markdown", "id": "90b3d014-9c8f-4c5b-a94e-bfb7f17380ad", "metadata": {}, "source": [ "The `collections` method lets you iterate through the results of the search so you can inspect the details of matching collections." ] }, { "cell_type": "code", "execution_count": null, "id": "006f13fd-5e58-4f3f-bd5a-707cd830caa1", "metadata": {}, "outputs": [], "source": [ "for result in collection_search.collections():\n", " print(result.id, f\"{collection.description}\", sep=\"\\n\")\n", " print(\"\\n\")" ] }, { "cell_type": "code", "execution_count": null, "id": "fef20a46", "metadata": {}, "outputs": [], "source": [ "collection = cat.get_collection(\"aster-l1t\")\n", "collection" ] }, { "cell_type": "markdown", "id": "47a540fe", "metadata": {}, "source": [ "# Items\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." ] }, { "cell_type": "code", "execution_count": null, "id": "17d6de4b", "metadata": {}, "outputs": [], "source": [ "items = collection.get_items()\n", "\n", "\n", "# flush stdout so we can see the exact order that things happen\n", "def get_ten_items(items):\n", " for i, item in enumerate(items):\n", " print(f\"{i}: {item}\", flush=True)\n", " if i == 9:\n", " return\n", "\n", "\n", "print(\"First page\", flush=True)\n", "get_ten_items(items)\n", "\n", "print(\"Second page\", flush=True)\n", "get_ten_items(items)" ] }, { "cell_type": "markdown", "id": "62e26114", "metadata": {}, "source": [ "# API Search\n", "\n", "If the Catalog is an API, we have the ability to search for items based on spatio-temporal properties." ] }, { "cell_type": "code", "execution_count": null, "id": "d8af6334", "metadata": {}, "outputs": [], "source": [ "# AOI around Delfzijl, in northern Netherlands\n", "geom = {\n", " \"type\": \"Polygon\",\n", " \"coordinates\": [\n", " [\n", " [6.42425537109375, 53.174765470134616],\n", " [7.344360351562499, 53.174765470134616],\n", " [7.344360351562499, 53.67393435835391],\n", " [6.42425537109375, 53.67393435835391],\n", " [6.42425537109375, 53.174765470134616],\n", " ]\n", " ],\n", "}\n", "\n", "# limit sets the # of items per page so we can see multiple pages getting fetched\n", "search = cat.search(\n", " max_items=15,\n", " limit=5,\n", " collections=\"aster-l1t\",\n", " intersects=geom,\n", " datetime=\"2000-01-01/2010-12-31\",\n", ")\n", "\n", "items = list(search.items())\n", "\n", "len(items)" ] }, { "cell_type": "code", "execution_count": null, "id": "275f316f", "metadata": {}, "outputs": [], "source": [ "# note that this will work in JupyterLab, but not in a Jupyter Notebook\n", "\n", "import IPython.display\n", "\n", "IPython.display.JSON([i.to_dict() for i in items])" ] }, { "cell_type": "code", "execution_count": null, "id": "68bf65f2", "metadata": {}, "outputs": [], "source": [ "# this cell can be used in Jupyter Notebook. Use above if using JupyterLab\n", "\n", "import json\n", "import uuid\n", "\n", "from IPython.display import display_html, display_javascript\n", "\n", "\n", "class RenderJSON(object):\n", " def __init__(self, json_data):\n", " if isinstance(json_data, dict) or isinstance(json_data, list):\n", " self.json_str = json.dumps(json_data)\n", " else:\n", " self.json_str = json_data\n", " self.uuid = str(uuid.uuid4())\n", "\n", " def _ipython_display_(self):\n", " display_html(\n", " '
'.format(self.uuid),\n", " raw=True,\n", " )\n", " display_javascript(\n", " \"\"\"\n", " require([\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\"],\n", " function() {\n", " renderjson.set_show_to_level(2);\n", " document.getElementById('%s').appendChild(renderjson(%s))\n", " });\n", " \"\"\"\n", " % (self.uuid, self.json_str),\n", " raw=True,\n", " )\n", "\n", "\n", "RenderJSON([i.to_dict() for i in items])" ] } ], "metadata": { "interpreter": { "hash": "6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/tutorials/stac-metadata-viz.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "ab31574b", "metadata": {}, "source": [ "# STAC Metadata Visualizations\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", "The libraries GeoPandas and hvplot are used for visualizations." ] }, { "cell_type": "code", "execution_count": null, "id": "3188b5a0", "metadata": {}, "outputs": [], "source": [ "# set pystac_client logger to DEBUG to see API calls\n", "import logging\n", "\n", "from pystac_client import Client\n", "\n", "logging.basicConfig()\n", "logger = logging.getLogger(\"pystac_client\")\n", "logger.setLevel(logging.DEBUG)" ] }, { "cell_type": "markdown", "id": "1d404982", "metadata": {}, "source": [ "Define the STAC API to use, along with any custom headers (such as for authentication)" ] }, { "cell_type": "code", "execution_count": null, "id": "d40ed5c1", "metadata": {}, "outputs": [], "source": [ "# STAC API root URL\n", "URL = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", "\n", "# custom headers\n", "headers = []\n", "\n", "cat = Client.open(URL, headers=headers)\n", "cat" ] }, { "cell_type": "markdown", "id": "e513e548", "metadata": {}, "source": [ "## Search\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." ] }, { "cell_type": "code", "execution_count": null, "id": "ae7a3eca", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# AOI around Delfzijl, in northern Netherlands\n", "geom = {\n", " \"type\": \"Polygon\",\n", " \"coordinates\": [\n", " [\n", " [6.42425537109375, 53.174765470134616],\n", " [7.344360351562499, 53.174765470134616],\n", " [7.344360351562499, 53.67393435835391],\n", " [6.42425537109375, 53.67393435835391],\n", " [6.42425537109375, 53.174765470134616],\n", " ]\n", " ],\n", "}\n", "\n", "# limit sets the # of items per page so we can see multiple pages getting fetched\n", "search = cat.search(\n", " max_items=50,\n", " collections=\"aster-l1t\",\n", " intersects=geom,\n", " datetime=\"2000-01-01/2010-12-31\",\n", ")\n", "\n", "# retrieve the items as dictionaries, rather than Item objects\n", "items = list(search.items_as_dicts())\n", "len(items)" ] }, { "cell_type": "markdown", "id": "2a23660f", "metadata": {}, "source": [ "## GeoPandas\n", "\n", "A GeoDataFrame is constructed from the AOI geometry, in order to make visualizations easy.\n", "\n", "The STAC Items, which are a GeoJSON FeatureCollection can be converted to a GeoDataFrame." ] }, { "cell_type": "code", "execution_count": null, "id": "ed0da77b", "metadata": {}, "outputs": [], "source": [ "from copy import deepcopy\n", "\n", "import geopandas as gpd\n", "import pandas as pd\n", "from shapely.geometry import shape\n", "\n", "\n", "# convert a list of STAC Items into a GeoDataFrame\n", "def items_to_geodataframe(items):\n", " _items = []\n", " for i in items:\n", " _i = deepcopy(i)\n", " _i[\"geometry\"] = shape(_i[\"geometry\"])\n", " _items.append(_i)\n", " gdf = gpd.GeoDataFrame(pd.json_normalize(_items))\n", " for field in [\"properties.datetime\", \"properties.created\", \"properties.updated\"]:\n", " if field in gdf:\n", " gdf[field] = pd.to_datetime(gdf[field])\n", " gdf.set_index(\"properties.datetime\", inplace=True)\n", " return gdf" ] }, { "cell_type": "code", "execution_count": null, "id": "c4bed793", "metadata": {}, "outputs": [], "source": [ "# convert geometry to a GeoDataFrame\n", "aoi_gdf = gpd.GeoDataFrame([{\"geometry\": shape(geom)}])\n", "aoi_gdf" ] }, { "cell_type": "code", "execution_count": null, "id": "d1fc8732", "metadata": {}, "outputs": [], "source": [ "# convert found items to a GeoDataFrame\n", "items_gdf = items_to_geodataframe(items)\n", "items_gdf.head()" ] }, { "cell_type": "markdown", "id": "4d570626", "metadata": {}, "source": [ "## Plot Geometries on a Map\n", "\n", "Holoviews is used to display geometries on a map by using `hvplot`. The The `*` Holoviews operator to overlay two plots" ] }, { "cell_type": "code", "execution_count": null, "id": "e8fbfad6", "metadata": {}, "outputs": [], "source": [ "import hvplot.pandas # noqa: F401\n", "\n", "\n", "# plot polygons on a map with background tiles.\n", "def plot_polygons(data, *args, **kwargs):\n", " return data.hvplot.polygons(\n", " *args,\n", " geo=True,\n", " projection=\"GOOGLE_MERCATOR\",\n", " xaxis=None,\n", " yaxis=None,\n", " frame_width=600,\n", " frame_height=600,\n", " fill_alpha=0,\n", " line_width=4,\n", " **kwargs,\n", " )" ] }, { "cell_type": "code", "execution_count": null, "id": "72094092", "metadata": {}, "outputs": [], "source": [ "plot_polygons(aoi_gdf, tiles=\"OSM\", line_color=\"red\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d7d6cfc6", "metadata": {}, "outputs": [], "source": [ "plot_polygons(items_gdf, tiles=\"OSM\")" ] }, { "cell_type": "code", "execution_count": null, "id": "0a07e87b", "metadata": {}, "outputs": [], "source": [ "plot_polygons(items_gdf, tiles=\"OSM\") * plot_polygons(aoi_gdf, line_color=\"red\")" ] }, { "cell_type": "markdown", "id": "b0604c84", "metadata": {}, "source": [ "## Line Plots\n", "\n", "Numeric STAC metadata can also be plotted, most often this will be plotted vs the Item `datetime`." ] }, { "cell_type": "code", "execution_count": null, "id": "c198e261", "metadata": {}, "outputs": [], "source": [ "items_df = pd.DataFrame(items_gdf)\n", "\n", "plot_fields = [\n", " \"properties.eo:cloud_cover\",\n", " \"properties.view:sun_azimuth\",\n", " \"properties.view:sun_elevation\",\n", "]\n", "\n", "items_df[plot_fields].hvplot(height=500, width=800).opts(legend_position=\"top_right\")" ] } ], "metadata": { "interpreter": { "hash": "6b6313dbab648ff537330b996f33bf845c0da10ea77ae70864d6ca8e2699c7ea" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: docs/tutorials.rst ================================================ .. _tutorials: Tutorials ######### PySTAC-Client Introduction -------------------------- - :tutorial:`GitHub version ` - :ref:`Docs version ` This tutorial gives an introduction to using pystac-client with a STAC API STAC Metadata Visualization --------------------------- - :tutorial:`GitHub version ` - :ref:`Docs version ` This tutorial gives an introduction to using hvplot to visualize STAC metadata and Item geometries on a map. CQL2 Filtering --------------------------- - :tutorial:`GitHub version ` - :ref:`Docs version ` This tutorial gives an introduction to using CQL2-JSON filtering in searches to search by arbitrary STAC Item properties. Item Search with Intersects ----------------------------------------------------- - :tutorial:`GitHub version ` - :ref:`Docs version ` This tutorial demonstrates the use of different Python library object types that can be used for the `intersects` search parameter. Calculating Coverage Percentage of the AOI by an Item ----------------------------------------------------- - :tutorial:`GitHub version ` - :ref:`Docs version ` This tutorial 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. Authentication -------------- - :tutorial:`GitHub version ` - :ref:`Docs version ` This tutorial demontrates different ways the pystac-client can be used to access a private stac api, when protected with various authentication mechanisms. ================================================ FILE: docs/usage.rst ================================================ Usage ##### PySTAC-Client (pystac-client) builds upon `PySTAC `_ library to add support for STAC APIs in addition to static STAC catalogs. PySTAC-Client can be used with static or dynamic (i.e., API) catalogs. Currently, pystac-client does not offer much in the way of additional functionality if using with static catalogs, as the additional features are for support STAC API endpoints such as `search`. However, in the future it is expected that pystac-client will offer additional convenience functions that may be useful for static and dynamic catalogs alike. The most basic implementation of a STAC API is an endpoint that returns a valid STAC Catalog, but also contains a ``"conformsTo"`` attribute that is a list of conformance URIs for the standards that the API supports. This section is organized by the classes that are used, which mirror parent classes from PySTAC: +------------------+------------+ | pystac-client | pystac | +==================+============+ | Client | Catalog | +------------------+------------+ | CollectionClient | Collection | +------------------+------------+ The classes offer all of the same functions for accessing and traversing Catalogs as in PySTAC. The documentation for pystac-client only includes new functions, it does not duplicate documentation for inherited functions. Client ++++++ The :class:`pystac_client.Client` class is the main interface for working with services that conform to the STAC API spec. This class inherits from the :class:`pystac.Catalog` class and in addition to the methods and attributes implemented by a Catalog, it also includes convenience methods and attributes for: * Checking conformance to various specs * Querying a search endpoint (if the API conforms to the STAC API - Item Search spec) * Getting jsonschema of queryables from `/queryables` endpoint (if the API conforms to the STAC API - Filter spec) The preferred way to interact with any STAC Catalog or API is to create an :class:`pystac_client.Client` instance with the ``pystac_client.Client.open`` method on a root Catalog. This calls the :meth:`pystac.STACObject.from_file` except it properly configures conformance and IO for reading from remote servers. The following code creates an instance by making a call to the Microsoft Planetary Computer root catalog. .. code-block:: python >>> from pystac_client import Client >>> catalog = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1') >>> catalog.title 'Microsoft Planetary Computer STAC API' Some functions, such as ``Client.search`` will throw an error if the provided Catalog/API does not support the required Conformance Class. In other cases, such as ``Client.get_collections``, API endpoints will be used if the API conforms, otherwise it will fall back to default behavior provided by :class:`pystac.Catalog`. When a ``Client`` does not conform to a particular Conformance Class, an informative warning is raised. Similarly when falling back to the :class:`pystac.Catalog` implementation a warning is raised. You can control the behavior of these warnings using the standard :py:mod:`warnings` or special context managers :func:`pystac_client.warnings.strict` and from :func:`pystac_client.warnings.ignore`. API Conformance --------------- This library is intended to work with any STAC static catalog or STAC API. A static catalog will be usable more or less the same as with PySTAC, except that pystac-client supports providing custom headers to API endpoints. (e.g., authenticating to an API with a token). A STAC API is a STAC Catalog that is required to advertise its capabilities in a `conformsTo` field and implements the `STAC API - Core` spec along with other optional specifications: * `CORE `__ * `ITEM_SEARCH `__ * `FIELDS `__ * `QUERY `__ * `SORT `__ * `CONTEXT `__ * `FILTER `__ * `COLLECTIONS `__ (based on the `Features Collection section of OGC APO - Features __`) * `FEATURES `__ (based on `OGC API - Features `__) The :meth:`pystac_client.Client.conforms_to` method is used to check conformance against conformance classes (specs). To check an API for support for a given spec, pass the `conforms_to` function the name of a :class:`ConformanceClasses`. .. code-block:: python >>> catalog.conforms_to("ITEM_SEARCH") True If the API does not advertise conformance with a particular spec, but it does support it you can update `conforms_to` on the client object. For instance in `v0` of earth-search there are no ``"conformsTo"`` uris set at all. But they can be explicitly set: .. code-block:: python >>> catalog = Client.open("https://earth-search.aws.element84.com/v0") :1: NoConformsTo: Server does not advertise any conformance classes. >>> catalog.conforms_to("ITEM_SEARCH") False >>> catalog.add_conforms_to("ITEM_SEARCH") Note, updating ``"conformsTo"`` does not change what the server supports, it just changes PySTAC client's understanding of what the server supports. Configuring retry behavior -------------------------- By default, **pystac-client** will retry requests that fail DNS lookup or have timeouts. If you'd like to configure this behavior, e.g. to retry on some ``50x`` responses, you can configure the StacApiIO's session: .. code-block:: python from requests.adapters import HTTPAdapter from urllib3 import Retry from pystac_client import Client from pystac_client.stac_api_io import StacApiIO retry = Retry( total=5, backoff_factor=1, status_forcelist=[502, 503, 504], allowed_methods=None ) stac_api_io = StacApiIO(max_retries=retry) client = Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", stac_io=stac_api_io ) Automatically modifying results ------------------------------- Some systems, like the `Microsoft Planetary Computer `__, have public STAC metadata but require some `authentication `__ to access the actual assets. ``pystac-client`` provides a ``modifier`` keyword that can automatically modify the STAC objects returned by the STAC API. .. code-block:: python >>> from pystac_client import Client >>> import planetary_computer, requests >>> catalog = Client.open( ... 'https://planetarycomputer.microsoft.com/api/stac/v1', ... modifier=planetary_computer.sign_inplace, ... ) >>> item = next(catalog.get_collection("sentinel-2-l2a").get_all_items()) >>> requests.head(item.assets["B02"].href).status_code 200 Without the modifier, we would have received a 404 error because the asset is in a private storage container. ``pystac-client`` expects that the ``modifier`` callable modifies the result object in-place and returns no result. A warning is emitted if your ``modifier`` returns a non-None result that is not the same object as the input. Here's an example of creating your own modifier. Because :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). Simplifying this interface is a space for future improvement. .. code-block:: python import urllib.parse import pystac from pystac_client import Client, Modifiable def modifier(modifiable: Modifiable) -> None: if isinstance(modifiable, dict): if modifiable["type"] == "FeatureCollection": new_features = list() for item_dict in modifiable["features"]: modifier(item_dict) new_features.append(item_dict) modifiable["features"] = new_features else: stac_object = pystac.read_dict(modifiable) modifier(stac_object) modifiable.update(stac_object.to_dict()) else: for key, asset in modifiable.assets.items(): url = urllib.parse.urlparse(asset.href) if not url.query: asset.href = urllib.parse.urlunparse(url._replace(query="foo=bar")) modifiable.assets[key] = asset client = Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=modifier ) item_search = client.search(collections=["landsat-c2-l2"], max_items=1) item = next(item_search.items()) asset = item.assets["red"] assert urllib.parse.urlparse(asset.href).query == "foo=bar" Using custom certificates ------------------------- If you need to use custom certificates in your ``pystac-client`` requests, you can customize the :class:`StacApiIO` instance before creating your :class:`Client`. .. code-block:: python >>> from pystac_client.stac_api_io import StacApiIO >>> from pystac_client.client import Client >>> stac_api_io = StacApiIO() >>> stac_api_io.session.verify = "/path/to/certfile" >>> client = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1", stac_io=stac_api_io) CollectionClient ++++++++++++++++ STAC APIs may optionally implement a ``/collections`` endpoint as described in the `STAC API - Collections spec `__. This endpoint allows clients to search or inspect items within a particular collection. .. code-block:: python >>> catalog = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1') >>> collection = catalog.get_collection("sentinel-2-l2a") >>> collection.title 'Sentinel-2 Level-2A' :class:`pystac_client.CollectionClient` overrides :meth:`pystac.Collection.get_items`. PySTAC will get items by iterating through all children until it gets to an ``item`` link. PySTAC client will use the API endpoint instead: `/collections//items` (as long as `STAC API - Item Search spec `__ is supported). .. code-block:: python >>> item = next(collection.get_items(), None) Note that calling list on this iterator will take a really long time since it will be retrieving every itme for the whole ``"sentinel-2-l2a"`` collection. CollectionSearch ++++++++++++++++ STAC API services may optionally implement a ``/collections`` endpoint as described in the `STAC API - Collections spec `__. The ``/collections`` endpoint can be extended with the `STAC API - Collection Search Extension `__ which adds the capability to apply filter parameters to the collection-level metadata. See the `Query Parameters and Fields `__ from that spec for details on the meaning of each parameter. The :meth:`pystac_client.Client.collection_search` method provides an interface for making requests to a service's "collections" endpoint. This method returns a :class:`pystac_client.CollectionSearch` instance. .. code-block:: python >>> from pystac_client import Client >>> catalog = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1') >>> results = catalog.collection_search( ... q="biomass", ... datetime="2022/.." ... ) Instances of :class:`~pystac_client.CollectionSearch` have a handful of methods for getting matching collections as Python objects. The right method to use depends on how many of the matches you want to consume (a single collection at a time, a page at a time, or everything) and whether you want plain Python dictionaries representing the collections, or :class:`pystac.Collection` objects. The following table shows the :class:`~pystac_client.CollectionSearch` methods for fetching matches, according to which set of matches to return and whether to return them as ``pystac`` objects or plain dictionaries. ====================== ======================================================= =============================================================== Matches to return PySTAC objects Plain dictionaries ====================== ======================================================= =============================================================== **Single collections** :meth:`~pystac_client.CollectionSearch.collections` :meth:`~pystac_client.CollectionSearch.collections_as_dicts` **Pages** :meth:`~pystac_client.CollectionSearch.pages` :meth:`~pystac_client.CollectionSearch.pages_as_dicts` **Everything** :meth:`~pystac_client.CollectionSearch.collection_list` :meth:`~pystac_client.CollectionSearch.collection_list_as_dict` ====================== ======================================================= =============================================================== Additionally, the ``matched`` method can be used to access result metadata about how many total items matched the query: * :meth:`CollectionSearch.matched `: returns the number of hits (collections) for this search. If the API supports the STAC API Context Extension this value will be returned directly from a search result with ``limit=1``. Otherwise ``pystac-client`` will count the results and return a value with an associated warning. .. code-block:: python >>> for collection in results.collections(): ... print(item.id) fia modis-13Q1-061 modis-13A1-061 sentinel-3-olci-lfr-l2-netcdf The :meth:`~pystac_client.CollectionSearch.collections` and related methods handle retrieval of successive pages of results by finding links with a ``"rel"`` type of ``"next"`` and parsing them to construct the next request. The default implementation of this ``"next"`` link parsing assumes that the link follows the spec for an extended STAC link as described in the `STAC API - Collections: Collection Paging `__ section. Alternatively, the Collections can be returned as a list, where each list is one page of results retrieved from search: .. code-block:: python >>> for page in results.pages(): ... for collection in page.collections(): ... print(collection.id) fia modis-13Q1-061 modis-13A1-061 sentinel-3-olci-lfr-l2-netcdf If you do not need the :class:`pystac.Collection` instances, you can instead use :meth:`CollectionSearch.collections_as_dicts ` to retrieve dictionary representation of the collections, without incurring the cost of creating the Collection objects. .. code-block:: python >>> for collection_dict in results.collections_as_dicts(): ... print(collection_dict["id"]) fia modis-13Q1-061 modis-13A1-061 sentinel-3-olci-lfr-l2-netcdf ItemSearch ++++++++++ STAC API services may optionally implement a ``/search`` endpoint as describe in the `STAC API - Item Search spec `__. This endpoint allows clients to query STAC Items across the entire service using a variety of filter parameters. See the `Query Parameter Table `__ from that spec for details on the meaning of each parameter. The :meth:`pystac_client.Client.search` method provides an interface for making requests to a service's "search" endpoint. This method returns a :class:`pystac_client.ItemSearch` instance. .. code-block:: python >>> from pystac_client import Client >>> catalog = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1') >>> results = catalog.search( ... max_items=5, ... bbox=[-73.21, 43.99, -73.12, 44.05], ... datetime=['2019-01-01T00:00:00Z', '2019-01-02T00:00:00Z'], ... ) Instances of :class:`~pystac_client.ItemSearch` have a handful of methods for getting matching items as Python objects. The right method to use depends on how many of the matches you want to consume (a single item at a time, a page at a time, or everything) and whether you want plain Python dictionaries representing the items, or :class:`pystac.Item` objects. The following table shows the :class:`~pystac_client.ItemSearch` methods for fetching matches, according to which set of matches to return and whether to return them as ``pystac`` objects or plain dictionaries. ================= ================================================= ========================================================= Matches to return PySTAC objects Plain dictionaries ================= ================================================= ========================================================= **Single items** :meth:`~pystac_client.ItemSearch.items` :meth:`~pystac_client.ItemSearch.items_as_dicts` **Pages** :meth:`~pystac_client.ItemSearch.pages` :meth:`~pystac_client.ItemSearch.pages_as_dicts` **Everything** :meth:`~pystac_client.ItemSearch.item_collection` :meth:`~pystac_client.ItemSearch.item_collection_as_dict` ================= ================================================= ========================================================= Additionally, the ``matched`` method can be used to access result metadata about how many total items matched the query: * :meth:`ItemSearch.matched `: returns the number of hits (items) for this search if the API supports the STAC API Context Extension. Not all APIs support returning a total count, in which case a warning will be issued. .. code-block:: python >>> for item in results.items(): ... print(item.id) S2B_OPER_MSI_L2A_TL_SGS__20190101T200120_A009518_T18TXP_N02.11 MCD43A4.A2019010.h12v04.006.2019022234410 MCD43A4.A2019009.h12v04.006.2019022222645 MYD11A1.A2019002.h12v04.006.2019003174703 MYD11A1.A2019001.h12v04.006.2019002165238 The :meth:`~pystac_client.ItemSearch.items` and related methods handle retrieval of successive pages of results by finding links with a ``"rel"`` type of ``"next"`` and parsing them to construct the next request. The default implementation of this ``"next"`` link parsing assumes that the link follows the spec for an extended STAC link as described in the `STAC API - Item Search: Paging `__ section. See the :mod:`Paging ` docs for details on how to customize this behavior. Alternatively, the Items can be returned within ItemCollections, where each ItemCollection is one page of results retrieved from search: .. code-block:: python >>> for ic in results.pages(): ... for item in ic.items: ... print(item.id) S2B_OPER_MSI_L2A_TL_SGS__20190101T200120_A009518_T18TXP_N02.11 MCD43A4.A2019010.h12v04.006.2019022234410 MCD43A4.A2019009.h12v04.006.2019022222645 MYD11A1.A2019002.h12v04.006.2019003174703 MYD11A1.A2019001.h12v04.006.2019002165238 If you do not need the :class:`pystac.Item` instances, you can instead use :meth:`ItemSearch.items_as_dicts ` to retrieve dictionary representation of the items, without incurring the cost of creating the Item objects. .. code-block:: python >>> for item_dict in results.items_as_dicts(): ... print(item_dict["id"]) S2B_OPER_MSI_L2A_TL_SGS__20190101T200120_A009518_T18TXP_N02.11 MCD43A4.A2019010.h12v04.006.2019022234410 MCD43A4.A2019009.h12v04.006.2019022222645 MYD11A1.A2019002.h12v04.006.2019003174703 MYD11A1.A2019001.h12v04.006.2019002165238 Query Extension --------------- If the Catalog supports the `Query extension `__, any Item property can also be included in the search. Rather than requiring the JSON syntax the Query extension uses, pystac-client can use a simpler syntax that it will translate to the JSON equivalent. Note however that when the simple syntax is used it sends all property values to the server as strings, except for ``gsd`` which it casts to ``float``. This means that if there are extensions in use with numeric properties these will be sent as strings. Some servers may automatically cast this to the appropriate data type, others may not. The query filter will also accept complete JSON as per the specification. :: where operator is one of `>=`, `<=`, `>`, `<`, `=` Examples: eo:cloud_cover<10 view:off_nadir<50 platform=landsat-8 Any number of properties can be included, and each can be included more than once to use additional operators. Sort Extension --------------- If the Catalog supports the `Sort extension `__, the search request can specify the order in which the results should be sorted with the ``sortby`` parameter. The ``sortby`` parameter can either be a string (e.g., ``"-properties.datetime,+id,collection"``), a list of strings (e.g., ``["-properties.datetime", "+id", "+collection"]``), or a dictionary representing the POST JSON format of sortby. In the string and list formats, a ``-`` prefix means a descending sort and a ``+`` prefix or no prefix means an ascending sort. .. code-block:: python >>> from pystac_client import Client >>> results = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1').search( ... sortby="properties.datetime" ... ) >>> results = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1').search( ... sortby="-properties.datetime,+id,+collection" ... ) >>> results = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1').search( ... sortby=["-properties.datetime", "+id" , "+collection" ] ... ) >>> results = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1').search( ... sortby=[ {"direction": "desc", "field": "properties.datetime"}, {"direction": "asc", "field": "id"}, {"direction": "asc", "field": "collection"}, ] ... ) Loading data ++++++++++++ Once you've fetched your STAC :class:`Items` with ``pystac-client``, you now can work with the data referenced by your :class:`Assets`. This is out of scope for ``pystac-client``, but there's a wide variety of tools and options available, and the correct choices depend on your type of data, your environment, and the type of analysis you're doing. For simple workflows, it can be easiest to load data directly using `rasterio `_, `fiona `_, and similar tools. Here is a simple example using **rasterio** to display data from a raster file. .. code-block:: python >>> import rasterio.plot.show >>> with rasterio.open(item.assets["data"].href) as dataset: ... rasterio.plot.show(dataset) For larger sets of data and more complex workflows, a common tool for working with a large number of raster files is `xarray `_, which provides data structures for labelled multi-dimensional arrays. `stackstac `_ and `odc-stac `_ are two similar tools that can load asset data from :class:`Items` or an :class:`ItemCollection` into an **xarray**. Here's a simple example from **odc-stac**'s documentation: .. code-block:: python >>> catalog = pystac_client.Client.open(...) >>> query = catalog.search(...) >>> xx = odc.stac.load( ... query.get_items(), ... bands=["red", "green", "blue"], ... resolution=100, ... ) >>> xx.red.plot.imshow(col="time") See each packages's respective documentation for more examples and tutorials. .. _how_to: How To ++++++++++++ Get the latest dataset across multiple collections ----------------------------------------- When 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. .. code-block:: python from pystac_client import Client client = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1") items = [] for collection in ["nasadem", "esa-worldcover", "jrc-gsw"]: items.append( next( client.search( collections=[collection], max_items=1, sortby="-properties.datetime" ).items() ) ) The result will be a list containing the most recent item from each collection: >>> items [, , ] ================================================ FILE: pyproject.toml ================================================ [project] name = "pystac-client" description = "Python library for searching SpatioTemporal Asset Catalog (STAC) APIs." readme = "README.md" authors = [ { name = "Jon Duckworth", email = "duckontheweb@gmail.com" }, { name = "Matthew Hanson", email = "matt.a.hanson@gmail.com" }, ] maintainers = [{ name = "Pete Gadomski", email = "pete.gadomski@gmail.com" }] keywords = ["pystac", "imagery", "raster", "catalog", "STAC"] license = { text = "Apache-2.0" } classifiers = [ "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Operating System :: OS Independent", "Natural Language :: English", "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Topic :: Scientific/Engineering :: GIS", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", ] requires-python = ">=3.10" dependencies = [ "requests>=2.28.2", "pystac[validation]>=1.10.0", "python-dateutil>=2.8.2", ] dynamic = ["version"] [project.scripts] stac-client = "pystac_client.cli:cli" [dependency-groups] dev = [ "codespell~=2.4.0", "coverage~=7.2", "cql2>=0.3.7", "doc8~=2.0.0", "importlib-metadata~=9.0", "mypy~=1.2", "orjson~=3.8", "pre-commit~=4.0", "pytest-benchmark~=5.2.0", "pytest-console-scripts~=1.4.0", "pytest-cov~=7.0", "pytest-recording~=0.13", "pytest~=9.0", "recommonmark~=0.7.1", "requests-mock~=1.12", "ruff==0.15.12", "tomli~=2.0; python_version<'3.11'", "types-python-dateutil>=2.8.19,<2.10.0", "types-requests~=2.33.0", "urllib3>=2.4", ] docs = [ "Sphinx~=8.0", "boto3~=1.26", "cartopy~=0.21", "geojson~=3.2.0", "geopandas~=1.1.1", "geoviews~=1.9", "hvplot~=0.12.0", "ipykernel~=7.0", "ipython~=8.12", "jinja2>=3.0,<4.0", "matplotlib~=3.8", "myst-parser~=4.0", "nbsphinx~=0.9", "pydata-sphinx-theme~=0.13", "pygeoif~=1.0", "scipy~=1.10", "sphinxcontrib-fulltoc~=1.2", ] [project.urls] homepage = "https://github.com/stac-utils/pystac-client" documentation = "https://pystac-client.readthedocs.io" repository = "https://github.com/stac-utils/pystac-client.git" changelog = "https://github.com/stac-utils/pystac-client/blob/main/CHANGELOG.md" discussions = "https://github.com/radiantearth/stac-spec/discussions/categories/stac-software" [tool.setuptools.packages.find] include = ["pystac_client*"] exclude = ["tests*"] [tool.setuptools.dynamic] version = { attr = "pystac_client.version.__version__" } [tool.doc8] ignore-path = "docs/_build,docs/tutorials" max-line-length = 130 [tool.ruff] line-length = 88 [tool.ruff.lint] ignore = ["E722", "E731"] select = ["E", "F", "W", "I"] [tool.ruff.lint.per-file-ignores] "__init__.py" = ["F401"] "test_item_search.py" = ["E501"] [tool.pytest.ini_options] markers = "vcr: records network activity" addopts = "--benchmark-skip --block-network -Werror" filterwarnings = [ "ignore::ResourceWarning", "ignore::pytest.PytestUnraisableExceptionWarning", ] [tool.mypy] show_error_codes = true strict = true [[tool.mypy.overrides]] module = ["jinja2"] ignore_missing_imports = true [build-system] requires = ["setuptools>=61.0"] build-backend = "setuptools.build_meta" ================================================ FILE: pystac_client/__init__.py ================================================ __all__ = [ "Client", "CollectionClient", "CollectionSearch", "ConformanceClasses", "ItemSearch", "Modifiable", "__version__", ] from pystac_client._utils import Modifiable from pystac_client.client import Client from pystac_client.collection_client import CollectionClient from pystac_client.collection_search import CollectionSearch from pystac_client.conformance import ConformanceClasses from pystac_client.item_search import ItemSearch from pystac_client.version import __version__ ================================================ FILE: pystac_client/_utils.py ================================================ import urllib import warnings from collections.abc import Callable from typing import Any, Union import pystac from pystac_client.errors import IgnoredResultWarning Modifiable = Union[ pystac.Collection, pystac.Item, pystac.ItemCollection, dict[Any, Any] ] def call_modifier( modifier: Callable[[Modifiable], None] | None, obj: Modifiable ) -> None: """Calls the user's modifier and validates that the result is None.""" if modifier is None: return None result = modifier(obj) if result is not None and result is not obj: warnings.warn( f"modifier '{modifier}' returned a result that's being ignored. " "You should ensure that 'modifier' is operating in-place and use " "a function that returns 'None' or silence this warning.", IgnoredResultWarning, ) def urljoin(href: str, name: str) -> str: """Joins a path onto an existing href, respecting query strings, etc.""" url = urllib.parse.urlparse(href) path = url.path if not path.endswith("/"): path += "/" return urllib.parse.urlunparse(url._replace(path=path + name)) ================================================ FILE: pystac_client/cli.py ================================================ import argparse import json import logging import os import re import sys import warnings from typing import Any from pystac import STACError, STACTypeError from .client import Client from .conformance import ConformanceClasses from .item_search import ( OPS, BBoxLike, CollectionsLike, DatetimeLike, FieldsLike, FilterLangLike, FilterLike, IDsLike, IntersectsLike, QueryLike, SortbyLike, ) from .version import __version__ from .warnings import ( DoesNotConformTo, FallbackToPystac, MissingLink, NoConformsTo, PystacClientWarning, ) logger = logging.getLogger(__name__) def search( client: Client, matched: bool = False, save: str | None = None, *, method: str = "GET", max_items: int | None = None, limit: int | None = None, ids: IDsLike | None = None, collections: CollectionsLike | None = None, bbox: BBoxLike | None = None, intersects: IntersectsLike | None = None, datetime: DatetimeLike | None = None, query: QueryLike | None = None, filter: FilterLike | None = None, filter_lang: FilterLangLike | None = None, sortby: SortbyLike | None = None, fields: FieldsLike | None = None, ) -> int: """Main function for performing a search""" result = client.search( method=method, max_items=max_items, limit=limit, ids=ids, collections=collections, bbox=bbox, intersects=intersects, datetime=datetime, query=query, filter=filter, filter_lang=filter_lang, sortby=sortby, fields=fields, ) if matched: if (nmatched := result.matched()) is not None: print(f"{nmatched} items matched") else: raise KeyError("'matched' is not supported for this catalog") else: feature_collection = result.item_collection_as_dict() if save: with open(save, "w") as f: f.write(json.dumps(feature_collection)) else: print(json.dumps(feature_collection)) return 0 def collections( client: Client, save: str | None = None, matched: bool = False, *, max_collections: int | None = None, limit: int | None = None, bbox: BBoxLike | None = None, datetime: DatetimeLike | None = None, q: str | None = None, query: QueryLike | None = None, filter: FilterLike | None = None, filter_lang: FilterLangLike | None = None, sortby: SortbyLike | None = None, fields: FieldsLike | None = None, ) -> int: """Fetch collections from collections endpoint""" try: result = client.collection_search( max_collections=max_collections, limit=limit, bbox=bbox, datetime=datetime, q=q, query=query, filter=filter, filter_lang=filter_lang, sortby=sortby, fields=fields, ) if matched: if (nmatched := result.matched()) is not None: print(f"{nmatched} collections matched") else: raise KeyError("'matched' is not supported for this catalog") else: collections_dicts = list(result.collections_as_dicts()) if save: with open(save, "w") as f: f.write(json.dumps(collections_dicts)) else: print(json.dumps(collections_dicts)) except STACTypeError as e: raise STACError( f"The client at {client.self_href} is OK, but one or more of the " "collections is invalid." ) from e return 0 def add_warnings_behavior(parser: argparse.ArgumentParser) -> None: warnings_group = parser.add_argument_group("warnings behavior") warnings_group.add_argument( "--error", nargs="*", choices=[ "no-conforms-to", "does-not-conform-to", "missing-link", "fallback-to-pystac", ], help="Error on all stac-client warnings or specific warnings.", ) warnings_group.add_argument( "--ignore", nargs="*", choices=[ "no-conforms-to", "does-not-conform-to", "missing-link", "fallback-to-pystac", ], help="Ignore all stac-client warnings or specific warnings.", ) def set_warnings(error: list[str] | None, ignore: list[str] | None) -> None: # First set filters on the base class if ignore is not None and len(ignore) == 0: warnings.filterwarnings("ignore", category=PystacClientWarning) if error is not None and len(error) == 0: warnings.filterwarnings("error", category=PystacClientWarning) # Then set filters on any specific classes category_options = { "no-conforms-to": NoConformsTo, "does-not-conform-to": DoesNotConformTo, "missing-link": MissingLink, "fallback-to-pystac": FallbackToPystac, } if ignore is not None: for w in ignore: warnings.filterwarnings("ignore", category=category_options[w]) if error is not None: for w in error: warnings.filterwarnings("error", category=category_options[w]) def set_conforms_to( client: Client, clear: bool, remove: list[str] | None, add: list[str] | None ) -> None: """Alters conforms_to settings on client in-place""" if clear: client.clear_conforms_to() if remove is not None: for conformance_class in remove: client.remove_conforms_to(conformance_class) if add is not None: for conformance_class in add: client.add_conforms_to(conformance_class) def parse_args(args: list[str]) -> dict[str, Any]: desc = "STAC Client" dhf = argparse.ArgumentDefaultsHelpFormatter parser0 = argparse.ArgumentParser(description=desc) parser0.add_argument( "--version", help="Print version and exit", action="version", version=__version__, ) parent = argparse.ArgumentParser(add_help=False) parent.add_argument("url", help="Root Catalog URL") parent.add_argument( "--logging", default="INFO", help="DEBUG, INFO, WARN, ERROR, CRITICAL" ) parent.add_argument( "--headers", nargs="*", help="Additional request headers (KEY=VALUE pairs)", default=None, ) parent.add_argument( "--add-conforms-to", choices=[c.name for c in ConformanceClasses], nargs="*", help="Specify conformance classes to add to client", ) parent.add_argument( "--remove-conforms-to", choices=[c.name for c in ConformanceClasses], nargs="*", help="Specify conformance classes to remove from client", ) parent.add_argument( "--clear-conforms-to", default=False, action="store_true", ) subparsers = parser0.add_subparsers(dest="command") # collections command parser = subparsers.add_parser( "collections", help="Get all collections in this Catalog", parents=[parent], formatter_class=dhf, ) add_warnings_behavior(parser) collections_group = parser.add_argument_group("collection search options") collections_group.add_argument( "--bbox", help="Bounding box (min lon, min lat, max lon, max lat)", nargs=4 ) collections_group.add_argument( "--datetime", help="Single datetime or begin and end datetime (e.g., 2017-01-01/2017-02-15)", ) collections_group.add_argument("--q", help="Free-text search query") collections_group.add_argument( "--query", nargs="*", help=f"Query properties of form KEY=VALUE ({','.join(OPS)} supported)", ) collections_group.add_argument( "--filter", help="Filter on queryables using language specified in filter-lang parameter", ) collections_group.add_argument( "--filter-lang", help="Filter language used within the filter parameter", default="cql2-json", ) collections_group.add_argument("--sortby", help="Sort by fields", nargs="*") collections_group.add_argument( "--fields", help="Control what fields get returned", nargs="*" ) collections_group.add_argument("--limit", help="Page size limit", type=int) collections_group.add_argument( "--max-collections", dest="max_collections", help="Max collections to retrieve from search", type=int, ) output_group = parser.add_argument_group("output options") output_group.add_argument( "--save", help="Filename to save collections to", default=None ) output_group.add_argument( "--matched", help="Print number matched", default=False, action="store_true" ) # search command parser = subparsers.add_parser( "search", help="Perform new search of items", parents=[parent], formatter_class=dhf, ) search_group = parser.add_argument_group("search options") search_group.add_argument( "-c", "--collections", help="One or more collection IDs", nargs="*" ) search_group.add_argument( "--ids", help="One or more Item IDs (ignores other parameters)", nargs="*" ) search_group.add_argument( "--bbox", help="Bounding box (min lon, min lat, max lon, max lat)", nargs=4 ) search_group.add_argument( "--intersects", help="GeoJSON Feature or geometry (file or string)" ) search_group.add_argument( "--datetime", help="Single datetime or begin and end datetime (e.g., 2017-01-01/2017-02-15)", ) search_group.add_argument( "--query", nargs="*", help=f"Query properties of form KEY=VALUE ({','.join(OPS)} supported)", ) search_group.add_argument( "--filter", help="Filter on queryables using language specified in filter-lang parameter", ) search_group.add_argument( "--filter-lang", help="Filter language used within the filter parameter", default="cql2-json", ) search_group.add_argument("--sortby", help="Sort by fields", nargs="*") search_group.add_argument( "--fields", help="Control what fields get returned", nargs="*" ) search_group.add_argument("--limit", help="Page size limit", type=int) search_group.add_argument( "--max-items", dest="max_items", help="Max items to retrieve from search", type=int, ) search_group.add_argument("--method", help="GET or POST", default="POST") add_warnings_behavior(parser) output_group = parser.add_argument_group("output options") output_group.add_argument( "--matched", help="Print number matched", default=False, action="store_true" ) output_group.add_argument( "--save", help="Filename to save Item collection to", default=None ) parsed_args = { k: v for k, v in vars(parser0.parse_args(args)).items() if v is not None } if "command" not in parsed_args: parser0.print_usage() return {} # if intersects is JSON file, read it in if "intersects" in parsed_args: if os.path.exists(parsed_args["intersects"]): with open(parsed_args["intersects"]) as f: data = json.loads(f.read()) if data["type"] == "Feature": parsed_args["intersects"] = data["geometry"] elif data["type"] == "FeatureCollection": logger.warning( "When the input to intersects is a FeatureCollection, " "only the first feature geometry is used." ) parsed_args["intersects"] = data["features"][0]["geometry"] else: parsed_args["intersects"] = data # if headers provided, parse it if "headers" in parsed_args: new_headers = {} splitter = re.compile("^([^=]+)=(.+)$") for head in parsed_args["headers"]: match = splitter.match(head) if match: new_headers[match.group(1)] = match.group(2) else: logger.warning(f"Unable to parse header {head}") parsed_args["headers"] = new_headers if "filter" in parsed_args: if "json" in parsed_args["filter_lang"]: parsed_args["filter"] = json.loads(parsed_args["filter"]) return parsed_args def cli() -> int: args = parse_args(sys.argv[1:]) if not args: return 1 loglevel = args.pop("logging") if args.get("save", False) or args.get("matched", False): logging.basicConfig(level=loglevel) # quiet loggers logging.getLogger("urllib3").propagate = False cmd = args.pop("command") try: url = args.pop("url") headers = args.pop("headers", {}) set_warnings(args.pop("error", None), args.pop("ignore", None)) client = Client.open(url, headers=headers) set_conforms_to( client, args.pop("clear_conforms_to"), args.pop("remove_conforms_to", None), args.pop("add_conforms_to", None), ) if cmd == "search": return search(client, **args) elif cmd == "collections": return collections(client, **args) else: logger.error( f"Command '{cmd}' is not a valid command. " "must be 'search' or 'collections'", ) return 1 except Exception as e: logger.error(e, exc_info=True) return 1 if __name__ == "__main__": return_code = cli() if return_code and return_code != 0: sys.exit(return_code) ================================================ FILE: pystac_client/client.py ================================================ import re import warnings from collections.abc import Callable, Iterator from functools import lru_cache from typing import ( TYPE_CHECKING, Any, cast, ) import pystac import pystac.utils import pystac.validation from pystac import CatalogType, Collection from pystac.layout import APILayoutStrategy, HrefLayoutStrategy from requests import Request from pystac_client._utils import Modifiable, call_modifier, urljoin from pystac_client.collection_client import CollectionClient from pystac_client.collection_search import CollectionSearch from pystac_client.conformance import ConformanceClasses from pystac_client.errors import ClientTypeError from pystac_client.exceptions import APIError from pystac_client.item_search import ( BBoxLike, CollectionsLike, DatetimeLike, FieldsLike, FilterLangLike, FilterLike, IDsLike, IntersectsLike, ItemSearch, QueryLike, SortbyLike, ) from pystac_client.mixins import QUERYABLES_ENDPOINT, QueryablesMixin from pystac_client.stac_api_io import StacApiIO, Timeout from pystac_client.warnings import ( DoesNotConformTo, FallbackToPystac, NoConformsTo, PystacClientWarning, ) if TYPE_CHECKING: from pystac.item import Item as Item_Type class Client(pystac.Catalog, QueryablesMixin): """A Client for interacting with the root of a STAC Catalog or API Instances of the ``Client`` class inherit from :class:`pystac.Catalog` and provide a convenient way of interacting with STAC Catalogs OR STAC APIs that conform to the `STAC API spec `_. In addition to being a valid `STAC Catalog `_ APIs that have a ``"conformsTo"`` indicate that it supports additional functionality on top of a normal STAC Catalog, such as searching items (e.g., /search endpoint). """ _stac_io: StacApiIO | None _fallback_strategy: HrefLayoutStrategy = APILayoutStrategy() def __init__( self, id: str, description: str, title: str | None = None, stac_extensions: list[str] | None = None, extra_fields: dict[str, Any] | None = None, href: str | None = None, catalog_type: CatalogType = CatalogType.ABSOLUTE_PUBLISHED, strategy: HrefLayoutStrategy | None = None, *, modifier: Callable[[Modifiable], None] | None = None, **kwargs: dict[str, Any], ): super().__init__( id, description, title=title, stac_extensions=stac_extensions, extra_fields=extra_fields, href=href, catalog_type=catalog_type, strategy=strategy, **kwargs, ) self.modifier = modifier def __repr__(self) -> str: return f"" @classmethod def open( cls, url: str, headers: dict[str, str] | None = None, parameters: dict[str, Any] | None = None, ignore_conformance: bool | None = None, modifier: Callable[[Modifiable], None] | None = None, request_modifier: Callable[[Request], Request | None] | None = None, stac_io: StacApiIO | None = None, timeout: Timeout | None = None, ) -> "Client": """Opens a STAC Catalog or API This function will read the root catalog of a STAC Catalog or API Args: url : The URL of a STAC Catalog. headers : A dictionary of additional headers to use in all requests made to any part of this Catalog/API. parameters: Optional dictionary of query string parameters to include in all requests. ignore_conformance (DEPRECATED) : Ignore any advertised Conformance Classes in this Catalog/API. This means that functions will skip checking conformance, and may throw an unknown error if that feature is not supported, rather than a :class:`NotImplementedError`. .. deprecated:: 0.7.0 Conformance can be altered rather than ignored using methods like :meth:`clear_conforms_to` and :meth:`add_conforms_to` modifier : A callable that modifies the children collection and items returned by this Client. This can be useful for injecting authentication parameters into child assets to access data from non-public sources. The callable should expect a single argument, which will be one of the following types: * :class:`pystac.Collection` * :class:`pystac.Item` * :class:`pystac.ItemCollection` * A STAC item-like :class:`dict` * A STAC collection-like :class:`dict` The callable should mutate the argument in place and return ``None``. ``modifier`` propagates recursively to children of this Client. After getting a child collection with, e.g. :meth:`Client.get_collection`, the child items of that collection will still be signed with ``modifier``. request_modifier: A callable that either modifies a `Request` instance or returns a new one. This can be useful for injecting Authentication headers and/or signing fully-formed requests (e.g. signing requests using AWS SigV4). The callable should expect a single argument, which will be an instance of :class:`requests.Request`. If the callable returns a `requests.Request`, that will be used. Alternately, the callable may simply modify the provided request object and return `None`. stac_io: A `StacApiIO` object to use for I/O requests. Generally, leave this to the default. However in cases where customized I/O processing is required, a custom instance can be provided here. timeout: Optional float or (float, float) tuple following the semantics defined by `Requests `__. Return: catalog : A :class:`Client` instance for this Catalog/API """ client: Client = cls.from_file( url, headers=headers, parameters=parameters, modifier=modifier, request_modifier=request_modifier, stac_io=stac_io, timeout=timeout, ) if ignore_conformance is not None: warnings.warn( ( "The `ignore_conformance` option is deprecated and will be " "removed in the next major release. Instead use `set_conforms_to` " "or `add_conforms_to` to control behavior." ), FutureWarning, ) if not client.has_conforms_to(): warnings.warn(NoConformsTo()) return client @classmethod def from_file( # type: ignore cls, href: str, stac_io: StacApiIO | None = None, headers: dict[str, str] | None = None, parameters: dict[str, Any] | None = None, modifier: Callable[[Modifiable], None] | None = None, request_modifier: Callable[[Request], Request | None] | None = None, timeout: Timeout | None = None, ) -> "Client": """Open a STAC Catalog/API Returns: Client: A Client (PySTAC Catalog) of the root Catalog for this Catalog/API """ if stac_io is None: stac_io = StacApiIO( headers=headers, parameters=parameters, request_modifier=request_modifier, timeout=timeout, ) else: stac_io.update( headers=headers, parameters=parameters, request_modifier=request_modifier, timeout=timeout, ) client: Client = super().from_file(href, stac_io) client.modifier = modifier return client def has_conforms_to(self) -> bool: """Whether server contains list of ``"conformsTo"`` URIs""" return "conformsTo" in self.extra_fields def get_conforms_to(self) -> list[str]: """List of ``"conformsTo"`` URIs Return: List[str]: List of URIs that the server conforms to """ return cast(list[str], self.extra_fields.get("conformsTo", []).copy()) def set_conforms_to(self, conformance_uris: list[str]) -> None: """Set list of ``"conformsTo"`` URIs Args: conformance_uris : URIs indicating what the server conforms to """ self.extra_fields["conformsTo"] = conformance_uris def clear_conforms_to(self) -> None: """Clear list of ``"conformsTo"`` urls Removes the entire list, so :py:meth:`has_conforms_to` will return False after using this method. """ self.extra_fields.pop("conformsTo", None) def add_conforms_to(self, name: str) -> None: """Add ``"conformsTo"`` by name. Args: name : name of :py:class:`ConformanceClasses` keys to add. """ conformance_class = ConformanceClasses.get_by_name(name) if not self.conforms_to(conformance_class): self.set_conforms_to([*self.get_conforms_to(), conformance_class.valid_uri]) def remove_conforms_to(self, name: str) -> None: """Remove ``"conformsTo"`` by name. Args: name : name of :py:class:`ConformanceClasses` keys to remove. """ conformance_class = ConformanceClasses.get_by_name(name) self.set_conforms_to( [ uri for uri in self.get_conforms_to() if not re.match(conformance_class.pattern, uri) ] ) def conforms_to(self, conformance_class: ConformanceClasses | str) -> bool: """Checks whether the API conforms to the given standard. This method only checks against the ``"conformsTo"`` property from the API landing page and does not make any additional calls to a ``/conformance`` endpoint even if the API provides such an endpoint. Args: name : name of :py:class:`ConformanceClasses` keys to check conformance against. Return: bool: Indicates if the API conforms to the given spec or URI. """ if isinstance(conformance_class, str): conformance_class = ConformanceClasses.get_by_name(conformance_class) return any( re.match(conformance_class.pattern, uri) for uri in self.get_conforms_to() ) @classmethod def from_dict( cls, d: dict[str, Any], href: str | None = None, root: pystac.Catalog | None = None, migrate: bool = False, preserve_dict: bool = True, modifier: Callable[[Modifiable], None] | None = None, ) -> "Client": try: # this will return a Client because we have used a StacApiIO instance result = super().from_dict( d=d, href=href, root=root, migrate=migrate, preserve_dict=preserve_dict ) except pystac.STACTypeError: raise ClientTypeError( f"Could not open Client (href={href}), " f"expected type=Catalog, found type={d.get('type', None)}" ) result.modifier = modifier return result def _supports_collections(self) -> bool: return self.conforms_to(ConformanceClasses.COLLECTIONS) or self.conforms_to( ConformanceClasses.FEATURES ) def _warn_about_fallback(self, *args: str) -> None: if self.has_conforms_to(): warnings.warn(DoesNotConformTo(*args), stacklevel=2) warnings.warn(FallbackToPystac(), stacklevel=2) def get_merged_queryables(self, collections: list[str]) -> dict[str, Any]: """Return the set of queryables in common to the specified collections. Queryables from multiple collections are unioned together, except in the case when the same queryable key has a different definition, in which case that key is dropped. Output is a dictionary that can be used in ``jsonshema.validate`` Args: collections List[str]: The IDs of the collections to inspect. Return: Dict[str, Any]: Dictionary containing queryable fields """ if not collections: raise ValueError("cannot get_merged_queryables from empty Iterable") if not self.conforms_to(ConformanceClasses.FILTER): raise DoesNotConformTo(ConformanceClasses.FILTER.name) response = self.get_queryables_from( self._get_collection_queryables_href(collections[0]) ) response.pop("$id") addl_props = response.get("additionalProperties", False) for collection in collections[1:]: resp = self.get_queryables_from( self._get_collection_queryables_href(collection) ) # additionalProperties is false if any collection doesn't support additional # properties addl_props &= resp.get("additionalProperties", False) # drop queryables if their keys match, but the descriptions differ for k in set(resp["properties"]).intersection(response["properties"]): if resp["properties"][k] != response["properties"][k]: resp["properties"].pop(k) response["properties"].pop(k) response["properties"].update(resp["properties"]) return response @lru_cache def get_collection( self, collection_id: str ) -> Collection | CollectionClient | None: """Get a single collection from this Catalog/API Args: collection_id: The Collection ID to get Returns: Collection | CollectionClient | None: A STAC Collection or None if it does not exist. """ collection: Collection | CollectionClient | None = None if not collection_id: raise ValueError("A `collection_id` must be provided.") if self._supports_collections(): assert self._stac_io is not None url = self._collections_href(collection_id) try: collection = CollectionClient.from_dict( self._stac_io.read_json(url), root=self, modifier=self.modifier, ) except APIError as err: if getattr(err, "status_code", None) and err.status_code == 404: return None else: raise err else: self._warn_about_fallback("COLLECTIONS", "FEATURES") for collection in super().get_collections(): if collection.id == collection_id: break if collection: call_modifier(self.modifier, collection) return collection def get_collections(self) -> Iterator[Collection]: """Get Collections in this Catalog Gets the collections from the /collections endpoint if supported, otherwise fall back to Catalog behavior of following child links Return: Iterator[Union[Collection, CollectionClient]]: Collections in Catalog/API """ collection: Collection | CollectionClient if self._supports_collections(): assert self._stac_io is not None url = self._collections_href() for page in self._stac_io.get_pages(url): if "collections" not in page: raise APIError("Invalid response from /collections") for col in page["collections"]: collection = CollectionClient.from_dict( col, root=self, modifier=self.modifier ) call_modifier(self.modifier, collection) yield collection else: self._warn_about_fallback("COLLECTIONS", "FEATURES") for collection in super().get_collections(): call_modifier(self.modifier, collection) yield collection def get_items( self, *ids: str, recursive: bool | None = None ) -> Iterator["Item_Type"]: """Return all items of this catalog. Args: ids: Zero or more item ids to find. recursive: If this client conforms to the ITEM_SEARCH conformance class, this is unused and this will always yield items recursively. Otherwise, this will only return items recursively if True or None. Return: Iterator[Item]: Iterator of items whose parent is this catalog. """ if self.conforms_to(ConformanceClasses.ITEM_SEARCH): if recursive is False: warnings.warn( "Getting items recursively using the /search endpoint " "(the recursive argument is being ignored).", PystacClientWarning, ) search = self.search(ids=ids) yield from search.items() else: self._warn_about_fallback("ITEM_SEARCH") for item in super().get_items( *ids, recursive=recursive is None or recursive ): call_modifier(self.modifier, item) yield item def get_all_items(self) -> Iterator["Item_Type"]: """Get all items from this catalog and all subcatalogs. Will traverse any subcatalogs recursively, or use the /search endpoint if supported Returns: Iterator[Item]: All items that belong to this catalog, and all catalogs or collections connected to this catalog through child links. """ yield from self.get_items() def search( self, *, method: str | None = "POST", max_items: int | None = None, limit: int | None = None, ids: IDsLike | None = None, collections: CollectionsLike | None = None, bbox: BBoxLike | None = None, intersects: IntersectsLike | None = None, datetime: DatetimeLike | None = None, query: QueryLike | None = None, filter: FilterLike | None = None, filter_lang: FilterLangLike | None = None, sortby: SortbyLike | None = None, fields: FieldsLike | None = None, ) -> ItemSearch: """Query the ``/search`` endpoint using the given parameters. This method returns an :class:`~pystac_client.ItemSearch` instance. See that class's documentation for details on how to get the number of matches and iterate over results. The ``url``, ``stac_io``, and ``client`` keywords are supplied by this Client instance. .. warning:: This method is only implemented if the API conforms to the `STAC API - Item Search `__ spec *and* contains a link with a ``"rel"`` type of ``"search"`` in its root catalog. If the API does not meet either of these criteria, this method will raise a :exc:`NotImplementedError`. Args: method : The HTTP method to use when making a request to the service. This must be either ``"GET"``, ``"POST"``, or ``None``. If ``None``, this will default to ``"POST"``. If a ``"POST"`` request receives a ``405`` status for the response, it will automatically retry with ``"GET"`` for all subsequent requests. max_items : The maximum number of items to return from the search, even if there are more matching results. This client to limit the total number of Items returned from the :meth:`items`, :meth:`item_collections`, and :meth:`items_as_dicts methods`. The client will continue to request pages of items until the number of max items is reached. Setting this to ``None`` will allow iteration over a possibly very large number of results. limit: A recommendation to the service as to the number of items to return *per page* of results. Defaults to 100. ids: List of one or more Item ids to filter on. collections: List of one or more Collection IDs or :class:`pystac.Collection` instances. Only Items in one of the provided Collections will be searched bbox: A list, tuple, or iterator representing a bounding box of 2D or 3D coordinates. Results will be filtered to only those intersecting the bounding box. intersects: A string or dictionary representing a GeoJSON geometry, or an object that implements a ``__geo_interface__`` property, as supported by several libraries including Shapely, ArcPy, PySAL, and geojson. Results filtered to only those intersecting the geometry. datetime: Either a single datetime or datetime range used to filter results. You may express a single datetime using a :class:`datetime.datetime` instance, a `RFC 3339-compliant `__ timestamp, or a simple date string (see below). Instances of :class:`datetime.datetime` may be either timezone aware or unaware. Timezone aware instances will be converted to a UTC timestamp before being passed to the endpoint. Timezone unaware instances are assumed to represent UTC timestamps. You may represent a datetime range using a ``"/"`` separated string as described in the spec, or a list, tuple, or iterator of 2 timestamps or datetime instances. For open-ended ranges, use either ``".."`` (``'2020-01-01:00:00:00Z/..'``, ``['2020-01-01:00:00:00Z', '..']``) or a value of ``None`` (``['2020-01-01:00:00:00Z', None]``). If using a simple date string, the datetime can be specified in ``YYYY-mm-dd`` format, optionally truncating to ``YYYY-mm`` or just ``YYYY``. Simple date strings will be expanded to include the entire time period, for example: - ``2017`` expands to ``2017-01-01T00:00:00Z/2017-12-31T23:59:59Z`` - ``2017-06`` expands to ``2017-06-01T00:00:00Z/2017-06-30T23:59:59Z`` - ``2017-06-10`` expands to ``2017-06-10T00:00:00Z/2017-06-10T23:59:59Z`` If used in a range, the end of the range expands to the end of that day/month/year, for example: - ``2017/2018`` expands to ``2017-01-01T00:00:00Z/2018-12-31T23:59:59Z`` - ``2017-06/2017-07`` expands to ``2017-06-01T00:00:00Z/2017-07-31T23:59:59Z`` - ``2017-06-10/2017-06-11`` expands to ``2017-06-10T00:00:00Z/2017-06-11T23:59:59Z`` query: List or JSON of query parameters as per the STAC API `query` extension filter: JSON of query parameters as per the STAC API `filter` extension filter_lang: Language variant used in the filter body. If `filter` is a dictionary or not provided, defaults to 'cql2-json'. If `filter` is a string, defaults to `cql2-text`. sortby: A single field or list of fields to sort the response by fields: A list of fields to include in the response. Note this may result in invalid STAC objects, as they may not have required fields. Use `items_as_dicts` to avoid object unmarshalling errors. Returns: search : An ItemSearch instance that can be used to iterate through Items. Raises: NotImplementedError: If the API does not conform to the `Item Search spec `__ or does not have a link with a ``"rel"`` type of ``"search"``. """ if not self.conforms_to(ConformanceClasses.ITEM_SEARCH): raise DoesNotConformTo( "ITEM_SEARCH", "There is no fallback option available for search." ) return ItemSearch( url=self._search_href(), method=method, max_items=max_items, client=self, limit=limit, ids=ids, collections=collections, bbox=bbox, intersects=intersects, datetime=datetime, query=query, filter=filter, filter_lang=filter_lang, sortby=sortby, fields=fields, modifier=self.modifier, ) def collection_search( self, *, max_collections: int | None = None, limit: int | None = None, bbox: BBoxLike | None = None, datetime: DatetimeLike | None = None, q: str | None = None, query: QueryLike | None = None, filter: FilterLike | None = None, filter_lang: FilterLangLike | None = None, sortby: SortbyLike | None = None, fields: FieldsLike | None = None, ) -> CollectionSearch: """Query the ``/collections`` endpoint using the given parameters. This method returns an :class:`~pystac_client.CollectionSearch` instance. See that class's documentation for details on how to get the number of matches and iterate over results. The ``url``, `stac_io``, and ``client`` keywords are supplied by this Client instance. .. warning:: This method is fully implemented if the API conforms to the `STAC API - Collection Search Extension `__ spec. If the API does not conform to the Collection Search Extension but conforms to `STAC API - Collections `__ (has a ``"rel"`` type of ``"data"`` in its root catalog), a limited client-side collection filtering process will be used. If the API does not meet either of these criteria, this method will raise a :exc:`NotImplementedError`. Client-side filtering will only use the ``bbox``, ``datetime``, and ``q`` (free-text search) parameters. Args: max_collections : The maximum number of collections to return from the search, even if there are more matching results. This client to limit the total number of Collections returned from the :meth:`collections`, :meth:`collection_list`, and :meth:`collections_as_dicts methods`. The client will continue to request pages of collections until the number of max collections is reached. Setting this to ``None`` will allow iteration over a possibly very large number of results. limit: A recommendation to the service as to the number of items to return *per page* of results. Defaults to 100. bbox: A list, tuple, or iterator representing a bounding box of 2D or 3D coordinates. Results will be filtered to only those intersecting the bounding box. datetime: Either a single datetime or datetime range used to filter results. You may express a single datetime using a :class:`datetime.datetime` instance, a `RFC 3339-compliant `__ timestamp, or a simple date string (see below). Instances of :class:`datetime.datetime` may be either timezone aware or unaware. Timezone aware instances will be converted to a UTC timestamp before being passed to the endpoint. Timezone unaware instances are assumed to represent UTC timestamps. You may represent a datetime range using a ``"/"`` separated string as described in the spec, or a list, tuple, or iterator of 2 timestamps or datetime instances. For open-ended ranges, use either ``".."`` (``'2020-01-01:00:00:00Z/..'``, ``['2020-01-01:00:00:00Z', '..']``) or a value of ``None`` (``['2020-01-01:00:00:00Z', None]``). If using a simple date string, the datetime can be specified in ``YYYY-mm-dd`` format, optionally truncating to ``YYYY-mm`` or just ``YYYY``. Simple date strings will be expanded to include the entire time period, for example: - ``2017`` expands to ``2017-01-01T00:00:00Z/2017-12-31T23:59:59Z`` - ``2017-06`` expands to ``2017-06-01T00:00:00Z/2017-06-30T23:59:59Z`` - ``2017-06-10`` expands to ``2017-06-10T00:00:00Z/2017-06-10T23:59:59Z`` If used in a range, the end of the range expands to the end of that day/month/year, for example: - ``2017/2018`` expands to ``2017-01-01T00:00:00Z/2018-12-31T23:59:59Z`` - ``2017-06/2017-07`` expands to ``2017-06-01T00:00:00Z/2017-07-31T23:59:59Z`` - ``2017-06-10/2017-06-11`` expands to ``2017-06-10T00:00:00Z/2017-06-11T23:59:59Z`` q: Free-text search query. See the `STAC API - Free Text Extension Spec `__ for details. query: List or JSON of query parameters as per the STAC API `query` extension filter: JSON of query parameters as per the STAC API `filter` extension filter_lang: Language variant used in the filter body. If `filter` is a dictionary or not provided, defaults to 'cql2-json'. If `filter` is a string, defaults to `cql2-text`. sortby: A single field or list of fields to sort the response by fields: A list of fields to include in the response. Note this may result in invalid STAC objects, as they may not have required fields. Use `items_as_dicts` to avoid object unmarshalling errors. Returns: collection_search : A CollectionSearch instance that can be used to iterate through Collections. Raises: NotImplementedError: If the API does not conform to either the `STAC API - Collection Search spec `__ or the `STAC API - Collections spec `__. """ if not ( self.conforms_to(ConformanceClasses.COLLECTION_SEARCH) or self.conforms_to(ConformanceClasses.COLLECTIONS) ) and any([bbox, datetime, q, query, filter, sortby, fields]): raise DoesNotConformTo( "COLLECTION_SEARCH or COLLECTIONS", "there is no fallback option available for search.", ) return CollectionSearch( url=self._collections_href(), client=self, max_collections=max_collections, limit=limit, bbox=bbox, datetime=datetime, q=q, query=query, filter=filter, filter_lang=filter_lang, sortby=sortby, fields=fields, modifier=self.modifier, ) def get_search_link(self) -> pystac.Link | None: """Returns this client's search link. Searches for a link with rel="search" and either a GEOJSON or JSON media type. Returns: Optional[pystac.Link]: The search link, or None if there is not one found. """ return next( ( link for link in self.links if link.rel == "search" and ( link.media_type == pystac.MediaType.GEOJSON or link.media_type == pystac.MediaType.JSON ) ), None, ) def _search_href(self) -> str: search_link = self.get_search_link() href = self._get_href("search", search_link, "search") return href def _collections_href(self, collection_id: str | None = None) -> str: data_link = self.get_single_link("data") href = self._get_href("data", data_link, "collections") if collection_id is not None: return urljoin(href, collection_id) return href def _get_collection_queryables_href(self, collection_id: str | None = None) -> str: href = self._collections_href(collection_id) return urljoin(href, QUERYABLES_ENDPOINT) ================================================ FILE: pystac_client/collection_client.py ================================================ from __future__ import annotations import warnings from collections.abc import Callable, Iterator from typing import ( TYPE_CHECKING, Any, Optional, cast, ) import pystac from pystac.layout import APILayoutStrategy, HrefLayoutStrategy from pystac_client._utils import Modifiable, call_modifier from pystac_client.conformance import ConformanceClasses from pystac_client.exceptions import APIError from pystac_client.item_search import ItemSearch from pystac_client.mixins import QueryablesMixin from pystac_client.stac_api_io import StacApiIO from pystac_client.warnings import FallbackToPystac if TYPE_CHECKING: from pystac.item import Item as Item_Type from pystac_client import Client class CollectionClient(pystac.Collection, QueryablesMixin): modifier: Callable[[Modifiable], None] _stac_io: StacApiIO _fallback_strategy: HrefLayoutStrategy = APILayoutStrategy() def __init__( self, id: str, description: str, extent: pystac.Extent, title: str | None = None, stac_extensions: list[str] | None = None, href: str | None = None, extra_fields: dict[str, Any] | None = None, catalog_type: pystac.CatalogType | None = None, license: str = "proprietary", keywords: list[str] | None = None, providers: list[pystac.Provider] | None = None, summaries: pystac.Summaries | None = None, assets: dict[str, pystac.Asset] | None = None, strategy: HrefLayoutStrategy | None = None, *, modifier: Callable[[Modifiable], None] | None = None, **kwargs: dict[str, Any], ): super().__init__( id, description, extent, title, stac_extensions, href, extra_fields, catalog_type, license, keywords, providers, summaries, assets, strategy, **kwargs, ) # error: Cannot assign to a method [assignment] # https://github.com/python/mypy/issues/2427 setattr(self, "modifier", modifier) @classmethod def from_dict( cls, d: dict[str, Any], href: str | None = None, root: pystac.Catalog | Client | None = None, migrate: bool = False, preserve_dict: bool = True, modifier: Callable[[Modifiable], None] | None = None, ) -> CollectionClient: result = super().from_dict(d, href, root, migrate, preserve_dict) # error: Cannot assign to a method [assignment] # https://github.com/python/mypy/issues/2427 setattr(result, "modifier", modifier) return result def __repr__(self) -> str: return f"" def set_root(self, root: pystac.Catalog | Client | None) -> None: # hook in to set_root and use it for setting _stac_io super().set_root(root=root) if root is None: raise ValueError("`CollectionClient.root` must be set") elif root._stac_io is not None and isinstance(root._stac_io, StacApiIO): self._stac_io = root._stac_io else: raise ValueError("`CollectionClient.root` must be a valid `Client` object") def get_root(self) -> Client: from pystac_client.client import Client root = super().get_root() if root is None or not isinstance(root, Client): raise ValueError( "`CollectionClient.root` is not have a valid `Client` object." ) return root def conforms_to(self, conformance_class: ConformanceClasses | str) -> bool: root = self.get_root() return root.conforms_to(conformance_class) def get_items(self, *ids: str, recursive: bool = False) -> Iterator[Item_Type]: """Return all items in this Collection or specific items. If the Collection contains a link of with a `rel` value of `items`, that link will be used to iterate through items. Otherwise, the default PySTAC behavior is assumed. Args: ids: Items ids to retrieve recursive: If True, search every child collection as well as this one. Return: Iterator[Item]: Iterator of items whose parent is this catalog. """ if recursive is True: yield from super().get_items(*ids, recursive=True) else: root = self.get_root() if root.conforms_to(ConformanceClasses.ITEM_SEARCH): url = root._search_href() if ids else self._items_href() search = ItemSearch( url=url, method="GET", client=root, ids=ids, collections=[self.id], modifier=self.modifier, ) yield from search.items() else: root._warn_about_fallback("ITEM_SEARCH") for item in super().get_items(*ids): call_modifier(self.modifier, item) yield item def get_item(self, id: str, recursive: bool = False) -> Item_Type | None: """Returns an item with a given ID. If the collection conforms to [ogcapi-features](https://github.com/radiantearth/stac-api-spec/blob/738f4837ac6bea041dc226219e6d13b2c577fb19/ogcapi-features/README.md), this will use the `/collections/{collectionId}/items/{featureId}`. If not, and the collection conforms to [item search](https://github.com/radiantearth/stac-api-spec/blob/2d3c0cf644af9976eecbf32aec77b9a137268e12/item-search/README.md), this will use `/search?ids={featureId}&collections={collectionId}`. Otherwise, the default PySTAC behavior is used. Args: id : The ID of the item to find. recursive : If True, search this catalog and all children for the item; otherwise, only search the items of this catalog. Defaults to False. Return: Item or None: The item with the given ID, or None if not found. """ if not recursive: root = self.get_root() if root.conforms_to(ConformanceClasses.FEATURES) and self._stac_io: url = f"{self._items_href().rstrip('/')}/{id}" try: obj = self._stac_io.read_stac_object(url, root=self) item = cast(Optional[pystac.Item], obj) except APIError as err: if getattr(err, "status_code", None) and err.status_code == 404: return None else: raise err elif root.conforms_to(ConformanceClasses.ITEM_SEARCH) and self._stac_io: item_search = ItemSearch( url=root._search_href(), method="GET", client=root, ids=[id], collections=[self.id], modifier=self.modifier, ) item = next(item_search.items(), None) else: root._warn_about_fallback("FEATURES", "ITEM_SEARCH") item = super().get_item(id, recursive=False) else: warnings.warn(FallbackToPystac()) item = super().get_item(id, recursive=True) if item: call_modifier(self.modifier, item) return item def _items_href(self) -> str: link = self.get_single_link("items") href = self._get_href("items", link, "items") return href ================================================ FILE: pystac_client/collection_search.py ================================================ import warnings from collections.abc import Callable, Iterator from datetime import datetime, timezone from functools import lru_cache from typing import ( TYPE_CHECKING, Any, Optional, ) from pystac import Collection, Extent from pystac_client._utils import Modifiable, call_modifier from pystac_client.conformance import ConformanceClasses from pystac_client.free_text import sqlite_text_search from pystac_client.item_search import ( BaseSearch, BBox, BBoxLike, Datetime, DatetimeLike, FieldsLike, FilterLangLike, FilterLike, QueryLike, SortbyLike, ) from pystac_client.stac_api_io import StacApiIO from pystac_client.warnings import DoesNotConformTo, PystacClientWarning if TYPE_CHECKING: from pystac_client import client as _client TemporalInterval = tuple[Optional[datetime], Optional[datetime]] def temporal_intervals_overlap( interval1: TemporalInterval, interval2: TemporalInterval, ) -> bool: start1, end1 = interval1 start2, end2 = interval2 dtmin = datetime.min.replace(tzinfo=timezone.utc) dtmax = datetime.max.replace(tzinfo=timezone.utc) return (start2 or dtmin) <= (end1 or dtmax) and (start1 or dtmin) <= (end2 or dtmax) def bboxes_overlap(bbox1: BBox, bbox2: BBox) -> bool: xmin1, ymin1, xmax1, ymax1 = bbox1 xmin2, ymin2, xmax2, ymax2 = bbox2 return xmin1 <= xmax2 and xmin2 <= xmax1 and ymin1 <= ymax2 and ymin2 <= ymax1 def _extent_matches( extent: Extent, bbox: BBox | None = None, temporal_interval_str: Datetime | None = None, ) -> tuple[bool, bool]: bbox_overlaps = not bbox or ( any( bboxes_overlap(bbox, tuple(collection_bbox)) for collection_bbox in extent.spatial.bboxes ) ) # check for overlap between the provided temporal interval and the collection's # temporal extent collection_temporal_extent = extent.temporal # process the user-provided temporal interval search_temporal_interval = ( temporal_interval_str.split("/") if temporal_interval_str else [] ) # replace .. in open intervals with actual strings if search_temporal_interval: if search_temporal_interval[0] == "..": search_temporal_interval[0] = datetime.min.replace( tzinfo=timezone.utc ).isoformat() if search_temporal_interval[1] == "..": search_temporal_interval[1] = datetime.max.replace( tzinfo=timezone.utc ).isoformat() datetime_overlaps = not temporal_interval_str or ( any( temporal_intervals_overlap( ( datetime.fromisoformat( search_temporal_interval[0].replace("Z", "+00:00") ), datetime.fromisoformat( search_temporal_interval[1].replace("Z", "+00:00") ), ), ( collection_temporal_interval[0], collection_temporal_interval[1], ), ) for collection_temporal_interval in collection_temporal_extent.intervals ) ) return bbox_overlaps, datetime_overlaps def collection_matches( collection_dict: dict[str, Any], bbox: BBox | None = None, temporal_interval_str: Datetime | None = None, q: str | None = None, ) -> bool: # check for overlap between provided bbox and the collection's spatial extent try: extent = Extent.from_dict(collection_dict.get("extent", {})) except Exception: warnings.warn( f"Unable to parse extent from collection={collection_dict.get('id', None)}", PystacClientWarning, ) bbox_overlaps = True datetime_overlaps = True else: bbox_overlaps, datetime_overlaps = _extent_matches( extent, bbox, temporal_interval_str ) # check for overlap between the provided free-text search query (q) and the # collection's title, description, and keywords text_fields: dict[str, str] = { key: text for key, text in collection_dict.items() if text and key in ["title", "description", "keywords"] } if text_fields.get("keywords"): text_fields["keywords"] = ", ".join(text_fields["keywords"]) text_overlaps = not q or sqlite_text_search(q, text_fields) return bbox_overlaps and datetime_overlaps and text_overlaps class CollectionSearch(BaseSearch): """Represents a deferred query to a STAC collection search endpoint as described in the `STAC API - Collection Search extension `__. Due to potential conflicts between the collection-search and transactions extensions pystac_client will only send GET requests to the /collections endpoint. No request is sent to the API until a method is called to iterate through the resulting STAC Collections, either :meth:`CollectionSearch.collections`, or :meth:`CollectionSearch.collections_as_dicts`. All parameters except `url``, ``method``, ``max_collections``, and ``client`` correspond to query parameters described in the `STAC API - Collection Extension: Query Parameters Table `__ docs. Please refer to those docs for details on how these parameters filter search results. Args: url: The URL to the search page of the STAC API. max_collections : The maximum number of collections to return from the search, even if there are more matching results. This client to limit the total number of Collections returned from the :meth:`collections`, :meth:`collection_list`, and :meth:`collections_as_dicts methods`. The client will continue to request pages of collections until the number of max collections is reached. Setting this to ``None`` will allow iteration over a possibly very large number of results. stac_io: An instance of StacIO for retrieving results. Normally comes from the Client that returns this CollectionSearch client: An instance of a root Client used to set the root on resulting Collections. client: An instance of Client for retrieving results. This is normally populated by the client that returns this CollectionSearch instance. limit: A recommendation to the service as to the number of collections to return *per page* of results. Defaults to 100. bbox: A list, tuple, or iterator representing a bounding box of 2D or 3D coordinates. Results will be filtered to only those intersecting the bounding box. datetime: Either a single datetime or datetime range used to filter results. You may express a single datetime using a :class:`datetime.datetime` instance, a `RFC 3339-compliant `__ timestamp, or a simple date string (see below). Instances of :class:`datetime.datetime` may be either timezone aware or unaware. Timezone aware instances will be converted to a UTC timestamp before being passed to the endpoint. Timezone unaware instances are assumed to represent UTC timestamps. You may represent a datetime range using a ``"/"`` separated string as described in the spec, or a list, tuple, or iterator of 2 timestamps or datetime instances. For open-ended ranges, use either ``".."`` (``'2020-01-01:00:00:00Z/..'``, ``['2020-01-01:00:00:00Z', '..']``) or a value of ``None`` (``['2020-01-01:00:00:00Z', None]``). If using a simple date string, the datetime can be specified in ``YYYY-mm-dd`` format, optionally truncating to ``YYYY-mm`` or just ``YYYY``. Simple date strings will be expanded to include the entire time period, for example: - ``2017`` expands to ``2017-01-01T00:00:00Z/2017-12-31T23:59:59Z`` - ``2017-06`` expands to ``2017-06-01T00:00:00Z/2017-06-30T23:59:59Z`` - ``2017-06-10`` expands to ``2017-06-10T00:00:00Z/2017-06-10T23:59:59Z`` If used in a range, the end of the range expands to the end of that day/month/year, for example: - ``2017/2018`` expands to ``2017-01-01T00:00:00Z/2018-12-31T23:59:59Z`` - ``2017-06/2017-07`` expands to ``2017-06-01T00:00:00Z/2017-07-31T23:59:59Z`` - ``2017-06-10/2017-06-11`` expands to ``2017-06-10T00:00:00Z/2017-06-11T23:59:59Z`` q: Free-text search query. See the `STAC API - Free Text Extension Spec `__ for syntax. query: List or JSON of query parameters as per the STAC API `query` extension filter: JSON of query parameters as per the STAC API `filter` extension filter_lang: Language variant used in the filter body. If `filter` is a dictionary or not provided, defaults to 'cql2-json'. If `filter` is a string, defaults to `cql2-text`. sortby: A single field or list of fields to sort the response by fields: A list of fields to include in the response. Note this may result in invalid STAC objects, as they may not have required fields. Use `collections_as_dicts` to avoid object unmarshalling errors. modifier : A callable that modifies the children collection and items returned by this Client. This can be useful for injecting authentication parameters into child assets to access data from non-public sources. The callable should expect a single argument, which will be one of the following types: * :class:`pystac.Collection` * :class:`pystac.Item` * :class:`pystac.ItemCollection` * A STAC item-like :class:`dict` * A STAC collection-like :class:`dict` The callable should mutate the argument in place and return ``None``. ``modifier`` propagates recursively to children of this Client. After getting a child collection with, e.g. :meth:`Client.get_collection`, the child items of that collection will still be signed with ``modifier``. """ _stac_io: StacApiIO _collection_search_extension_enabled: bool _collection_search_free_text_enabled: bool def __init__( self, url: str, *, max_collections: int | None = None, stac_io: StacApiIO | None = None, client: Optional["_client.Client"] = None, limit: int | None = None, bbox: BBoxLike | None = None, datetime: DatetimeLike | None = None, query: QueryLike | None = None, filter: FilterLike | None = None, filter_lang: FilterLangLike | None = None, sortby: SortbyLike | None = None, fields: FieldsLike | None = None, q: str | None = None, modifier: Callable[[Modifiable], None] | None = None, collection_search_extension_enabled: bool = False, collection_search_free_text_enabled: bool = False, ): super().__init__( url=url, method="GET", max_items=max_collections, stac_io=stac_io, client=client, limit=limit, bbox=bbox, datetime=datetime, query=query, filter=filter, filter_lang=filter_lang, sortby=sortby, fields=fields, q=q, modifier=modifier, ) if client and client._stac_io is not None and stac_io is None: self._stac_io = client._stac_io self._collection_search_extension_enabled = client.conforms_to( ConformanceClasses.COLLECTION_SEARCH ) self._collection_search_free_text_enabled = client.conforms_to( ConformanceClasses.COLLECTION_SEARCH_FREE_TEXT ) if any([bbox, datetime, q, query, filter, sortby, fields]): if not self._collection_search_extension_enabled: warnings.warn( DoesNotConformTo( "COLLECTION_SEARCH", "Filtering will be performed client-side where only bbox, " "datetime, and q arguments are supported", ) ) self._validate_client_side_args() else: if not self._collection_search_free_text_enabled: warnings.warn( DoesNotConformTo( "COLLECTION_SEARCH#FREE_TEXT", "Free-text search is not enabled for collection search" "Free-text filters will be applied client-side.", ) ) else: self._stac_io = stac_io or StacApiIO() self._collection_search_extension_enabled = ( collection_search_extension_enabled ) self._collection_search_free_text_enabled = ( collection_search_free_text_enabled ) self._validate_client_side_args() def _validate_client_side_args(self) -> None: """Client-side filtering only supports the bbox, datetime, and q parameters.""" args = {key for key, value in self._parameters.items() if value is not None} extras = args - {"bbox", "datetime", "q", "limit", "max_items"} if extras: raise ValueError( "Only the limit, max_collections, bbox, datetime, and q arguments are " "supported for client-side filtering but these extra arguments were " "provided: " + ",".join(extras) ) @lru_cache(1) def matched(self) -> int | None: """Return number matched for search Returns the value from the `numberMatched` or `context.matched` field. Not all APIs will support counts in which case a warning will be issued Returns: int: Total count of matched collections. If counts are not supported `None` is returned. """ found = None iter = self.pages_as_dicts() page = next(iter, None) if not page: return 0 # if collection search and free-text are fully supported, try reading a value # from the search result context if ( self._collection_search_extension_enabled and self._collection_search_free_text_enabled ): if "context" in page: found = page["context"].get("matched", None) elif "numberMatched" in page: found = page["numberMatched"] if not found: count = len(page["collections"]) for page in iter: count += len(page["collections"]) found = count return found # ------------------------------------------------------------------------ # Result sets # ------------------------------------------------------------------------ # By collection def collections(self) -> Iterator[Collection]: """Iterator that yields :class:`pystac.Collection` instances for each collection matching the given search parameters. Yields: Collection : each Collection matching the search criteria """ for collection in self.collections_as_dicts(): # already signed in collections_as_dicts yield Collection.from_dict( collection, root=self.client, preserve_dict=False ) @lru_cache(1) def collections_as_dicts(self) -> Iterator[dict[str, Any]]: """Iterator that yields :class:`dict` instances for each collection matching the given search parameters. Yields: Collection : each Collection matching the search criteria """ for page in self.pages_as_dicts(): yield from page.get("collections", []) # ------------------------------------------------------------------------ # By Page def pages(self) -> Iterator[list[Collection]]: """Iterator that yields lists of Collection objects. Each list is a page of results from the search. Yields: List[Collection] : a group of Collections matching the search criteria """ if isinstance(self._stac_io, StacApiIO): for page in self.pages_as_dicts(): # already signed in pages_as_dicts yield [ Collection.from_dict( collection, preserve_dict=False, root=self.client ) for collection in page["collections"] ] def pages_as_dicts(self) -> Iterator[dict[str, Any]]: """Iterator that yields :class:`dict` instances for each page of results from the search. Yields: Dict : a group of collections matching the search criteria as a feature-collection-like dictionary. """ if isinstance(self._stac_io, StacApiIO): num_collections = 0 for page in self._stac_io.get_pages( self.url, self.method, self.get_parameters() ): call_modifier(self.modifier, page) collections = page.get("collections", []) page_has_collections = len(collections) > 0 # apply client-side filter if the collection search extension # is not enabled in the API if not self._collection_search_extension_enabled: args = { "bbox": self._parameters.get("bbox"), "temporal_interval_str": self._parameters.get("datetime"), "q": self._parameters.get("q"), } collections = [ collection for collection in filter( lambda x: collection_matches(x, **args), collections, ) ] # apply client-side free-text filter if free-text extension is not # enabled in the API elif not self._collection_search_free_text_enabled: if q := self._parameters.get("q"): collections = [ collection for collection in filter( lambda x: collection_matches(x, q=q), collections, ) ] if collections: num_collections += len(collections) if self._max_items and num_collections > self._max_items: # Slice the features down to make sure we hit max_collections page["collections"] = collections[ 0 : -(num_collections - self._max_items) ] else: page["collections"] = collections yield page if self._max_items and num_collections >= self._max_items: return # if there were collections on this page but they got filtered out keep # going elif page_has_collections: continue else: return # ------------------------------------------------------------------------ # Everything @lru_cache(1) def collection_list(self) -> list[Collection]: """ Get the matching collections as a list of :py:class:`pystac.Collection` objects. Return: List[Collection]: The list of collections """ # Bypass the cache here, so that we can pass __preserve_dict__ # without mutating what's in the cache. collection_list = self.collections_as_dicts.__wrapped__(self) # already signed in collections_as_dicts return [ Collection.from_dict(collection, preserve_dict=False, root=self.client) for collection in collection_list ] @lru_cache(1) def collection_list_as_dict(self) -> dict[str, Any]: """ Get the matching collections as a dict. The dictionary will have a single key: 1. ``'collections'`` with the value being a list of dictionaries for the matching collections. Return: Dict : A dictionary with the list of matching collections """ collections = [] for page in self.pages_as_dicts(): for collection in page["collections"]: collections.append(collection) return {"collections": collections} ================================================ FILE: pystac_client/conformance.py ================================================ from __future__ import annotations import re from enum import Enum class ConformanceClasses(Enum): """Enumeration class for Conformance Classes""" # defined conformance classes regexes CORE = "/core" COLLECTIONS = "/collections" # this is ogcapi-features instead of just features for historical reasons, # even thought this is a STAC API conformance class FEATURES = "/ogcapi-features" ITEM_SEARCH = "/item-search" CONTEXT = "/item-search#context" FIELDS = "/item-search#fields" SORT = "/item-search#sort" QUERY = "/item-search#query" FILTER = "/item-search#filter" # collection search COLLECTION_SEARCH = "/collection-search" COLLECTION_SEARCH_FREE_TEXT = "/collection-search#free-text" @classmethod def get_by_name(cls, name: str) -> ConformanceClasses: for member in cls: if member.name == name.upper(): return member raise ValueError( f"Invalid conformance class '{name}'. Options are: {list(cls)}" ) def __str__(self) -> str: return f"{self.name}" def __repr__(self) -> str: return str(self) @property def valid_uri(self) -> str: return f"https://api.stacspec.org/v1.0.*{self.value}" @property def pattern(self) -> re.Pattern[str]: return re.compile( rf"{re.escape('https://api.stacspec.org/v1.0.')}(.*){re.escape(self.value)}" ) ================================================ FILE: pystac_client/errors.py ================================================ class ClientTypeError(Exception): """Raised when trying to open a Client on a non-catalog STAC Object.""" pass class IgnoredResultWarning(RuntimeWarning): """ Warning raised when a 'modifier' callable returns a result. """ ================================================ FILE: pystac_client/exceptions.py ================================================ from requests import Response class APIError(Exception): """Raised when unexpected server error.""" status_code: int | None @classmethod def from_response(cls, response: Response) -> "APIError": error = cls(response.text) error.status_code = response.status_code return error class ParametersError(Exception): """Raised when invalid parameters are used in a query""" ================================================ FILE: pystac_client/free_text.py ================================================ """Client-side free-text search filtering as described in `OGC API - Features - Part 9: Text Search `__ Uses the `SQLite FTS5 Extension `__ to implement free-text search filtering. """ import re import sqlite3 def parse_query_for_sqlite(q: str) -> str: """Translate an OGC Features API free-text search query into the SQLite text search syntax """ # separate out search terms, quoted exact phrases, commas, and exact phrases tokens = [token.strip() for token in re.findall(r'"[^"]*"|,|[\(\)]|[^,\s\(\)]+', q)] # special characters that need to be escaped or quoted for sqlite fts5 special_chars = set("-@&:^~<>=") for i, token in enumerate(tokens): if token.startswith("+"): tokens[i] = token[1:].strip() elif token.startswith("-"): tokens[i] = "NOT " + token[1:].strip() elif token == ",": tokens[i] = "OR" elif any(char in token for char in special_chars): # Escape any existing double quotes in the token escaped_token = token.replace('"', '""') tokens[i] = f'"{escaped_token}"' return " ".join(tokens) def sqlite_text_search(q: str, text_fields: dict[str, str]) -> bool: """Perform a free-text search against a set of text fields for a single collection to determine if that collection matches the query. Creates an in-memory SQLite database with a single table and a single row then runs the MATCH query to determine if the row matches the search criteria. """ column_clause = ", ".join(text_fields.keys()) value_clause = ", ".join(["?" for _ in text_fields.keys()]) with sqlite3.connect(":memory:") as conn: # Use an in-memory database cursor = conn.cursor() cursor.execute( f""" CREATE VIRTUAL TABLE collections USING fts5({column_clause}); """ ) cursor.execute( f""" INSERT INTO collections ({column_clause}) VALUES ({value_clause}); """, tuple(text_fields.values()), ) cursor.execute( f""" SELECT COUNT(*) FROM collections WHERE collections MATCH '{parse_query_for_sqlite(q)}'; """ ) return bool(cursor.fetchone()[0]) ================================================ FILE: pystac_client/item_search.py ================================================ import json import re import warnings from abc import ABC from collections.abc import Callable, Iterable, Iterator, Mapping from copy import deepcopy from datetime import datetime as datetime_ from datetime import timezone from functools import lru_cache from itertools import chain from typing import ( TYPE_CHECKING, Any, Optional, Protocol, Union, ) from dateutil.relativedelta import relativedelta from dateutil.tz import tzutc from pystac import Collection, Item, ItemCollection from requests import Request from pystac_client._utils import Modifiable, call_modifier from pystac_client.conformance import ConformanceClasses from pystac_client.stac_api_io import StacApiIO from pystac_client.warnings import DoesNotConformTo if TYPE_CHECKING: from pystac_client import client as _client DATETIME_REGEX = re.compile( r"^(?P\d{4})(-(?P\d{2})(-(?P\d{2})" r"(?P([Tt])\d{2}:\d{2}:\d{2}(\.\d+)?" r"(?P[Zz]|([-+])(\d{2}):(\d{2}))?)?)?)?$" ) class GeoInterface(Protocol): @property def __geo_interface__(self) -> dict[str, Any]: ... DatetimeOrTimestamp = Optional[Union[datetime_, str]] Datetime = str DatetimeLike = Union[ DatetimeOrTimestamp, tuple[DatetimeOrTimestamp, DatetimeOrTimestamp], list[DatetimeOrTimestamp], Iterator[DatetimeOrTimestamp], ] BBox = tuple[float, ...] BBoxLike = Union[BBox, list[float], Iterator[float], str] Collections = tuple[str, ...] CollectionsLike = Union[list[str], Iterator[str], str] IDs = tuple[str, ...] IDsLike = Union[IDs, str, list[str], Iterator[str]] Intersects = dict[str, Any] IntersectsLike = Union[str, GeoInterface, Intersects] Query = dict[str, Any] QueryLike = Union[Query, list[str]] FilterLangLike = str FilterLike = Union[dict[str, Any], str] Sortby = list[dict[str, str]] SortbyLike = Union[Sortby, str, list[str]] Fields = dict[str, list[str]] FieldsLike = Union[Fields, str, list[str]] # these cannot be reordered or parsing will fail! OP_MAP = { ">=": "gte", "<=": "lte", "=": "eq", "<>": "neq", ">": "gt", "<": "lt", } OPS = list(OP_MAP.keys()) # from https://gist.github.com/angstwad/bf22d1822c38a92ec0a9#gistcomment-2622319 def dict_merge( dct: dict[Any, Any], merge_dct: dict[Any, Any], add_keys: bool = True ) -> dict[Any, Any]: """Recursive dict merge. Inspired by :meth:``dict.update()``, instead of updating only top-level keys, dict_merge recurses down into dicts nested to an arbitrary depth, updating keys. The ``merge_dct`` is merged into ``dct``. This version will return a copy of the dictionary and leave the original arguments untouched. The optional argument ``add_keys``, determines whether keys which are present in ``merge_dict`` but not ``dct`` should be included in the new dict. Args: dct (dict) onto which the merge is executed merge_dct (dict): dct merged into dct add_keys (bool): whether to add new keys Return: dict: updated dict """ dct = dct.copy() if not add_keys: merge_dct = {k: merge_dct[k] for k in set(dct).intersection(set(merge_dct))} for k, v in merge_dct.items(): if k in dct and isinstance(dct[k], dict) and isinstance(merge_dct[k], Mapping): dct[k] = dict_merge(dct[k], merge_dct[k], add_keys=add_keys) else: dct[k] = merge_dct[k] return dct class BaseSearch(ABC): _stac_io: StacApiIO def __init__( self, url: str, *, method: str | None = "POST", max_items: int | None = None, stac_io: StacApiIO | None = None, client: Optional["_client.Client"] = None, limit: int | None = None, ids: IDsLike | None = None, collections: CollectionsLike | None = None, bbox: BBoxLike | None = None, intersects: IntersectsLike | None = None, datetime: DatetimeLike | None = None, query: QueryLike | None = None, filter: FilterLike | None = None, filter_lang: FilterLangLike | None = None, sortby: SortbyLike | None = None, fields: FieldsLike | None = None, modifier: Callable[[Modifiable], None] | None = None, q: str | None = None, ): self.url = url self.client = client self._max_items = max_items if self._max_items is not None and limit is not None: limit = min(limit, self._max_items) if limit is not None and (limit < 1 or limit > 10000): raise Exception(f"Invalid limit of {limit}, must be between 1 and 10,000") self.method = method self.modifier = modifier params = { "limit": limit, "bbox": self._format_bbox(bbox), "datetime": self._format_datetime(datetime), "ids": self._format_ids(ids), "collections": self._format_collections(collections), "intersects": self._format_intersects(intersects), "query": self._format_query(query), "filter": self._format_filter(method, filter_lang, filter), "filter-lang": self._format_filter_lang(method, filter, filter_lang), "sortby": self._format_sortby(sortby), "fields": self._format_fields(fields), "q": q, } self._parameters: dict[str, Any] = { k: v for k, v in params.items() if v is not None } def get_parameters(self) -> dict[str, Any]: if self.method == "POST": return self._parameters elif self.method == "GET": return self._clean_params_for_get_request() else: raise Exception(f"Unsupported method {self.method}") def _clean_params_for_get_request(self) -> dict[str, Any]: params = deepcopy(self._parameters) if "bbox" in params: params["bbox"] = ",".join(map(str, params["bbox"])) if "ids" in params: params["ids"] = ",".join(params["ids"]) if "collections" in params: params["collections"] = ",".join(params["collections"]) if "intersects" in params: params["intersects"] = json.dumps( params["intersects"], separators=(",", ":") ) if "query" in params: params["query"] = json.dumps(params["query"], separators=(",", ":")) if "sortby" in params: params["sortby"] = self._sortby_dict_to_str(params["sortby"]) if "fields" in params: params["fields"] = self._fields_dict_to_str(params["fields"]) if "filter" in params and isinstance(params["filter"], dict): params["filter"] = json.dumps(params["filter"]) return params def url_with_parameters(self) -> str: """Returns the search url with parameters, appropriate for a GET request. Examples: >>> search = ItemSearch( ... url="https://planetarycomputer.microsoft.com/api/stac/v1/search", ... collections=["cop-dem-glo-30"], ... bbox=[88.214, 27.927, 88.302, 28.034], ... ) >>> assert ( ... search.url_with_parameters() ... == "https://planetarycomputer.microsoft.com/api/stac/v1/search?" ... "limit=100&bbox=88.214,27.927,88.302,28.034&collections=cop-dem-glo-30" ... ) Returns: str: The search url with parameters. """ params = self._clean_params_for_get_request() request = Request("GET", self.url, params=params) url = request.prepare().url if url is None: raise ValueError("Could not construct a full url") return url def _format_query(self, value: QueryLike | None) -> dict[str, Any] | None: if value is None: return None if self.client and not self.client.conforms_to(ConformanceClasses.QUERY): warnings.warn(DoesNotConformTo("QUERY")) if isinstance(value, dict): return value elif isinstance(value, list): query: dict[str, Any] = {} for q in value: if isinstance(q, str): try: query = dict_merge(query, json.loads(q)) except json.decoder.JSONDecodeError: for op in OPS: parts = q.split(op) if len(parts) == 2: param = parts[0] val: str | float = parts[1] if param == "gsd": val = float(val) query = dict_merge(query, {parts[0]: {OP_MAP[op]: val}}) break else: raise Exception("Unsupported query format, must be a List[str].") else: raise Exception("Unsupported query format, must be a Dict or List[str].") return query @staticmethod def _format_filter_lang( method: str | None, _filter: FilterLike | None, value: FilterLangLike | None, ) -> str | None: if _filter is None: return None if value is not None: return value if method == "GET": return "cql2-text" if method == "POST": return "cql2-json" return None def _format_filter( self, method: str | None, filter_lang: FilterLangLike | None, value: FilterLike | None, ) -> FilterLike | None: if not value: return None if self.client and not self.client.conforms_to(ConformanceClasses.FILTER): warnings.warn(DoesNotConformTo("FILTER")) if method == "GET" and isinstance(value, str): return value if method == "POST" and isinstance(value, dict): return value # if filter_lang is specified, do not coerce if filter_lang is not None: return value try: import cql2 if isinstance(value, dict): expr = cql2.parse_json(json.dumps(value)) else: # could be cql2-text or stringified cql2-json expr = cql2.Expr(value) except ImportError as e: raise ValueError( "Unless you specify ``filter_lang`` pystac-client will try to convert " "the filter to cql2-text or cql2-json based on the HTTP method " "provided.\n" "Resolve this error by installing ``cql2``: ``pip install cql2``" ) from e if method == "GET": return str(expr.to_text()) if method == "POST": return dict(expr.to_json()) return value @staticmethod def _format_bbox(value: BBoxLike | None) -> BBox | None: if value is None: return None if isinstance(value, str): bbox = tuple(map(float, value.split(","))) else: bbox = tuple(map(float, value)) return bbox @staticmethod def _to_utc_isoformat(dt: datetime_) -> str: if dt.tzinfo is not None: dt = dt.astimezone(timezone.utc) dt = dt.replace(tzinfo=None) return f"{dt.isoformat('T')}Z" def _to_isoformat_range( self, component: DatetimeOrTimestamp, ) -> tuple[str, str | None]: """Converts a single DatetimeOrTimestamp into one or two Datetimes. This is required to expand a single value like "2017" out to the whole year. This function returns two values. The first value is always a valid Datetime. The second value can be None or a Datetime. If it is None, this means that the first value was an exactly specified value (e.g. a `datetime.datetime`). If the second value is a Datetime, then it will be the end of the range at the resolution of the component, e.g. if the component were "2017" the second value would be the last second of the last day of 2017. """ if component is None: return "..", None elif isinstance(component, str): if component == "..": return component, None elif component == "": return "..", None match = DATETIME_REGEX.match(component) if not match: raise Exception(f"invalid datetime component: {component}") elif match.group("remainder"): if match.group("tz_info"): return component, None else: return f"{component}Z", None else: year = int(match.group("year")) optional_month = match.group("month") optional_day = match.group("day") if optional_day is not None: start = datetime_( year, int(optional_month), int(optional_day), 0, 0, 0, tzinfo=tzutc(), ) end = start + relativedelta(days=1, seconds=-1) elif optional_month is not None: start = datetime_(year, int(optional_month), 1, 0, 0, 0, tzinfo=tzutc()) end = start + relativedelta(months=1, seconds=-1) else: start = datetime_(year, 1, 1, 0, 0, 0, tzinfo=tzutc()) end = start + relativedelta(years=1, seconds=-1) return self._to_utc_isoformat(start), self._to_utc_isoformat(end) else: return self._to_utc_isoformat(component), None def _format_datetime(self, value: DatetimeLike | None) -> Datetime | None: if value is None: return None elif isinstance(value, datetime_): return self._to_utc_isoformat(value) elif isinstance(value, str): components = value.split("/") else: components = list(value) # type: ignore if not components: return None elif len(components) == 1: if components[0] is None: raise Exception("cannot create a datetime query with None") start, end = self._to_isoformat_range(components[0]) if end is not None: return f"{start}/{end}" else: return start elif len(components) == 2: if all(c is None for c in components): raise Exception("cannot create a double open-ended interval") start, _ = self._to_isoformat_range(components[0]) backup_end, end = self._to_isoformat_range(components[1]) return f"{start}/{end or backup_end}" else: raise Exception( "too many datetime components " f"(max=2, actual={len(components)}): {value}" ) @staticmethod def _format_collections(value: CollectionsLike | None) -> Collections | None: def _format(c: Any) -> Collections: if isinstance(c, str): return (c,) if isinstance(c, Iterable): return tuple(map(lambda x: _format(x)[0], c)) return (c.id,) if value is None: return None if isinstance(value, str): return tuple(map(lambda x: _format(x)[0], value.split(","))) if isinstance(value, Collection): return _format(value) return _format(value) @staticmethod def _format_ids(value: IDsLike | None) -> IDs | None: if value is None or isinstance(value, (tuple, list)) and not value: # We can't just check for truthiness here because of the Iterator[str] case return None elif isinstance(value, str): # We could check for str in the first branch, but then we'd be checking # for str twice #microoptimizations if value: return tuple(value.split(",")) else: return None else: return tuple(value) def _format_sortby(self, value: SortbyLike | None) -> Sortby | None: if value is None: return None if self.client and not self.client.conforms_to(ConformanceClasses.SORT): warnings.warn(DoesNotConformTo("SORT")) if isinstance(value, str): return [self._sortby_part_to_dict(part) for part in value.split(",")] if isinstance(value, list): if value and isinstance(value[0], str): return [self._sortby_part_to_dict(str(v)) for v in value] elif value and isinstance(value[0], dict): return value # type: ignore raise Exception( "sortby must be of type None, str, List[str], or List[Dict[str, str]" ) @staticmethod def _sortby_part_to_dict(part: str) -> dict[str, str]: if part.startswith("-"): return {"field": part[1:], "direction": "desc"} elif part.startswith("+"): return {"field": part[1:], "direction": "asc"} else: return {"field": part, "direction": "asc"} @staticmethod def _sortby_dict_to_str(sortby: Sortby) -> str: return ",".join( [ f"{'+' if sort['direction'] == 'asc' else '-'}{sort['field']}" for sort in sortby ] ) def _format_fields(self, value: FieldsLike | None) -> Fields | None: if value is None: return None if self.client and not self.client.conforms_to(ConformanceClasses.FIELDS): warnings.warn(DoesNotConformTo("FIELDS")) if isinstance(value, str): return self._fields_to_dict(value.split(",")) if isinstance(value, list): if len(value) == 1: return self._fields_to_dict(value[0].split(",")) return self._fields_to_dict(value) if isinstance(value, dict): return value raise Exception( "sortby must be of type None, str, List[str], or List[Dict[str, str]" ) @staticmethod def _fields_to_dict(fields: list[str]) -> Fields: includes: list[str] = [] excludes: list[str] = [] for field in fields: if field.startswith("-"): excludes.append(field[1:]) elif field.startswith("+"): includes.append(field[1:]) else: includes.append(field) return {"include": includes, "exclude": excludes} @staticmethod def _fields_dict_to_str(fields: Fields) -> str: includes = [f"+{x}" for x in fields.get("include", [])] excludes = [f"-{x}" for x in fields.get("exclude", [])] return ",".join(chain(includes, excludes)) @staticmethod def _format_intersects(value: IntersectsLike | None) -> Intersects | None: if value is None: return None if isinstance(value, dict): if value.get("type") == "Feature": return deepcopy(value.get("geometry")) else: return deepcopy(value) if isinstance(value, str): return dict(json.loads(value)) if hasattr(value, "__geo_interface__"): return dict(deepcopy(getattr(value, "__geo_interface__"))) raise Exception( "intersects must be of type None, str, dict, or an object that " "implements __geo_interface__" ) if TYPE_CHECKING: from pystac_client import client as _client def __getattr__(name: str) -> Any: if name in ("DEFAUL_LIMIT", "DEFAULT_LIMIT_AND_MAX_ITEMS"): warnings.warn( f"{name} is deprecated and will be removed in v0.8", DeprecationWarning ) return 100 raise AttributeError(f"module {__name__} has no attribute {name}") class ItemSearch(BaseSearch): """Represents a deferred query to a STAC search endpoint as described in the `STAC API - Item Search spec `__. No request is sent to the API until a method is called to iterate through the resulting STAC Items, either :meth:`ItemSearch.item_collections`, :meth:`ItemSearch.items`, or :meth:`ItemSearch.items_as_dicts`. All parameters except ``url``, ``method``, ``max_items``, and ``client`` correspond to query parameters described in the `STAC API - Item Search: Query Parameters Table `__ docs. Please refer to those docs for details on how these parameters filter search results. Args: url: The URL to the search page of the STAC API. method : The HTTP method to use when making a request to the service. This must be either ``"GET"``, ``"POST"``, or ``None``. If ``None``, this will default to ``"POST"``. If a ``"POST"`` request receives a ``405`` status for the response, it will automatically retry with ``"GET"`` for all subsequent requests. max_items : The maximum number of items to return from the search, even if there are more matching results. This allows the client to limit the total number of Items returned from the :meth:`items`, :meth:`item_collections`, and :meth:`items_as_dicts methods`. The client will continue to request pages of items until the number of max items is reached. By default (``max_items=None``) all items matching the query will be returned. stac_io: An instance of StacIO for retrieving results. Normally comes from the Client that returns this ItemSearch client: An instance of a root Client used to set the root on resulting Items. client: An instance of Client for retrieving results. This is normally populated by the client that returns this ItemSearch instance. limit: A recommendation to the service as to the number of items to return *per page* of results. Defaults to 100. ids: List of one or more Item ids to filter on. collections: List of one or more Collection IDs or :class:`pystac.Collection` instances. bbox: A list, tuple, or iterator representing a bounding box of 2D or 3D coordinates. Results will be filtered to only those intersecting the bounding box. intersects: A string or dictionary representing a GeoJSON geometry or feature, or an object that implements a ``__geo_interface__`` property, as supported by several libraries including Shapely, ArcPy, PySAL, and geojson. Results filtered to only those intersecting the geometry. datetime: Either a single datetime or datetime range used to filter results. You may express a single datetime using a :class:`datetime.datetime` instance, a `RFC 3339-compliant `__ timestamp, or a simple date string (see below). Instances of :class:`datetime.datetime` may be either timezone aware or unaware. Timezone aware instances will be converted to a UTC timestamp before being passed to the endpoint. Timezone unaware instances are assumed to represent UTC timestamps. You may represent a datetime range using a ``"/"`` separated string as described in the spec, or a list, tuple, or iterator of 2 timestamps or datetime instances. For open-ended ranges, use either ``".."`` (``'2020-01-01:00:00:00Z/..'``, ``['2020-01-01:00:00:00Z', '..']``) or a value of ``None`` (``['2020-01-01:00:00:00Z', None]``). If using a simple date string, the datetime can be specified in ``YYYY-mm-dd`` format, optionally truncating to ``YYYY-mm`` or just ``YYYY``. Simple date strings will be expanded to include the entire time period, for example: - ``2017`` expands to ``2017-01-01T00:00:00Z/2017-12-31T23:59:59Z`` - ``2017-06`` expands to ``2017-06-01T00:00:00Z/2017-06-30T23:59:59Z`` - ``2017-06-10`` expands to ``2017-06-10T00:00:00Z/2017-06-10T23:59:59Z`` If used in a range, the end of the range expands to the end of that day/month/year, for example: - ``2017/2018`` expands to ``2017-01-01T00:00:00Z/2018-12-31T23:59:59Z`` - ``2017-06/2017-07`` expands to ``2017-06-01T00:00:00Z/2017-07-31T23:59:59Z`` - ``2017-06-10/2017-06-11`` expands to ``2017-06-10T00:00:00Z/2017-06-11T23:59:59Z`` query: List or JSON of query parameters as per the STAC API `query` extension filter: JSON of query parameters as per the STAC API `filter` extension filter_lang: Language variant used in the filter body. If `filter` is a dictionary or not provided, defaults to 'cql2-json'. If `filter` is a string, defaults to `cql2-text`. sortby: A single field or list of fields to sort the response by fields: A list of fields to include in the response. Note this may result in invalid STAC objects, as they may not have required fields. Use `items_as_dicts` to avoid object unmarshalling errors. modifier : A callable that modifies the children collection and items returned by this Client. This can be useful for injecting authentication parameters into child assets to access data from non-public sources. The callable should expect a single argument, which will be one of the following types: * :class:`pystac.Collection` * :class:`pystac.Item` * :class:`pystac.ItemCollection` * A STAC item-like :class:`dict` * A STAC collection-like :class:`dict` The callable should mutate the argument in place and return ``None``. ``modifier`` propagates recursively to children of this Client. After getting a child collection with, e.g. :meth:`Client.get_collection`, the child items of that collection will still be signed with ``modifier``. """ _stac_io: StacApiIO def __init__( self, url: str, *, method: str | None = "POST", max_items: int | None = None, stac_io: StacApiIO | None = None, client: Optional["_client.Client"] = None, limit: int | None = None, ids: IDsLike | None = None, collections: CollectionsLike | None = None, bbox: BBoxLike | None = None, intersects: IntersectsLike | None = None, datetime: DatetimeLike | None = None, query: QueryLike | None = None, filter: FilterLike | None = None, filter_lang: FilterLangLike | None = None, sortby: SortbyLike | None = None, fields: FieldsLike | None = None, modifier: Callable[[Modifiable], None] | None = None, ): super().__init__( url=url, method=method, max_items=max_items, stac_io=stac_io, client=client, limit=limit, ids=ids, collections=collections, bbox=bbox, intersects=intersects, datetime=datetime, query=query, filter=filter, filter_lang=filter_lang, sortby=sortby, fields=fields, modifier=modifier, ) if client and client._stac_io is not None and stac_io is None: self._stac_io = client._stac_io if not client.conforms_to(ConformanceClasses.ITEM_SEARCH): warnings.warn(DoesNotConformTo("ITEM_SEARCH")) else: self._stac_io = stac_io or StacApiIO() @lru_cache(1) def matched(self) -> int | None: """Return number matched for search Returns the value from the `numberMatched` or `context.matched` field. Not all APIs will support counts in which case a warning will be issued Returns: int: Total count of matched items. If counts are not supported `None` is returned. """ params = {**self.get_parameters(), "limit": 1} resp = self._stac_io.read_json(self.url, method=self.method, parameters=params) found = None if "context" in resp: found = resp["context"].get("matched", None) elif "numberMatched" in resp: found = resp["numberMatched"] if found is None: warnings.warn("numberMatched or context.matched not in response") return found # ------------------------------------------------------------------------ # Result sets # ------------------------------------------------------------------------ # By item def items(self) -> Iterator[Item]: """Iterator that yields :class:`pystac.Item` instances for each item matching the given search parameters. Yields: Item : each Item matching the search criteria """ for item in self.items_as_dicts(): # already signed in items_as_dicts yield Item.from_dict(item, root=self.client, preserve_dict=False) def items_as_dicts(self) -> Iterator[dict[str, Any]]: """Iterator that yields :class:`dict` instances for each item matching the given search parameters. Yields: Item : each Item matching the search criteria """ for page in self.pages_as_dicts(): yield from page.get("features", []) # ------------------------------------------------------------------------ # By Page def pages(self) -> Iterator[ItemCollection]: """Iterator that yields ItemCollection objects. Each ItemCollection is a page of results from the search. Yields: ItemCollection : a group of Items matching the search criteria within an ItemCollection """ if isinstance(self._stac_io, StacApiIO): for page in self.pages_as_dicts(): # already signed in pages_as_dicts yield ItemCollection.from_dict( page, preserve_dict=False, root=self.client ) def pages_as_dicts(self) -> Iterator[dict[str, Any]]: """Iterator that yields :class:`dict` instances for each page of results from the search. Yields: Dict : a group of items matching the search criteria as a feature-collection-like dictionary. """ if isinstance(self._stac_io, StacApiIO): num_items = 0 for page in self._stac_io.get_pages( self.url, self.method, self.get_parameters() ): call_modifier(self.modifier, page) features = page.get("features", []) if features: num_items += len(features) if self._max_items and num_items > self._max_items: # Slice the features down to make sure we hit max_items page["features"] = features[0 : -(num_items - self._max_items)] yield page if self._max_items and num_items >= self._max_items: return else: return # ------------------------------------------------------------------------ # Everything @lru_cache(1) def item_collection(self) -> ItemCollection: """ Get the matching items as a :py:class:`pystac.ItemCollection`. Return: ItemCollection: The item collection """ # Bypass the cache here, so that we can pass __preserve_dict__ # without mutating what's in the cache. feature_collection = self.item_collection_as_dict.__wrapped__(self) # already signed in item_collection_as_dict return ItemCollection.from_dict( feature_collection, preserve_dict=False, root=self.client ) @lru_cache(1) def item_collection_as_dict(self) -> dict[str, Any]: """ Get the matching items as an item-collection-like dict. The dictionary will have two keys: 1. ``'type'`` with the value ``'FeatureCollection'`` 2. ``'features'`` with the value being a list of dictionaries for the matching items. Return: Dict : A GeoJSON FeatureCollection """ features = [] for page in self.pages_as_dicts(): for feature in page["features"]: features.append(feature) feature_collection = {"type": "FeatureCollection", "features": features} return feature_collection # Deprecated methods # not caching these, since they're cached in the implementation def get_item_collections(self) -> Iterator[ItemCollection]: """DEPRECATED .. deprecated:: 0.4.0 Use :meth:`ItemSearch.pages` instead. Yields: ItemCollection : a group of Items matching the search criteria. """ warnings.warn( "get_item_collections() is deprecated, use pages() instead", FutureWarning, ) return self.pages() def item_collections(self) -> Iterator[ItemCollection]: """DEPRECATED .. deprecated:: 0.5.0 Use :meth:`ItemSearch.pages` instead. Yields: ItemCollection : a group of Items matching the search criteria within an ItemCollection """ warnings.warn( "item_collections() is deprecated, use pages() instead", FutureWarning, ) return self.pages() def get_items(self) -> Iterator[Item]: """DEPRECATED. .. deprecated:: 0.4.0 Use :meth:`ItemSearch.items` instead. Yields: Item : each Item matching the search criteria """ warnings.warn( "get_items() is deprecated, use items() instead", FutureWarning, ) return self.items() def get_all_items(self) -> ItemCollection: """DEPRECATED .. deprecated:: 0.4.0 Use :meth:`ItemSearch.item_collection` instead. Return: item_collection : ItemCollection """ warnings.warn( "get_all_items() is deprecated, use item_collection() instead.", FutureWarning, ) return self.item_collection() def get_all_items_as_dict(self) -> dict[str, Any]: """DEPRECATED .. deprecated:: 0.4.0 Use :meth:`ItemSearch.item_collection_as_dict` instead. Return: Dict : A GeoJSON FeatureCollection """ warnings.warn( "get_all_items_as_dict() is deprecated, use item_collection_as_dict() " "instead.", FutureWarning, ) return self.item_collection_as_dict() ================================================ FILE: pystac_client/mixins.py ================================================ import warnings from typing import Any import pystac from pystac_client._utils import urljoin from pystac_client.conformance import ConformanceClasses from pystac_client.exceptions import APIError from pystac_client.stac_api_io import StacApiIO from pystac_client.warnings import DoesNotConformTo, MissingLink QUERYABLES_REL = "http://www.opengis.net/def/rel/ogc/1.0/queryables" QUERYABLES_ENDPOINT = "queryables" class StacAPIObject(pystac.STACObject): _stac_io: StacApiIO | None def conforms_to(self, conformance_class: str | ConformanceClasses) -> bool: raise NotImplementedError class BaseMixin(StacAPIObject): def _get_href(self, rel: str, link: pystac.Link | None, endpoint: str) -> str: if link and isinstance(link.href, str): href = link.absolute_href else: warnings.warn(MissingLink(rel, self.__class__.__name__), stacklevel=2) href = urljoin(self.self_href, endpoint) return href class QueryablesMixin(BaseMixin): """Mixin for adding support for /queryables endpoint""" def get_queryables_from(self, url: str) -> dict[str, Any]: """Return all queryables. Output is a dictionary that can be used in ``jsonshema.validate`` Args: url: a queryables url Return: Dict[str, Any]: Dictionary containing queryable fields """ if self._stac_io is None: raise APIError("API access is not properly configured") result = self._stac_io.read_json(url) if "properties" not in result: raise APIError( f"Invalid response from {QUERYABLES_ENDPOINT}: " "expected 'properties' attribute" ) return result def get_queryables(self) -> dict[str, Any]: url = self._get_queryables_href() return self.get_queryables_from(url) def _get_queryables_href(self) -> str: if not self.conforms_to(ConformanceClasses.FILTER): raise DoesNotConformTo(ConformanceClasses.FILTER.name) link = self.get_single_link(QUERYABLES_REL) href = self._get_href(QUERYABLES_REL, link, QUERYABLES_ENDPOINT) return href ================================================ FILE: pystac_client/py.typed ================================================ ================================================ FILE: pystac_client/stac_api_io.py ================================================ import json import logging import warnings from collections.abc import Callable, Iterator from copy import deepcopy from typing import ( TYPE_CHECKING, Any, Optional, Union, ) from urllib.parse import urlparse import pystac from pystac.link import Link from pystac.serialization import ( identify_stac_object, identify_stac_object_type, merge_common_properties, migrate_to_latest, ) from pystac.stac_io import DefaultStacIO from requests import Request, Session from requests.adapters import HTTPAdapter from urllib3 import Retry import pystac_client from .exceptions import APIError if TYPE_CHECKING: from pystac.catalog import Catalog as Catalog_Type from pystac.stac_object import STACObject as STACObject_Type logger = logging.getLogger(__name__) Timeout = Union[float, tuple[float, float], tuple[float, None]] class StacApiIO(DefaultStacIO): def __init__( self, headers: dict[str, str] | None = None, conformance: list[str] | None = None, parameters: dict[str, Any] | None = None, request_modifier: Callable[[Request], Request | None] | None = None, timeout: Timeout | None = None, max_retries: int | Retry | None = 5, ): """Initialize class for API IO Args: headers : Optional dictionary of headers to include in all requests conformance (DEPRECATED) : Optional list of `Conformance Classes `__. .. deprecated:: 0.7.0 Conformance can be altered on the client class directly parameters: Optional dictionary of query string parameters to include in all requests. request_modifier: Optional callable that can be used to modify Request objects before they are sent. If provided, the callable receives a `request.Request` and must either modify the object directly or return a new / modified request instance. timeout: Optional float or (float, float) tuple following the semantics defined by `Requests `__. max_retries: The number of times to retry requests. Set to ``None`` to disable retries. Return: StacApiIO : StacApiIO instance """ # TODO - this should super() to parent class if conformance is not None: warnings.warn( ( "The `conformance` option is deprecated and will be " "removed in the next major release. Instead use " "`Client.set_conforms_to` or `Client.add_conforms_to` to control " "behavior." ), category=FutureWarning, ) self.session = Session() if max_retries: self.session.mount("http://", HTTPAdapter(max_retries=max_retries)) self.session.mount("https://", HTTPAdapter(max_retries=max_retries)) self.timeout = timeout self.update( headers=headers, parameters=parameters, request_modifier=request_modifier, timeout=timeout, ) def update( self, headers: dict[str, str] | None = None, parameters: dict[str, Any] | None = None, request_modifier: Callable[[Request], Request | None] | None = None, timeout: Timeout | None = None, ) -> None: """Updates this StacApi's headers, parameters, and/or request_modifer. Args: headers : Optional dictionary of headers to include in all requests parameters: Optional dictionary of query string parameters to include in all requests. request_modifier: Optional callable that can be used to modify Request objects before they are sent. If provided, the callable receives a `request.Request` and must either modify the object directly or return a new / modified request instance. timeout: Optional float or (float, float) tuple following the semantics defined by `Requests `__. """ self.session.headers.update(headers or {}) self.session.params.update(parameters or {}) # type: ignore self._req_modifier = request_modifier self.timeout = timeout def read_text(self, source: pystac.link.HREF, *args: Any, **kwargs: Any) -> str: """Read text from the given URI. Overwrites the default method for reading text from a URL or file to allow :class:`urllib.request.Request` instances as input. This method also raises any :exc:`urllib.error.HTTPError` exceptions rather than catching them to allow us to handle different response status codes as needed. """ if isinstance(source, Link): link = source.to_dict() href = link["href"] # get headers and body from Link and add to request from simple STAC # resolver merge = bool(link.get("merge", False)) # If the link object includes a "method" property, use that. If not # fall back to 'GET'. method = link.get("method", "GET") # If the link object includes a "headers" property, use that and # respect the "merge" property. headers = link.get("headers", None) # If "POST" use the body object that and respect the "merge" property. link_body = link.get("body", {}) if method == "POST": parameters = ( {**(kwargs.get("parameters", {})), **link_body} if merge else link_body ) else: # parameters are already in the link href parameters = {} return self.request( href, method=method, headers=headers, parameters=parameters ) else: # str or something that can be str'ed href = str(source) if _is_url(href): return self.request(href, *args, **kwargs) else: with open(href) as f: href_contents = f.read() return href_contents def request( self, href: str, method: str | None = None, headers: dict[str, str] | None = None, parameters: dict[str, Any] | None = None, ) -> str: """Makes a request to an http endpoint Args: href (str): The request URL method (Optional[str], optional): The http method to use, 'GET' or 'POST'. Defaults to None, which will result in 'GET' being used. headers (Optional[Dict[str, str]], optional): Additional headers to include in request. Defaults to None. parameters (Optional[Dict[str, Any]], optional): parameters to send with request. Defaults to None. Raises: APIError: raised if the server returns an error response Return: str: The decoded response from the endpoint """ if method == "POST": request = Request(method=method, url=href, headers=headers, json=parameters) else: params = deepcopy(parameters) or {} request = Request(method="GET", url=href, headers=headers, params=params) try: modified = self._req_modifier(request) if self._req_modifier else None prepped = self.session.prepare_request(modified or request) msg = f"{prepped.method} {prepped.url} Headers: {prepped.headers}" if method == "POST": msg += f" Payload: {json.dumps(request.json)}" if self.timeout is not None: msg += f" Timeout: {self.timeout}" logger.debug(msg) send_kwargs = self.session.merge_environment_settings( prepped.url, proxies={}, stream=None, verify=True, cert=None ) resp = self.session.send(prepped, timeout=self.timeout, **send_kwargs) except Exception as err: logger.debug(err) raise APIError(str(err)) if resp.status_code != 200: raise APIError.from_response(resp) try: return resp.content.decode("utf-8") except Exception as err: raise APIError(str(err)) def write_text_to_href(self, href: str, *args: Any, **kwargs: Any) -> None: if _is_url(href): raise APIError("Transactions not supported") else: return super().write_text_to_href(href, *args, **kwargs) def stac_object_from_dict( self, d: dict[str, Any], href: pystac.link.HREF | None = None, root: Optional["Catalog_Type"] = None, preserve_dict: bool = True, ) -> "STACObject_Type": """Deserializes a :class:`~pystac.STACObject` sub-class instance from a dictionary. Args: d : The dictionary to deserialize href : Optional href to associate with the STAC object root : Optional root :class:`~pystac.Catalog` to associate with the STAC object. preserve_dict: If ``False``, the dict parameter ``d`` may be modified during this method call. Otherwise the dict is not mutated. Defaults to ``True``, which results results in a deepcopy of the parameter. Set to ``False`` when possible to avoid the performance hit of a deepcopy. """ if identify_stac_object_type(d) == pystac.STACObjectType.ITEM: collection_cache = None if root is not None: collection_cache = root._resolved_objects.as_collection_cache() # Merge common properties in case this is an older STAC object. merge_common_properties( d, json_href=str(href), collection_cache=collection_cache ) info = identify_stac_object(d) d = migrate_to_latest(d, info) if info.object_type == pystac.STACObjectType.CATALOG: result = pystac_client.client.Client.from_dict( d, href=str(href), root=root, migrate=False, preserve_dict=preserve_dict ) result._stac_io = self return result if info.object_type == pystac.STACObjectType.COLLECTION: collection_client = ( pystac_client.collection_client.CollectionClient.from_dict( d, href=str(href), root=root, migrate=False, preserve_dict=preserve_dict, ) ) collection_client._stac_io = self return collection_client if info.object_type == pystac.STACObjectType.ITEM: return pystac.Item.from_dict( d, href=str(href), root=root, migrate=False, preserve_dict=preserve_dict ) raise ValueError(f"Unknown STAC object type {info.object_type}") def get_pages( self, url: str, method: str | None = None, parameters: dict[str, Any] | None = None, ) -> Iterator[dict[str, Any]]: """Iterator that yields dictionaries for each page at a STAC paging endpoint, e.g., /collections, /search Return: Dict[str, Any] : JSON content from a single page """ page = self.read_json(url, method=method, parameters=parameters) if not (page.get("features") or page.get("collections")): return None yield page next_link = next( (link for link in page.get("links", []) if link["rel"] == "next"), None ) while next_link: link = Link.from_dict(next_link) page = self.read_json(link, parameters=parameters) if not (page.get("features") or page.get("collections")): return None yield page # get the next link and make the next request next_link = next( (link for link in page.get("links", []) if link["rel"] == "next"), None ) def _is_url(href: str) -> bool: url = urlparse(href) return bool(url.scheme) and bool(url.netloc) ================================================ FILE: pystac_client/version.py ================================================ __version__ = "0.9.0" ================================================ FILE: pystac_client/warnings.py ================================================ import warnings from collections.abc import Iterator from contextlib import contextmanager class PystacClientWarning(UserWarning): """Base warning class""" ... class NoConformsTo(PystacClientWarning): """Inform user when client does not have "conformsTo" set""" def __str__(self) -> str: return "Server does not advertise any conformance classes." class DoesNotConformTo(PystacClientWarning): """Inform user when client does not conform to extension""" def __str__(self) -> str: return "Server does not conform to {}".format(", ".join(self.args)) class MissingLink(PystacClientWarning): """Inform user when link is not found""" def __str__(self) -> str: return "No link with rel='{}' could be found on this {}.".format(*self.args) class FallbackToPystac(PystacClientWarning): """Inform user when falling back to pystac implementation""" def __str__(self) -> str: return "Falling back to pystac. This might be slow." @contextmanager def strict() -> Iterator[None]: """Context manager for raising all pystac-client warnings as errors For more fine-grained control or to filter warnings in the whole python session, use the :py:mod:`warnings` module directly. Examples: >>> from pystac_client import Client >>> from pystac_client.warnings import strict >>> with strict(): ... Client.open("https://perfect-api.test") For finer-grained control: >>> import warnings >>> from pystac_client import Client >>> from pystac_client.warnings import MissingLink >>> warnings.filterwarnings("error", category=FallbackToPystac) >>> Client.open("https://imperfect-api.test") """ warnings.filterwarnings("error", category=PystacClientWarning) try: yield finally: warnings.filterwarnings("default", category=PystacClientWarning) @contextmanager def ignore() -> Iterator[None]: """Context manager for ignoring all pystac-client warnings For more fine-grained control or to set filter warnings in the whole python session, use the ``warnings`` module directly. Examples: >>> from pystac_client import Client >>> from pystac_client.warnings import ignore >>> with ignore(): ... Client.open("https://imperfect-api.test") For finer-grained control: >>> import warnings >>> from pystac_client import Client >>> from pystac_client.warnings import MissingLink >>> warnings.filterwarnings("ignore", category=MissingLink) >>> Client.open("https://imperfect-api.test") """ warnings.filterwarnings("ignore", category=PystacClientWarning) try: yield finally: warnings.filterwarnings("default", category=PystacClientWarning) ================================================ FILE: scripts/build-docs ================================================ #!/bin/bash set -e if [[ -n "${CI}" ]]; then set -x fi function usage() { echo -n \ "Usage: $(basename "$0") Build documentation " } if [ "${BASH_SOURCE[0]}" = "${0}" ]; then if [ "${1:-}" = "--help" ]; then usage else uv run -- sphinx-build -M html docs docs/build fi fi ================================================ FILE: scripts/format ================================================ #!/bin/bash set -e if [[ -n "${CI}" ]]; then set -x fi function usage() { echo -n \ "Usage: $(basename "$0") Format code with ruff " } if [ "${BASH_SOURCE[0]}" = "${0}" ]; then if [ "${1:-}" = "--help" ]; then usage else pre-commit run ruff-format --all-files fi fi ================================================ FILE: scripts/lint ================================================ #!/bin/bash set -e if [[ -n "${CI}" ]]; then set -x fi function usage() { echo -n \ "Usage: $(basename "$0") Execute project linters. " } if [ "${BASH_SOURCE[0]}" = "${0}" ]; then if [ "${1:-}" = "--help" ]; then usage else pre-commit run codespell --all-files pre-commit run doc8 --all-files pre-commit run ruff --all-files pre-commit run mypy --all-files fi fi ================================================ FILE: scripts/test ================================================ #!/bin/bash set -e if [[ -n "${CI}" ]]; then set -x fi function usage() { echo -n \ "Usage: $(basename "$0") Execute project linters and test suites. " } if [ "${BASH_SOURCE[0]}" = "${0}" ]; then if [ "${1:-}" = "--help" ]; then usage else ./scripts/lint ./scripts/format # Test suite with coverage enabled pytest --cov pystac_client --cov-report term-missing coverage xml fi fi ================================================ FILE: tests/__init__.py ================================================ ================================================ FILE: tests/cassettes/test_cli/TestCLICollections.test_collection_search[inprocess].yaml ================================================ interactions: - request: body: null headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - python-requests/2.32.4 method: GET uri: https://earth-search.aws.element84.com/v1 response: body: string: '{"stac_version":"1.0.0","type":"Catalog","id":"earth-search-aws","title":"Earth Search by Element 84","description":"A STAC API of public datasets on 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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 Conic Conformal (2SP)\"},\"parameters\":[{\"id\":{\"code\":8823,\"authority\":\"EPSG\"},\"name\":\"Latitude of 1st standard parallel\",\"unit\":\"degree\",\"value\":25},{\"id\":{\"code\":8824,\"authority\":\"EPSG\"},\"name\":\"Latitude of 2nd standard parallel\",\"unit\":\"degree\",\"value\":60},{\"id\":{\"code\":8821,\"authority\":\"EPSG\"},\"name\":\"Latitude of false origin\",\"unit\":\"degree\",\"value\":42.5},{\"id\":{\"code\":8822,\"authority\":\"EPSG\"},\"name\":\"Longitude of false origin\",\"unit\":\"degree\",\"value\":-100},{\"id\":{\"code\":8826,\"authority\":\"EPSG\"},\"name\":\"Easting at false origin\",\"unit\":\"metre\",\"value\":0},{\"id\":{\"code\":8827,\"authority\":\"EPSG\"},\"name\":\"Northing 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\"}}},\"y\":{\"axis\":\"y\",\"step\":-1000.0,\"type\":\"spatial\",\"extent\":[-622000.0,-39000.0],\"description\":\"y coordinate of 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parallel\",\"unit\":\"degree\",\"value\":60},{\"id\":{\"code\":8821,\"authority\":\"EPSG\"},\"name\":\"Latitude of false origin\",\"unit\":\"degree\",\"value\":42.5},{\"id\":{\"code\":8822,\"authority\":\"EPSG\"},\"name\":\"Longitude of false origin\",\"unit\":\"degree\",\"value\":-100},{\"id\":{\"code\":8826,\"authority\":\"EPSG\"},\"name\":\"Easting at false origin\",\"unit\":\"metre\",\"value\":0},{\"id\":{\"code\":8827,\"authority\":\"EPSG\"},\"name\":\"Northing 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 of the 'time_bnds' variable.\"},\"time\":{\"type\":\"temporal\",\"extent\":[\"1980-01-01T12:00:00Z\",\"2020-12-30T12:00:00Z\"],\"description\":\"24-hour day based on local time\"}},\"msft:group_keys\":[\"daily\",\"hawaii\"],\"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 surface weather data on a 1-km grid for 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 Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-seamless\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 3DEP Seamless DEMs\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/threedep.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"USGS 3DEP\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-seamless.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported to GeoParquet 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 digital elevation data at horizontal resolutions ranging from one to sixty meters.\\n\\nThe [USGS 3D Elevation Program (3DEP) Datasets](https://www.usgs.gov/core-science-systems/ngp/3dep) from the [National Map](https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map) are the primary elevation data product produced and distributed by the USGS. The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \\n\\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; 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 digital elevation data at horizontal resolutions ranging from one to sixty 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 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dsm\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 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 Lidar COG\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-lidar-dsm.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Surface Model (DSM) using [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"COG data\",\"raster:bands\":[{\"type\":\"Z\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"float32\",\"description\":\"Z PDAL Dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 Lidar collection for the Digital Surface Model (DSM) Cloud Optimized Geotiffs (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 Open Access Data Use Agreement\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/fia\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Forest 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 Description and User Guide\"},\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/fia.png\",\"type\":\"image/gif\",\"title\":\"Forest Inventory and Analysis\"},\"geoparquet-items\":{\"href\":\"abfs://items/fia.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Service\"],\"providers\":[{\"url\":\"https://www.fia.fs.fed.us/\",\"name\":\"Forest Inventory & Analysis\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://carbonplan.org/\",\"name\":\"CarbonPlan\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\\n\\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land.\\n\\nDomain: continental U.S., 1928-2018\\n\\nResolution: plot-level (irregular polygon)\\n\\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Dataset root\",\"table:storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"stac_version\":\"1.0.0\",\"table:tables\":[{\"name\":\"Survey Table\",\"description\":\"Survey table. This table contains one record for each year an inventory is conducted in a State for annual inventory or one record for each periodic inventory.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

* PLOTSNAP.CN = PLOT.CN links the 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 and trends on U.S. forest location, health, growth, mortality, and production, 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 1.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gridmet\",\"title\":\"Human 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 thumbnail\"},\"zarr-abfs\":{\"href\":\"abfs://gridmet/gridmet.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\"],\"description\":\"Azure Blob File System URI of the gridMET Zarr Group on Azure Blob Storage for use 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 HTTPS Zarr root\",\"description\":\"HTTPS URI of the gridMET Zarr Group on 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 Pressure\",\"Climate\"],\"providers\":[{\"url\":\"https://developmentseed.org/\",\"name\":\"Development Seed\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]},{\"url\":\"http://www.climatologylab.org/gridmet.html\",\"name\":\"Climatology Lab\",\"roles\":[\"producer\"]},{\"url\":\"http://onlinelibrary.wiley.com/doi/10.1002/joc.3413/full\",\"name\":\"Abatzoglou, J.T.\",\"roles\":[\"producer\"]}],\"description\":\"gridMET is a dataset of daily surface meteorological data at approximately four-kilometer resolution, covering the contiguous U.S. from 1979 to the present. 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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 Accumulated Precipitation\"},\"dead_fuel_moisture_100hr\":{\"type\":\"data\",\"unit\":\"Percent\",\"attrs\":{\"units\":\"Percent\",\"long_name\":\"fm100\",\"dimensions\":\"lon 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 hour fuel moisture\"},\"dead_fuel_moisture_1000hr\":{\"type\":\"data\",\"unit\":\"Percent\",\"attrs\":{\"units\":\"Percent\",\"long_name\":\"fm1000\",\"dimensions\":\"lon lat time\",\"description\":\"1000 hour fuel 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Hawaii\",\"msft:region\":\"westeurope\"},{\"id\":\"daymet-monthly-pr\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-monthly-pr/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/daymet-monthly-pr\"},{\"rel\":\"license\",\"href\":\"https://science.nasa.gov/earth-science/earth-science-data/data-information-policy\",\"title\":\"EOSDIS Data Use Policy\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.3334/ORNLDAAC/1855\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/daymet-monthly-pr\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Daymet Monthly Puerto Rico\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/daymet-monthly-pr.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Daymet monthly Puerto Rico map thumbnail\"},\"zarr-abfs\":{\"href\":\"abfs://daymet-zarr/monthly/pr.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\",\"abfs\"],\"title\":\"Monthly Puerto Rico Daymet Azure Blob File System Zarr root\",\"description\":\"Azure Blob File System of the monthly Puerto Rico Daymet Zarr Group on Azure Blob Storage for use with adlfs.\",\"xarray:open_kwargs\":{\"consolidated\":true},\"xarray:storage_options\":{\"account_name\":\"daymeteuwest\"}},\"zarr-https\":{\"href\":\"https://daymeteuwest.blob.core.windows.net/daymet-zarr/monthly/pr.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\",\"https\"],\"title\":\"Monthly Puerto Rico Daymet HTTPS Zarr root\",\"description\":\"HTTPS URI of the monthly Puerto Rico Daymet Zarr Group on Azure Blob Storage.\",\"xarray:open_kwargs\":{\"consolidated\":true}}},\"extent\":{\"spatial\":{\"bbox\":[[-67.9927,16.8444,-64.1196,19.9382]]},\"temporal\":{\"interval\":[[\"1980-01-16T12:00:00Z\",\"2020-12-16T00:00:00Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.3334/ORNLDAAC/1855\",\"keywords\":[\"Daymet\",\"Puerto Rico\",\"Temperature\",\"Precipitation\",\"Vapor Pressure\",\"Climate\"],\"providers\":[{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]},{\"url\":\"https://doi.org/10.3334/ORNLDAAC/1855\",\"name\":\"ORNL DAAC\",\"roles\":[\"producer\"]}],\"description\":\"Monthly climate summaries derived from [Daymet](https://daymet.ornl.gov) Version 4 daily data at a 1 km x 1 km spatial resolution for five variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable.\\n\\n[Daymet](https://daymet.ornl.gov/) provides measurements of near-surface meteorological conditions; the main purpose is to provide data estimates where no instrumentation exists. The dataset covers the period from January 1, 1980 to the present. Each year is processed individually at the close of a calendar year. Data are in a Lambert conformal conic projection for North America and are distributed in Zarr and NetCDF formats, compliant with the [Climate and Forecast (CF) metadata conventions (version 1.6)](http://cfconventions.org/).\\n\\nUse the DOI at [https://doi.org/10.3334/ORNLDAAC/1855](https://doi.org/10.3334/ORNLDAAC/1855) to cite your usage of the data.\\n\\nThis dataset provides coverage for Hawaii; North America and Puerto Rico are provided in [separate datasets](https://planetarycomputer.microsoft.com/dataset/group/daymet#monthly).\\n\",\"sci:citation\":\"Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Monthly Climate Summaries on a 1-km Grid for North America, Version 4. 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temperature\"},\"time_bnds\":{\"type\":\"data\",\"attrs\":{},\"shape\":[492,2],\"chunks\":[12,2],\"dimensions\":[\"time\",\"nv\"]},\"lambert_conformal_conic\":{\"type\":\"data\",\"attrs\":{\"false_easting\":0.0,\"false_northing\":0.0,\"semi_major_axis\":6378137.0,\"grid_mapping_name\":\"lambert_conformal_conic\",\"standard_parallel\":[25.0,60.0],\"inverse_flattening\":298.257223563,\"latitude_of_projection_origin\":42.5,\"longitude_of_central_meridian\":-100.0},\"shape\":[],\"dimensions\":[]}},\"msft:container\":\"daymet-zarr\",\"cube:dimensions\":{\"x\":{\"axis\":\"x\",\"step\":1000.0,\"type\":\"spatial\",\"extent\":[3445750.0,3808750.0],\"description\":\"x coordinate of 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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 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 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 Overview Slides\",\"description\":\"Slides giving a high level overview of the gNATSGO dataset\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/publicdomain/zero/1.0/\",\"title\":\"CC0 1.0 Universal Public Domain Dedication\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"gNATSGO 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 States\"],\"providers\":[{\"url\":\"https://www.nrcs.usda.gov/\",\"name\":\"United 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 collection contains the table data for gNATSGO. This table data can be used to determine the values of raster data cells for Items in the [gNATSGO Rasters](https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters) Collection.\\n\\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\\n\\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\\n\\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\\n\\nSee the [official 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 Horizon AASHTO table contains the American Association of State Highway Transportation Officials classification(s) for the referenced horizon. One row in this table is marked as the representative AASHTO classification for the horizon.\"},{\"name\":\"chconsistence\",\"description\":\"The Horizon Consistence table contains descriptive terms of soil consistence -- rupture resistance, plasticity, and stickiness -- for the referenced horizon. \ One row in this table is marked as having the representative characteristics for the horizon.\"},{\"name\":\"chdesgnsuffix\",\"description\":\"The Horizon Designation Suffix table contains the designation suffix(es), one per row, \ for the referenced horizon. For example, the \\\"h\\\" and \\\"s\\\" of a Bhs horizon appear as two rows in this table.\"},{\"name\":\"chfrags\",\"description\":\"The Horizon Fragments table lists the mineral and organic fragments that generally occur in the referenced horizon. If the Volume % is greater than zero (low=5, RV=10, high=15) in a row, the kind and size of fragment in that row exists everywhere this horizon and component occur in the map unit. If the Volume % includes zero (low=0, RV=5, high=10), the kind and size of fragment may exist in some places, but not in others.\"},{\"name\":\"chorizon\",\"description\":\"The Horizon table lists the horizon(s) and related data for the referenced map unit component. If the horizon thickness is greater than zero (low=5, RV=8, high=12), the horizon exists everywhere this component occurs. If the horizon thickness includes zero (low=0, RV=1, high=3), the horizon may exist in some places, but not in other places.\\r\\nHorizons that have two distinct parts, such as E/B or E&Bt horizons, are recorded twice. Once for the characteristics of the first part; and again on another row, using the same depths and thicknesses, for the characteristics of the other part.\"},{\"name\":\"chpores\",\"description\":\"The Horizon Pores table lists the voids for the referenced horizon. If the Quantity is greater than zero (low=2, RV=5, high=10) in a row, the voids in that row exist everywhere the horizon and component occur in the map unit. If the Quantity includes zero (low=0, RV=2, high=5), the voids may exist in some places, but not in others. More than one row can be marked as an RV row because a horizon may have more than one size or shape of void.\"},{\"name\":\"chstruct\",\"description\":\"The Horizon Structure table lists the individual soil structure size, grade, and shape terms for the referenced horizon. Terms in this table are assembled into a structure group string which is recorded in the Horizon Structure Group table.\"},{\"name\":\"chstructgrp\",\"description\":\"The Horizon Structure Group table lists the ranges of soil structure for the referenced horizon. \ The row with the typically occurring structure is marked as being representative. \ The entry in this table is based on grouping of entries in the Horizon Structure table.\"},{\"name\":\"chtext\",\"description\":\"The Horizon Text table contains notes and narrative descriptions related to the referenced horizon. Some notes may provide additional information about the horizon for which there is no explicit column for such data. In many cases, the table is empty for a particular horizon.\"},{\"name\":\"chtexture\",\"description\":\"The Horizon Texture table lists the individual texture(s), or term(s) used in lieu of texture, for the referenced horizon. Only the unmodified texture terms are listed in the Horizon Texture table; modifiers are listed in the Horizon Texture Modifier table. For example, a gravelly loamy sand is shown as \\\"GR-LS\\\" in the Horizon Texture Group table, \\\"ls\\\" in the Horizon Texture table, and \\\"gr\\\" in the Horizon Texture Modifier table.\"},{\"name\":\"chtexturegrp\",\"description\":\"The Horizon Texture Group table lists the range of textures for the referenced horizon as a concatenation of horizon texture and texture modifier(s). For example, a horizon that is gravelly loamy sand in some places and gravelly loamy coarse sand in other places is shown as GR-LS on one row and GR-LCOS on another row in this table. The row with the typically occurring texture is identified as the RV row. Stratified textures are shown in one row. For example, a horizon that is stratified gravelly loamy fine sand and cobbly coarse sand is shown as SR- GR-LFS CB-COS on one row and the Stratified? column for that row is marked \\\"yes\\\". If two or more textures always occur together but are not stratified, all of the textures are listed on one row and the Stratified? column for that row is marked \\\"no\\\".\"},{\"name\":\"chtexturemod\",\"description\":\"The Horizon Texture Modifier table lists the texture modifier(s) for the referenced texture. For example, a gravelly loamy sand is shown as \\\"GR-LS\\\" in the Horizon Texture Group table, \\\"ls\\\" in the Horizon Texture table, and \\\"gr\\\" in this table.\"},{\"name\":\"chunified\",\"description\":\"The Horizon Unified table contains the Unified Soil Classification(s) for the referenced horizon. One row in the Horizon Unified table is marked as the representative Unified classification for the horizon.\"},{\"name\":\"cocanopycover\",\"description\":\"The Component Canopy Cover table lists the overstory plants that typically occur on the referenced map unit component.\"},{\"name\":\"cocropyld\",\"description\":\"The Component Crop Yield table lists commonly grown crops and their expected range in yields when grown on the referenced map unit component. Yields for the map unit as a whole are given in the Mapunit Crop Yield table.\"},{\"name\":\"codiagfeatures\",\"description\":\"The Component Diagnostic Features table lists the typical soil features, such as ochric epipedon or cambic horizon, for the referenced map unit component.\"},{\"name\":\"coecoclass\",\"description\":\"The Component Ecological Classification table identifies the ecological sites typically associated with the referenced map unit component. These may include the official NRCS forestland and rangland ecological sites, as well as those of other classification systems, such as the USFS Habitat Types.\"},{\"name\":\"coeplants\",\"description\":\"The Component Existing Plants table lists the plants, either rangeland or forestland plants, that typically occur on the referenced map unit component.\"},{\"name\":\"coerosionacc\",\"description\":\"The Component Erosion Accelerated table lists the kinds of accelerated erosion that occur on the referenced map unit component. One row in this table is marked as the representative kind of accelerated erosion for that component.\"},{\"name\":\"coforprod\",\"description\":\"The Component Forest Productivity table lists the site index and the annual productivity in cubic feet per acre per year (CAMI) of forest overstory tree species that typically occur on the referenced map unit component.\"},{\"name\":\"coforprodo\",\"description\":\"The Component Forest Productivity - Other table lists the site index and annual productivity of forest overstory tree species in units other than cubic feet per acre per year for trees that typically occur on the referenced map unit component.\"},{\"name\":\"cogeomordesc\",\"description\":\"The Component Geomorphic Description table lists the geomorphic features on which the referenced map unit component typically occurs.\"},{\"name\":\"cohydriccriteria\",\"description\":\"The Component Hydric Criteria table lists the hydric soil criteria met for those referenced map unit components that are classified as a \\\"hydric soil.\\\"\"},{\"name\":\"cointerp\",\"description\":\"The Component Interpretation table lists the predictions of behavior and limiting features for specified uses made for the referenced map unit component.\"},{\"name\":\"comonth\",\"description\":\"The Component Month table lists the monthly flooding and ponding characteristics for the referenced map unit component. This table has one row for each month of the year.\"},{\"name\":\"component\",\"description\":\"The Component table lists the map unit components identified in the referenced map unit, and selected properties of each component. If the Component % is greater than zero (low=65, RV=75, high=90) for a component, that component exists in every delineation of that mapunit. If the Component % includes zero (low=0, RV=50, high=90), the component may exist in some delineations, but not in others.\"},{\"name\":\"copm\",\"description\":\"The Component Parent Material table lists the individual parent material(s) for the referenced map unit component. In some cases where soils developed in multiple materials in a vertical sequence, that sequence will be noted. \ In other cases multiple entries with no vertical sequence noted indicates the soil may have formed in one of the materials listed.\"},{\"name\":\"copmgrp\",\"description\":\"The Component Parent Material Group table lists the concatenated string of parent material(s) in which the referenced map unit component formed based on entries in the Component Parent Material table. For example, a component formed in one parent material, such as loess, or one vertical sequence of parent materials, such as loamy glacial drift over silty residuum weathered from shale, has one row in this table. A component formed in one parent material in some locations, but another parent material (or sequence of parent materials) in other locations has two rows in this table, one for each parent material (or sequence of parent materials). One row is identified as the representative parent material.\"},{\"name\":\"copwindbreak\",\"description\":\"The Component Potential Windbreak table lists the windbreak plant species commonly recommended for the referenced map unit component. A windbreak plant listed in this table may be used alone or in combination with other plants.\"},{\"name\":\"corestrictions\",\"description\":\"The Component Restrictions table lists the root restrictive feature(s) or layer(s) for the referenced map unit component. If the thickness of the restrictive layer is greater than zero (low=5, RV=8, high=10), the restrictive layer exists in all delineations of the map unit where the component occurs. If the thickness of the restrictive layer includes zero (low=0, RV=2, high=5), the restrictive layer may exist in some delineations, but not in others. This table will be empty if the component does not have restrictive features, but could have several rows if several restrictive features occur in the soil.\"},{\"name\":\"cosoilmoist\",\"description\":\"The Component Soil Moisture table describes the typical soil moisture profile for the referenced map unit component during the month referenced in the Component Month table. The soil moisture profiles for each month, taken as a group of twelve months, describe the representative situation for the component throughout the year.\"},{\"name\":\"cosoiltemp\",\"description\":\"The Component Soil Temperature table describes the typical soil temperature profile for the referenced map unit component during the month referenced in the Component Month table. The soil temperature profiles for each month, taken as a group of twelve months, describe the representative situation for the component throughout the year.\"},{\"name\":\"cosurffrags\",\"description\":\"The Component Surface Fragments table lists the organic or mineral fragments that generally occur on the surface of the referenced map unit component. If the cover percent is greater than zero (low=0.1, RV=1, high=3) for a row in this table, the fragment is in every delineation of the map unit where the referenced component occurs. If the Cover % includes zero (low=0, RV=0.01, high=1) for a row in this table, the fragment may exist in some delineations and not in others.\"},{\"name\":\"cosurfmorphgc\",\"description\":\"The Component Three Dimensional Surface Morphometry table lists the typical geomorphic position (s) of the referenced map unit component, in three dimension terms. \ The geomorphic position(s) listed in this table apply to the geomorphic feature referenced in the Component Geomorphic Description table.\"},{\"name\":\"cosurfmorphhpp\",\"description\":\"The Component Two Dimensional Surface Morphometry table lists the geomorphic position(s) of the referenced map unit component, in two dimensional hillslope profile terms. The geomorphic position(s) listed in this table apply to the geomorphic feature referenced in the Component Geomorphic Description table.\"},{\"name\":\"cosurfmorphmr\",\"description\":\"The Component Microrelief Surface Morphometry table lists microrelief features associated with the referenced geomorphic (microfeature) feature shown in the Component Geomorphic Description table.\"},{\"name\":\"cosurfmorphss\",\"description\":\"The Component Slope Shape Surface Morphometry table lists the geomorphic shape(s) of the referenced map unit component, in slope shape terms. The slope shape terms listed in this table apply to the referenced geomorphic feature shown in the Component Geomorphic Description table.\"},{\"name\":\"cotaxfmmin\",\"description\":\"The Component Taxonomic Family Mineralogy table lists the mineralogy characteristics, as defined in Soil Taxonomy, that apply to the referenced map unit component.\"},{\"name\":\"cotaxmoistcl\",\"description\":\"The Component Taxonomic Moisture Class table provides clear identification of the intended taxonomic moisture class, as defined in Soil Taxonomy, that apply to the referenced map unit component, even though moisture class is implied at a higher taxonomic level. The class or classes listed in this table describe the representative situation for the component.\"},{\"name\":\"cotext\",\"description\":\"The Component Text table contains notes and narrative descriptions for the referenced map unit component. In many cases, the table will be empty for a particular component.\"},{\"name\":\"cotreestomng\",\"description\":\"The Component Trees To Manage table lists the trees commonly recommended for managing on the referenced map unit component.\"},{\"name\":\"cotxfmother\",\"description\":\"The Component Taxonomic Family Other Criteria table lists the other taxonomic characteristics, such as classes of coatings or permanent cracks, as defined in Soil Taxonomy, that apply to the referenced map unit component. The characteristics listed in this table describe the representative situation for the component.\"},{\"name\":\"distinterpmd\",\"description\":\"The Distribution Interp Metadata table records the set of NASIS fuzzy logic interpretations which were generated for the map unit components included in a set of distribution data.\"},{\"name\":\"distlegendmd\",\"description\":\"The Distribution Legend Metadata table records information about the legends or soil survey areas selected for inclusion in a set of distributed data. The presence of a legend in this table does not imply that all of the available data for that legend was included in the set of data that was distributed. Only certain map units and components for that legend may have been selected. The record of the criteria used for selecting map units and components may be found in the Distribution Metadata table.\"},{\"name\":\"distmd\",\"description\":\"The Distribution Metadata table records information associated with the selection of a set of data for distribution to some entity or information system external to NASIS. A set of distribution data may include only selected map units from a legend or legends, and only selected components of those map units. This table records the criteria used for selecting map units and components for inclusion in the set of distributed data. Other recorded information includes the name of the NASIS user who initiated a distribution request, and the times when that request was made, and when that request was ultimately processed.\"},{\"name\":\"laoverlap\",\"description\":\"The Legend Area Overlap table lists the geographic areas that are coincident with the soil survey area identified in the Legend table. For example, a survey area that covers two counties would have two rows in this table, one for each county. Other types of geographic areas listed might include state, MLRA, rainfall (R) factor area, climate (C) factor area, etc.\"},{\"name\":\"legend\",\"description\":\"The Legend table identifies the soil survey area that the legend is related to, and related information about that legend.\"},{\"name\":\"legendtext\",\"description\":\"The Legend Text table contains notes and narrative descriptions related to the referenced legend. Legend text is optional. In many cases, this table is empty.\"},{\"name\":\"mapunit\",\"description\":\"The Mapunit table identifies the map units included in the referenced legend. Data related the map unit as a whole are also given.\"},{\"name\":\"muaggatt\",\"description\":\"The Mapunit Aggregated Attribute table records a variety of soil attributes and interpretations that have been aggregated from the component level to a single value at the map unit level. They have been aggregated by one or more appropriate means in order to express a consolidated value or interpretation for the map unit as a whole.\"},{\"name\":\"muaoverlap\",\"description\":\"The Mapunit Area Overlap table lists the map units that exist in the overlap between the entire soil survey area and the referenced geographic area in the Legend Area Overlap table.\"},{\"name\":\"mucropyld\",\"description\":\"The Mapunit Crop Yield table lists commonly grown crops and their expected yields for the referenced map unit as a whole. Yields for individual map unit components are given in the Component Crop Yield table.\"},{\"name\":\"mutext\",\"description\":\"The Mapunit Text table contains notes and narrative descriptions related to the referenced map unit.\"},{\"name\":\"sacatalog\",\"description\":\"This table records the primary dynamic metadata associated with a soil survey area. This includes such things as survey area version, tabular data version, etc. The remaining dynamic metadata, which soil interpretations were generated for the corresponding soil survey area, is recorded in the Survey Area Interpretation table.\"},{\"name\":\"sainterp\",\"description\":\"This table records information about the soil interpretations that were generated for a soil survey area.\"},{\"name\":\"valu1\",\"description\":\"Included with the gSSURGO database, but not a part of the standard SSURGO dataset is a table called Valu1. This table contains 57 pre-summarized or 'ready to map' attributes derived from the official SSURGO database. These attribute data are pre-summarized to the map unit level using best-practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. These themes were prepared to better meet the mapping needs of users of soil survey information and can be used with both SSURGO and gridded 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 gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United 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 Data Use Policy\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/hgb\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"HGB: 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 Global Biomass\"},\"geoparquet-items\":{\"href\":\"abfs://items/hgb.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at 300m resolution. The aboveground biomass map integrates land-cover-specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover-specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree/land cover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.\\n\",\"item_assets\":{\"aboveground\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Global above-ground biomass\"},\"belowground\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Global below-ground biomass\"},\"aboveground_uncertainty\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Uncertainty associated with global above-ground biomass\"},\"belowground_uncertainty\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Uncertainty 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 maps of aboveground and belowground biomass carbon density for the year 2010 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 DEM License\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-30\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Copernicus 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 DEM GLO-30\"},\"geoparquet-items\":{\"href\":\"abfs://items/cop-dem-glo-30.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\\n\\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 30 meters.\\n\\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\\n\\nSee the dataset page on OpenTopography: \\n\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; 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 DEM 30 is a near-global digital surface model (DSM) with a horizontal resolution of approximately 30 meters, derived from radar satellite data acquired during 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 DEM License\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cop-dem-glo-90\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Copernicus 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 DEM GLO-90\"},\"geoparquet-items\":{\"href\":\"abfs://items/cop-dem-glo-90.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 Copernicus DEM is a digital surface model (DSM), which represents the surface of the Earth including buildings, infrastructure, and vegetation. This DSM is based on radar satellite data acquired during the TanDEM-X Mission, which was funded by a public-private partnership between the German Aerospace Centre (DLR) and Airbus Defence and Space.\\n\\nCopernicus DEM is available at both 30-meter and 90-meter resolution; this dataset has a horizontal resolution of approximately 90 meters.\\n\\nSee the [Product Handbook](https://object.cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/Copernicus_metadata.pdf) for more information.\\n\\nSee the dataset page on OpenTopography: \\n\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; 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 DEM 90 is a near-global digital surface model (DSM) with a horizontal resolution of approximately 90 meters, derived from radar satellite data acquired during the TanDEM-X mission.\",\"msft:region\":\"westeurope\"},{\"id\":\"terraclimate\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/terraclimate/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/terraclimate\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/publicdomain/zero/1.0/legalcode\",\"type\":\"text/html\",\"title\":\"CC0-1.0 License\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/terraclimate\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"TerraClimate\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/terraclimate.png\",\"role\":[\"thumbnail\"],\"type\":\"image/png\",\"title\":\"TerraClimate thumbnail\"},\"zarr-abfs\":{\"href\":\"az://cpdata/terraclimate.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\",\"abfs\"],\"title\":\"TerraClimate Azure Blob File System Zarr root\",\"description\":\"Azure Blob File System URI of the TerraClimate Zarr Group on Azure Blob Storage for use with adlfs.\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true,\"storage_options\":{\"account_name\":\"cpdataeuwest\"}}},\"zarr-https\":{\"href\":\"https://cpdataeuwest.blob.core.windows.net/cpdata/terraclimate.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\",\"https\"],\"title\":\"TerraClimate HTTPS Zarr root\",\"description\":\"HTTPS URI of the TerraClimate Zarr Group on Azure Blob Storage.\",\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":true}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"1958-01-01T00:00:00Z\",\"2021-12-01T00:00:00Z\"]]}},\"license\":\"CC0-1.0\",\"keywords\":[\"TerraClimate\",\"Water\",\"Precipitation\",\"Temperature\",\"Vapor Pressure\",\"Climate\"],\"providers\":[{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]},{\"url\":\"http://www.climatologylab.org/terraclimate.html\",\"name\":\"Climatology Lab\",\"roles\":[\"producer\"]},{\"url\":\"https://www.nature.com/articles/sdata2017191\",\"name\":\"Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch\",\"roles\":[\"producer\"]}],\"description\":\"[TerraClimate](http://www.climatologylab.org/terraclimate.html) is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958 to the present. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. This dataset is provided in [Zarr](https://zarr.readthedocs.io/) format.\\n\",\"stac_version\":\"1.0.0\",\"cube:variables\":{\"q\":{\"type\":\"data\",\"unit\":\"mm\",\"attrs\":{\"units\":\"mm\",\"long_name\":\"runoff_amount\",\"dimensions\":\"lon lat time\",\"description\":\"Runoff\",\"grid_mapping\":\"crs\",\"standard_name\":\"runoff_amount\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[768,4320,8640],\"chunks\":[12,1024,1024],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Runoff\"},\"ws\":{\"type\":\"data\",\"unit\":\"m/s\",\"attrs\":{\"units\":\"m/s\",\"long_name\":\"wind_speed\",\"dimensions\":\"lon lat time\",\"description\":\"Wind Speed at 10-m\",\"grid_mapping\":\"crs\",\"standard_name\":\"wind_speed\",\"coordinate_system\":\"WGS84,EPSG:4326\"},\"shape\":[768,4320,8640],\"chunks\":[12,1024,1024],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Wind Speed at 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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 NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four \u201CTier 1\u201D greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.\\n\\nThe [NASA Center for Climate Simulation](https://www.nccs.nasa.gov/) maintains the [next-gddp-cmip6 product page](https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6) where you can find more information about these datasets. Users are encouraged to review the [technote](https://www.nccs.nasa.gov/sites/default/files/NEX-GDDP-CMIP6-Tech_Note.pdf), provided alongside the data set, where more detailed information, references and acknowledgements can be found.\\n\\nThis collection contains many NetCDF files. There is one NetCDF file per `(model, scenario, variable, year)` tuple.\\n\\n- **model** is the name of a modeling group (e.g. \\\"ACCESS-CM-2\\\"). See the `cmip6:model` summary in the STAC collection for a full list of models.\\n- **scenario** is one of \\\"historical\\\", \\\"ssp245\\\" or \\\"ssp585\\\".\\n- **variable** is one of \\\"hurs\\\", \\\"huss\\\", \\\"pr\\\", \\\"rlds\\\", \\\"rsds\\\", \\\"sfcWind\\\", \\\"tas\\\", \\\"tasmax\\\", \\\"tasmin\\\".\\n- **year** depends on the value of *scenario*. For \\\"historical\\\", the values range from 1950 to 2014 (inclusive). For \\\"ssp245\\\" and \\\"ssp585\\\", the years range from 2015 to 2100 (inclusive).\\n\\nIn addition to the NetCDF files, we provide some *experimental* **reference files** as collection-level dataset assets. These are JSON files implementing the [references specification](https://fsspec.github.io/kerchunk/spec.html).\\nThese files include the positions of data variables within the binary NetCDF files, which can speed up reading the metadata. See the example notebook for more.\",\"item_assets\":{\"pr\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Precipitation\",\"description\":\"Precipitation\"},\"tas\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Daily Near-Surface Air Temperature\",\"description\":\"Daily Near-Surface Air Temperature\"},\"hurs\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Near-Surface Relative Humidity\",\"description\":\"Near-Surface Relative Humidity\"},\"huss\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Near-Surface Specific Humidity\",\"description\":\"Near-Surface Specific Humidity\"},\"rlds\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Surface Downwelling Longwave Radiation\",\"description\":\"Surface Downwelling Longwave Radiation\"},\"rsds\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Surface Downwelling Shortwave Radiation\",\"description\":\"Surface Downwelling Shortwave Radiation\"},\"tasmax\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Daily Maximum Near-Surface Air Temperature\",\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Daily Minimum Near-Surface Air Temperature\",\"description\":\"Daily Minimum Near-Surface Air Temperature\"},\"sfcWind\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Daily-Mean Near-Surface Wind Speed\",\"description\":\"Daily-Mean Near-Surface Wind Speed\"}},\"sci:citation\":\"Climate scenarios used were from the NEX-GDDP-CMIP6 dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).\",\"stac_version\":\"1.0.0\",\"cube:variables\":{\"pr\":{\"type\":\"data\",\"unit\":\"kg m-2 s-1\",\"attrs\":{\"units\":\"kg m-2 s-1\",\"comment\":\"includes both liquid and solid phases\",\"long_name\":\"Precipitation\",\"cell_methods\":\"area: time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"precipitation_flux\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Precipitation\"},\"tas\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"near-surface (usually, 2 meter) air temperature; derived from downscaled tasmax & tasmin\",\"long_name\":\"Daily Near-Surface Air Temperature\",\"cell_methods\":\"area: mean time: maximum\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"air_temperature\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily Near-Surface Air Temperature\"},\"hurs\":{\"type\":\"data\",\"unit\":\"%\",\"attrs\":{\"units\":\"%\",\"comment\":\"The relative humidity with respect to liquid water for T> 0 C, and with respect to ice for T<0 C.\",\"long_name\":\"Near-Surface Relative Humidity\",\"cell_methods\":\"area: time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"relative_humidity\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Near-Surface Relative Humidity\"},\"huss\":{\"type\":\"data\",\"unit\":\"1\",\"attrs\":{\"units\":\"1\",\"comment\":\"Near-surface (usually, 2 meter) specific humidity.\",\"long_name\":\"Near-Surface Specific Humidity\",\"cell_methods\":\"area: time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"specific_humidity\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Near-Surface Specific Humidity\"},\"rlds\":{\"type\":\"data\",\"unit\":\"W m-2\",\"attrs\":{\"units\":\"W m-2\",\"comment\":\"The surface called 'surface' means the lower boundary of the atmosphere. 'longwave' means longwave radiation. Downwelling radiation is radiation from above. It does not mean 'net downward'. When thought of as being incident on a surface, a radiative flux is sometimes called 'irradiance'. In addition, it is identical with the quantity measured by a cosine-collector light-meter and sometimes called 'vector irradiance'. In accordance with common usage in geophysical disciplines, 'flux' implies per unit area, called 'flux density' in physics.\",\"long_name\":\"Surface Downwelling Longwave Radiation\",\"cell_methods\":\"area: time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"surface_downwelling_longwave_flux_in_air\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Surface Downwelling Longwave Radiation\"},\"rsds\":{\"type\":\"data\",\"unit\":\"W m-2\",\"attrs\":{\"units\":\"W m-2\",\"comment\":\"Surface solar irradiance for UV calculations.\",\"long_name\":\"Surface Downwelling Shortwave Radiation\",\"cell_methods\":\"area: time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"surface_downwelling_shortwave_flux_in_air\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Surface Downwelling Shortwave Radiation\"},\"tasmax\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')\",\"long_name\":\"Daily Maximum Near-Surface Air Temperature\",\"cell_methods\":\"area: mean time: maximum\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"air_temperature\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')\",\"long_name\":\"Daily Minimum Near-Surface Air Temperature\",\"cell_methods\":\"area: mean time: minimum\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"air_temperature\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily Minimum Near-Surface Air Temperature\"},\"sfcWind\":{\"type\":\"data\",\"unit\":\"m s-1\",\"attrs\":{\"units\":\"m s-1\",\"comment\":\"near-surface (usually, 10 meters) wind speed.\",\"long_name\":\"Daily-Mean Near-Surface Wind Speed\",\"cell_methods\":\"area: time: mean\",\"cell_measures\":\"area: areacella\",\"standard_name\":\"wind_speed\"},\"shape\":[365,600,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily-Mean Near-Surface Wind Speed\"}},\"msft:container\":\"nex-gddp-cmip6\",\"cube:dimensions\":{\"lat\":{\"axis\":\"y\",\"step\":0.25,\"type\":\"spatial\",\"extent\":[-59.875,89.875],\"description\":\"latitude\",\"reference_system\":4326},\"lon\":{\"axis\":\"x\",\"step\":0.25,\"type\":\"spatial\",\"extent\":[0.125,359.875],\"description\":\"longitude\",\"reference_system\":4326},\"time\":{\"step\":\"P1DT0H0M0S\",\"type\":\"temporal\",\"extent\":[\"1950-01-01T12:00:00Z\",\"2100-12-31T00:00:00Z\"],\"description\":\"time\"}},\"stac_extensions\":[\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\",\"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\"],\"msft:storage_account\":\"nasagddp\",\"msft:short_description\":[\"Global downscaled climate scenarios derived from the General Circulation Model conducted under CMIP6.\"],\"msft:region\":\"westeurope\"},{\"id\":\"gpm-imerg-hhr\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gpm-imerg-hhr/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/gpm-imerg-hhr\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gpm-imerg-hhr\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"GPM IMERG\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gpm-imerg-hhr.png\",\"role\":[\"thumbnail\"],\"type\":\"image/png\",\"title\":\"gpm-imerg-hhr thumbnail\"},\"zarr-abfs\":{\"href\":\"abfs://imerg/gpm-imerg-hhr.zarr\",\"type\":\"application/vnd+zarr\",\"roles\":[\"data\",\"zarr\"],\"description\":\"Azure Blob File System URI of the gpm-imerg-hhr Zarr Group on Azure Blob Storage for use with adlfs.\",\"xarray:open_kwargs\":{\"use_cftime\":true,\"consolidated\":true},\"xarray:storage_options\":{\"account_name\":\"ai4edataeuwest\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2000-06-01T00:00:00Z\",\"2021-05-31T23:30:00Z\"]]}},\"license\":\"proprietary\",\"sci:doi\":\"10.5067/GPM/IMERG/3B-HH/06\",\"keywords\":[\"IMERG\",\"GPM\",\"Precipitation\"],\"providers\":[{\"url\":\"https://developmentseed.org/\",\"name\":\"Development Seed\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]},{\"url\":\"https://gpm.nasa.gov/data/directory\",\"name\":\"NASA\",\"roles\":[\"producer\"]}],\"description\":\"The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the [GPM satellite constellation](https://gpm.nasa.gov/missions/gpm/constellation) to estimate precipitation over the majority of the Earth's surface. This algorithm is particularly valuable over the majority of the Earth's surface that lacks precipitation-measuring instruments on the ground. Now in the latest Version 06 release of IMERG the algorithm fuses the early precipitation estimates collected during the operation of the TRMM satellite (2000 - 2015) with more recent precipitation estimates collected during operation of the GPM satellite (2014 - present). The longer the record, the more valuable it is, as researchers and application developers will attest. By being able to compare and contrast past and present data, researchers are better informed to make climate and weather models more accurate, better understand normal and extreme rain and snowfall around the world, and strengthen applications for current and future disasters, disease, resource management, energy production and food security.\\n\\nFor more, see the [IMERG homepage](https://gpm.nasa.gov/data/imerg) The [IMERG Technical documentation](https://gpm.nasa.gov/sites/default/files/2020-10/IMERG_doc_201006.pdf) provides more information on the algorithm, input datasets, and output products.\",\"sci:citation\":\"Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC)\",\"stac_version\":\"1.0.0\",\"cube:variables\":{\"lat_bnds\":{\"type\":\"auxiliary\",\"unit\":\"degrees_north\",\"attrs\":{\"Units\":\"degrees_north\",\"units\":\"degrees_north\",\"coordinates\":\"lat latv\",\"DimensionNames\":\"lat,latv\"},\"shape\":[1800,2],\"chunks\":[1800,2],\"dimensions\":[\"lat\",\"latv\"]},\"lon_bnds\":{\"type\":\"auxiliary\",\"unit\":\"degrees_east\",\"attrs\":{\"Units\":\"degrees_east\",\"units\":\"degrees_east\",\"coordinates\":\"lon lonv\",\"DimensionNames\":\"lon,lonv\"},\"shape\":[3600,2],\"chunks\":[3600,2],\"dimensions\":[\"lon\",\"lonv\"]},\"time_bnds\":{\"type\":\"auxiliary\",\"attrs\":{\"Units\":\"seconds since 1970-01-01 00:00:00 UTC\",\"coordinates\":\"time nv\",\"DimensionNames\":\"time,nv\"},\"shape\":[368160,2],\"chunks\":[12,2],\"dimensions\":[\"time\",\"nv\"]},\"randomError\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"HQprecipSource\":{\"type\":\"data\",\"attrs\":{\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"HQprecipitation\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"IRprecipitation\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"precipitationCal\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"HQobservationTime\":{\"type\":\"data\",\"attrs\":{\"Units\":\"minutes\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"precipitationUncal\":{\"type\":\"data\",\"unit\":\"mm/hr\",\"attrs\":{\"Units\":\"mm/hr\",\"units\":\"mm/hr\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"IRkalmanFilterWeight\":{\"type\":\"data\",\"attrs\":{\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"precipitationQualityIndex\":{\"type\":\"data\",\"attrs\":{\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999.9\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]},\"probabilityLiquidPrecipitation\":{\"type\":\"data\",\"unit\":\"percent\",\"attrs\":{\"Units\":\"percent\",\"units\":\"percent\",\"DimensionNames\":\"time,lon,lat\",\"CodeMissingValue\":\"-9999\"},\"shape\":[368160,3600,1800],\"chunks\":[12,3600,1800],\"dimensions\":[\"time\",\"lon\",\"lat\"]}},\"msft:container\":\"imerg\",\"cube:dimensions\":{\"lat\":{\"axis\":\"y\",\"type\":\"spatial\",\"extent\":[-89.94999694824219,89.94999694824219],\"reference_system\":4326},\"lon\":{\"axis\":\"x\",\"type\":\"spatial\",\"extent\":[-179.9499969482422,179.9499969482422],\"reference_system\":4326},\"time\":{\"step\":\"P0DT0H30M0S\",\"type\":\"temporal\",\"extent\":[\"2000-06-01T00:00:00Z\",\"2021-05-31T23:30:00Z\"]}},\"stac_extensions\":[\"https://stac-extensions.github.io/xarray-assets/v1.0.0/schema.json\",\"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\":\"ai4edataeuwest\",\"msft:short_description\":\"The Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation over the majority of the Earth's surface.\",\"msft:region\":\"westeurope\"},{\"id\":\"gnatsgo-rasters\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/gnatsgo-rasters/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-rasters\"},{\"rel\":\"about\",\"href\":\"https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf\",\"type\":\"application/pdf\",\"title\":\"gSSURGO 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 Overview Slides\",\"description\":\"Slides giving a high level overview of the gNATSGO dataset\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/publicdomain/zero/1.0/\",\"title\":\"CC0 1.0 Universal Public Domain Dedication\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gnatsgo-rasters\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"gNATSGO Soil Database - Rasters\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gnatsgo-rasters.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"gNATSGO\"},\"geoparquet-items\":{\"href\":\"abfs://items/gnatsgo-rasters.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 States\"],\"providers\":[{\"url\":\"https://www.nrcs.usda.gov/\",\"name\":\"United 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 collection contains the raster data for gNATSGO. In order to use the map unit values contained in the `mukey` raster asset, you'll need to join to tables represented as Items in the [gNATSGO Tables](https://planetarycomputer.microsoft.com/dataset/gnatsgo-tables) Collection. Many items have commonly used values encoded in additional raster assets.\\n\\nThe gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase.\\n\\nSSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods.\\n\\nThe gNATSGO database is composed primarily of SSURGO data, but STATSGO2 data was used to fill in the gaps. The RSSs are newer product with relatively limited spatial extent. These RSSs were merged into the gNATSGO after combining the SSURGO and STATSGO2 data. The extent of RSS is expected to increase in the coming years.\\n\\nSee the [official documentation](https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625)\",\"item_assets\":{\"mukey\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"mukey\",\"description\":\"Map unit key is the unique identifier of a record in the Mapunit table. Use this column to join the Component table to the Map Unit table and the Valu1 table to the MapUnitRaster_10m raster map layer to map valu1 themes.\"},\"aws0_5\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_5\",\"description\":\"Available water storage estimate (AWS) in a standard zone 1 (0-5 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"soc0_5\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_5\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard layer 1 or standard zone 1 (0-5 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 5 cm. NULL values are presented where data are incomplete or not available.\"},\"tk0_5a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_5a\",\"description\":\"Thickness of soil components used in standard layer 1 or standard zone 1 (0-5 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_5s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_5s\",\"description\":\"Thickness of soil components used in standard layer 1 or standard zone 1 (0-5 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"aws0_20\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_20\",\"description\":\"Available water storage estimate (AWS) in standard zone 2 (0-20 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"aws0_30\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_30\",\"description\":\"Available water storage estimate (AWS) in standard zone 3 (0-30 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"aws5_20\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws5_20\",\"description\":\"Available water storage estimate (AWS) in standard layer 2 (5-20 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"soc0_20\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_20\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard zone 2 (0-20 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 20 cm. NULL values are presented where data are incomplete or not available.\"},\"soc0_30\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_30\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard zone 3 (0-30 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 30 cm. NULL values are presented where data are incomplete or not available.\"},\"soc5_20\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc5_20\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard layer 2 (5-20 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 5-20 cm layer. NULL values are presented where data are incomplete or not available.\"},\"tk0_20a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_20a\",\"description\":\"Thickness of soil components used in standard zone 2 (0-20 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_20s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_20s\",\"description\":\"Thickness of soil components used in standard zone 2 (0-20 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_30a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_30a\",\"description\":\"Thickness of soil components used in standard zone 3 (0-30 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_30s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_30s\",\"description\":\"Thickness of soil components used in standard zone 3 (0-30 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"tk5_20a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk5_20a\",\"description\":\"Thickness of soil components used in standard layer 2 (5-20 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk5_20s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk5_20s\",\"description\":\"Thickness of soil components used in standard layer 2 (5-20 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"aws0_100\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_100\",\"description\":\"Available water storage estimate (AWS) in standard zone 4 (0-100 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"aws0_150\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_150\",\"description\":\"Available water storage estimate (AWS) in standard zone 5 (0-150 cm depth), expressed in mm. The volume of plant available water that the soil can store in this zone based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"aws0_999\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws0_999\",\"description\":\"Available water storage estimate (AWS) in total soil profile (0 cm to the reported depth of the soil profile), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"aws20_50\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws20_50\",\"description\":\"Available water storage estimate (AWS) in standard layer 3 (20-50 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"droughty\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"droughty\",\"description\":\"zone for commodity crops that is less than or equal to 6 inches (152 mm) expressed as \\\"1\\\" for a drought vulnerable soil landscape map unit or \\\"0\\\" for a non-droughty soil landscape map unit or NULL for miscellaneous areas (includes water bodies) or where data were not available. It is computed as a weighted average for major earthy components. Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes'\"},\"nccpi3sg\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3sg\",\"description\":\"National Commodity Crop Productivity Index for Small Grains (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available.\"},\"soc0_100\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_100\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard zone 4 (0-100 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 100 cm. NULL values are presented where data are incomplete or not available.\"},\"soc0_150\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_150\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard zone 5 (0-150 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter to a depth of 150 cm. NULL values are presented where data are incomplete or not available.\"},\"soc0_999\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc0_999\",\"description\":\"Soil organic carbon stock estimate (SOC) in total soil profile (0 cm to the reported depth of the soil profile). The concentration of organic carbon present in the soil expressed in grams C per square meter for the total reported soil profile depth. NULL values are presented where data are incomplete or not available.\"},\"soc20_50\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc20_50\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard layer 3 (20-50 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 20-50 cm layer. NULL values are presented where data are incomplete or not available.\"},\"tk0_100a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_100a\",\"description\":\"Thickness of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_100s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_100s\",\"description\":\"Thickness of soil components used in standard zone 4 (0-100 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_150a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_150a\",\"description\":\"Thickness of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_150s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_150s\",\"description\":\"Thickness of soil components used in standard zone 5 (0-150 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_999a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_999a\",\"description\":\"Thickness of soil components used in total soil profile (0 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk0_999s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk0_999s\",\"description\":\"Thickness of soil components used in total soil profile (0 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"tk20_50a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk20_50a\",\"description\":\"Thickness of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk20_50s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk20_50s\",\"description\":\"Thickness of soil components used in standard layer 3 (20-50 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"aws50_100\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws50_100\",\"description\":\"Available water storage estimate (AWS) in standard layer 3 (50-100 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"musumcpct\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"musumcpct\",\"description\":\"The sum of the comppct_r (SSURGO component table) values for all listed components in the map unit. Useful metadata information. NULL values are presented where data are incomplete or not available.\"},\"nccpi3all\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3all\",\"description\":\"National Commodity Crop Productivity Index that has the highest value among Corn and Soybeans, Small Grains, or Cotton (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available.\"},\"nccpi3cot\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3cot\",\"description\":\"National Commodity Crop Productivity Index for Cotton (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available.\"},\"nccpi3soy\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3soy\",\"description\":\"National Commodity Crop Productivity Index for Soybeans (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available.\"},\"pwsl1pomu\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"pwsl1pomu\",\"description\":\"Potential Wetland Soil Landscapes (PWSL) is expressed as the percentage of the map unit that meets the PWSL criteria. The hydric rating (soil component variable \u201Chydricrating\u201D) is an indicator of wet soils. For version 1 (pwsl1), those soil components that meet the following criteria are tagged as PWSL and their comppct_r values are summed for each map unit. Soil components with hydricrating = 'YES' are considered PWSL. Soil components with hydricrating = \u201CNO\u201D are not PWSL. Soil components with hydricrating = 'UNRANKED' are tested using other attributes, and will be considered PWSL if any of the following conditions are met: drainagecl = 'Poorly drained' or 'Very poorly drained' or the localphase or the otherph data fields contain any of the phrases \\\"drained\\\" or \\\"undrained\\\" or \\\"channeled\\\" or \\\"protected\\\" or \\\"ponded\\\" or \\\"flooded\\\". If these criteria do not determine the PWSL for a component and hydricrating = 'UNRANKED', then the map unit will be classified as PWSL if the map unit name contains any of the phrases \\\"drained\\\" or \\\"undrained\\\" or \\\"channeled\\\" or \\\"protected\\\" or \\\"ponded\\\" or \\\"flooded\\\". For version 1 (pwsl1), waterbodies are identified as \\\"999\\\" when map unit names match a list of terms that identify water or intermittent water or map units have a sum of the comppct_r for \\\"Water\\\" that is 80% or greater. NULL values are presented where data are incomplete or not available.\"},\"rootznaws\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"rootznaws\",\"description\":\"Root zone (commodity crop) available water storage estimate (RZAWS) , expressed in mm, is the volume of plant available water that the soil can store within the root zone based on all map unit earthy major components (weighted average). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available.\"},\"rootznemc\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"rootznemc\",\"description\":\"Root zone depth is the depth within the soil profile that commodity crop (cc) roots can effectively extract water and nutrients for growth. Root zone depth influences soil productivity significantly. Soil component horizon criteria for root-limiting depth include: presence of hard bedrock, soft bedrock, a fragipan, a duripan, sulfuric material, a dense layer, a layer having a pH of less than 3.5, or a layer having an electrical conductivity of more than 12 within the component soil profile. If no root-restricting zone is identified, a depth of 150 cm is used to approximate the root zone depth (Dobos et al., 2012). Root zone depth is computed for all map unit major earthy components (weighted average). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available.\"},\"soc50_100\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc50_100\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard layer 4 (50-100 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 50-100 cm layer. NULL values are presented where data are incomplete or not available.\"},\"tk50_100a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk50_100a\",\"description\":\"Thickness of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk50_100s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk50_100s\",\"description\":\"Thickness of soil components used in standard layer 4 (50-100 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"aws100_150\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws100_150\",\"description\":\"Available water storage estimate (AWS) in standard layer 5 (100-150 cm depth), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"aws150_999\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"aws150_999\",\"description\":\"Available water storage estimate (AWS) in standard layer 6 (150 cm to the reported depth of the soil profile), expressed in mm. The volume of plant available water that the soil can store in this layer based on all map unit components (weighted average). NULL values are presented where data are incomplete or not available.\"},\"musumcpcta\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"musumcpcta\",\"description\":\"The sum of the comppct_r (SSURGO component table) values used in the available water storage calculation for the map unit. Useful metadata information. NULL values are presented where data are incomplete or not available.\"},\"musumcpcts\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"musumcpcts\",\"description\":\"The sum of the comppct_r (SSURGO component table) values used in the soil organic carbon calculation for the map unit. Useful metadata information. NULL values are presented where data are incomplete or not available.\"},\"nccpi3corn\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"nccpi3corn\",\"description\":\"National Commodity Crop Productivity Index for Corn (weighted average) for major earthy components. Values range from .01 (low productivity) to .99 (high productivity). Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). NULL values are presented where data are incomplete or not available.\"},\"pctearthmc\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"pctearthmc\",\"description\":\"The National Commodity Crop Productivity Index map unit percent earthy is the map unit summed comppct_r for major earthy components. Earthy components are those soil series or higher level taxa components that can support crop growth (Dobos et al., 2012). Major components are those soil components where the majorcompflag = 'Yes' (SSURGO component table). Useful metadata information. NULL values are presented where data are incomplete or not available.\"},\"soc100_150\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc100_150\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard layer 5 (100-150 cm depth). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 100-150 cm layer. NULL values are presented where data are incomplete or not available.\"},\"soc150_999\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"soc150_999\",\"description\":\"Soil organic carbon stock estimate (SOC) in standard layer 6 (150 cm to the reported depth of the soil profile). The concentration of organic carbon present in the soil expressed in grams C per square meter for the 150 cm and greater depth layer. NULL values are presented where data are incomplete or not available.\"},\"tk100_150a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk100_150a\",\"description\":\"Thickness of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk100_150s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk100_150s\",\"description\":\"Thickness of soil components used in standard layer 5 (100-150 cm) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or not available.\"},\"tk150_999a\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk150_999a\",\"description\":\"Thickness of soil components used in standard layer 6 (150 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the available water storage calculation. NULL values are presented where data are incomplete or not available.\"},\"tk150_999s\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"tk150_999s\",\"description\":\"Thickness of soil components used in standard layer 6 (150 cm to the reported depth of the soil profile) expressed in cm (weighted average) for the Soil Organic Carbon calculation. NULL values are presented where data are incomplete or 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 data for the gridded National Soil Survey Geographic Database (gNATSGO), a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of 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 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-hag\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 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 Lidar COG\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-lidar-hag.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 COG type is generated using the Z dimension of the [COPC data](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc) data and removes noise, water, and using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) followed by [pdal.filters.hag_nn](https://pdal.io/stages/filters.hag_nn.html#filters-hag-nn).\\n\\nThe Height Above Ground Nearest Neighbor filter takes as input a point cloud with Classification set to 2 for ground points. It creates a new dimension, HeightAboveGround, that contains the normalized height values.\\n\\nGround points may be generated with [`pdal.filters.pmf`](https://pdal.io/stages/filters.pmf.html#filters-pmf) or [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf), but you can use any method you choose, as long as the ground returns are marked.\\n\\nNormalized heights are a commonly used attribute of point cloud data. This can also be referred to as height above ground (HAG) or above ground level (AGL) heights. In the end, it is simply a measure of a point's relative height as opposed to its raw elevation value.\\n\\nThe filter finds the number of ground points nearest to the non-ground point under consideration. It calculates an average ground height weighted by the distance of each ground point from the non-ground point. The HeightAboveGround is the difference between the Z value of the non-ground point and the interpolated ground height.\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG data\",\"raster:bands\":[{\"name\":\"HeightAboveGround\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"float32\",\"description\":\"Raster for Height Above Ground (HAG)\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 Lidar collection for the Height Above Ground (HAG or Elevation) Cloud Optimized Geotiffs (COGs). All USGS HAG COG STAC items will be associated with this 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 BY 4.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"10m 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 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Cover\",\"Land Use\",\"Sentinel\"],\"providers\":[{\"url\":\"https://www.esri.com/\",\"name\":\"Esri\",\"roles\":[\"licensor\"]},{\"url\":\"https://www.impactobservatory.com/\",\"name\":\"Impact Observatory\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"raster:bands\":[{\"nodata\":0,\"spatial_resolution\":10}]},\"description\":\"Time series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2023. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\\n\\nThis dataset, produced by [Impact Observatory](http://impactobservatory.com/), Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\\n\\nThese maps have been improved from Impact Observatory\u2019s [previous release](https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class) and provide a relative reduction in the amount of anomalous change between classes, particularly between \u201CBare\u201D and any of the vegetative classes \u201CTrees,\u201D \u201CCrops,\u201D \u201CFlooded Vegetation,\u201D and \u201CRangeland\u201D. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery.\\n\\nAll years are available under a Creative Commons BY-4.0.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Global land cover data\",\"file:values\":[{\"values\":[0],\"summary\":\"No Data\"},{\"values\":[1],\"summary\":\"Water\"},{\"values\":[2],\"summary\":\"Trees\"},{\"values\":[4],\"summary\":\"Flooded vegetation\"},{\"values\":[5],\"summary\":\"Crops\"},{\"values\":[7],\"summary\":\"Built 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 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 Domain\"},{\"rel\":\"preview\",\"href\":\"https://planetarycomputer.microsoft.com/api/data/v1/collection/map?collection=goes-cmi\",\"title\":\"Map of collection mosaic\",\"type\":\"text/html\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/goes-cmi\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"GOES-R Cloud & Moisture Imagery\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/goes-cmi-thumb.png\",\"title\":\"GOES CMIP\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/goes-cmi.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"W-MON\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}},\"tilejson\":{\"title\":\"Mosaic 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 DISK\",\"CONUS\",\"MESOSCALE\"],\"goes:processing-level\":[\"L2\"]},\"description\":\"The GOES-R Advanced Baseline Imager (ABI) L2 Cloud and Moisture Imagery product provides 16 reflective and emissive bands at high temporal cadence over the Western Hemisphere.\\n\\nThe GOES-R series is the latest in the Geostationary Operational Environmental Satellites (GOES) program, which has been operated in a collaborative effort by NOAA and NASA since 1975. The operational GOES-R Satellites, GOES-16, GOES-17, and GOES-18, capture 16-band imagery from geostationary orbits over the Western Hemisphere via the Advance Baseline Imager (ABI) radiometer. The ABI captures 2 visible, 4 near-infrared, and 10 infrared channels at resolutions between 0.5km and 2km.\\n\\n### Geographic coverage\\n\\nThe ABI captures three levels of coverage, each at a different temporal cadence depending on the modes described below. The geographic coverage for each image is described by the `goes:image-type` STAC Item property.\\n\\n- _FULL DISK_: a circular image depicting nearly full coverage of the Western Hemisphere.\\n- _CONUS_: a 3,000 (lat) by 5,000 (lon) km rectangular image depicting the Continental U.S. (GOES-16) or the Pacific Ocean including Hawaii (GOES-17).\\n- _MESOSCALE_: a 1,000 by 1,000 km rectangular image. GOES-16 and 17 both alternate between two different mesoscale geographic regions.\\n\\n### Modes\\n\\nThere are three standard scanning modes for the ABI instrument: Mode 3, Mode 4, and Mode 6.\\n\\n- Mode _3_ consists of one observation of the full disk scene of the Earth, three observations of the continental United States (CONUS), and thirty observations for each of two distinct mesoscale views every fifteen minutes.\\n- Mode _4_ consists of the observation of the full disk scene every five minutes.\\n- Mode _6_ consists of one observation of the full disk scene of the Earth, two observations of the continental United States (CONUS), and twenty observations for each of two distinct mesoscale views every ten minutes.\\n\\nThe mode that each image was captured with is described by the `goes:mode` STAC Item property.\\n\\nSee this [ABI Scan Mode Demonstration](https://youtu.be/_c5H6R-M0s8) video for an idea of how the ABI scans multiple geographic regions over time.\\n\\n### Cloud and Moisture Imagery\\n\\nThe Cloud and Moisture Imagery product contains one or more images with pixel values identifying \\\"brightness values\\\" that are scaled to support visual analysis. Cloud and Moisture Imagery product (CMIP) files are generated for each of the sixteen ABI reflective and emissive bands. In addition, there is a multi-band product file that includes the imagery at all bands (MCMIP).\\n\\nThe Planetary Computer STAC Collection `goes-cmi` captures both the CMIP and MCMIP product files into individual STAC Items for each observation from a GOES-R satellite. It contains the original CMIP and MCMIP NetCDF files, as well as cloud-optimized GeoTIFF (COG) exports of the data from each MCMIP band (2km); the full-resolution CMIP band for bands 1, 2, 3, and 5; and a Web Mercator COG of bands 1, 2 and 3, which are useful for rendering.\\n\\nThis product is not in a standard coordinate reference system (CRS), which can cause issues with some tooling that does not handle non-standard large geographic regions.\\n\\n### For more information\\n- [Beginner\u2019s Guide to GOES-R Series Data](https://www.goes-r.gov/downloads/resources/documents/Beginners_Guide_to_GOES-R_Series_Data.pdf)\\n- [GOES-R Series Product Definition and Users\u2019 Guide: Volume 5 (Level 2A+ Products)](https://www.goes-r.gov/products/docs/PUG-L2+-vol5.pdf) ([Spanish 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 01 (full resolution)\",\"eo:bands\":[{\"name\":\"ABI Band 1\",\"common_name\":\"blue\",\"description\":\"Daytime aerosol over land, coastal water mapping\",\"center_wavelength\":0.47}]},\"C01_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 01\",\"eo:bands\":[{\"name\":\"ABI Band 1\",\"common_name\":\"blue\",\"description\":\"Daytime aerosol over land, coastal water mapping\",\"center_wavelength\":0.47}]},\"C02_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 02\",\"eo:bands\":[{\"name\":\"ABI Band 2\",\"common_name\":\"red\",\"description\":\"Daytime clouds, fog, insolation, winds\",\"center_wavelength\":0.64}]},\"C03_1km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 03 (full resolution)\",\"eo:bands\":[{\"name\":\"ABI Band 3\",\"common_name\":\"nir09\",\"description\":\"Daytime vegetation, burn scar, aerosol over water, winds\",\"center_wavelength\":0.87}]},\"C03_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 03\",\"eo:bands\":[{\"name\":\"ABI Band 3\",\"common_name\":\"nir09\",\"description\":\"Daytime vegetation, burn scar, aerosol over water, winds\",\"center_wavelength\":0.87}]},\"C04_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 04\",\"eo:bands\":[{\"name\":\"ABI Band 4\",\"common_name\":\"cirrus\",\"description\":\"Daytime cirrus cloud\",\"center_wavelength\":1.38}]},\"C05_1km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 05 (full resolution)\",\"eo:bands\":[{\"name\":\"ABI Band 5\",\"common_name\":\"swir16\",\"description\":\"Daytime cloud-top phase and particle size, snow\",\"center_wavelength\":1.61}]},\"C05_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 05\",\"eo:bands\":[{\"name\":\"ABI Band 5\",\"common_name\":\"swir16\",\"description\":\"Daytime cloud-top phase and particle size, snow\",\"center_wavelength\":1.61}]},\"C06_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery reflectance factor - Band 06\",\"eo:bands\":[{\"name\":\"ABI Band 6\",\"common_name\":\"swir22\",\"description\":\"Daytime land, cloud properties, particle size, vegetation, snow\",\"center_wavelength\":2.25}]},\"C07_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 07\",\"eo:bands\":[{\"name\":\"ABI Band 7\",\"description\":\"Surface and cloud, fog at night, fire, winds\",\"center_wavelength\":3.89}]},\"C08_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 08\",\"eo:bands\":[{\"name\":\"ABI Band 8\",\"description\":\"High-level atmospheric water vapor, winds, rainfall\",\"center_wavelength\":6.17}]},\"C09_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 09\",\"eo:bands\":[{\"name\":\"ABI Band 9\",\"description\":\"Midlevel atmospheric water vapor, winds, rainfall\",\"center_wavelength\":6.93}]},\"C10_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 10\",\"eo:bands\":[{\"name\":\"ABI Band 10\",\"description\":\"Lower-level water vapor, winds, and silicon dioxide\",\"center_wavelength\":7.34}]},\"C11_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 11\",\"eo:bands\":[{\"name\":\"ABI Band 11\",\"description\":\"Total water for stability, cloud phase, dust, silicon dioxide, rainfall\",\"center_wavelength\":8.44}]},\"C12_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 12\",\"eo:bands\":[{\"name\":\"ABI Band 12\",\"description\":\"Total ozone, turbulence, winds\",\"center_wavelength\":9.61}]},\"C13_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 13\",\"eo:bands\":[{\"name\":\"ABI Band 13\",\"description\":\"Surface and clouds\",\"center_wavelength\":10.33}]},\"C14_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 14\",\"eo:bands\":[{\"name\":\"ABI Band 14\",\"description\":\"Imagery, sea surface temperature, clouds, rainfall\",\"center_wavelength\":11.19}]},\"C15_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 15\",\"eo:bands\":[{\"name\":\"ABI Band 15\",\"description\":\"Total water, volcanic ash, sea surface temperature\",\"center_wavelength\":12.27}]},\"C16_2km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"Cloud and Moisture Imagery brightness temperature at top of atmosphere - Band 16\",\"eo:bands\":[{\"name\":\"ABI Band 16\",\"description\":\"Air temperature, cloud heights\",\"center_wavelength\":13.27}]},\"MCMIP-nc\":{\"type\":\"application/netcdf\",\"roles\":[\"data\"],\"title\":\"Cloud and Moisture Imagery - Multiband\",\"eo:bands\":[{\"name\":\"ABI Band 1\",\"common_name\":\"blue\",\"description\":\"Daytime aerosol over land, coastal water mapping\",\"center_wavelength\":0.47},{\"name\":\"ABI Band 2\",\"common_name\":\"red\",\"description\":\"Daytime clouds, fog, insolation, winds\",\"center_wavelength\":0.64},{\"name\":\"ABI Band 3\",\"common_name\":\"nir09\",\"description\":\"Daytime vegetation, burn scar, aerosol over water, winds\",\"center_wavelength\":0.87},{\"name\":\"ABI Band 4\",\"common_name\":\"cirrus\",\"description\":\"Daytime cirrus cloud\",\"center_wavelength\":1.38},{\"name\":\"ABI Band 5\",\"common_name\":\"swir16\",\"description\":\"Daytime cloud-top phase and particle size, snow\",\"center_wavelength\":1.61},{\"name\":\"ABI Band 6\",\"common_name\":\"swir22\",\"description\":\"Daytime land, cloud properties, particle size, vegetation, snow\",\"center_wavelength\":2.25},{\"name\":\"ABI Band 7\",\"description\":\"Surface and cloud, fog at night, fire, winds\",\"center_wavelength\":3.89},{\"name\":\"ABI Band 8\",\"description\":\"High-level atmospheric water vapor, winds, rainfall\",\"center_wavelength\":6.17},{\"name\":\"ABI Band 9\",\"description\":\"Midlevel atmospheric water vapor, winds, rainfall\",\"center_wavelength\":6.93},{\"name\":\"ABI Band 10\",\"description\":\"Lower-level water vapor, winds, and silicon dioxide\",\"center_wavelength\":7.34},{\"name\":\"ABI Band 11\",\"description\":\"Total water for stability, cloud phase, dust, silicon dioxide, rainfall\",\"center_wavelength\":8.44},{\"name\":\"ABI Band 12\",\"description\":\"Total ozone, turbulence, winds\",\"center_wavelength\":9.61},{\"name\":\"ABI Band 13\",\"description\":\"Surface and clouds\",\"center_wavelength\":10.33},{\"name\":\"ABI Band 14\",\"description\":\"Imagery, sea surface temperature, clouds, rainfall\",\"center_wavelength\":11.19},{\"name\":\"ABI Band 15\",\"description\":\"Total water, volcanic ash, sea surface temperature\",\"center_wavelength\":12.27},{\"name\":\"ABI Band 16\",\"description\":\"Air temperature, cloud heights\",\"center_wavelength\":13.27}],\"description\":\"The Cloud and Moisture Imagery product contains one or more Earth-view images with pixel values identifying 'brightness values' that are scaled to support visual analysis.\"},\"C02_0.5km\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud and Moisture Imagery reflectance factor - 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CONUS404, so named because it covers the CONterminous United States for over 40 years at 4 km resolution, was produced by the Weather Research and Forecasting (WRF) model simulations run by NCAR as part of a collaboration with the USGS Water Mission Area. The CONUS404 includes 42 years of data (water years 1980-2021) and the spatial domain extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for CONUS surface waters.\\n\\nThe CONUS404 dataset, produced using WRF version 3.9.1.1, is the successor to the CONUS1 dataset in [ds612.0](https://rda.ucar.edu/datasets/ds612.0/) (Liu, et al., 2017) with improved representation of weather and climate conditions in the central United States due to the addition of a shallow groundwater module and several other improvements in the NOAH-Multiparameterization land surface model. It also uses a more up-to-date and higher-resolution reanalysis dataset (ERA5) as input and covers a longer period than CONUS1.\",\"msft:region\":\"eastus\",\"sci:citation\":\"Rasmussen, R. M., Chen, F., Liu, C.H., Ikeda, K., Prein, A., Kim, J., Schneider, T., Dai, A., Gochis, D., Dugger, A., Zhang, Y., Jaye, A., Dudhia, J., He, C., Harrold, M., Xue, L., Chen, S., Newman, A., Dougherty, E., Abolafia-Rosenzweig, R., Lybarger, N. D., Viger, R., Lesmes, D., Skalak, K., Brakebill, J., Cline, D., Dunne, K., Rasmussen, K., & Miguez-Macho, G. (2023). CONUS404: The NCAR\u2013USGS 4-km Long-Term Regional Hydroclimate Reanalysis over the CONUS. 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index.\"},\"snso_layers_stag\":{\"type\":\"count\",\"extent\":[0,6],\"description\":\"snso layer index.\"},\"soil_layers_stag\":{\"type\":\"count\",\"extent\":[0,4],\"description\":\"soil layer index.\"},\"south_north_stag\":{\"type\":\"count\",\"extent\":[0,1015],\"description\":\"south_north_stag index.\"}},\"stac_extensions\":[\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\",\"https://stac-extensions.github.io/xarray-assets/v1.0.0/schema.json\",\"https://stac-extensions.github.io/datacube/v2.2.0/schema.json\"],\"msft:storage_account\":\"azureopendatastorage\",\"msft:short_description\":\"High-resolution hydro-climate dataset for forcing hydrological models and meteorological analysis over the conterminous United 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 BY 4.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel 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 1 RTC\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-1-rtc.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Sentinel-1 Radiometrically Terrain Corrected (RTC) data in this collection is a radiometrically terrain corrected product derived from the [Ground Range Detected (GRD) Level-1](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) products produced by the European Space Agency. The RTC processing is performed by [Catalyst](https://catalyst.earth/).\\n\\nRadiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return, as expressed in radar geometry. Without treatment, the hill-slope modulations of the radiometry threaten to overwhelm weaker thematic land cover-induced backscatter differences. Additionally, comparison of backscatter from multiple satellites, modes, or tracks loses meaning.\\n\\nA Planetary Computer account is required to retrieve SAS tokens to read the RTC data. See the [documentation](http://planetarycomputer.microsoft.com/docs/concepts/sas/#when-an-account-is-needed) for more information.\\n\\n### Methodology\\n\\nThe Sentinel-1 GRD product is converted to calibrated intensity using the conversion algorithm described in the ESA technical note ESA-EOPG-CSCOP-TN-0002, [Radiometric Calibration 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). The flat earth calibration values for gamma correction (i.e. perpendicular to the radar line of sight) are extracted from the GRD metadata. The calibration coefficients are applied as a two-dimensional correction in range (by sample number) and azimuth (by time). All available polarizations are calibrated and written as separate layers of a single file. The calibrated SAR output is reprojected to nominal map orientation with north at the top and west to the left.\\n\\nThe data is then radiometrically terrain corrected using PlanetDEM as the elevation source. The correction algorithm is nominally based upon D. Small, [\u201CFlattening Gamma: Radiometric Terrain Correction for SAR Imagery\u201D](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/sentinel-1/2011_Flattening_Gamma.pdf), IEEE Transactions on Geoscience and Remote Sensing, Vol 49, No 8., August 2011, pp 3081-3093. For each image scan line, the digital elevation model is interpolated to determine the elevation corresponding to the position associated with the known near slant range distance and arc length for each input pixel. The elevations at the four corners of each pixel are estimated using bilinear resampling. The four elevations are divided into two triangular facets and reprojected onto the plane perpendicular to the radar line of sight to provide an estimate of the area illuminated by the radar for each earth flattened pixel. The uncalibrated sum at each earth flattened pixel is normalized by dividing by the flat earth surface area. The adjustment for gamma intensity is given by dividing the normalized result by the cosine of the incident angle. Pixels which are not illuminated by the radar due to the viewing geometry are flagged as shadow.\\n\\nCalibrated data is then orthorectified to the appropriate UTM projection. The orthorectified output maintains the original sample sizes (in range and azimuth) and was not shifted to any specific grid.\\n\\nRTC data is processed only for the Interferometric Wide Swath (IW) mode, which is the main acquisition mode over land and satisfies the majority of service requirements.\\n\",\"item_assets\":{\"hh\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"HH: horizontal transmit, horizontal receive\",\"description\":\"Terrain-corrected gamma naught values of signal transmitted with horizontal polarization and received with horizontal polarization with radiometric terrain correction applied.\",\"raster:bands\":[{\"nodata\":-32768,\"data_type\":\"float32\",\"spatial_resolution\":10}]},\"hv\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"HV: horizontal transmit, vertical receive\",\"description\":\"Terrain-corrected gamma naught values of signal transmitted with horizontal polarization and received with vertical polarization with radiometric terrain correction applied.\",\"raster:bands\":[{\"nodata\":-32768,\"data_type\":\"float32\",\"spatial_resolution\":10}]},\"vh\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VH: vertical transmit, horizontal receive\",\"description\":\"Terrain-corrected gamma naught values of signal transmitted with vertical polarization and received with horizontal polarization with radiometric terrain correction applied.\",\"raster:bands\":[{\"nodata\":-32768,\"data_type\":\"float32\",\"spatial_resolution\":10}]},\"vv\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VV: vertical transmit, vertical receive\",\"description\":\"Terrain-corrected gamma naught values of signal transmitted with vertical polarization and received 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 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 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-intensity\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 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 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 collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the pulse return magnitude.\\n\\nThe values are based on the Intensity [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG data\",\"raster:bands\":[{\"name\":\"Intensity\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"uint16\",\"description\":\"Intensity PDAL dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 Lidar collection for the Cloud Optimized Geotiffs (COGs) based on the Intensity 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 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-pointsourceid\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 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 Lidar COG\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-lidar-pointsourceid.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the file source ID from which the point originated. Zero indicates that the point originated in the current file.\\n\\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG data\",\"raster:bands\":[{\"name\":\"PointSourceId\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"int32\",\"description\":\"PointSourceId PDAL dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 Lidar collection for Cloud Optimized Geotiffs (COGs) using PDAL's PointSourceId 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 BY\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/mtbs\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MTBS: 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 Trends in Burn Severity](https://www.mtbs.gov/) (MTBS) is an inter-agency program whose goal is to consistently map the burn severity and extent of large fires across the United States from 1984 to the present. This includes all fires 1000 acres or greater in the Western United States and 500 acres or greater in the Eastern United States. The burn severity mosaics in this dataset consist of thematic raster images of MTBS burn severity classes for 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Burn severity\",\"file:values\":[{\"values\":[0],\"summary\":\"No Data\"},{\"values\":[1],\"summary\":\"Unburned to low\"},{\"values\":[2],\"summary\":\"Low\"},{\"values\":[3],\"summary\":\"Moderate\"},{\"values\":[4],\"summary\":\"High\"},{\"values\":[5],\"summary\":\"Increased 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 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 - C-CAP Products\"},{\"rel\":\"describedby\",\"href\":\"https://coast.noaa.gov/data/digitalcoast/pdf/ccap-faq-regional.pdf\",\"type\":\"application/pdf\",\"title\":\"Frequently Asked Questions\"},{\"rel\":\"describedby\",\"href\":\"https://coast.noaa.gov/data/digitalcoast/pdf/ccap-class-scheme-regional.pdf\",\"type\":\"application/pdf\",\"title\":\"C-CAP Classification Scheme and Class Definitions\"},{\"rel\":\"license\",\"href\":\"https://coast.noaa.gov/digitalcoast/data/ccapregional.html\",\"type\":\"text/html\",\"title\":\"C-CAP Regional Land Cover and Change\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-c-cap\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"C-CAP 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 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 C-CAP\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-c-cap.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Cover\",\"Land Use\",\"NOAA\",\"Coastal\"],\"providers\":[{\"url\":\"https://coast.noaa.gov/digitalcoast/data/ccapregional.html\",\"name\":\"National Oceanic and Atmospheric Administration, Office for Coastal Management\",\"roles\":[\"licensor\",\"producer\",\"processor\"]},{\"url\":\"https//planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"Nationally standardized, raster-based inventories of land cover for the coastal areas of the U.S. Data are derived, through the Coastal Change Analysis Program, from the analysis of multiple dates of remotely sensed imagery. Two file types are available: individual dates that supply a wall-to-wall map, and change files that compare one date to another. The use of standardized data and procedures assures consistency through time and across geographies. C-CAP data forms the coastal expression of the National Land Cover Database (NLCD) and the A-16 land cover theme of the National Spatial Data Infrastructure. \ The data are updated every 5 years.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:classes\":[{\"value\":0,\"description\":\"Background\"},{\"value\":1,\"description\":\"Unclassified (Cloud, Shadow, etc)\"},{\"value\":2,\"color-hint\":\"FFFFFF\",\"description\":\"High Intensity Developed\"},{\"value\":3,\"color-hint\":\"C1C1C1\",\"description\":\"Medium Intensity Developed\"},{\"value\":4,\"color-hint\":\"979797\",\"description\":\"Low Intensity Developed\"},{\"value\":5,\"color-hint\":\"CCCA42\",\"description\":\"Developed Open Space\"},{\"value\":6,\"color-hint\":\"4E2409\",\"description\":\"Cultivated Crops\"},{\"value\":7,\"color-hint\":\"BCA05B\",\"description\":\"Pasture/Hay\"},{\"value\":8,\"color-hint\":\"EABB8E\",\"description\":\"Grassland/Herbaceous\"},{\"value\":9,\"color-hint\":\"6FEC48\",\"description\":\"Deciduous Forest\"},{\"value\":10,\"color-hint\":\"15380A\",\"description\":\"Evergreen Forest\"},{\"value\":11,\"color-hint\":\"489D48\",\"description\":\"Mixed Forest\"},{\"value\":12,\"color-hint\":\"6D6C1F\",\"description\":\"Scrub/Shrub\"},{\"value\":13,\"color-hint\":\"245A5A\",\"description\":\"Palustrine Forested Wetland\"},{\"value\":14,\"color-hint\":\"E2742D\",\"description\":\"Palustrine Scrub/Shrub Wetland\"},{\"value\":15,\"color-hint\":\"DE3BEA\",\"description\":\"Palustrine Emergent Wetland (Persistent)\"},{\"value\":16,\"color-hint\":\"37083B\",\"description\":\"Estuarine Forested Wetland\"},{\"value\":17,\"color-hint\":\"631569\",\"description\":\"Estuarine Scrub/Shrub Wetland\"},{\"value\":18,\"color-hint\":\"A028A9\",\"description\":\"Estuarine Emergent Wetland\"},{\"value\":19,\"color-hint\":\"6DEEF0\",\"description\":\"Unconsolidated Shore\"},{\"value\":20,\"color-hint\":\"F3F050\",\"description\":\"Bare Land\"},{\"value\":21,\"color-hint\":\"000A72\",\"description\":\"Open Water\"},{\"value\":22,\"color-hint\":\"0021E8\",\"description\":\"Palustrine Aquatic Bed\"},{\"value\":23,\"color-hint\":\"6777D4\",\"description\":\"Estuarine Aquatic Bed\"},{\"value\":24,\"color-hint\":\"F3D147\",\"description\":\"Tundra\"},{\"value\":25,\"color-hint\":\"BBF7EF\",\"description\":\"Perennial Ice/Snow\"}]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"]}},\"sci:citation\":\"National Oceanic and Atmospheric Administration, Office for Coastal Management. Name of Data Set: Coastal Change Analysis Program (C-CAP) Regional Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed April 2022 at www.coast.noaa.gov/htdata/raster1/landcover/bulkdownload/30m_lc/.\",\"stac_version\":\"1.0.0\",\"msft:container\":\"noaa-c-cap\",\"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/v1.0.0/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\":\"ai4edataeuwest\",\"msft:short_description\":\"Periodic coastal landcover classifications back to 1975\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-copc\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-copc/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-copc\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 3DEP Lidar Point Cloud\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/3dep-lidar-copc-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Thumbnail\"}},\"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\",\"Point cloud\"],\"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 collection contains source data from the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program) reformatted into the [COPC](https://copc.io) format. A COPC file is a LAZ 1.4 file that stores point data organized in a clustered octree. It contains a VLR that describes the octree organization of data that are stored in LAZ 1.4 chunks. The end product is a one-to-one mapping of LAZ to UTM-reprojected COPC files.\\n\\nLAZ data is geospatial [LiDAR point cloud](https://en.wikipedia.org/wiki/Point_cloud) (LPC) content stored in the compressed [LASzip](https://laszip.org?) format. Data were reorganized and stored in LAZ-compatible [COPC](https://copc.io) organization for use in Planetary Computer, which supports incremental spatial access and cloud streaming.\\n\\nLPC can be summarized for construction of digital terrain models (DTM), filtered for extraction of features like vegetation and buildings, and visualized to provide a point cloud map of the physical spaces the laser scanner interacted with. LPC content from 3DEP is used to compute and extract a variety of landscape characterization products, and some of them are provided by Planetary Computer, including Height Above Ground, Relative Intensity Image, and DTM and Digital Surface Models.\\n\\nThe LAZ tiles represent a one-to-one mapping of original tiled content as provided by the [USGS 3DEP program](https://www.usgs.gov/3d-elevation-program), with the exception that the data were reprojected and normalized into appropriate UTM zones for their location without adjustment to the vertical datum. In some cases, vertical datum description may not match actual data values, especially for pre-2010 USGS 3DEP point cloud data.\\n\\nIn addition to these COPC files, various higher-level derived products are available as Cloud Optimized GeoTIFFs in [other collections](https://planetarycomputer.microsoft.com/dataset/group/3dep-lidar).\",\"item_assets\":{\"data\":{\"type\":\"application/vnd.laszip+copc\",\"roles\":[\"data\"],\"title\":\"COPC data\",\"pc:type\":\"lidar\",\"pc:encoding\":\"application/vnd.laszip+copc\"},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 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 User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD64A1\",\"title\":\"MCD64A1 file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MCD64A1.061\",\"title\":\"LP DAAC - MCD64A1\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-64A1-061\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS 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 Burned Area Monthly thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-64A1-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. 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The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year.\",\"item_assets\":{\"QA\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Quality Assurance Indicators\",\"raster:bands\":[{\"data_type\":\"uint8\",\"spatial_resolution\":500}]},\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source data containing all bands\"},\"Last_Day\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Last day of the year of reliable change detection\",\"raster:bands\":[{\"unit\":\"Day\",\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal Geographic Data Committee (FGDC) Metadata\"},\"Burn_Date\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Burn day of year\",\"raster:bands\":[{\"unit\":\"Day\",\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"First_Day\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"First day of the year of reliable change detection\",\"raster:bands\":[{\"unit\":\"Day\",\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"Burn_Date_Uncertainty\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Estimated 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, L., Justice, C., Boschetti, L., & Roy, D. (2021). MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V061 [Data set]. 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Here, the \\\"forest\\\" is defined as the tree covered land with an area larger than 0.5 ha and a canopy cover of over 10 %, in accordance with the FAO definition of forest. The classification results are presented in four categories, with two categories of forest areas: forests with a canopy cover of 90 % or more and forests with a canopy cover of 10 % to 90 %, depending on the density of the forest area.\\n\\nSee the [Product Description](https://www.eorc.jaxa.jp/ALOS/en/dataset/pdf/DatasetDescription_PALSAR2_FNF_V200.pdf) for more details.\\n\",\"item_assets\":{\"C\":{\"role\":\"data\",\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"C\",\"description\":\"Forest vs Non-Forest classification\",\"raster:bands\":[{\"nodata\":0,\"data_type\":\"uint8\"}],\"classification:classes\":[{\"name\":\"no_data\",\"value\":0,\"description\":\"No data\"},{\"name\":\"forest (>90%)\",\"value\":1,\"description\":\"Forest (>90% canopy cover)\"},{\"name\":\"forest (10-90%)\",\"value\":2,\"description\":\"Forest (10-90% canopy cover)\"},{\"name\":\"non_forest\",\"value\":3,\"description\":\"Non-forest\"},{\"name\":\"water\",\"value\":4,\"description\":\"Water\"}]}},\"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/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 25m resolution map classifying \\\"forest\\\" and \\\"non-forest\\\" landcover.\",\"msft:region\":\"westeurope\"},{\"id\":\"3dep-lidar-returns\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/3dep-lidar-returns/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-returns\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/3d-elevation-program/about-3dep-products-services\",\"title\":\"About 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-returns\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 3DEP Lidar Returns\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/numberofreturns-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP Lidar COG\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-lidar-returns.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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\",\"NumberOfReturns\"],\"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 collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It is a collection of Cloud Optimized GeoTIFFs representing the number of returns for a given pulse.\\n\\nThis values are based on the PointSourceId [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\\n\\nThe values are based on the NumberOfReturns [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.outlier`](https://pdal.io/stages/filters.outlier.html#filters-outlier) and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to remove outliers and noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG data\",\"raster:bands\":[{\"name\":\"ReturnNumber\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"int16\",\"description\":\"Raster for ReturnNumber PDAL dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 Lidar collection for the Cloud Optimized Geotiffs (COGs) based on the ReturnNumber 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 MoBI license\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/mobi\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MoBI: 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-130.23751729209377,21.736663926419077,-63.66198266211968,49.184200486300604]]},\"temporal\":{\"interval\":[[\"2020-04-14T00:00:00Z\",\"2020-04-14T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"MoBI\",\"Natureserve\",\"United States\",\"Biodiversity\"],\"providers\":[{\"url\":\"http://natureserve.org/\",\"name\":\"NatureServe\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[990]},\"description\":\"The [Map of Biodiversity Importance](https://www.natureserve.org/conservation-tools/projects/map-biodiversity-importance) (MoBI) consists of raster maps that combine habitat information for 2,216 imperiled species occurring in the conterminous United States, using weightings based on range size and degree of protection to identify areas of high importance for biodiversity conservation. Species included in the project are those which, as of September 2018, had a global conservation status of G1 (critical imperiled) or G2 (imperiled) or which are listed as threatened or endangered at the full species level under the United States Endangered Species Act. Taxonomic groups included in the project are vertebrates (birds, mammals, amphibians, reptiles, turtles, crocodilians, and freshwater and anadromous fishes), vascular plants, selected aquatic invertebrates (freshwater mussels and crayfish) and selected pollinators (bumblebees, butterflies, and skippers).\\n\\nThere are three types of spatial data provided, described in more detail below: species richness, range-size rarity, and protection-weighted range-size rarity. For each type, this data set includes five different layers – one for all species combined, and four additional layers that break the data down by taxonomic group (vertebrates, plants, freshwater invertebrates, and pollinators) – for a total of fifteen layers.\\n\\nThese data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the [NatureServe Network](https://www.natureserve.org/natureserve-network).\\n\",\"item_assets\":{\"RSR_All\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size rarity for all species\"},\"RSR_Plants\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size rarity for vascular plants\"},\"RSR_Vertebrates\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size rarity for vertebrates\"},\"RSR_AquaticInverts\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Range-size rarity for aquatic invertebrates\"},\"PWRSR_GAP12_SUM_All\":{\"type\":\"image/tiff; application=geotiff; 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vertebrates\"},\"SpeciesRichness_Vertebrates\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Species richness for vertebrates\"},\"PWRSR_GAP12_SUM_AquaticInverts\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Protection-weighted range-size rarity for aquatic invertebrates\"},\"SpeciesRichness_AquaticInverts\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Species richness for aquatic invertebrates\"},\"PWRSR_GAP12_SUM_PollinatorInverts\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Protection-weighted range-size rarity for pollinators\"},\"SpeciesRichness_PollinatorInverts\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Species richness for pollinators\"}},\"stac_version\":\"1.0.0\",\"msft:container\":\"mobi\",\"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\":\"ai4edataeuwest\",\"msft:short_description\":\"Habitat information for 2,216 imperiled species occurring in the continental United 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Level-2\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/core-science-systems/hdds/data-policy\",\"title\":\"Public Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/landsat-c2-l2\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Landsat Collection 2 Level-2\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/landsat-c2-l2-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Landsat Collection 2 Level-2 thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/landsat-c2-l2.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"MS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-90.0,180.0,90.0]]},\"temporal\":{\"interval\":[[\"1982-08-22T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Landsat\",\"USGS\",\"NASA\",\"Satellite\",\"Global\",\"Imagery\",\"Reflectance\",\"Temperature\"],\"providers\":[{\"url\":\"https://landsat.gsfc.nasa.gov/\",\"name\":\"NASA\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products\",\"name\":\"USGS\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"gsd\":[30,60,100,120],\"sci:doi\":[\"10.5066/P9IAXOVV\",\"10.5066/P9C7I13B\",\"10.5066/P9OGBGM6\"],\"eo:bands\":[{\"name\":\"TM_B1\",\"common_name\":\"blue\",\"description\":\"Visible blue (Thematic Mapper)\",\"center_wavelength\":0.49,\"full_width_half_max\":0.07},{\"name\":\"TM_B2\",\"common_name\":\"green\",\"description\":\"Visible green (Thematic Mapper)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.08},{\"name\":\"TM_B3\",\"common_name\":\"red\",\"description\":\"Visible red (Thematic Mapper)\",\"center_wavelength\":0.66,\"full_width_half_max\":0.06},{\"name\":\"TM_B4\",\"common_name\":\"nir08\",\"description\":\"Near infrared (Thematic Mapper)\",\"center_wavelength\":0.83,\"full_width_half_max\":0.14},{\"name\":\"TM_B5\",\"common_name\":\"swir16\",\"description\":\"Short-wave infrared (Thematic Mapper)\",\"center_wavelength\":1.65,\"full_width_half_max\":0.2},{\"name\":\"TM_B6\",\"common_name\":\"lwir\",\"description\":\"Long-wave infrared (Thematic Mapper)\",\"center_wavelength\":11.45,\"full_width_half_max\":2.1},{\"name\":\"TM_B7\",\"common_name\":\"swir22\",\"description\":\"Short-wave infrared (Thematic Mapper)\",\"center_wavelength\":2.22,\"full_width_half_max\":0.27},{\"name\":\"ETM_B1\",\"common_name\":\"blue\",\"description\":\"Visible blue (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":0.48,\"full_width_half_max\":0.07},{\"name\":\"ETM_B2\",\"common_name\":\"green\",\"description\":\"Visible green (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.08},{\"name\":\"ETM_B3\",\"common_name\":\"red\",\"description\":\"Visible red (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":0.66,\"full_width_half_max\":0.06},{\"name\":\"ETM_B4\",\"common_name\":\"nir08\",\"description\":\"Near infrared (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":0.84,\"full_width_half_max\":0.13},{\"name\":\"ETM_B5\",\"common_name\":\"swir16\",\"description\":\"Short-wave infrared (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":1.65,\"full_width_half_max\":0.2},{\"name\":\"ETM_B6\",\"common_name\":\"lwir\",\"description\":\"Long-wave infrared (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":11.34,\"full_width_half_max\":2.05},{\"name\":\"ETM_B7\",\"common_name\":\"swir22\",\"description\":\"Short-wave infrared (Enhanced Thematic Mapper Plus)\",\"center_wavelength\":2.2,\"full_width_half_max\":0.28},{\"name\":\"OLI_B1\",\"common_name\":\"coastal\",\"description\":\"Coastal/Aerosol (Operational Land Imager)\",\"center_wavelength\":0.44,\"full_width_half_max\":0.02},{\"name\":\"OLI_B2\",\"common_name\":\"blue\",\"description\":\"Visible blue (Operational Land Imager)\",\"center_wavelength\":0.48,\"full_width_half_max\":0.06},{\"name\":\"OLI_B3\",\"common_name\":\"green\",\"description\":\"Visible green (Operational Land Imager)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.06},{\"name\":\"OLI_B4\",\"common_name\":\"red\",\"description\":\"Visible red (Operational Land Imager)\",\"center_wavelength\":0.65,\"full_width_half_max\":0.04},{\"name\":\"OLI_B5\",\"common_name\":\"nir08\",\"description\":\"Near infrared (Operational Land Imager)\",\"center_wavelength\":0.87,\"full_width_half_max\":0.03},{\"name\":\"OLI_B6\",\"common_name\":\"swir16\",\"description\":\"Short-wave infrared (Operational Land Imager)\",\"center_wavelength\":1.61,\"full_width_half_max\":0.09},{\"name\":\"OLI_B7\",\"common_name\":\"swir22\",\"description\":\"Short-wave infrared (Operational Land Imager)\",\"center_wavelength\":2.2,\"full_width_half_max\":0.19},{\"name\":\"TIRS_B10\",\"common_name\":\"lwir11\",\"description\":\"Long-wave 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 Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\\n\\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\\n\",\"item_assets\":{\"qa\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Surface Temperature Quality Assessment Band\",\"description\":\"Collection 2 Level-2 Quality Assessment 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 Coefficients File\",\"description\":\"Collection 2 Level-1 Angle Coefficients File\"},\"red\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Red 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Blue 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Downwelled Radiance Band\",\"description\":\"Collection 2 Level-2 Downwelled Radiance 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Emissivity Band\",\"description\":\"Collection 2 Level-2 Emissivity Band (ST_EMIS) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"emissivity coefficient\",\"scale\":0.0001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"emsd\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Emissivity Standard Deviation Band\",\"description\":\"Collection 2 Level-2 Emissivity Standard Deviation Band (ST_EMSD) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"emissivity coefficient\",\"scale\":0.0001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"lwir\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"temperature\"],\"title\":\"Surface Temperature Band\",\"eo:bands\":[{\"common_name\":\"lwir\",\"description\":\"Long-wave infrared\"}],\"description\":\"Collection 2 Level-2 Thermal Infrared Band (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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Thermal Radiance Band\",\"description\":\"Collection 2 Level-2 Thermal Radiance Band (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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Upwelled Radiance Band\",\"description\":\"Collection 2 Level-2 Upwelled Radiance Band (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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Atmospheric Transmittance Band\",\"description\":\"Collection 2 Level-2 Atmospheric Transmittance Band (ST_ATRAN) Surface Temperature Product\",\"raster:bands\":[{\"scale\":0.0001,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"cdist\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Cloud Distance Band\",\"description\":\"Collection 2 Level-2 Cloud Distance Band (ST_CDIST) Surface Temperature Product\",\"raster:bands\":[{\"unit\":\"kilometer\",\"scale\":0.01,\"nodata\":-9999,\"data_type\":\"int16\",\"spatial_resolution\":30}]},\"green\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Green Band\",\"eo:bands\":[{\"common_name\":\"green\",\"description\":\"Visible 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Near Infrared Band 0.8\",\"eo:bands\":[{\"common_name\":\"nir08\",\"description\":\"Near infrared\"}],\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"lwir11\":{\"gsd\":100,\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"temperature\"],\"title\":\"Surface Temperature Band\",\"eo:bands\":[{\"name\":\"TIRS_B10\",\"common_name\":\"lwir11\",\"description\":\"Long-wave infrared\",\"center_wavelength\":10.9,\"full_width_half_max\":0.59}],\"description\":\"Collection 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Short-wave Infrared Band 1.6\",\"eo:bands\":[{\"common_name\":\"swir16\",\"description\":\"Short-wave infrared\"}],\"raster:bands\":[{\"scale\":0.0000275,\"nodata\":0,\"offset\":-0.2,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"swir22\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Short-wave Infrared Band 2.2\",\"eo:bands\":[{\"common_name\":\"swir22\",\"description\":\"Short-wave infrared\"}],\"description\":\"Collection 2 Level-2 Short-wave Infrared Band 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Coastal/Aerosol Band\",\"eo:bands\":[{\"name\":\"OLI_B1\",\"common_name\":\"coastal\",\"description\":\"Coastal/Aerosol\",\"center_wavelength\":0.44,\"full_width_half_max\":0.02}],\"description\":\"Collection 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 Metadata File (txt)\",\"description\":\"Collection 2 Level-2 Product Metadata File (txt)\"},\"mtl.xml\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product Metadata File (xml)\",\"description\":\"Collection 2 Level-2 Product Metadata File (xml)\"},\"cloud_qa\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"cloud\",\"cloud-shadow\",\"snow-ice\",\"water-mask\"],\"title\":\"Cloud Quality Assessment Band\",\"description\":\"Collection 2 Level-2 Cloud Quality Assessment Band (SR_CLOUD_QA) Surface Reflectance Product\",\"raster:bands\":[{\"unit\":\"bit index\",\"data_type\":\"uint8\",\"spatial_resolution\":30}],\"classification:bitfields\":[{\"name\":\"ddv\",\"length\":1,\"offset\":0,\"classes\":[{\"name\":\"not_ddv\",\"value\":0,\"description\":\"Pixel has no DDV\"},{\"name\":\"ddv\",\"value\":1,\"description\":\"Pixel has DDV\"}],\"description\":\"Dense Dark Vegetation (DDV)\"},{\"name\":\"cloud\",\"length\":1,\"offset\":1,\"classes\":[{\"name\":\"not_cloud\",\"value\":0,\"description\":\"Pixel has no cloud\"},{\"name\":\"cloud\",\"value\":1,\"description\":\"Pixel has cloud\"}],\"description\":\"Cloud mask\"},{\"name\":\"cloud_shadow\",\"length\":1,\"offset\":2,\"classes\":[{\"name\":\"not_shadow\",\"value\":0,\"description\":\"Pixel has no cloud shadow\"},{\"name\":\"shadow\",\"value\":1,\"description\":\"Pixel has cloud shadow\"}],\"description\":\"Cloud shadow mask\"},{\"name\":\"cloud_adjacent\",\"length\":1,\"offset\":3,\"classes\":[{\"name\":\"not_adjacent\",\"value\":0,\"description\":\"Pixel is not adjacent to cloud\"},{\"name\":\"adjacent\",\"value\":1,\"description\":\"Pixel is adjacent to cloud\"}],\"description\":\"Cloud adjacency\"},{\"name\":\"snow\",\"length\":1,\"offset\":4,\"classes\":[{\"name\":\"not_snow\",\"value\":0,\"description\":\"Pixel is not snow\"},{\"name\":\"shadow\",\"value\":1,\"description\":\"Pixel is snow\"}],\"description\":\"Snow mask\"},{\"name\":\"water\",\"length\":1,\"offset\":5,\"classes\":[{\"name\":\"not_water\",\"value\":0,\"description\":\"Pixel is not water\"},{\"name\":\"water\",\"value\":1,\"description\":\"Pixel is water\"}],\"description\":\"Water mask\"}]},\"mtl.json\":{\"type\":\"application/json\",\"roles\":[\"metadata\"],\"title\":\"Product Metadata File (json)\",\"description\":\"Collection 2 Level-2 Product Metadata File (json)\"},\"qa_pixel\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"cloud\",\"cloud-shadow\",\"snow-ice\",\"water-mask\"],\"title\":\"Pixel Quality Assessment Band\",\"description\":\"Collection 2 Level-1 Pixel Quality Assessment Band (QA_PIXEL)\",\"raster:bands\":[{\"unit\":\"bit index\",\"nodata\":1,\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"qa_radsat\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"saturation\"],\"raster:bands\":[{\"unit\":\"bit index\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"qa_aerosol\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data-mask\",\"water-mask\"],\"title\":\"Aerosol Quality Assessment Band\",\"description\":\"Collection 2 Level-2 Aerosol Quality Assessment Band (SR_QA_AEROSOL) Surface Reflectance Product\",\"raster:bands\":[{\"unit\":\"bit 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 is not fill\"},{\"name\":\"fill\",\"value\":1,\"description\":\"Pixel is fill\"}],\"description\":\"Image or fill data\"},{\"name\":\"retrieval\",\"length\":1,\"offset\":1,\"classes\":[{\"name\":\"not_valid\",\"value\":0,\"description\":\"Pixel retrieval is not valid\"},{\"name\":\"valid\",\"value\":1,\"description\":\"Pixel retrieval is valid\"}],\"description\":\"Valid aerosol retrieval\"},{\"name\":\"water\",\"length\":1,\"offset\":2,\"classes\":[{\"name\":\"not_water\",\"value\":0,\"description\":\"Pixel is not water\"},{\"name\":\"water\",\"value\":1,\"description\":\"Pixel is water\"}],\"description\":\"Water mask\"},{\"name\":\"interpolated\",\"length\":1,\"offset\":5,\"classes\":[{\"name\":\"not_interpolated\",\"value\":0,\"description\":\"Pixel is not interpolated aerosol\"},{\"name\":\"interpolated\",\"value\":1,\"description\":\"Pixel is interpolated aerosol\"}],\"description\":\"Aerosol interpolation\"},{\"name\":\"level\",\"length\":2,\"offset\":6,\"classes\":[{\"name\":\"climatology\",\"value\":0,\"description\":\"No aerosol correction applied\"},{\"name\":\"low\",\"value\":1,\"description\":\"Low aerosol level\"},{\"name\":\"medium\",\"value\":2,\"description\":\"Medium aerosol level\"},{\"name\":\"high\",\"value\":3,\"description\":\"High aerosol level\"}],\"description\":\"Aerosol level\"}]},\"atmos_opacity\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Atmospheric Opacity Band\",\"description\":\"Collection 2 Level-2 Atmospheric Opacity 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 Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 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 to Use Copernicus Products\"},{\"rel\":\"describedby\",\"href\":\"https://confluence.ecmwf.int/display/CKB/ERA5\",\"type\":\"text/html\",\"title\":\"Project 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 to cite\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/era5-pds\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ERA5 - 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 STAC items\",\"media_type\":\"application/x-parquet\",\"description\":\"Snapshot 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 OS\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"era5:kind\":[\"fc\",\"an\"]},\"description\":\"ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate\\ncovering the period from January 1950 to present. ERA5 is produced by the\\nCopernicus Climate Change Service (C3S) at ECMWF.\\n\\nReanalysis combines model data with observations from across the world into a\\nglobally complete and consistent dataset using the laws of physics. This\\nprinciple, called data assimilation, is based on the method used by numerical\\nweather prediction centres, where every so many hours (12 hours at ECMWF) a\\nprevious forecast is combined with newly available observations in an optimal\\nway to produce a new best estimate of the state of the atmosphere, called\\nanalysis, from which an updated, improved forecast is issued. Reanalysis works\\nin the same way, but at reduced resolution to allow for the provision of a\\ndataset spanning back several decades. Reanalysis does not have the constraint\\nof issuing timely forecasts, so there is more time to collect observations, and\\nwhen going further back in time, to allow for the ingestion of improved versions\\nof the original observations, which all benefit the quality of the reanalysis\\nproduct.\\n\\nThis dataset was converted to Zarr by [Planet OS](https://planetos.com/).\\nSee [their documentation](https://github.com/planet-os/notebooks/blob/master/aws/era5-pds.md)\\nfor more.\\n\\n## STAC Metadata\\n\\nTwo types of data variables are provided: \\\"forecast\\\" (`fc`) and \\\"analysis\\\" (`an`).\\n\\n* An **analysis**, of the atmospheric conditions, is a blend of observations\\n with a previous forecast. An analysis can only provide\\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\\n \ parameters (parameters valid at a specific time, e.g temperature at 12:00),\\n \ but not accumulated parameters, mean rates or min/max parameters.\\n* A **forecast** starts with an analysis at a specific time (the 'initialization\\n \ time'), and a model computes the atmospheric conditions for a number of\\n \ 'forecast steps', at increasing 'validity times', into the future. A forecast\\n \ can provide\\n [instantaneous](https://confluence.ecmwf.int/display/CKB/Model+grid+box+and+time+step)\\n \ parameters, accumulated parameters, mean rates, and min/max parameters.\\n\\nEach [STAC](https://stacspec.org/) item in this collection covers a single month\\nand the entire globe. There are two STAC items per month, one for each type of data\\nvariable (`fc` and `an`). The STAC items include an `ecmwf:kind` properties to\\nindicate which kind of variables that STAC item catalogs.\\n\\n## How to acknowledge, cite and refer to ERA5\\n\\nAll users of data on the Climate Data Store (CDS) disks (using either the web interface or the CDS API) must provide clear and visible attribution to the Copernicus programme and are asked to cite and reference the dataset provider:\\n\\nAcknowledge according to the [licence to use Copernicus Products](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf).\\n\\nCite each dataset used as indicated on the relevant CDS entries (see link to \\\"Citation\\\" under References on the Overview page of the dataset entry).\\n\\nThroughout the content of your publication, the dataset used is referred to as Author (YYYY).\\n\\nThe 3-steps procedure above is illustrated with this example: [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 complete details, please refer to [How to acknowledge and cite a Climate Data 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 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 surface 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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 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 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 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 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 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 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 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 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 pressure\",\"nameECMWF\":\"Surface pressure\",\"product_type\":\"analysis\",\"standard_name\":\"surface_air_pressure\",\"shortNameECMWF\":\"sp\"},\"shape\":[null,721,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Surface pressure\"},\"sea_surface_temperature\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"nameCDM\":\"Sea_surface_temperature_surface\",\"long_name\":\"Sea 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 surface temperature\"},\"eastward_wind_at_10_metres\":{\"type\":\"data\",\"unit\":\"m s**-1\",\"attrs\":{\"units\":\"m s**-1\",\"nameCDM\":\"10_metre_U_wind_component_surface\",\"long_name\":\"10 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 metre U wind component\"},\"air_temperature_at_2_metres\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"nameCDM\":\"2_metre_temperature_surface\",\"long_name\":\"2 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 metre temperature\"},\"eastward_wind_at_100_metres\":{\"type\":\"data\",\"unit\":\"m s**-1\",\"attrs\":{\"units\":\"m s**-1\",\"nameCDM\":\"100_metre_U_wind_component_surface\",\"long_name\":\"100 metre U wind component\",\"nameECMWF\":\"100 metre U wind component\",\"product_type\":\"analysis\",\"standard_name\":\"eastward_wind\",\"shortNameECMWF\":\"100u\"},\"shape\":[null,721,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"100 metre U wind component\"},\"northward_wind_at_10_metres\":{\"type\":\"data\",\"unit\":\"m s**-1\",\"attrs\":{\"units\":\"m s**-1\",\"nameCDM\":\"10_metre_V_wind_component_surface\",\"long_name\":\"10 metre V wind component\",\"nameECMWF\":\"10 metre V wind 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pressure\",\"product_type\":\"analysis\",\"standard_name\":\"air_pressure_at_mean_sea_level\",\"shortNameECMWF\":\"msl\"},\"shape\":[null,721,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Mean sea level pressure\"},\"dew_point_temperature_at_2_metres\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"nameCDM\":\"2_metre_dewpoint_temperature_surface\",\"long_name\":\"2 metre dewpoint temperature\",\"nameECMWF\":\"2 metre dewpoint temperature\",\"product_type\":\"analysis\",\"standard_name\":\"dew_point_temperature\",\"shortNameECMWF\":\"2d\"},\"shape\":[null,721,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"2 metre dewpoint temperature\"},\"precipitation_amount_1hour_Accumulation\":{\"type\":\"data\",\"unit\":\"m\",\"attrs\":{\"units\":\"m\",\"nameCDM\":\"Total_precipitation_1hour_Accumulation\",\"long_name\":\"Total precipitation\",\"nameECMWF\":\"Total precipitation\",\"product_type\":\"forecast\",\"standard_name\":\"precipitation_amount\",\"shortNameECMWF\":\"tp\"},\"shape\":[null,721,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Total precipitation\"},\"air_temperature_at_2_metres_1hour_Maximum\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"nameCDM\":\"Maximum_temperature_at_2_metres_since_previous_post-processing_surface_1_Hour_2\",\"long_name\":\"Maximum temperature at 2 metres since previous post-processing\",\"nameECMWF\":\"Maximum temperature at 2 metres since previous post-processing\",\"product_type\":\"forecast\",\"standard_name\":\"air_temperature\",\"shortNameECMWF\":\"mx2t\"},\"shape\":[null,721,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Maximum temperature at 2 metres since previous 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downwards\",\"product_type\":\"forecast\",\"standard_name\":\"integral_wrt_time_of_surface_direct_downwelling_shortwave_flux_in_air\",\"shortNameECMWF\":\"ssrd\"},\"shape\":[null,721,1440],\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Surface solar radiation 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 comprehensive reanalysis, which assimilates as many observations as possible 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 Commons Attribution Non Commercial Share Alike 4.0 International\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/chloris-biomass\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Chloris Biomass\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/chloris-biomass.jpg\",\"type\":\"image/jpg\",\"roles\":[\"thumbnail\"],\"title\":\"Chloris Biomass\"},\"geoparquet-items\":{\"href\":\"abfs://items/chloris-biomass.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems. It covers the period 2003 - 2019, at annual time steps. The global dataset has a circa 4.6 km spatial resolution.\\n\\nThe maps and data sets were generated by combining multiple remote sensing measurements from space borne satellites, processed using state-of-the-art machine learning and statistical methods, validated with field data from multiple countries. The dataset provides direct estimates of aboveground stock and change, and are not based on land use or land cover area change, and as such they include gains and losses of carbon stock in all types of woody vegetation - whether natural or plantations.\\n\\nAnnual stocks are expressed in units of tons of biomass. Annual changes in stocks are expressed in units of CO2 equivalent, i.e., the amount of CO2 released from or taken up by terrestrial ecosystems for that specific pixel.\\n\\nThe spatial data sets are available on [Microsoft\u2019s Planetary Computer](https://planetarycomputer.microsoft.com/dataset/chloris-biomass) under a Creative Common license of the type Attribution-Non Commercial-Share Alike [CC BY-NC-SA](https://spdx.org/licenses/CC-BY-NC-SA-4.0.html).\\n\\n[Chloris Geospatial](https://chloris.earth/) is a mission-driven technology company that develops software and data products on the state of natural capital for use by business, governments, and the social sector.\\n\",\"item_assets\":{\"biomass\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual estimates of aboveground woody biomass.\",\"raster:bands\":[{\"unit\":\"tonnes\",\"nodata\":2147483647,\"data_type\":\"uint32\"}]},\"biomass_wm\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual estimates of aboveground woody biomass (Web Mercator).\",\"raster:bands\":[{\"unit\":\"tonnes\",\"nodata\":2147483647,\"data_type\":\"uint32\"}]},\"biomass_change\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual estimates of changes (gains and losses) in aboveground woody biomass from the previous year.\",\"raster:bands\":[{\"unit\":\"tonnes\",\"nodata\":-32768,\"data_type\":\"int16\"}]},\"biomass_change_wm\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual estimates of changes (gains and losses) in aboveground woody biomass from 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 Chloris Global Biomass 2003 - 2019 dataset provides estimates of stock and change in aboveground biomass for Earth's terrestrial woody vegetation ecosystems during the period 2003 - 2019, at annual time steps. The global dataset has 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 Data License Agreement \u2013 Sharing, Version 1.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/kaza-hydroforecast\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"HydroForecast - 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 Tech\"],\"providers\":[{\"url\":\"https://www.upstream.tech\",\"name\":\"Upstream Tech\",\"roles\":[\"producer\",\"processor\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"This dataset is a daily updated set of HydroForecast seasonal river flow forecasts at six locations in the Kwando and Upper Zambezi river basins. More details about the locations, project context, and to interactively view current and previous forecasts, visit our [public website](https://dashboard.hydroforecast.com/public/wwf-kaza).\\n\\n## Flow forecast dataset and model description\\n\\n[HydroForecast](https://www.upstream.tech/hydroforecast) is a theory-guided machine learning hydrologic model that predicts streamflow in basins across the world. For the Kwando and Upper Zambezi, HydroForecast makes daily predictions of streamflow rates using a [seasonal analog approach](https://support.upstream.tech/article/125-seasonal-analog-model-a-technical-overview). The model's output is probabilistic and the mean, median and a range of quantiles are available at each forecast step.\\n\\nThe underlying model has the following attributes: \\n\\n* Timestep: 10 days\\n* Horizon: 10 to 180 days \\n* Update frequency: daily\\n* Units: cubic meters per second (m\xB3/s)\\n \\n## Site details\\n\\nThe model produces output for six locations in the Kwando and Upper Zambezi river basins.\\n\\n* Upper Zambezi sites\\n * Zambezi at Chavuma\\n * Luanginga at Kalabo\\n* Kwando basin sites\\n * Kwando at Kongola -- total basin flows\\n * Kwando Sub-basin 1\\n * Kwando Sub-basin 2 \\n * Kwando Sub-basin 3\\n * Kwando Sub-basin 4\\n * Kwando Kongola Sub-basin\\n\\n## STAC metadata\\n\\nThere is one STAC item per location. Each STAC item has a single asset linking to a Parquet file in Azure Blob Storage.\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Forecast Data\",\"description\":\"All seasonal river flow forecasts for a specific forecast site, produced by Upstream Tech's HydroForecast model\"}},\"stac_version\":\"1.0.0\",\"table:columns\":[{\"name\":\"initialization_time\",\"type\":\"timestamp\",\"description\":\"The initial timestamp in the forecast model which created this forecast step. All rows with the same initialization time are part of the same \\\"forecast\\\" and are produced at the same time.\"},{\"name\":\"valid_time\",\"type\":\"timestamp\",\"description\":\"The point in time this forecast step is predicting. valid_time = initialization_time + lead_time_hours. The discharge values in this row are the average rate from this time (inclusive) until the next valid time (exclusive).\"},{\"name\":\"lead_time_hours\",\"type\":\"int64\",\"description\":\"The number of hours ahead of the initialization time this forecast step is predicting.\"},{\"name\":\"discharge_mean\",\"type\":\"double\",\"description\":\"The mean of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_median\",\"type\":\"double\",\"description\":\"The median of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.01\",\"type\":\"double\",\"description\":\"The quantile 0.01 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.025\",\"type\":\"double\",\"description\":\"The quantile 0.025 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.05\",\"type\":\"double\",\"description\":\"The quantile 0.05 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.1\",\"type\":\"double\",\"description\":\"The quantile 0.1 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.25\",\"type\":\"double\",\"description\":\"The quantile 0.25 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.5\",\"type\":\"double\",\"description\":\"The quantile 0.5 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.75\",\"type\":\"double\",\"description\":\"The quantile 0.75 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.9\",\"type\":\"double\",\"description\":\"The quantile 0.9 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.95\",\"type\":\"double\",\"description\":\"The quantile 0.95 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.975\",\"type\":\"double\",\"description\":\"The quantile 0.975 of the probabilistic streamflow prediction, as an average rate across the forecast step in m\xB3/s.\"},{\"name\":\"discharge_q0.99\",\"type\":\"double\",\"description\":\"The quantile 0.99 of the probabilistic streamflow prediction, as an average rate 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 river flow forecasts for the Kwando and Upper Zambezi rivers in the KAZA region 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 License Agreement.\"},{\"rel\":\"documentation\",\"href\":\"https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf\",\"type\":\"application/pdf\",\"title\":\"Participant License Agreement.\"},{\"rel\":\"documentation\",\"href\":\"https://www.planet.com/nicfi/\",\"type\":\"text/html\",\"title\":\"NICFI Program - Satellite Imagery and Monitoring | Planet\"},{\"rel\":\"documentation\",\"href\":\"https://developers.planet.com/nicfi/\",\"type\":\"text/html\",\"title\":\"NICFI Program Resource Center\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/planet-nicfi-analytic\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Planet-NICFI 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 blue\"},{\"name\":\"Green\",\"common_name\":\"green\",\"description\":\"visible green\"},{\"name\":\"Red\",\"common_name\":\"red\",\"description\":\"visible red\"},{\"name\":\"NIR\",\"common_name\":\"nir\",\"description\":\"near-infrared\"}],\"planet-nicfi:cadence\":[\"biannual\",\"monthly\"],\"planet-nicfi:percent_covered\":{\"maximum\":100,\"minimum\":0}},\"description\":\"*Note: Assets in this collection are only available to winners of the [GEO-Microsoft Planetary Computer RFP](https://www.earthobservations.org/geo_blog_obs.php?id=528). 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/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\\n\\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\\n\\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\\n\\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\\n* Increase transparency and accountability in the tropics.\\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\\n\\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\\n\\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Data\",\"description\":\"a 'ground truth' representation of spatially accurate data with minimized effects 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 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 User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD17A2H\",\"title\":\"MOD17A2H file specification\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD17A2H\",\"title\":\"MYD17A2H file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MOD17A2H.061\",\"title\":\"LP DAAC - MOD17A2H\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MYD17A2H.061\",\"title\":\"LP DAAC - MYD17A2H\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-17A2H-061\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS 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 Gross Primary Productivity 8-Day thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-17A2H-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN.\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source data containing all bands\"},\"Gpp_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Gross Primary Productivity\",\"raster:bands\":[{\"unit\":\"kg C/m^2\",\"scale\":0.0001,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal Geographic Data Committee (FGDC) Metadata\"},\"PsnNet_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Net Photosynthesis\",\"raster:bands\":[{\"unit\":\"kg C/m^2\",\"scale\":0.0001,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"Psn_QC_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Quality 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, S., Mu, Q., & Zhao, M. 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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 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dtm-native\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 3DEP Lidar Digital Terrain Model (Native)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/3dep-lidar-dtm-native-thumbnail.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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\",\"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 collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using the vendor provided (native) ground classification and [`pdal.filters.range`](https://pdal.io/stages/filters.range.html#filters-range) to output a collection of Cloud Optimized GeoTIFFs, removing all points that have been classified as noise.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG data\",\"raster:bands\":[{\"name\":\"Z\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"float32\",\"description\":\"Raster for Z PDAL Dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 Lidar collection for the Digital Terrain Model (DTM) Cloud Optimized Geotiffs (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 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-classification\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 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 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 collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It uses the [ASPRS](https://www.asprs.org/) (American Society for Photogrammetry and Remote Sensing) [Lidar point classification](https://desktop.arcgis.com/en/arcmap/latest/manage-data/las-dataset/lidar-point-classification.htm). See [LAS specification](https://www.ogc.org/standards/LAS) for details.\\n\\nThis COG type is based on the Classification [PDAL dimension](https://pdal.io/dimensions.html) and uses [`pdal.filters.range`](https://pdal.io/stages/filters.range.html) to select a subset of interesting classifications. Do note that not all LiDAR collections contain a full compliment of classification labels.\\nTo remove outliers, the PDAL pipeline uses a noise filter and then outputs the Classification dimension.\\n\\nThe STAC collection implements the [`item_assets`](https://github.com/stac-extensions/item-assets) and [`classification`](https://github.com/stac-extensions/classification) extensions. These classes are displayed in the \\\"Item assets\\\" below. You can programmatically access the full list of class values and descriptions using the `classification:classes` field form the `data` asset on the STAC collection.\\n\\nClassification rasters were produced as a subset of LiDAR classification categories:\\n\\n```\\n0, Never Classified\\n1, Unclassified\\n2, Ground\\n3, Low Vegetation\\n4, Medium Vegetation\\n5, High Vegetation\\n6, Building\\n9, Water\\n10, Rail\\n11, Road\\n17, Bridge Deck\\n```\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG data\",\"raster:bands\":[{\"name\":\"Classification\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"int16\",\"description\":\"Raster for Classification PDAL dimension, ASPRS Lidar Point Classification Standard\"}],\"classification:classes\":[{\"value\":0,\"description\":\"Never Classified\"},{\"value\":1,\"description\":\"Unclassified\"},{\"value\":2,\"description\":\"Ground\"},{\"value\":3,\"description\":\"Low Vegetation\"},{\"value\":4,\"description\":\"Medium Vegetation\"},{\"value\":5,\"description\":\"High Vegetation\"},{\"value\":6,\"description\":\"Building\"},{\"value\":9,\"description\":\"Water\"},{\"value\":10,\"description\":\"Rail\"},{\"value\":11,\"description\":\"Road\"},{\"value\":17,\"description\":\"Bridge Deck\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 Lidar collection for Cloud Optimized Geotiffs (COGs) created according to 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 3DEP Products & Services\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/3dep-lidar-dtm\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 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 Lidar COG\"},\"geoparquet-items\":{\"href\":\"abfs://items/3dep-lidar-dtm.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 collection is derived from the [USGS 3DEP COPC collection](https://planetarycomputer.microsoft.com/dataset/3dep-lidar-copc). It creates a Digital Terrain Model (DTM) using [`pdal.filters.smrf`](https://pdal.io/stages/filters.smrf.html#filters-smrf) to output a collection of Cloud Optimized GeoTIFFs.\\n\\nThe Simple Morphological Filter (SMRF) classifies ground points based on the approach outlined in [Pingel2013](https://pdal.io/references.html#pingel2013).\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"COG data\",\"raster:bands\":[{\"type\":\"Z\",\"unit\":\"metre\",\"sampling\":\"point\",\"data_type\":\"float32\",\"description\":\"Z PDAL Dimension\"}]},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"3DEP 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 Lidar collection for the Digital Terrain Model (DTM) Cloud Optimized Geotiffs (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 Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gap\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS Gap Land Cover\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gap.png\",\"type\":\"image/png\",\"roles\":[\"data\"],\"title\":\"USGS GAP\"},\"geoparquet-items\":{\"href\":\"abfs://items/gap.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 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 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 source data\",\"description\":\"Original source data for the continental United 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 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 Florida Bayhead Swamp\",\"South Florida Cypress Dome\",\"South Florida Dwarf Cypress Savanna\",\"South Florida Mangrove Swamp\",\"South Florida Hardwood Hammock\",\"Southeast Florida Coastal Strand and Maritime Hammock\",\"Southwest Florida Coastal Strand and Maritime Hammock\",\"South Florida Pine Rockland\",\"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Open Understory\",\"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory\",\"Atlantic Coastal Plain Upland Longleaf Pine Woodland\",\"Atlantic Coastal Plain Xeric River Dune\",\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Open Understory Modifier\",\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Scrub/Shrub Modifier\",\"Florida Longleaf Pine Sandhill - Scrub/Shrub Understory Modifier\",\"Florida Longleaf Pine Sandhill- Open Understory Modifier\",\"West Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland\",\"Atlantic Coastal Plain Central Maritime Forest\",\"Atlantic Coastal Plain Southern Maritime Forest\",\"Central and South Texas Coastal Fringe Forest and Woodland\",\"East Gulf Coastal Plain Limestone Forest\",\"East Gulf Coastal Plain Maritime Forest\",\"East Gulf Coastal Plain Southern Loess Bluff Forest\",\"East Gulf Coastal Plain Southern Mesic Slope Forest\",\"Mississippi Delta Maritime Forest\",\"Southern Coastal Plain Dry Upland Hardwood Forest\",\"Southern Coastal Plain Oak Dome and Hammock\",\"West Gulf Coastal Plain Chenier and Upper Texas Coastal Fringe Forest and Woodland\",\"West Gulf Coastal Plain Mesic Hardwood Forest\",\"East-Central Texas Plains Pine Forest and Woodland\",\"West Gulf Coastal Plain Pine-Hardwood Forest\",\"West Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest and Woodland\",\"Atlantic Coastal Plain Fall-Line Sandhills Longleaf Pine Woodland - Loblolly Modifier\",\"Deciduous Plantations\",\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Loblolly Modifier\",\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood Modifier\",\"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Offsite Hardwood Modifier\",\"Evergreen Plantation or Managed Pine\",\"California Central Valley Mixed Oak Savanna\",\"California Coastal Closed-Cone Conifer Forest and Woodland\",\"California Coastal Live Oak Woodland and Savanna\",\"California Lower Montane Blue Oak-Foothill Pine Woodland and Savanna\",\"Central and Southern California Mixed Evergreen Woodland\",\"Mediterranean California Lower Montane Black Oak-Conifer Forest and Woodland\",\"Southern California Oak Woodland and Savanna\",\"Madrean Encinal\",\"Madrean Pinyon-Juniper Woodland\",\"Madrean Pine-Oak Forest and Woodland\",\"Madrean Upper Montane Conifer-Oak Forest and Woodland\",\"Edwards Plateau Dry-Mesic Slope Forest and Woodland\",\"Edwards Plateau Limestone Savanna and Woodland\",\"Edwards Plateau Mesic Canyon\",\"Llano Uplift Acidic Forest, Woodland and Glade\",\"East Cascades Oak-Ponderosa Pine Forest and Woodland\",\"Mediterranean California Mixed Evergreen Forest\",\"Mediterranean California Mixed Oak Woodland\",\"North Pacific Dry Douglas-fir-(Madrone) Forest and Woodland\",\"North Pacific Oak Woodland\",\"Edwards Plateau Limestone Shrubland\",\"Allegheny-Cumberland Dry Oak Forest and Woodland - Hardwood\",\"Allegheny-Cumberland Dry Oak Forest and Woodland - Pine Modifier\",\"Central and Southern Appalachian Montane Oak Forest\",\"Central and Southern Appalachian Northern Hardwood Forest\",\"Central Appalachian Oak and Pine Forest\",\"Crosstimbers Oak Forest and Woodland\",\"East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Woodland Modifier\",\"East Gulf Coastal Plain Northern Dry Upland Hardwood Forest\",\"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Hardwood Modifier\",\"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Juniper Modifier\",\"East-Central Texas Plains Post Oak Savanna and Woodland\",\"Lower Mississippi River Dune Woodland and Forest\",\"Mississippi River Alluvial Plain Dry-Mesic Loess Slope Forest\",\"North-Central Interior Dry Oak Forest and Woodland\",\"North-Central Interior Dry-Mesic Oak Forest and Woodland\",\"Northeastern Interior Dry Oak Forest - Mixed Modifier\",\"Northeastern Interior Dry Oak Forest - Virginia/Pitch Pine Modifier\",\"Northeastern Interior Dry Oak Forest-Hardwood Modifier\",\"Northeastern Interior Dry-Mesic Oak Forest\",\"Northern Atlantic Coastal Plain Dry Hardwood Forest\",\"Crowley's Ridge Sand Forest\",\"Ouachita Montane Oak Forest\",\"Ozark-Ouachita Dry Oak Woodland\",\"Ozark-Ouachita Dry-Mesic Oak Forest\",\"Southern and Central Appalachian Oak Forest\",\"Southern and Central Appalachian Oak Forest - Xeric\",\"Southern Interior Low Plateau Dry-Mesic Oak Forest\",\"Southern Ridge and Valley Dry Calcareous Forest\",\"Southern Ridge and Valley Dry Calcareous Forest - Pine modifier\",\"East Gulf Coastal Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier\",\"Managed Tree Plantation\",\"Ruderal forest\",\"Southern Piedmont Dry Oak-(Pine) Forest - Loblolly Pine Modifier\",\"Acadian Low-Elevation Spruce-Fir-Hardwood Forest\",\"Acadian-Appalachian Montane Spruce-Fir Forest\",\"Appalachian Hemlock-Hardwood Forest\",\"Central and Southern Appalachian Spruce-Fir Forest\",\"0\",\"Laurentian-Acadian Northern Hardwoods Forest\",\"Laurentian-Acadian Northern Pine-(Oak) Forest\",\"Laurentian-Acadian Pine-Hemlock-Hardwood Forest\",\"Paleozoic Plateau Bluff and Talus\",\"Southern Appalachian Northern Hardwood Forest\",\"Atlantic Coastal Plain Dry and Dry-Mesic Oak Forest\",\"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Offsite Hardwood\",\"East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Hardwood Modifier\",\"East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Mixed Modifier\",\"Ozark-Ouachita Shortleaf Pine-Bluestem Woodland\",\"Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland\",\"Southeastern Interior Longleaf Pine Woodland\",\"Southern Appalachian Low Mountain Pine Forest\",\"Southern Piedmont Dry Oak-(Pine) Forest\",\"Southern Piedmont Dry Oak-(Pine) Forest - Hardwood Modifier\",\"Southern Piedmont Dry Oak-(Pine) Forest - Mixed Modifier\",\"Southern Piedmont Dry Oak-Heath Forest - Mixed Modifier\",\"Eastern Great Plains Tallgrass Aspen Parkland\",\"Northwestern Great Plains Aspen Forest and Parkland\",\"Northwestern Great Plains Shrubland\",\"Western Great Plains Dry Bur Oak Forest and Woodland\",\"Western Great Plains Wooded Draw and Ravine\",\"Southern Atlantic Coastal Plain Mesic Hardwood Forest\",\"East Gulf Coastal Plain Northern Loess Bluff Forest\",\"East Gulf Coastal Plain Northern Mesic Hardwood Forest\",\"North-Central Interior Beech-Maple Forest\",\"North-Central Interior Maple-Basswood Forest\",\"Ozark-Ouachita Mesic Hardwood Forest\",\"South-Central Interior Mesophytic Forest\",\"Southern and Central Appalachian Cove Forest\",\"Crowley's Ridge Mesic Loess Slope Forest\",\"Southern Piedmont Mesic Forest\",\"Appalachian Shale Barrens\",\"Atlantic Coastal Plain Northern Maritime Forest\",\"Laurentian Pine-Oak Barrens\",\"Northeastern Interior Pine Barrens\",\"Northern Atlantic Coastal Plain Pitch Pine Barrens\",\"Southern Appalachian Montane Pine Forest and Woodland\",\"East Cascades Mesic Montane Mixed-Conifer Forest and Woodland\",\"Middle Rocky Mountain Montane Douglas-fir Forest and Woodland\",\"Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest\",\"Northern Rocky Mountain Foothill Conifer Wooded Steppe\",\"Northern Rocky Mountain Mesic Montane Mixed Conifer Forest\",\"Northern Rocky Mountain Ponderosa Pine Woodland and Savanna\",\"Northern Rocky Mountain Western Larch Savanna\",\"Northwestern Great Plains - Black Hills Ponderosa Pine Woodland and Savanna\",\"Rocky Mountain Foothill Limber Pine-Juniper Woodland\",\"Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland\",\"Inter-Mountain Basins Subalpine Limber-Bristlecone Pine Woodland\",\"Northern Rocky Mountain Subalpine Woodland and Parkland\",\"Rocky Mountain Aspen Forest and Woodland\",\"Rocky Mountain Lodgepole Pine Forest\",\"Rocky Mountain Poor-Site Lodgepole Pine Forest\",\"Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland\",\"Rocky Mountain Subalpine Mesic Spruce-Fir Forest and Woodland\",\"Rocky Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland\",\"Rocky Mountain Bigtooth Maple Ravine Woodland\",\"Southern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland\",\"Southern Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland\",\"Southern Rocky Mountain Ponderosa Pine Savanna\",\"Southern Rocky Mountain Ponderosa Pine Woodland\",\"California Montane Jeffrey Pine-(Ponderosa Pine) Woodland\",\"Klamath-Siskiyou Lower Montane Serpentine Mixed Conifer Woodland\",\"Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland\",\"Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland\",\"Mediterranean California Mesic Mixed Conifer Forest and Woodland\",\"Sierran-Intermontane Desert Western White Pine-White Fir Woodland\",\"California Coastal Redwood Forest\",\"North Pacific Broadleaf Landslide Forest and Shrubland\",\"North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest\",\"North Pacific Hypermaritime Sitka Spruce Forest\",\"North Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest\",\"North Pacific Lowland Mixed Hardwood-Conifer Forest and Woodland\",\"North Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest\",\"North Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest\",\"North Pacific Mesic Western Hemlock-Silver Fir Forest\",\"North Pacific Wooded Volcanic Flowage\",\"Mediterranean California Red Fir Forest\",\"Mediterranean California Subalpine Woodland\",\"North Pacific Maritime Mesic Subalpine Parkland\",\"North Pacific Mountain Hemlock Forest\",\"Northern California Mesic Subalpine Woodland\",\"Northern Pacific Mesic Subalpine Woodland\",\"Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland\",\"Columbia Plateau Western Juniper Woodland and Savanna\",\"Great Basin Pinyon-Juniper Woodland\",\"Inter-Mountain Basins Curl-leaf Mountain Mahogany Woodland and Shrubland\",\"Inter-Mountain Basins Juniper Savanna\",\"Colorado Plateau Pinyon-Juniper Shrubland\",\"Colorado Plateau Pinyon-Juniper Woodland\",\"Southern Rocky Mountain Juniper Woodland and Savanna\",\"Southern Rocky Mountain Pinyon-Juniper Woodland\",\"Northwestern Great Plains Floodplain\",\"Northwestern Great Plains Riparian\",\"Western Great Plains Floodplain\",\"Western Great Plains Floodplain Systems\",\"Western Great Plains Riparian Woodland and Shrubland\",\"Central Appalachian Floodplain - Forest Modifier\",\"Central Appalachian Riparian - Forest Modifier\",\"Central Interior and Appalachian Floodplain Systems\",\"Central Interior and Appalachian Riparian Systems\",\"Laurentian-Acadian Floodplain Systems\",\"Ozark-Ouachita Riparian\",\"South-Central Interior Large Floodplain\",\"South-Central Interior Large Floodplain - Forest Modifier\",\"South-Central Interior Small Stream and Riparian\",\"North-Central Interior and Appalachian Rich Swamp\",\"0\",\"0\",\"Laurentian-Acadian Swamp Systems\",\"North-Central Interior Wet Flatwoods\",\"0\",\"South-Central Interior / Upper Coastal Plain Wet Flatwoods\",\"0\",\"Southern Piedmont/Ridge and Valley Upland Depression Swamp\",\"Atlantic Coastal Plain Blackwater Stream Floodplain Forest - Forest Modifier\",\"Atlantic Coastal Plain Brownwater Stream Floodplain Forest\",\"Atlantic Coastal Plain Northern Tidal Wooded Swamp\",\"Atlantic Coastal Plain Small Blackwater River Floodplain Forest\",\"Atlantic Coastal Plain Small Brownwater River Floodplain Forest\",\"Atlantic Coastal Plain Southern Tidal Wooded Swamp\",\"East Gulf Coastal Plain Large River Floodplain Forest - Forest Modifier\",\"East Gulf Coastal Plain Small Stream and River Floodplain Forest\",\"East Gulf Coastal Plain Tidal Wooded Swamp\",\"0\",\"Southeastern Great Plains Riparian Forest\",\"Southeastern Great Plains Floodplain Forest\",\"Mississippi River Bottomland Depression\",\"Mississippi River Floodplain and Riparian Forest\",\"Mississippi River Low Floodplain (Bottomland) Forest\",\"Mississippi River Riparian Forest\",\"Red River Large Floodplain Forest\",\"Southern Coastal Plain Blackwater River Floodplain Forest\",\"Southern Piedmont Large Floodplain Forest - Forest Modifier\",\"Southern Piedmont Small Floodplain and Riparian Forest\",\"West Gulf Coastal Plain Large River Floodplain Forest\",\"West Gulf Coastal Plain Near-Coast Large River Swamp\",\"West Gulf Coastal Plain Small Stream and River Forest\",\"Atlantic Coastal Plain Streamhead Seepage Swamp - Pocosin - and Baygall\",\"Gulf and Atlantic Coastal Plain Swamp Systems\",\"Southern Coastal Plain Hydric Hammock\",\"Southern Coastal Plain Seepage Swamp and Baygall\",\"West Gulf Coastal Plain Seepage Swamp and Baygall\",\"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Taxodium/Nyssa Modifier\",\"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Oak Dominated Modifier\",\"East Gulf Coastal Plain Southern Loblolly-Hardwood Flatwoods\",\"Lower Mississippi River Bottomland Depressions - Forest Modifier\",\"Lower Mississippi River Flatwoods\",\"Northern Atlantic Coastal Plain Basin Swamp and Wet Hardwood Forest\",\"Southern Coastal Plain Nonriverine Basin Swamp\",\"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier\",\"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier\",\"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier\",\"West Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods\",\"West Gulf Coastal Plain Pine-Hardwood Flatwoods\",\"Edwards Plateau Riparian\",\"Atlantic Coastal Plain Clay-Based Carolina Bay Forested Wetland\",\"Atlantic Coastal Plain Clay-Based Carolina Bay Herbaceous Wetland\",\"Atlantic Coastal Plain Southern Wet Pine Savanna and Flatwoods\",\"Central Atlantic Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\",\"Central Florida Pine Flatwoods\",\"East Gulf Coastal Plain Near-Coast Pine Flatwoods\",\"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Open Understory Modifier\",\"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Scrub/Shrub Understory Modifier\",\"South Florida Pine Flatwoods\",\"Southern Coastal Plain Nonriverine Cypress Dome\",\"West Gulf Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\",\"Columbia Basin Foothill Riparian Woodland and Shrubland\",\"Great Basin Foothill and Lower Montane Riparian Woodland and Shrubland\",\"0\",\"Northern Rocky Mountain Conifer Swamp\",\"Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland\",\"Rocky Mountain Lower Montane Riparian Woodland and Shrubland\",\"Rocky Mountain Montane Riparian Systems\",\"Rocky Mountain Subalpine-Montane Riparian Woodland\",\"North Pacific Hardwood-Conifer Swamp\",\"North Pacific Lowland Riparian Forest and Shrubland\",\"North Pacific Montane Riparian Woodland and Shrubland\",\"North Pacific Shrub Swamp\",\"California Central Valley Riparian Woodland and Shrubland\",\"Mediterranean California Foothill and Lower Montane Riparian Woodland\",\"Mediterranean California Serpentine Foothill and Lower Montane Riparian Woodland and Seep\",\"North American Warm Desert Lower Montane Riparian Woodland and Shrubland\",\"North American Warm Desert Riparian Systems\",\"North American Warm Desert Riparian Woodland and Shrubland\",\"Tamaulipan Floodplain\",\"Tamaulipan Riparian Systems\",\"Boreal Aspen-Birch Forest\",\"Boreal Jack Pine-Black Spruce Forest\",\"Boreal White Spruce-Fir-Hardwood Forest\",\"Boreal-Laurentian Conifer Acidic Swamp and Treed Poor Fen\",\"Eastern Boreal Floodplain\",\"South Florida Shell Hash Beach\",\"Southeast Florida Beach\",\"Southwest Florida Beach\",\"South Florida Everglades Sawgrass Marsh\",\"South Florida Freshwater Slough and Gator Hole\",\"South Florida Wet Marl Prairie\",\"California Maritime Chaparral\",\"California Mesic Chaparral\",\"California Xeric Serpentine Chaparral\",\"Klamath-Siskiyou Xeromorphic Serpentine Savanna and Chaparral\",\"Mediterranean California Mesic Serpentine Woodland and Chaparral\",\"Northern and Central California Dry-Mesic Chaparral\",\"Southern California Dry-Mesic Chaparral\",\"Southern California Coastal Scrub\",\"California Central Valley and Southern Coastal Grassland\",\"California Mesic Serpentine Grassland\",\"Columbia Basin Foothill and Canyon Dry Grassland\",\"Columbia Basin Palouse Prairie\",\"North Pacific Alpine and Subalpine Dry Grassland\",\"North Pacific Montane Grassland\",\"North Pacific Montane Shrubland\",\"Northern Rocky Mountain Lower Montane, Foothill and Valley Grassland\",\"Northern Rocky Mountain Montane-Foothill Deciduous Shrubland\",\"Northern Rocky Mountain Subalpine Deciduous Shrubland\",\"Northern Rocky Mountain Subalpine-Upper Montane Grassland\",\"Southern Rocky Mountain Montane-Subalpine Grassland\",\"Rocky Mountain Gambel Oak-Mixed Montane Shrubland\",\"Rocky Mountain Lower Montane-Foothill Shrubland\",\"California Northern Coastal Grassland\",\"North Pacific Herbaceous Bald and Bluff\",\"North Pacific Hypermaritime Shrub and Herbaceous Headland\",\"Willamette Valley Upland Prairie and Savanna\",\"Mediterranean California Subalpine Meadow\",\"Rocky Mountain Subalpine-Montane Mesic Meadow\",\"Central Mixedgrass Prairie\",\"Northwestern Great Plains Mixedgrass Prairie\",\"Western Great Plains Foothill and Piedmont Grassland\",\"Western Great Plains Tallgrass Prairie\",\"Western Great Plains Sand Prairie\",\"Western Great Plains Sandhill Steppe\",\"Western Great Plains Mesquite Woodland and Shrubland\",\"Western Great Plains Shortgrass Prairie\",\"Arkansas Valley Prairie and Woodland\",\"Central Tallgrass Prairie\",\"North-Central Interior Oak Savanna\",\"North-Central Interior Sand and Gravel Tallgrass Prairie\",\"North-Central Oak Barrens\",\"Northern Tallgrass Prairie\",\"Southeastern Great Plains Tallgrass Prairie\",\"Texas Blackland Tallgrass Prairie\",\"Texas-Louisiana Coastal Prairie\",\"Central Appalachian Pine-Oak Rocky Woodland\",\"Southern Appalachian Grass and Shrub Bald\",\"Southern Appalachian Grass and Shrub Bald - Herbaceous Modifier\",\"Southern Appalachian Grass and Shrub Bald - Shrub Modifier\",\"Central Appalachian Alkaline Glade and Woodland\",\"Central Interior Highlands Calcareous Glade and Barrens\",\"Central Interior Highlands Dry Acidic Glade and Barrens\",\"Cumberland Sandstone Glade and Barrens\",\"Great Lakes Alvar\",\"Nashville Basin Limestone Glade\",\"Southern Ridge and Valley / Cumberland Dry Calcareous Forest\",\"Southern Piedmont Glade and Barrens\",\"East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Herbaceous Modifier\",\"East Gulf Coastal Plain Jackson Prairie and Woodland\",\"Eastern Highland Rim Prairie and Barrens - Dry Modifier\",\"Coahuilan Chaparral\",\"Madrean Oriental Chaparral\",\"Mogollon Chaparral\",\"Sonora-Mojave Semi-Desert Chaparral\",\"California Montane Woodland and Chaparral\",\"Great Basin Semi-Desert Chaparral\",\"Florida Dry Prairie\",\"Florida Peninsula Inland Scrub\",\"West Gulf Coastal Plain Catahoula Barrens\",\"West Gulf Coastal Plain Nepheline Syenite Glade\",\"East Gulf Coastal Plain Jackson Plain Dry Flatwoods - Open Understory Modifier\",\"West Gulf Coastal Plain Northern Calcareous Prairie\",\"West Gulf Coastal Plain Southern Calcareous Prairie\",\"Acadian-Appalachian Subalpine Woodland and Heath-Krummholz\",\"Atlantic and Gulf Coastal Plain Interdunal Wetland\",\"Atlantic Coastal Plain Southern Dune and Maritime Grassland\",\"Central and Upper Texas Coast Dune and Coastal Grassland\",\"East Gulf Coastal Plain Dune and Coastal Grassland\",\"Great Lakes Dune\",\"Northern Atlantic Coastal Plain Dune and Swale\",\"Northern Atlantic Coastal Plain Heathland and Grassland\",\"South Texas Dune and Coastal Grassland\",\"South Texas Sand Sheet Grassland\",\"Southwest Florida Dune and Coastal Grassland\",\"North Pacific Coastal Cliff and Bluff\",\"North Pacific Maritime Coastal Sand Dune and Strand\",\"Northern California Coastal Scrub\",\"Mediterranean California Coastal Bluff\",\"Mediterranean California Northern Coastal Dune\",\"Mediterranean California Southern Coastal Dune\",\"Atlantic Coastal Plain Northern Sandy Beach\",\"Atlantic Coastal Plain Sea Island Beach\",\"Atlantic Coastal Plain Southern Beach\",\"Florida Panhandle Beach Vegetation\",\"Louisiana Beach\",\"Northern Atlantic Coastal Plain Sandy Beach\",\"Texas Coastal Bend Beach\",\"Upper Texas Coast Beach\",\"0\",\"Mediterranean California Serpentine Fen\",\"Mediterranean California Subalpine-Montane Fen\",\"North Pacific Bog and Fen\",\"Rocky Mountain Subalpine-Montane Fen\",\"Atlantic Coastal Plain Peatland Pocosin\",\"Southern and Central Appalachian Bog and Fen\",\"Atlantic Coastal Plain Central Fresh-Oligohaline Tidal Marsh\",\"Atlantic Coastal Plain Embayed Region Tidal Freshwater Marsh\",\"Atlantic Coastal Plain Northern Fresh and Oligohaline Tidal Marsh\",\"Florida Big Bend Fresh-Oligohaline Tidal Marsh\",\"Atlantic Coastal Plain Depression Pondshore\",\"Atlantic Coastal Plain Large Natural Lakeshore\",\"Central Florida Herbaceous Pondshore\",\"Central Florida Herbaceous Seep\",\"East Gulf Coastal Plain Savanna and Wet Prairie\",\"East Gulf Coastal Plain Depression Pondshore\",\"Floridian Highlands Freshwater Marsh\",\"Southern Coastal Plain Herbaceous Seepage Bog\",\"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier\",\"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier\",\"Texas-Louisiana Coastal Prairie Slough\",\"Central Interior and Appalachian Shrub-Herbaceous Wetland Systems\",\"Great Lakes Coastal Marsh Systems\",\"0\",\"0\",\"Laurentian-Acadian Shrub-Herbaceous Wetland Systems\",\"0\",\"Eastern Great Plains Wet Meadow, Prairie and Marsh\",\"Great Lakes Wet-Mesic Lakeplain Prairie\",\"Great Plains Prairie Pothole\",\"Western Great Plains Closed Depression Wetland\",\"Western Great Plains Depressional Wetland Systems\",\"Western Great Plains Open Freshwater Depression Wetland\",\"Cumberland Riverscour\",\"Inter-Mountain Basins Interdunal Swale Wetland\",\"North Pacific Avalanche Chute Shrubland\",\"North Pacific Intertidal Freshwater Wetland\",\"Temperate Pacific Freshwater Emergent Marsh\",\"Temperate Pacific Freshwater Mudflat\",\"Columbia Plateau Vernal Pool\",\"Northern California Claypan Vernal Pool\",\"Northern Rocky Mountain Wooded Vernal Pool\",\"Columbia Plateau Silver Sagebrush Seasonally Flooded Shrub-Steppe\",\"Rocky Mountain Alpine-Montane Wet Meadow\",\"Rocky Mountain Subalpine-Montane Riparian Shrubland\",\"Temperate Pacific Montane Wet Meadow\",\"Willamette Valley Wet Prairie\",\"Chihuahuan-Sonoran Desert Bottomland and Swale Grassland\",\"North American Arid West Emergent Marsh\",\"North American Warm Desert Riparian Mesquite Bosque\",\"Western Great Plains Saline Depression Wetland\",\"Acadian Salt Marsh and Estuary Systems\",\"Atlantic Coastal Plain Central Salt and Brackish Tidal Marsh\",\"Atlantic Coastal Plain Embayed Region Tidal Salt and Brackish Marsh\",\"Atlantic Coastal Plain Indian River Lagoon Tidal Marsh\",\"Atlantic Coastal Plain Northern Tidal Salt Marsh\",\"Florida Big Bend Salt-Brackish Tidal Marsh\",\"Gulf and Atlantic Coastal Plain Tidal Marsh Systems\",\"Mississippi Sound Salt and Brackish Tidal Marsh\",\"Texas Saline Coastal Prairie\",\"Temperate Pacific Tidal Salt and Brackish Marsh\",\"Inter-Mountain Basins Alkaline Closed Depression\",\"Inter-Mountain Basins Greasewood Flat\",\"Inter-Mountain Basins Playa\",\"North American Warm Desert Playa\",\"Apacherian-Chihuahuan Mesquite Upland Scrub\",\"Apacherian-Chihuahuan Semi-Desert Grassland and Steppe\",\"Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub\",\"Chihuahuan Gypsophilous Grassland and Steppe\",\"Chihuahuan Loamy Plains Desert Grassland\",\"Chihuahuan Mixed Desert and Thorn Scrub\",\"Chihuahuan Sandy Plains Semi-Desert Grassland\",\"Chihuahuan Stabilized Coppice Dune and Sand Flat Scrub\",\"Chihuahuan Succulent Desert Scrub\",\"Madrean Juniper Savanna\",\"Mojave Mid-Elevation Mixed Desert Scrub\",\"North American Warm Desert Active and Stabilized Dune\",\"Sonora-Mojave Creosotebush-White Bursage Desert Scrub\",\"Sonoran Mid-Elevation Desert Scrub\",\"Sonoran Paloverde-Mixed Cacti Desert Scrub\",\"Chihuahuan Mixed Salt Desert Scrub\",\"Sonora-Mojave Mixed Salt Desert Scrub\",\"North American Warm Desert Wash\",\"South Texas Lomas\",\"Tamaulipan Calcareous Thornscrub\",\"Tamaulipan Clay Grassland\",\"Tamaulipan Mesquite Upland Scrub\",\"Tamaulipan Mixed Deciduous Thornscrub\",\"Tamaulipan Savanna Grassland\",\"Inter-Mountain Basins Mat Saltbush Shrubland\",\"Inter-Mountain Basins Mixed Salt Desert Scrub\",\"Inter-Mountain Basins Wash\",\"Columbia Plateau Steppe and Grassland\",\"Great Basin Xeric Mixed Sagebrush Shrubland\",\"Inter-Mountain Basins Big Sagebrush Shrubland\",\"Inter-Mountain Basins Big Sagebrush Steppe\",\"Inter-Mountain Basins Montane Sagebrush Steppe\",\"Colorado Plateau Mixed Low Sagebrush Shrubland\",\"Columbia Plateau Low Sagebrush Steppe\",\"Columbia Plateau Scabland Shrubland\",\"Wyoming Basins Dwarf Sagebrush Shrubland and Steppe\",\"Colorado Plateau Blackbrush-Mormon-tea Shrubland\",\"Inter-Mountain Basins Semi-Desert Grassland\",\"Inter-Mountain Basins Semi-Desert Shrub Steppe\",\"Southern Colorado Plateau Sand Shrubland\",\"Acadian-Appalachian Alpine Tundra\",\"Rocky Mountain Alpine Dwarf-Shrubland\",\"Rocky Mountain Alpine Fell-Field\",\"Rocky Mountain Alpine Turf\",\"Mediterranean California Alpine Dry Tundra\",\"Mediterranean California Alpine Fell-Field\",\"North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow\",\"Rocky Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit\",\"Temperate Pacific Intertidal Mudflat\",\"Mediterranean California Eelgrass Bed\",\"North Pacific Maritime Eelgrass Bed\",\"South-Central Interior Large Floodplain - Herbaceous Modifier\",\"East Gulf Coastal Plain Large River Floodplain Forest - Herbaceous Modifier\",\"Temperate Pacific Freshwater Aquatic Bed\",\"Central California Coast Ranges Cliff and Canyon\",\"Mediterranean California Serpentine Barrens\",\"Southern California Coast Ranges Cliff and Canyon\",\"Central Interior Acidic Cliff and Talus\",\"Central Interior Calcareous Cliff and Talus\",\"East Gulf Coastal Plain Dry Chalk Bluff\",\"North-Central Appalachian Acidic Cliff and Talus\",\"North-Central Appalachian Circumneutral Cliff and Talus\",\"Southern Appalachian Montane Cliff\",\"Southern Interior Acid Cliff\",\"Southern Interior Calcareous Cliff\",\"Southern Piedmont Cliff\",\"Southern Appalachian Granitic Dome\",\"Southern Appalachian Rocky Summit\",\"Southern Piedmont Granite Flatrock\",\"Rocky Mountain Cliff, Canyon and Massive Bedrock\",\"Klamath-Siskiyou Cliff and Outcrop\",\"North Pacific Montane Massive Bedrock, Cliff and Talus\",\"North Pacific Serpentine Barren\",\"North Pacific Active Volcanic Rock and Cinder Land\",\"Sierra Nevada Cliff and Canyon\",\"Western Great Plains Badland\",\"Southwestern Great Plains Canyon\",\"Western Great Plains Cliff and Outcrop\",\"North American Warm Desert Badland\",\"North American Warm Desert Bedrock Cliff and Outcrop\",\"North American Warm Desert Pavement\",\"North American Warm Desert Volcanic Rockland\",\"Colorado Plateau Mixed Bedrock Canyon and Tableland\",\"Columbia Plateau Ash and Tuff Badland\",\"Geysers and Hot Springs\",\"Inter-Mountain Basins Active and Stabilized Dune\",\"Inter-Mountain Basins Cliff and Canyon\",\"Inter-Mountain Basins Shale Badland\",\"Inter-Mountain Basins Volcanic Rock and Cinder Land\",\"Rocky Mountain Alpine Bedrock and Scree\",\"Mediterranean California Alpine Bedrock and Scree\",\"North Pacific Alpine and Subalpine Bedrock and Scree\",\"Unconsolidated Shore\",\"Undifferentiated Barren Land\",\"North American Alpine Ice Field\",\"Orchards Vineyards and Other High Structure Agriculture\",\"Cultivated Cropland\",\"Pasture/Hay\",\"Introduced Upland Vegetation - Annual Grassland\",\"Introduced Upland Vegetation - Perennial Grassland and Forbland\",\"Modified/Managed Southern Tall Grassland\",\"Introduced Upland Vegetation - Shrub\",\"Introduced Riparian and Wetland Vegetation\",\"Introduced Upland Vegetation - Treed\",\"0\",\"Disturbed, Non-specific\",\"Recently Logged Areas\",\"Harvested Forest - Grass/Forb Regeneration\",\"Harvested Forest-Shrub Regeneration\",\"Harvested Forest - Northwestern Conifer Regeneration\",\"Recently Burned\",\"Recently burned grassland\",\"Recently burned shrubland\",\"Recently burned forest\",\"Disturbed/Successional - Grass/Forb Regeneration\",\"Disturbed/Successional - Shrub Regeneration\",\"Disturbed/Successional - Recently Chained Pinyon-Juniper\",\"Open Water (Aquaculture)\",\"Open Water (Brackish/Salt)\",\"Open Water (Fresh)\",\"Quarries, Mines, Gravel Pits and Oil Wells\",\"Developed, Open Space\",\"Developed, Low Intensity\",\"Developed, Medium Intensity\",\"Developed, High Intensity\"]}]},\"description\":\"The [USGS GAP/LANDFIRE National Terrestrial Ecosystems data](https://www.sciencebase.gov/catalog/item/573cc51be4b0dae0d5e4b0c5), based on the [NatureServe Terrestrial Ecological Systems](https://www.natureserve.org/products/terrestrial-ecological-systems-united-states), 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](https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/land-cover) component of the GAP/LANDFIRE project.\\n\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"GeoTIFF data\",\"file:values\":[{\"values\":[0],\"summary\":\"0\"},{\"values\":[1],\"summary\":\"South Florida Bayhead Swamp\"},{\"values\":[2],\"summary\":\"South Florida Cypress Dome\"},{\"values\":[3],\"summary\":\"South Florida Dwarf Cypress Savanna\"},{\"values\":[4],\"summary\":\"South Florida Mangrove Swamp\"},{\"values\":[5],\"summary\":\"South Florida Hardwood Hammock\"},{\"values\":[6],\"summary\":\"Southeast Florida Coastal Strand and Maritime Hammock\"},{\"values\":[7],\"summary\":\"Southwest Florida Coastal Strand and Maritime Hammock\"},{\"values\":[8],\"summary\":\"South Florida Pine Rockland\"},{\"values\":[9],\"summary\":\"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Open Understory\"},{\"values\":[10],\"summary\":\"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory\"},{\"values\":[11],\"summary\":\"Atlantic Coastal Plain Upland Longleaf Pine Woodland\"},{\"values\":[12],\"summary\":\"Atlantic Coastal Plain Xeric River Dune\"},{\"values\":[13],\"summary\":\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Open Understory Modifier\"},{\"values\":[14],\"summary\":\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Scrub/Shrub Modifier\"},{\"values\":[15],\"summary\":\"Florida Longleaf Pine Sandhill - Scrub/Shrub Understory Modifier\"},{\"values\":[16],\"summary\":\"Florida Longleaf Pine Sandhill- Open Understory Modifier\"},{\"values\":[17],\"summary\":\"West Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland\"},{\"values\":[18],\"summary\":\"Atlantic Coastal Plain Central Maritime Forest\"},{\"values\":[19],\"summary\":\"Atlantic Coastal Plain Southern Maritime Forest\"},{\"values\":[20],\"summary\":\"Central and South Texas Coastal Fringe Forest and Woodland\"},{\"values\":[21],\"summary\":\"East Gulf Coastal Plain Limestone Forest\"},{\"values\":[22],\"summary\":\"East Gulf Coastal Plain Maritime Forest\"},{\"values\":[23],\"summary\":\"East Gulf Coastal Plain Southern Loess Bluff Forest\"},{\"values\":[24],\"summary\":\"East Gulf Coastal Plain Southern Mesic Slope Forest\"},{\"values\":[25],\"summary\":\"Mississippi Delta Maritime Forest\"},{\"values\":[26],\"summary\":\"Southern Coastal Plain Dry Upland Hardwood Forest\"},{\"values\":[27],\"summary\":\"Southern Coastal Plain Oak Dome and Hammock\"},{\"values\":[28],\"summary\":\"West Gulf Coastal Plain Chenier and Upper Texas Coastal Fringe Forest and Woodland\"},{\"values\":[29],\"summary\":\"West Gulf Coastal Plain Mesic Hardwood Forest\"},{\"values\":[30],\"summary\":\"East-Central Texas Plains Pine Forest and Woodland\"},{\"values\":[31],\"summary\":\"West Gulf Coastal Plain Pine-Hardwood Forest\"},{\"values\":[32],\"summary\":\"West Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest and Woodland\"},{\"values\":[33],\"summary\":\"Atlantic Coastal Plain Fall-Line Sandhills Longleaf Pine Woodland - Loblolly Modifier\"},{\"values\":[34],\"summary\":\"Deciduous Plantations\"},{\"values\":[35],\"summary\":\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Loblolly Modifier\"},{\"values\":[36],\"summary\":\"East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood Modifier\"},{\"values\":[37],\"summary\":\"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Offsite Hardwood Modifier\"},{\"values\":[38],\"summary\":\"Evergreen Plantation or Managed Pine\"},{\"values\":[39],\"summary\":\"California Central Valley Mixed Oak Savanna\"},{\"values\":[40],\"summary\":\"California Coastal Closed-Cone Conifer Forest and Woodland\"},{\"values\":[41],\"summary\":\"California Coastal Live Oak Woodland and Savanna\"},{\"values\":[42],\"summary\":\"California Lower Montane Blue Oak-Foothill Pine Woodland and Savanna\"},{\"values\":[43],\"summary\":\"Central and Southern California Mixed Evergreen Woodland\"},{\"values\":[44],\"summary\":\"Mediterranean California Lower Montane Black Oak-Conifer Forest and Woodland\"},{\"values\":[45],\"summary\":\"Southern California Oak Woodland and Savanna\"},{\"values\":[46],\"summary\":\"Madrean Encinal\"},{\"values\":[47],\"summary\":\"Madrean Pinyon-Juniper Woodland\"},{\"values\":[48],\"summary\":\"Madrean Pine-Oak Forest and Woodland\"},{\"values\":[49],\"summary\":\"Madrean Upper Montane Conifer-Oak Forest and Woodland\"},{\"values\":[50],\"summary\":\"Edwards Plateau Dry-Mesic Slope Forest and Woodland\"},{\"values\":[51],\"summary\":\"Edwards Plateau Limestone Savanna and Woodland\"},{\"values\":[52],\"summary\":\"Edwards Plateau Mesic Canyon\"},{\"values\":[53],\"summary\":\"Llano Uplift Acidic Forest, Woodland and Glade\"},{\"values\":[54],\"summary\":\"East Cascades Oak-Ponderosa Pine Forest and Woodland\"},{\"values\":[55],\"summary\":\"Mediterranean California Mixed Evergreen Forest\"},{\"values\":[56],\"summary\":\"Mediterranean California Mixed Oak Woodland\"},{\"values\":[57],\"summary\":\"North Pacific Dry Douglas-fir-(Madrone) Forest and Woodland\"},{\"values\":[58],\"summary\":\"North Pacific Oak Woodland\"},{\"values\":[59],\"summary\":\"Edwards Plateau Limestone Shrubland\"},{\"values\":[60],\"summary\":\"Allegheny-Cumberland Dry Oak Forest and Woodland - Hardwood\"},{\"values\":[61],\"summary\":\"Allegheny-Cumberland Dry Oak Forest and Woodland - Pine Modifier\"},{\"values\":[62],\"summary\":\"Central and Southern Appalachian Montane Oak Forest\"},{\"values\":[63],\"summary\":\"Central and Southern Appalachian Northern Hardwood Forest\"},{\"values\":[64],\"summary\":\"Central Appalachian Oak and Pine Forest\"},{\"values\":[65],\"summary\":\"Crosstimbers Oak Forest and Woodland\"},{\"values\":[66],\"summary\":\"East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Woodland Modifier\"},{\"values\":[67],\"summary\":\"East Gulf Coastal Plain Northern Dry Upland Hardwood Forest\"},{\"values\":[68],\"summary\":\"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Hardwood Modifier\"},{\"values\":[69],\"summary\":\"East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Juniper Modifier\"},{\"values\":[70],\"summary\":\"East-Central Texas Plains Post Oak Savanna and Woodland\"},{\"values\":[71],\"summary\":\"Lower Mississippi River Dune Woodland and Forest\"},{\"values\":[72],\"summary\":\"Mississippi River Alluvial Plain Dry-Mesic Loess Slope Forest\"},{\"values\":[73],\"summary\":\"North-Central Interior Dry Oak Forest and Woodland\"},{\"values\":[74],\"summary\":\"North-Central Interior Dry-Mesic Oak Forest and Woodland\"},{\"values\":[75],\"summary\":\"Northeastern Interior Dry Oak Forest - Mixed Modifier\"},{\"values\":[76],\"summary\":\"Northeastern Interior Dry Oak Forest - Virginia/Pitch Pine Modifier\"},{\"values\":[77],\"summary\":\"Northeastern Interior Dry Oak Forest-Hardwood Modifier\"},{\"values\":[78],\"summary\":\"Northeastern Interior Dry-Mesic Oak Forest\"},{\"values\":[79],\"summary\":\"Northern Atlantic Coastal Plain Dry Hardwood Forest\"},{\"values\":[80],\"summary\":\"Crowley's Ridge Sand Forest\"},{\"values\":[81],\"summary\":\"Ouachita Montane Oak Forest\"},{\"values\":[82],\"summary\":\"Ozark-Ouachita Dry Oak Woodland\"},{\"values\":[83],\"summary\":\"Ozark-Ouachita Dry-Mesic Oak Forest\"},{\"values\":[84],\"summary\":\"Southern and Central Appalachian Oak Forest\"},{\"values\":[85],\"summary\":\"Southern and Central Appalachian Oak Forest - Xeric\"},{\"values\":[86],\"summary\":\"Southern Interior Low Plateau Dry-Mesic Oak Forest\"},{\"values\":[87],\"summary\":\"Southern Ridge and Valley Dry Calcareous Forest\"},{\"values\":[88],\"summary\":\"Southern Ridge and Valley Dry Calcareous Forest - Pine modifier\"},{\"values\":[89],\"summary\":\"East Gulf Coastal Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier\"},{\"values\":[90],\"summary\":\"Managed Tree Plantation\"},{\"values\":[91],\"summary\":\"Ruderal forest\"},{\"values\":[92],\"summary\":\"Southern Piedmont Dry Oak-(Pine) Forest - Loblolly Pine Modifier\"},{\"values\":[93],\"summary\":\"Acadian Low-Elevation Spruce-Fir-Hardwood Forest\"},{\"values\":[94],\"summary\":\"Acadian-Appalachian Montane Spruce-Fir Forest\"},{\"values\":[95],\"summary\":\"Appalachian Hemlock-Hardwood Forest\"},{\"values\":[96],\"summary\":\"Central and Southern Appalachian Spruce-Fir Forest\"},{\"values\":[97],\"summary\":\"0\"},{\"values\":[98],\"summary\":\"Laurentian-Acadian Northern Hardwoods Forest\"},{\"values\":[99],\"summary\":\"Laurentian-Acadian Northern Pine-(Oak) Forest\"},{\"values\":[100],\"summary\":\"Laurentian-Acadian Pine-Hemlock-Hardwood Forest\"},{\"values\":[101],\"summary\":\"Paleozoic Plateau Bluff and Talus\"},{\"values\":[102],\"summary\":\"Southern Appalachian Northern Hardwood Forest\"},{\"values\":[103],\"summary\":\"Atlantic Coastal Plain Dry and Dry-Mesic Oak Forest\"},{\"values\":[104],\"summary\":\"Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Offsite Hardwood\"},{\"values\":[105],\"summary\":\"East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Hardwood Modifier\"},{\"values\":[106],\"summary\":\"East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Mixed Modifier\"},{\"values\":[107],\"summary\":\"Ozark-Ouachita Shortleaf Pine-Bluestem Woodland\"},{\"values\":[108],\"summary\":\"Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland\"},{\"values\":[109],\"summary\":\"Southeastern Interior Longleaf Pine Woodland\"},{\"values\":[110],\"summary\":\"Southern Appalachian Low Mountain Pine Forest\"},{\"values\":[111],\"summary\":\"Southern Piedmont Dry Oak-(Pine) Forest\"},{\"values\":[112],\"summary\":\"Southern Piedmont Dry Oak-(Pine) Forest - Hardwood Modifier\"},{\"values\":[113],\"summary\":\"Southern Piedmont Dry Oak-(Pine) Forest - Mixed Modifier\"},{\"values\":[114],\"summary\":\"Southern Piedmont Dry Oak-Heath Forest - Mixed Modifier\"},{\"values\":[115],\"summary\":\"Eastern Great Plains Tallgrass Aspen Parkland\"},{\"values\":[116],\"summary\":\"Northwestern Great Plains Aspen Forest and Parkland\"},{\"values\":[117],\"summary\":\"Northwestern Great Plains Shrubland\"},{\"values\":[118],\"summary\":\"Western Great Plains Dry Bur Oak Forest and Woodland\"},{\"values\":[119],\"summary\":\"Western Great Plains Wooded Draw and Ravine\"},{\"values\":[120],\"summary\":\"Southern Atlantic Coastal Plain Mesic Hardwood Forest\"},{\"values\":[121],\"summary\":\"East Gulf Coastal Plain Northern Loess Bluff Forest\"},{\"values\":[122],\"summary\":\"East Gulf Coastal Plain Northern Mesic Hardwood Forest\"},{\"values\":[123],\"summary\":\"North-Central Interior Beech-Maple Forest\"},{\"values\":[124],\"summary\":\"North-Central Interior Maple-Basswood Forest\"},{\"values\":[125],\"summary\":\"Ozark-Ouachita Mesic Hardwood Forest\"},{\"values\":[126],\"summary\":\"South-Central Interior Mesophytic Forest\"},{\"values\":[127],\"summary\":\"Southern and Central Appalachian Cove Forest\"},{\"values\":[128],\"summary\":\"Crowley's Ridge Mesic Loess Slope Forest\"},{\"values\":[129],\"summary\":\"Southern Piedmont Mesic Forest\"},{\"values\":[130],\"summary\":\"Appalachian Shale Barrens\"},{\"values\":[131],\"summary\":\"Atlantic Coastal Plain Northern Maritime Forest\"},{\"values\":[132],\"summary\":\"Laurentian Pine-Oak Barrens\"},{\"values\":[133],\"summary\":\"Northeastern Interior Pine Barrens\"},{\"values\":[134],\"summary\":\"Northern Atlantic Coastal Plain Pitch Pine Barrens\"},{\"values\":[135],\"summary\":\"Southern Appalachian Montane Pine Forest and Woodland\"},{\"values\":[136],\"summary\":\"East Cascades Mesic Montane Mixed-Conifer Forest and Woodland\"},{\"values\":[137],\"summary\":\"Middle Rocky Mountain Montane Douglas-fir Forest and Woodland\"},{\"values\":[138],\"summary\":\"Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest\"},{\"values\":[139],\"summary\":\"Northern Rocky Mountain Foothill Conifer Wooded Steppe\"},{\"values\":[140],\"summary\":\"Northern Rocky Mountain Mesic Montane Mixed Conifer Forest\"},{\"values\":[141],\"summary\":\"Northern Rocky Mountain Ponderosa Pine Woodland and Savanna\"},{\"values\":[142],\"summary\":\"Northern Rocky Mountain Western Larch Savanna\"},{\"values\":[143],\"summary\":\"Northwestern Great Plains - Black Hills Ponderosa Pine Woodland and Savanna\"},{\"values\":[144],\"summary\":\"Rocky Mountain Foothill Limber Pine-Juniper Woodland\"},{\"values\":[145],\"summary\":\"Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland\"},{\"values\":[146],\"summary\":\"Inter-Mountain Basins Subalpine Limber-Bristlecone Pine Woodland\"},{\"values\":[147],\"summary\":\"Northern Rocky Mountain Subalpine Woodland and Parkland\"},{\"values\":[148],\"summary\":\"Rocky Mountain Aspen Forest and Woodland\"},{\"values\":[149],\"summary\":\"Rocky Mountain Lodgepole Pine Forest\"},{\"values\":[150],\"summary\":\"Rocky Mountain Poor-Site Lodgepole Pine Forest\"},{\"values\":[151],\"summary\":\"Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland\"},{\"values\":[152],\"summary\":\"Rocky Mountain Subalpine Mesic Spruce-Fir Forest and Woodland\"},{\"values\":[153],\"summary\":\"Rocky Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland\"},{\"values\":[154],\"summary\":\"Rocky Mountain Bigtooth Maple Ravine Woodland\"},{\"values\":[155],\"summary\":\"Southern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland\"},{\"values\":[156],\"summary\":\"Southern Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland\"},{\"values\":[157],\"summary\":\"Southern Rocky Mountain Ponderosa Pine Savanna\"},{\"values\":[158],\"summary\":\"Southern Rocky Mountain Ponderosa Pine Woodland\"},{\"values\":[159],\"summary\":\"California Montane Jeffrey Pine-(Ponderosa Pine) Woodland\"},{\"values\":[160],\"summary\":\"Klamath-Siskiyou Lower Montane Serpentine Mixed Conifer Woodland\"},{\"values\":[161],\"summary\":\"Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland\"},{\"values\":[162],\"summary\":\"Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland\"},{\"values\":[163],\"summary\":\"Mediterranean California Mesic Mixed Conifer Forest and Woodland\"},{\"values\":[164],\"summary\":\"Sierran-Intermontane Desert Western White Pine-White Fir Woodland\"},{\"values\":[165],\"summary\":\"California Coastal Redwood Forest\"},{\"values\":[166],\"summary\":\"North Pacific Broadleaf Landslide Forest and Shrubland\"},{\"values\":[167],\"summary\":\"North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest\"},{\"values\":[168],\"summary\":\"North Pacific Hypermaritime Sitka Spruce Forest\"},{\"values\":[169],\"summary\":\"North Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest\"},{\"values\":[170],\"summary\":\"North Pacific Lowland Mixed Hardwood-Conifer Forest and Woodland\"},{\"values\":[171],\"summary\":\"North Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest\"},{\"values\":[172],\"summary\":\"North Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest\"},{\"values\":[173],\"summary\":\"North Pacific Mesic Western Hemlock-Silver Fir Forest\"},{\"values\":[174],\"summary\":\"North Pacific Wooded Volcanic Flowage\"},{\"values\":[175],\"summary\":\"Mediterranean California Red Fir Forest\"},{\"values\":[176],\"summary\":\"Mediterranean California Subalpine Woodland\"},{\"values\":[177],\"summary\":\"North Pacific Maritime Mesic Subalpine Parkland\"},{\"values\":[178],\"summary\":\"North Pacific Mountain Hemlock Forest\"},{\"values\":[179],\"summary\":\"Northern California Mesic Subalpine Woodland\"},{\"values\":[180],\"summary\":\"Northern Pacific Mesic Subalpine Woodland\"},{\"values\":[181],\"summary\":\"Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland\"},{\"values\":[182],\"summary\":\"Columbia Plateau Western Juniper Woodland and Savanna\"},{\"values\":[183],\"summary\":\"Great Basin Pinyon-Juniper Woodland\"},{\"values\":[184],\"summary\":\"Inter-Mountain Basins Curl-leaf Mountain Mahogany Woodland and Shrubland\"},{\"values\":[185],\"summary\":\"Inter-Mountain Basins Juniper Savanna\"},{\"values\":[186],\"summary\":\"Colorado Plateau Pinyon-Juniper Shrubland\"},{\"values\":[187],\"summary\":\"Colorado Plateau Pinyon-Juniper Woodland\"},{\"values\":[188],\"summary\":\"Southern Rocky Mountain Juniper Woodland and Savanna\"},{\"values\":[189],\"summary\":\"Southern Rocky Mountain Pinyon-Juniper Woodland\"},{\"values\":[190],\"summary\":\"Northwestern Great Plains Floodplain\"},{\"values\":[191],\"summary\":\"Northwestern Great Plains Riparian\"},{\"values\":[192],\"summary\":\"Western Great Plains Floodplain\"},{\"values\":[193],\"summary\":\"Western Great Plains Floodplain Systems\"},{\"values\":[194],\"summary\":\"Western Great Plains Riparian Woodland and Shrubland\"},{\"values\":[195],\"summary\":\"Central Appalachian Floodplain - Forest Modifier\"},{\"values\":[196],\"summary\":\"Central Appalachian Riparian - Forest Modifier\"},{\"values\":[197],\"summary\":\"Central Interior and Appalachian Floodplain Systems\"},{\"values\":[198],\"summary\":\"Central Interior and Appalachian Riparian Systems\"},{\"values\":[199],\"summary\":\"Laurentian-Acadian Floodplain Systems\"},{\"values\":[200],\"summary\":\"Ozark-Ouachita Riparian\"},{\"values\":[201],\"summary\":\"South-Central Interior Large Floodplain\"},{\"values\":[202],\"summary\":\"South-Central Interior Large Floodplain - Forest Modifier\"},{\"values\":[203],\"summary\":\"South-Central Interior Small Stream and Riparian\"},{\"values\":[204],\"summary\":\"North-Central Interior and Appalachian Rich Swamp\"},{\"values\":[205],\"summary\":\"0\"},{\"values\":[206],\"summary\":\"0\"},{\"values\":[207],\"summary\":\"Laurentian-Acadian Swamp Systems\"},{\"values\":[208],\"summary\":\"North-Central Interior Wet Flatwoods\"},{\"values\":[209],\"summary\":\"0\"},{\"values\":[210],\"summary\":\"South-Central Interior / Upper Coastal Plain Wet Flatwoods\"},{\"values\":[211],\"summary\":\"0\"},{\"values\":[212],\"summary\":\"Southern Piedmont/Ridge and Valley Upland Depression Swamp\"},{\"values\":[213],\"summary\":\"Atlantic Coastal Plain Blackwater Stream Floodplain Forest - Forest Modifier\"},{\"values\":[214],\"summary\":\"Atlantic Coastal Plain Brownwater Stream Floodplain Forest\"},{\"values\":[215],\"summary\":\"Atlantic Coastal Plain Northern Tidal Wooded Swamp\"},{\"values\":[216],\"summary\":\"Atlantic Coastal Plain Small Blackwater River Floodplain Forest\"},{\"values\":[217],\"summary\":\"Atlantic Coastal Plain Small Brownwater River Floodplain Forest\"},{\"values\":[218],\"summary\":\"Atlantic Coastal Plain Southern Tidal Wooded Swamp\"},{\"values\":[219],\"summary\":\"East Gulf Coastal Plain Large River Floodplain Forest - Forest Modifier\"},{\"values\":[220],\"summary\":\"East Gulf Coastal Plain Small Stream and River Floodplain Forest\"},{\"values\":[221],\"summary\":\"East Gulf Coastal Plain Tidal Wooded Swamp\"},{\"values\":[222],\"summary\":\"0\"},{\"values\":[223],\"summary\":\"Southeastern Great Plains Riparian Forest\"},{\"values\":[224],\"summary\":\"Southeastern Great Plains Floodplain Forest\"},{\"values\":[225],\"summary\":\"Mississippi River Bottomland Depression\"},{\"values\":[226],\"summary\":\"Mississippi River Floodplain and Riparian Forest\"},{\"values\":[227],\"summary\":\"Mississippi River Low Floodplain (Bottomland) Forest\"},{\"values\":[228],\"summary\":\"Mississippi River Riparian Forest\"},{\"values\":[229],\"summary\":\"Red River Large Floodplain Forest\"},{\"values\":[230],\"summary\":\"Southern Coastal Plain Blackwater River Floodplain Forest\"},{\"values\":[231],\"summary\":\"Southern Piedmont Large Floodplain Forest - Forest Modifier\"},{\"values\":[232],\"summary\":\"Southern Piedmont Small Floodplain and Riparian Forest\"},{\"values\":[233],\"summary\":\"West Gulf Coastal Plain Large River Floodplain Forest\"},{\"values\":[234],\"summary\":\"West Gulf Coastal Plain Near-Coast Large River Swamp\"},{\"values\":[235],\"summary\":\"West Gulf Coastal Plain Small Stream and River Forest\"},{\"values\":[236],\"summary\":\"Atlantic Coastal Plain Streamhead Seepage Swamp - Pocosin - and Baygall\"},{\"values\":[237],\"summary\":\"Gulf and Atlantic Coastal Plain Swamp Systems\"},{\"values\":[238],\"summary\":\"Southern Coastal Plain Hydric Hammock\"},{\"values\":[239],\"summary\":\"Southern Coastal Plain Seepage Swamp and Baygall\"},{\"values\":[240],\"summary\":\"West Gulf Coastal Plain Seepage Swamp and Baygall\"},{\"values\":[241],\"summary\":\"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Taxodium/Nyssa Modifier\"},{\"values\":[242],\"summary\":\"Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Oak Dominated Modifier\"},{\"values\":[243],\"summary\":\"East Gulf Coastal Plain Southern Loblolly-Hardwood Flatwoods\"},{\"values\":[244],\"summary\":\"Lower Mississippi River Bottomland Depressions - Forest Modifier\"},{\"values\":[245],\"summary\":\"Lower Mississippi River Flatwoods\"},{\"values\":[246],\"summary\":\"Northern Atlantic Coastal Plain Basin Swamp and Wet Hardwood Forest\"},{\"values\":[247],\"summary\":\"Southern Coastal Plain Nonriverine Basin Swamp\"},{\"values\":[248],\"summary\":\"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier\"},{\"values\":[249],\"summary\":\"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier\"},{\"values\":[250],\"summary\":\"Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier\"},{\"values\":[251],\"summary\":\"West Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods\"},{\"values\":[252],\"summary\":\"West Gulf Coastal Plain Pine-Hardwood Flatwoods\"},{\"values\":[253],\"summary\":\"Edwards Plateau Riparian\"},{\"values\":[254],\"summary\":\"Atlantic Coastal Plain Clay-Based Carolina Bay Forested Wetland\"},{\"values\":[255],\"summary\":\"Atlantic Coastal Plain Clay-Based Carolina Bay Herbaceous Wetland\"},{\"values\":[256],\"summary\":\"Atlantic Coastal Plain Southern Wet Pine Savanna and Flatwoods\"},{\"values\":[257],\"summary\":\"Central Atlantic Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\"},{\"values\":[258],\"summary\":\"Central Florida Pine Flatwoods\"},{\"values\":[259],\"summary\":\"East Gulf Coastal Plain Near-Coast Pine Flatwoods\"},{\"values\":[260],\"summary\":\"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Open Understory Modifier\"},{\"values\":[261],\"summary\":\"East Gulf Coastal Plain Near-Coast Pine Flatwoods - Scrub/Shrub Understory Modifier\"},{\"values\":[262],\"summary\":\"South Florida Pine Flatwoods\"},{\"values\":[263],\"summary\":\"Southern Coastal Plain Nonriverine Cypress Dome\"},{\"values\":[264],\"summary\":\"West Gulf Coastal Plain Wet Longleaf Pine Savanna and Flatwoods\"},{\"values\":[265],\"summary\":\"Columbia Basin Foothill Riparian Woodland and Shrubland\"},{\"values\":[266],\"summary\":\"Great Basin Foothill and Lower Montane Riparian Woodland and Shrubland\"},{\"values\":[267],\"summary\":\"0\"},{\"values\":[268],\"summary\":\"Northern Rocky Mountain Conifer Swamp\"},{\"values\":[269],\"summary\":\"Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland\"},{\"values\":[270],\"summary\":\"Rocky Mountain Lower Montane Riparian Woodland and Shrubland\"},{\"values\":[271],\"summary\":\"Rocky Mountain Montane Riparian Systems\"},{\"values\":[272],\"summary\":\"Rocky Mountain Subalpine-Montane Riparian Woodland\"},{\"values\":[273],\"summary\":\"North Pacific Hardwood-Conifer Swamp\"},{\"values\":[274],\"summary\":\"North Pacific Lowland Riparian Forest and Shrubland\"},{\"values\":[275],\"summary\":\"North Pacific Montane Riparian Woodland and Shrubland\"},{\"values\":[276],\"summary\":\"North Pacific Shrub Swamp\"},{\"values\":[277],\"summary\":\"California Central Valley Riparian Woodland and Shrubland\"},{\"values\":[278],\"summary\":\"Mediterranean California Foothill and Lower Montane Riparian Woodland\"},{\"values\":[279],\"summary\":\"Mediterranean California Serpentine Foothill and Lower Montane Riparian Woodland and Seep\"},{\"values\":[280],\"summary\":\"North American Warm Desert Lower Montane Riparian Woodland and Shrubland\"},{\"values\":[281],\"summary\":\"North American Warm Desert Riparian Systems\"},{\"values\":[282],\"summary\":\"North American Warm Desert Riparian Woodland and Shrubland\"},{\"values\":[283],\"summary\":\"Tamaulipan Floodplain\"},{\"values\":[284],\"summary\":\"Tamaulipan Riparian Systems\"},{\"values\":[285],\"summary\":\"Boreal Aspen-Birch Forest\"},{\"values\":[286],\"summary\":\"Boreal Jack Pine-Black Spruce Forest\"},{\"values\":[287],\"summary\":\"Boreal White Spruce-Fir-Hardwood Forest\"},{\"values\":[288],\"summary\":\"Boreal-Laurentian Conifer Acidic Swamp and Treed Poor Fen\"},{\"values\":[289],\"summary\":\"Eastern Boreal Floodplain\"},{\"values\":[290],\"summary\":\"South Florida Shell Hash Beach\"},{\"values\":[291],\"summary\":\"Southeast Florida Beach\"},{\"values\":[292],\"summary\":\"Southwest Florida Beach\"},{\"values\":[293],\"summary\":\"South Florida Everglades Sawgrass Marsh\"},{\"values\":[294],\"summary\":\"South Florida Freshwater Slough and Gator Hole\"},{\"values\":[295],\"summary\":\"South Florida Wet Marl Prairie\"},{\"values\":[296],\"summary\":\"California Maritime Chaparral\"},{\"values\":[297],\"summary\":\"California Mesic Chaparral\"},{\"values\":[298],\"summary\":\"California Xeric Serpentine Chaparral\"},{\"values\":[299],\"summary\":\"Klamath-Siskiyou Xeromorphic Serpentine Savanna and Chaparral\"},{\"values\":[300],\"summary\":\"Mediterranean California Mesic Serpentine Woodland and Chaparral\"},{\"values\":[301],\"summary\":\"Northern and Central California Dry-Mesic Chaparral\"},{\"values\":[302],\"summary\":\"Southern California Dry-Mesic Chaparral\"},{\"values\":[303],\"summary\":\"Southern California Coastal Scrub\"},{\"values\":[304],\"summary\":\"California Central Valley and Southern Coastal Grassland\"},{\"values\":[305],\"summary\":\"California Mesic Serpentine Grassland\"},{\"values\":[306],\"summary\":\"Columbia Basin Foothill and Canyon Dry Grassland\"},{\"values\":[307],\"summary\":\"Columbia Basin Palouse Prairie\"},{\"values\":[308],\"summary\":\"North Pacific Alpine and Subalpine Dry Grassland\"},{\"values\":[309],\"summary\":\"North Pacific Montane Grassland\"},{\"values\":[310],\"summary\":\"North Pacific Montane Shrubland\"},{\"values\":[311],\"summary\":\"Northern Rocky Mountain Lower Montane, Foothill and Valley Grassland\"},{\"values\":[312],\"summary\":\"Northern Rocky Mountain Montane-Foothill Deciduous Shrubland\"},{\"values\":[313],\"summary\":\"Northern Rocky Mountain Subalpine Deciduous Shrubland\"},{\"values\":[314],\"summary\":\"Northern Rocky Mountain Subalpine-Upper Montane Grassland\"},{\"values\":[315],\"summary\":\"Southern Rocky Mountain Montane-Subalpine Grassland\"},{\"values\":[316],\"summary\":\"Rocky Mountain Gambel Oak-Mixed Montane Shrubland\"},{\"values\":[317],\"summary\":\"Rocky Mountain Lower Montane-Foothill Shrubland\"},{\"values\":[318],\"summary\":\"California Northern Coastal Grassland\"},{\"values\":[319],\"summary\":\"North Pacific Herbaceous Bald and Bluff\"},{\"values\":[320],\"summary\":\"North Pacific Hypermaritime Shrub and Herbaceous Headland\"},{\"values\":[321],\"summary\":\"Willamette Valley Upland Prairie and Savanna\"},{\"values\":[322],\"summary\":\"Mediterranean California Subalpine Meadow\"},{\"values\":[323],\"summary\":\"Rocky Mountain Subalpine-Montane Mesic Meadow\"},{\"values\":[324],\"summary\":\"Central Mixedgrass Prairie\"},{\"values\":[325],\"summary\":\"Northwestern Great Plains Mixedgrass Prairie\"},{\"values\":[326],\"summary\":\"Western Great Plains Foothill and Piedmont Grassland\"},{\"values\":[327],\"summary\":\"Western Great Plains Tallgrass Prairie\"},{\"values\":[328],\"summary\":\"Western Great Plains Sand Prairie\"},{\"values\":[329],\"summary\":\"Western Great Plains Sandhill Steppe\"},{\"values\":[330],\"summary\":\"Western Great Plains Mesquite Woodland and Shrubland\"},{\"values\":[331],\"summary\":\"Western Great Plains Shortgrass Prairie\"},{\"values\":[332],\"summary\":\"Arkansas Valley Prairie and Woodland\"},{\"values\":[333],\"summary\":\"Central Tallgrass Prairie\"},{\"values\":[334],\"summary\":\"North-Central Interior Oak Savanna\"},{\"values\":[335],\"summary\":\"North-Central Interior Sand and Gravel Tallgrass Prairie\"},{\"values\":[336],\"summary\":\"North-Central Oak Barrens\"},{\"values\":[337],\"summary\":\"Northern Tallgrass Prairie\"},{\"values\":[338],\"summary\":\"Southeastern Great Plains Tallgrass Prairie\"},{\"values\":[339],\"summary\":\"Texas Blackland Tallgrass Prairie\"},{\"values\":[340],\"summary\":\"Texas-Louisiana Coastal Prairie\"},{\"values\":[341],\"summary\":\"Central Appalachian Pine-Oak Rocky Woodland\"},{\"values\":[342],\"summary\":\"Southern Appalachian Grass and Shrub Bald\"},{\"values\":[343],\"summary\":\"Southern Appalachian Grass and Shrub Bald - Herbaceous Modifier\"},{\"values\":[344],\"summary\":\"Southern Appalachian Grass and Shrub Bald - Shrub Modifier\"},{\"values\":[345],\"summary\":\"Central Appalachian Alkaline Glade and Woodland\"},{\"values\":[346],\"summary\":\"Central Interior Highlands Calcareous Glade and Barrens\"},{\"values\":[347],\"summary\":\"Central Interior Highlands Dry Acidic Glade and Barrens\"},{\"values\":[348],\"summary\":\"Cumberland Sandstone Glade and Barrens\"},{\"values\":[349],\"summary\":\"Great Lakes Alvar\"},{\"values\":[350],\"summary\":\"Nashville Basin Limestone Glade\"},{\"values\":[351],\"summary\":\"Southern Ridge and Valley / Cumberland Dry Calcareous Forest\"},{\"values\":[352],\"summary\":\"Southern Piedmont Glade and Barrens\"},{\"values\":[353],\"summary\":\"East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Herbaceous Modifier\"},{\"values\":[354],\"summary\":\"East Gulf Coastal Plain Jackson Prairie and Woodland\"},{\"values\":[355],\"summary\":\"Eastern Highland Rim Prairie and Barrens - Dry Modifier\"},{\"values\":[356],\"summary\":\"Coahuilan Chaparral\"},{\"values\":[357],\"summary\":\"Madrean Oriental Chaparral\"},{\"values\":[358],\"summary\":\"Mogollon Chaparral\"},{\"values\":[359],\"summary\":\"Sonora-Mojave Semi-Desert Chaparral\"},{\"values\":[360],\"summary\":\"California Montane Woodland and Chaparral\"},{\"values\":[361],\"summary\":\"Great Basin Semi-Desert Chaparral\"},{\"values\":[362],\"summary\":\"Florida Dry Prairie\"},{\"values\":[363],\"summary\":\"Florida Peninsula Inland Scrub\"},{\"values\":[364],\"summary\":\"West Gulf Coastal Plain Catahoula Barrens\"},{\"values\":[365],\"summary\":\"West Gulf Coastal Plain Nepheline Syenite Glade\"},{\"values\":[366],\"summary\":\"East Gulf Coastal Plain Jackson Plain Dry Flatwoods - Open Understory Modifier\"},{\"values\":[367],\"summary\":\"West Gulf Coastal Plain Northern Calcareous Prairie\"},{\"values\":[368],\"summary\":\"West Gulf Coastal Plain Southern Calcareous Prairie\"},{\"values\":[369],\"summary\":\"Acadian-Appalachian Subalpine Woodland and Heath-Krummholz\"},{\"values\":[370],\"summary\":\"Atlantic and Gulf Coastal Plain Interdunal Wetland\"},{\"values\":[371],\"summary\":\"Atlantic Coastal Plain Southern Dune and Maritime Grassland\"},{\"values\":[372],\"summary\":\"Central and Upper Texas Coast Dune and Coastal Grassland\"},{\"values\":[373],\"summary\":\"East Gulf Coastal Plain Dune and Coastal Grassland\"},{\"values\":[374],\"summary\":\"Great Lakes Dune\"},{\"values\":[375],\"summary\":\"Northern Atlantic Coastal Plain Dune and Swale\"},{\"values\":[376],\"summary\":\"Northern Atlantic Coastal Plain Heathland and Grassland\"},{\"values\":[377],\"summary\":\"South Texas Dune and Coastal Grassland\"},{\"values\":[378],\"summary\":\"South Texas Sand Sheet Grassland\"},{\"values\":[379],\"summary\":\"Southwest Florida Dune and Coastal Grassland\"},{\"values\":[380],\"summary\":\"North Pacific Coastal Cliff and Bluff\"},{\"values\":[381],\"summary\":\"North Pacific Maritime Coastal Sand Dune and Strand\"},{\"values\":[382],\"summary\":\"Northern California Coastal Scrub\"},{\"values\":[383],\"summary\":\"Mediterranean California Coastal Bluff\"},{\"values\":[384],\"summary\":\"Mediterranean California Northern Coastal Dune\"},{\"values\":[385],\"summary\":\"Mediterranean California Southern Coastal Dune\"},{\"values\":[386],\"summary\":\"Atlantic Coastal Plain Northern Sandy Beach\"},{\"values\":[387],\"summary\":\"Atlantic Coastal Plain Sea Island Beach\"},{\"values\":[388],\"summary\":\"Atlantic Coastal Plain Southern Beach\"},{\"values\":[389],\"summary\":\"Florida Panhandle Beach Vegetation\"},{\"values\":[390],\"summary\":\"Louisiana Beach\"},{\"values\":[391],\"summary\":\"Northern Atlantic Coastal Plain Sandy Beach\"},{\"values\":[392],\"summary\":\"Texas Coastal Bend Beach\"},{\"values\":[393],\"summary\":\"Upper Texas Coast Beach\"},{\"values\":[394],\"summary\":\"0\"},{\"values\":[395],\"summary\":\"Mediterranean California Serpentine Fen\"},{\"values\":[396],\"summary\":\"Mediterranean California Subalpine-Montane Fen\"},{\"values\":[397],\"summary\":\"North Pacific Bog and Fen\"},{\"values\":[398],\"summary\":\"Rocky Mountain Subalpine-Montane Fen\"},{\"values\":[399],\"summary\":\"Atlantic Coastal Plain Peatland Pocosin\"},{\"values\":[400],\"summary\":\"Southern and Central Appalachian Bog and Fen\"},{\"values\":[401],\"summary\":\"Atlantic Coastal Plain Central Fresh-Oligohaline Tidal Marsh\"},{\"values\":[402],\"summary\":\"Atlantic Coastal Plain Embayed Region Tidal Freshwater Marsh\"},{\"values\":[403],\"summary\":\"Atlantic Coastal Plain Northern Fresh and Oligohaline Tidal Marsh\"},{\"values\":[404],\"summary\":\"Florida Big Bend Fresh-Oligohaline Tidal Marsh\"},{\"values\":[405],\"summary\":\"Atlantic Coastal Plain Depression Pondshore\"},{\"values\":[406],\"summary\":\"Atlantic Coastal Plain 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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/) and email [planetarycomputer@microsoft.com](mailto:planetarycomputer@microsoft.com).*\\n\\nThrough Norway\u2019s International Climate & Forests Initiative (NICFI), users can access Planet\u2019s high-resolution, analysis-ready mosaics of the world\u2019s tropics in order to help reduce and reverse the loss of tropical forests, combat climate change, conserve biodiversity, and facilitate sustainable development.\\n\\nIn support of NICFI\u2019s mission, you can use this data for a number of projects including, but not limited to:\\n\\n* Advance scientific research about the world\u2019s tropical forests and the critical services they provide.\\n* Implement and improve policies for sustainable forest management and land use in developing tropical forest countries and jurisdictions.\\n* Increase transparency and accountability in the tropics.\\n* Protect and improve the rights of indigenous peoples and local communities in tropical forest countries.\\n* Innovate solutions towards reducing pressure on forests from global commodities and financial markets.\\n* In short, the primary purpose of the NICFI Program is to support reducing and reversing the loss of tropical forests, contributing to combating climate change, conserving biodiversity, contributing to forest regrowth, restoration, and enhancement, and facilitating sustainable development, all of which must be Non-Commercial Use.\\n\\nTo learn how more about the NICFI program, streaming and downloading basemaps please read the [NICFI Data Program User Guide](https://assets.planet.com/docs/NICFI_UserGuidesFAQ.pdf).\\n\\nThis collection contains both monthly and biannual mosaics. Biannual mosaics are available from December 2015 - August 2020. Monthly mosaics are available from September 2020. The STAC items include a `planet-nicfi:cadence` field indicating the type of mosaic.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Data\",\"description\":\"a 'true-colour' representation of spatially accurate data with minimized haze, 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 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 Terms of Use\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/gbif\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Global Biodiversity Information Facility (GBIF)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gbif.png\",\"type\":\"image/png\",\"title\":\"Forest Inventory and Analysis\"},\"geoparquet-items\":{\"href\":\"abfs://items/gbif.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Biodiversity Information Facility\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"The [Global Biodiversity Information Facility](https://www.gbif.org) (GBIF) is an international network and data infrastructure funded by the world's governments, providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences.\\n\\nThe GBIF occurrence dataset combines data from a wide array of sources, including specimen-related data from natural history museums, observations from citizen science networks, and automated environmental surveys. While these data are constantly changing at [GBIF.org](https://www.gbif.org), periodic snapshots are taken and made available here. \\n\\nData are stored in [Parquet](https://parquet.apache.org/) format; the Parquet file schema is described below. Most field names correspond to [terms from the Darwin Core standard](https://dwc.tdwg.org/terms/), and have been interpreted by GBIF's systems to align taxonomy, location, dates, etc. Additional information may be retrieved using the [GBIF API](https://www.gbif.org/developer/summary).\\n\\nPlease refer to the GBIF [citation guidelines](https://www.gbif.org/citation-guidelines) for information about how to cite GBIF data in publications.. For analyses using the whole dataset, please use the following citation:\\n\\n> GBIF.org ([Date]) GBIF Occurrence Data [DOI of dataset]\\n\\nFor analyses where data are significantly filtered, please track the datasetKeys used and use a \\\"[derived dataset](https://www.gbif.org/citation-guidelines#derivedDatasets)\\\" record for citing the data.\\n\\nThe [GBIF data blog](https://data-blog.gbif.org/categories/gbif/) contains a number of articles that can help you analyze GBIF data.\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Dataset root\",\"table:storage_options\":{\"account_name\":\"ai4edataeuwest\"}}},\"stac_version\":\"1.0.0\",\"table:columns\":[{\"name\":\"gbifid\",\"type\":\"int64\",\"description\":\"GBIF's identifier for the occurrence\"},{\"name\":\"datasetkey\",\"type\":\"byte_array\",\"description\":\"GBIF's UUID for the [dataset](https://www.gbif.org/developer/registry#datasets) containing this occurrence\"},{\"name\":\"occurrenceid\",\"type\":\"byte_array\",\"description\":\"See [dwc:occurrenceID](https://dwc.tdwg.org/terms/#occurrenceID).\"},{\"name\":\"kingdom\",\"type\":\"byte_array\",\"description\":\"See [dwc:kingdom](https://dwc.tdwg.org/terms/#kingdom). This field has been aligned with the [GBIF backbone taxonomy](https://doi.org/10.15468/39omei).\"},{\"name\":\"phylum\",\"type\":\"byte_array\",\"description\":\"See [dwc:phylum](https://dwc.tdwg.org/terms/#phylum). This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"class\",\"type\":\"byte_array\",\"description\":\"See [dwc:class](https://dwc.tdwg.org/terms/#class). This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"order\",\"type\":\"byte_array\",\"description\":\"See [dwc:order](https://dwc.tdwg.org/terms/#order). This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"family\",\"type\":\"byte_array\",\"description\":\"See [dwc:family](https://dwc.tdwg.org/terms/#family). This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"genus\",\"type\":\"byte_array\",\"description\":\"See [dwc:genus](https://dwc.tdwg.org/terms/#genus). This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"species\",\"type\":\"byte_array\",\"description\":\"See [dwc:species](https://dwc.tdwg.org/terms/#species). This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"infraspecificepithet\",\"type\":\"byte_array\",\"description\":\"See [dwc:infraspecificEpithet](https://dwc.tdwg.org/terms/#infraspecificEpithet). \ This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"taxonrank\",\"type\":\"byte_array\",\"description\":\"See [dwc:taxonRank](https://dwc.tdwg.org/terms/#taxonRank). This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"scientificname\",\"type\":\"byte_array\",\"description\":\"See [dwc:scientificName](https://dwc.tdwg.org/terms/#scientificName). This field has been aligned with the GBIF backbone taxonomy.\"},{\"name\":\"verbatimscientificname\",\"type\":\"byte_array\",\"description\":\"The scientific name as provided by the data publisher\"},{\"name\":\"verbatimscientificnameauthorship\",\"type\":\"byte_array\",\"description\":\"The scientific name authorship provided by the data publisher.\"},{\"name\":\"countrycode\",\"type\":\"byte_array\",\"description\":\"See [dwc:countryCode](https://dwc.tdwg.org/terms/#countryCode). GBIF's interpretation has set this to an ISO 3166-2 code.\"},{\"name\":\"locality\",\"type\":\"byte_array\",\"description\":\"See [dwc:locality](https://dwc.tdwg.org/terms/#locality).\"},{\"name\":\"stateprovince\",\"type\":\"byte_array\",\"description\":\"See [dwc:stateProvince](https://dwc.tdwg.org/terms/#stateProvince).\"},{\"name\":\"occurrencestatus\",\"type\":\"byte_array\",\"description\":\"See [dwc:occurrenceStatus](https://dwc.tdwg.org/terms/#occurrenceStatus). Either the value `PRESENT` or `ABSENT`. **Many users will wish to filter for `PRESENT` data.**\"},{\"name\":\"individualcount\",\"type\":\"int32\",\"description\":\"See [dwc:individualCount](https://dwc.tdwg.org/terms/#individualCount).\"},{\"name\":\"publishingorgkey\",\"type\":\"byte_array\",\"description\":\"GBIF's UUID for the [organization](https://www.gbif.org/developer/registry#organizations) publishing this occurrence.\"},{\"name\":\"decimallatitude\",\"type\":\"double\",\"description\":\"See [dwc:decimalLatitude](https://dwc.tdwg.org/terms/#decimalLatitude). GBIF's interpretation has normalized this to a WGS84 coordinate.\"},{\"name\":\"decimallongitude\",\"type\":\"double\",\"description\":\"See [dwc:decimalLongitude](https://dwc.tdwg.org/terms/#decimalLongitude). GBIF's interpretation has normalized this to a WGS84 coordinate.\"},{\"name\":\"coordinateuncertaintyinmeters\",\"type\":\"double\",\"description\":\"See [dwc:coordinateUncertaintyInMeters](https://dwc.tdwg.org/terms/#coordinateUncertaintyInMeters).\"},{\"name\":\"coordinateprecision\",\"type\":\"double\",\"description\":\"See [dwc:coordinatePrecision](https://dwc.tdwg.org/terms/#coordinatePrecision).\"},{\"name\":\"elevation\",\"type\":\"double\",\"description\":\"See [dwc:elevation](https://dwc.tdwg.org/terms/#elevation). If provided by the data publisher, GBIF's interpretation has normalized this value to metres.\"},{\"name\":\"elevationaccuracy\",\"type\":\"double\",\"description\":\"See [dwc:elevationAccuracy](https://dwc.tdwg.org/terms/#elevationAccuracy). If provided by the data publisher, GBIF's interpretation has normalized this value to metres.\"},{\"name\":\"depth\",\"type\":\"double\",\"description\":\"See [dwc:depth](https://dwc.tdwg.org/terms/#depth). If provided by the data publisher, GBIF's interpretation has normalized this value to metres.\"},{\"name\":\"depthaccuracy\",\"type\":\"double\",\"description\":\"See [dwc:depthAccuracy](https://dwc.tdwg.org/terms/#depthAccuracy). If provided by the data publisher, GBIF's interpretation has normalized this value to metres.\"},{\"name\":\"eventdate\",\"type\":\"byte_array\",\"description\":\"See [dwc:eventDate](https://dwc.tdwg.org/terms/#eventDate). GBIF's interpretation has normalized this value to an ISO 8601 date with a local time.\"},{\"name\":\"day\",\"type\":\"int32\",\"description\":\"See [dwc:day](https://dwc.tdwg.org/terms/#day).\"},{\"name\":\"month\",\"type\":\"int32\",\"description\":\"See [dwc:month](https://dwc.tdwg.org/terms/#month).\"},{\"name\":\"year\",\"type\":\"int32\",\"description\":\"See [dwc:year](https://dwc.tdwg.org/terms/#year).\"},{\"name\":\"taxonkey\",\"type\":\"int32\",\"description\":\"The numeric identifier for the [taxon](https://www.gbif.org/developer/species#nameUsages) in GBIF's backbone taxonomy corresponding to `scientificname`.\"},{\"name\":\"specieskey\",\"type\":\"int32\",\"description\":\"The numeric identifier for the taxon in GBIF's backbone taxonomy corresponding to `species`.\"},{\"name\":\"basisofrecord\",\"type\":\"byte_array\",\"description\":\"See [dwc:basisOfRecord](https://dwc.tdwg.org/terms/#basisOfRecord). One of `PRESERVED_SPECIMEN`, `FOSSIL_SPECIMEN`, `LIVING_SPECIMEN`, `OBSERVATION`, `HUMAN_OBSERVATION`, `MACHINE_OBSERVATION`, `MATERIAL_SAMPLE`, `LITERATURE`, `UNKNOWN`.\"},{\"name\":\"institutioncode\",\"type\":\"byte_array\",\"description\":\"See [dwc:institutionCode](https://dwc.tdwg.org/terms/#institutionCode).\"},{\"name\":\"collectioncode\",\"type\":\"byte_array\",\"description\":\"See [dwc:collectionCode](https://dwc.tdwg.org/terms/#collectionCode).\"},{\"name\":\"catalognumber\",\"type\":\"byte_array\",\"description\":\"See [dwc:catalogNumber](https://dwc.tdwg.org/terms/#catalogNumber).\"},{\"name\":\"recordnumber\",\"type\":\"byte_array\",\"description\":\"See [dwc:recordNumber](https://dwc.tdwg.org/terms/#recordNumber).\"},{\"name\":\"identifiedby\",\"type\":\"byte_array\",\"description\":\"See [dwc:identifiedBy](https://dwc.tdwg.org/terms/#identifiedBy).\"},{\"name\":\"dateidentified\",\"type\":\"byte_array\",\"description\":\"See [dwc:dateIdentified](https://dwc.tdwg.org/terms/#dateIdentified). An ISO 8601 date.\"},{\"name\":\"license\",\"type\":\"byte_array\",\"description\":\"See [dwc:license](https://dwc.tdwg.org/terms/#license). Either [`CC0_1_0`](https://creativecommons.org/publicdomain/zero/1.0/) or [`CC_BY_4_0`](https://creativecommons.org/licenses/by/4.0/). `CC_BY_NC_4_0` records are not present in this snapshot.\"},{\"name\":\"rightsholder\",\"type\":\"byte_array\",\"description\":\"See [dwc:rightsHolder](https://dwc.tdwg.org/terms/#rightsHolder).\"},{\"name\":\"recordedby\",\"type\":\"byte_array\",\"description\":\"See [dwc:recordedBy](https://dwc.tdwg.org/terms/#recordedBy).\"},{\"name\":\"typestatus\",\"type\":\"byte_array\",\"description\":\"See [dwc:typeStatus](https://dwc.tdwg.org/terms/#typeStatus).\"},{\"name\":\"establishmentmeans\",\"type\":\"byte_array\",\"description\":\"See [dwc:establishmentMeans](https://dwc.tdwg.org/terms/#establishmentMeans).\"},{\"name\":\"lastinterpreted\",\"type\":\"byte_array\",\"description\":\"The ISO 8601 date when the record was last processed by GBIF. Data are reprocessed for several reasons, including changes to the backbone taxonomy, so this date is not necessarily the date the occurrence record last changed.\"},{\"name\":\"mediatype\",\"type\":\"byte_array\",\"description\":\"See [dwc:mediaType](https://dwc.tdwg.org/terms/#mediaType). May contain `StillImage`, `MovingImage` or `Sound` (from [enumeration](http://api.gbif.org/v1/enumeration/basic/MediaType), detailing whether the occurrence has this media available.\"},{\"name\":\"issue\",\"type\":\"byte_array\",\"description\":\"A list of [issues](https://gbif.github.io/gbif-api/apidocs/org/gbif/api/vocabulary/OccurrenceIssue.html) encountered by GBIF in processing this record. More details are available on these issues and flags in [this blog post](https://data-blog.gbif.org/post/issues-and-flags/).\"}],\"msft:container\":\"gbif\",\"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\":\"Global biodiversity observation records, documenting over 1.6 billion species occurrences\",\"msft:region\":\"westeurope\"},{\"id\":\"modis-17A3HGF-061\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-17A3HGF-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-17A3HGF-061\"},{\"rel\":\"help\",\"href\":\"https://lpdaac.usgs.gov/documents/972/MOD17_User_Guide_V61.pdf\",\"title\":\"MOD17 User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD17A3HGF\",\"title\":\"MOD17A3HGF file specification\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD17A3HGF\",\"title\":\"MYD17A3HGF file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MOD17A3HGF.061\",\"title\":\"LP DAAC - MOD17A3HGF\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MYD17A3HGF.061\",\"title\":\"LP DAAC - MYD17A3HGF\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-17A3HGF-061\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS Net Primary Production Yearly Gap-Filled\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-17A3HGF-061.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MODIS Net Primary Production Yearly Gap-Filled thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-17A3HGF-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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\",\"MOD17A3HGF\",\"MYD17A3HGF\"],\"providers\":[{\"url\":\"https://lpdaac.usgs.gov/\",\"name\":\"NASA 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 Version 6.1 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual Moderate Resolution Imaging Spectroradiometer (MODIS) NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The product will be generated at the end of each year when the entire yearly 8-day 15A2H is available. Hence, the gap-filled product is the improved 17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year.\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source data containing all bands\"},\"Gpp_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Gross Primary Productivity\",\"raster:bands\":[{\"unit\":\"kg C/m^2\",\"scale\":0.0001,\"data_type\":\"uint16\",\"spatial_resolution\":500}]},\"Npp_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Net Primary Productivity\",\"raster:bands\":[{\"unit\":\"kg C/m^2\",\"scale\":0.0001,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal Geographic Data Committee (FGDC) Metadata\"},\"Npp_QC_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Quality control\",\"raster:bands\":[{\"unit\":\"Percent\",\"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/MOD17A3HGF.061\",\"citation\":\"Running, S., & Zhao, M. (2021). MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD17A3HGF.061\"},{\"doi\":\"10.5067/MODIS/MYD17A3HGF.061\",\"citation\":\"Running, S., & Zhao, M. (2021). MODIS/Aqua Net Primary Production Gap-Filled Yearly L4 Global 500m SIN Grid V061 [Data set]. 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When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source data containing all bands\"},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal Geographic Data Committee (FGDC) Metadata\"},\"sur_refl_b01\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Surface 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Surface 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; 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application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Surface Reflectance Band 6 (1628-1652 nm)\",\"eo:bands\":[{\"name\":\"sur_refl_b06\",\"common_name\":\"swir16\",\"center_wavelength\":1.64,\"full_width_half_max\":0.24}],\"raster:bands\":[{\"scale\":0.0001,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"sur_refl_b07\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"reflectance\"],\"title\":\"Surface Reflectance Band 7 (2105-2155 nm)\",\"eo:bands\":[{\"name\":\"sur_refl_b07\",\"common_name\":\"swir22\",\"center_wavelength\":2.13,\"full_width_half_max\":0.5}],\"raster:bands\":[{\"scale\":0.0001,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"sur_refl_raz\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"MODIS relative azimuth angle\",\"raster:bands\":[{\"unit\":\"Degree\",\"scale\":0.01,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"sur_refl_szen\":{\"type\":\"image/tiff; 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application=geotiff; profile=cloud-optimized\",\"title\":\"HH\",\"description\":\"HH polarization backscattering coefficient, 16-bit DN.\"},\"HV\":{\"role\":\"data\",\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"HV\",\"description\":\"HV polarization backscattering coefficient, 16-bit DN.\"},\"VH\":{\"role\":\"data\",\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"VH\",\"description\":\"VH polarization backscattering coefficient, 16-bit DN (high-sensitive beam quad-mode only).\"},\"VV\":{\"role\":\"data\",\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"VV\",\"description\":\"VV polarization backscattering coefficient, 16-bit DN (high-sensitive beam quad-mode only).\"},\"date\":{\"role\":\"date\",\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"date\",\"description\":\"Observation date (days since Jan 1, 1970).\"},\"mask\":{\"role\":\"data-mask\",\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"mask\",\"description\":\"Quality Mask\",\"raster:bands\":[{\"nodata\":0,\"data_type\":\"uint8\"}],\"classification:classes\":[{\"name\":\"no_data\",\"value\":0,\"description\":\"No data\"},{\"name\":\"water\",\"value\":50,\"description\":\"Water\"},{\"name\":\"lay_over\",\"value\":100,\"description\":\"Lay over\"},{\"name\":\"shadowing\",\"value\":150,\"description\":\"Shadowing\"},{\"name\":\"land\",\"value\":255,\"description\":\"Land\"}]},\"linci\":{\"role\":\"local-incidence-angle\",\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"title\":\"linci\",\"description\":\"Local incidence angle (degrees).\"},\"metadata\":{\"type\":\"application/xml\",\"title\":\"metadata\",\"description\":\"Product 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 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 Guide\"},{\"rel\":\"license\",\"href\":\"https://cdla.dev/permissive-1-0/\",\"type\":\"text/html\",\"title\":\"Community Data License Agreement - Permissive, Version 1.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/deltares-water-availability\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Deltares 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 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/) has produced a hydrological model approach to simulate historical daily reservoir variations for 3,236 locations across the globe for the period 1970-2020 using the distributed [wflow_sbm](https://deltares.github.io/Wflow.jl/stable/model_docs/model_configurations/) model. The model outputs long-term daily information on reservoir volume, inflow and outflow dynamics, as well as information on upstream hydrological forcing.\\n\\nThey hydrological model was forced with 5 different precipitation products. Two products (ERA5 and CHIRPS) are available at the global scale, while for Europe, USA and Australia a regional product was use (i.e. EOBS, NLDAS and BOM, respectively). Using these different precipitation products, it becomes possible to assess the impact of uncertainty in the model forcing. A different number of basins upstream of reservoirs are simulated, given the spatial coverage of each precipitation product.\\n\\nSee the complete [methodology documentation](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_docs/pc-deltares-water-availability-documentation.pdf) for more information.\\n\\n## Dataset coverages\\n\\n| Name | Scale | Period | Number of basins |\\n|--------|--------------------------|-----------|------------------|\\n| ERA5 | Global | 1967-2020 | 3236 |\\n| CHIRPS | Global (+/- 50 latitude) | 1981-2020 | 2951 |\\n| EOBS | Europe/North Africa | 1979-2020 | 682 |\\n| NLDAS | USA | 1979-2020 | 1090 |\\n| BOM | Australia | 1979-2020 | 116 |\\n\\n## STAC Metadata\\n\\nThis STAC collection includes one STAC item per dataset. The item includes a `deltares:reservoir` property that can be used to query for the URL of a specific dataset.\\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 Map\",\"description\":\"Inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level.\"},\"index\":{\"type\":\"application/json\",\"roles\":[\"index\"],\"title\":\"Index file\",\"description\":\"Kerchunk index file.\"}},\"stac_version\":\"1.0.0\",\"cube:variables\":{\"P\":{\"type\":\"data\",\"unit\":\"mm per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Average precipitation upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average precipitation upstream of reservoir\"},\"ETa\":{\"type\":\"data\",\"unit\":\"mm per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Average simulated actual evapotransporation upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average simulated actual evapotransporation upstream of reservoir\"},\"PET\":{\"type\":\"data\",\"unit\":\"mm per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Average potential evapotranspiration upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average potential evapotranspiration upstream of reservoir\"},\"Melt\":{\"type\":\"data\",\"unit\":\"mm per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Average simulated snow melt upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average simulated snow melt upstream of reservoir\"},\"Snow\":{\"type\":\"data\",\"unit\":\"mm\",\"attrs\":{\"units\":\"mm\",\"description\":\"Average simulated snow depth upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average simulated snow depth upstream of reservoir\"},\"Temp\":{\"type\":\"data\",\"unit\":\"degrees C\",\"attrs\":{\"units\":\"degrees C\",\"description\":\"Average surface temperature upstream of reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Average surface temperature upstream of reservoir\"},\"P_res\":{\"type\":\"data\",\"unit\":\"mm per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Precipitation reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Precipitation reservoir\"},\"S_res\":{\"type\":\"data\",\"unit\":\"m3\",\"attrs\":{\"units\":\"m3\",\"description\":\"Simulated reservoir volume\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated reservoir volume\"},\"Ea_res\":{\"type\":\"data\",\"unit\":\"mm per day\",\"attrs\":{\"units\":\"mm per day\",\"description\":\"Simulated actual evaporation reservoir\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated actual evaporation reservoir\"},\"Qin_res\":{\"type\":\"data\",\"unit\":\"m3 per s\",\"attrs\":{\"units\":\"m3 per s\",\"description\":\"Simulated reservoir inflow (surface+subsurface)\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated reservoir inflow (surface+subsurface)\"},\"FracFull\":{\"type\":\"data\",\"unit\":\"m3\",\"attrs\":{\"units\":\"m3\",\"description\":\"Simulated reservoir volume\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated reservoir volume\"},\"Qout_res\":{\"type\":\"data\",\"unit\":\"m3 per s\",\"attrs\":{\"units\":\"m3 per s\",\"description\":\"Simulated reservoir outflow\"},\"dimensions\":[\"time\",\"GrandID\",\"ksathorfrac\"],\"description\":\"Simulated reservoir outflow\"},\"latitude\":{\"type\":\"data\",\"unit\":\"degrees\",\"attrs\":{\"units\":\"degrees\",\"description\":\"Latitude of reservoir\"},\"dimensions\":[\"GrandID\"],\"description\":\"Latitude of reservoir\"},\"longitude\":{\"type\":\"data\",\"unit\":\"degrees\",\"attrs\":{\"units\":\"degrees\",\"description\":\"Longitude of reservoir\"},\"dimensions\":[\"GrandID\"],\"description\":\"Longitude of reservoir\"}},\"msft:container\":\"reservoirs\",\"cube:dimensions\":{\"time\":{\"step\":\"P1DT0H0M0S\",\"type\":\"temporal\",\"extent\":[null,\"2020-12-31T00:00:00Z\"]},\"GrandID\":{\"type\":\"identifier\",\"extent\":[null,null],\"description\":\"GrandID number of the reservoir of interest\"},\"ksathorfrac\":{\"type\":\"level\",\"values\":[5,20,50,100,250],\"description\":\"Five different value lateral anisotropy values used\"}},\"stac_extensions\":[\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"deltaresreservoirssa\",\"msft:short_description\":\"Historical daily reservoir variations.\",\"msft:region\":\"westeurope\"},{\"id\":\"modis-16A3GF-061\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-16A3GF-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-16A3GF-061\"},{\"rel\":\"help\",\"href\":\"https://lpdaac.usgs.gov/documents/931/MOD16_User_Guide_V61.pdf\",\"title\":\"MOD16 User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD16A3GF\",\"title\":\"MOD16A3GF file specification\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD16A3GF\",\"title\":\"MYD16A3GF file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MOD16A3GF.061\",\"title\":\"LP DAAC - MOD16A3GF\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MYD16A3GF.061\",\"title\":\"LP DAAC - MYD16A3GF\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-16A3GF-061\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS Net Evapotranspiration Yearly Gap-Filled\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-16A3GF-061.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MODIS Net Evapotranspiration Yearly Gap-Filled thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-16A3GF-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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\":[[\"2001-01-01T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"NASA\",\"MODIS\",\"Satellite\",\"Global\",\"Vegetation\",\"MOD16A3GF\",\"MYD16A3GF\"],\"providers\":[{\"url\":\"https://lpdaac.usgs.gov/\",\"name\":\"NASA 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 Moderate Resolution Imaging Spectroradiometer (MODIS) MOD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The product will be generated at the end of each year when the entire yearly 8-day MOD15A2H/MYD15A2H is available. Hence, the gap-filled product is the improved 16, which has cleaned the poor-quality inputs from yearly Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get this product in near-real time because it will be generated only at the end of a given year. Provided in the product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year.\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source data containing all bands\"},\"ET_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Total of Evapotranspiration\",\"raster:bands\":[{\"unit\":\"kg/m^2/year\",\"scale\":0.1,\"data_type\":\"uint16\",\"spatial_resolution\":500}]},\"LE_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Average of Latent Heat Flux\",\"raster:bands\":[{\"unit\":\"J/m^2/day\",\"scale\":10000,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"PET_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Total Potential Evapotranspiration\",\"raster:bands\":[{\"unit\":\"kg/m^2/year\",\"scale\":0.1,\"data_type\":\"uint16\",\"spatial_resolution\":500}]},\"PLE_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Average of Potential Latent Heat Flux\",\"raster:bands\":[{\"unit\":\"J/m^2/day\",\"scale\":10000,\"data_type\":\"int16\",\"spatial_resolution\":500}]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal Geographic Data Committee (FGDC) Metadata\"},\"ET_QC_500m\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Evapotranspiration Quality Assessment\",\"raster:bands\":[{\"unit\":\"Percent\",\"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/MOD16A3GF.061\",\"citation\":\"Running, S., Mu, Q., Zhao, M., & Moreno, A. (2021). MODIS/Terra Net Evapotranspiration Gap-Filled Yearly L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD16A3GF.061\"},{\"doi\":\"10.5067/MODIS/MYD16A3GF.061\",\"citation\":\"Running, S., Mu, Q., Zhao, M., & Moreno, A. (2021). MODIS/Aqua Net Evapotranspiration Gap-Filled Yearly L4 Global 500m SIN Grid V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD16A3GF.061\"}],\"msft:storage_account\":\"modiseuwest\",\"msft:short_description\":\"MODIS Net Evapotranspiration Yearly Gap-Filled\",\"msft:region\":\"westeurope\"},{\"id\":\"modis-21A2-061\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-21A2-061/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-21A2-061\"},{\"rel\":\"help\",\"href\":\"https://lpdaac.usgs.gov/documents/620/MOD21_User_Guide_V61.pdf\",\"title\":\"MOD21 User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MOD21A2\",\"title\":\"MOD21A2 file specification\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MYD21A2\",\"title\":\"MYD21A2 file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MOD21A2.061\",\"title\":\"LP DAAC - MOD21A2\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MYD21A2.061\",\"title\":\"LP DAAC - MYD21A2\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-21A2-061\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS Land Surface Temperature/3-Band Emissivity 8-Day\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-21A2-061.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MODIS Land Surface Temperature/3-Band Emissivity 8-Day thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-21A2-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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\":[[\"2000-02-16T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"NASA\",\"MODIS\",\"Satellite\",\"Global\",\"Temperature\",\"MOD21A2\",\"MYD21A2\"],\"providers\":[{\"url\":\"https://lpdaac.usgs.gov/\",\"name\":\"NASA 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\":\"A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MOD21 Land Surface Temperatuer (LST) algorithm differs from the algorithm of the MOD11 LST products, in that the MOD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MOD11 uses the split-window technique. The MOD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. This dataset is an 8-day composite LST product at 1,000 meter spatial resolution that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free 21A1D and 21A1N daily acquisitions from the 8-day period. Unlike the 21A1 data sets where the daytime and nighttime acquisitions are separate products, the 21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD).\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source data containing all bands\"},\"QC_Day\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Day Quality Control\",\"raster:bands\":[{\"data_type\":\"uint8\",\"spatial_resolution\":1000}]},\"Emis_29\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Average Day/Night Band 29 emissivity\",\"raster:bands\":[{\"scale\":0.002,\"data_type\":\"uint8\",\"spatial_resolution\":1000}]},\"Emis_31\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Average Day/Night Band 31 emissivity\",\"raster:bands\":[{\"scale\":0.002,\"data_type\":\"uint8\",\"spatial_resolution\":1000}]},\"Emis_32\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Average Day/Night Band 32 emissivity\",\"raster:bands\":[{\"scale\":0.002,\"data_type\":\"uint8\",\"spatial_resolution\":1000}]},\"QC_Night\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Night Quality Control\",\"raster:bands\":[{\"data_type\":\"uint8\",\"spatial_resolution\":1000}]},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal Geographic Data Committee (FGDC) Metadata\"},\"LST_Day_1KM\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Day Land Surface Temperature\",\"raster:bands\":[{\"unit\":\"Kelvin\",\"scale\":0.02,\"data_type\":\"uint16\",\"spatial_resolution\":1000}]},\"LST_Night_1KM\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Night Land Surface Temperature\",\"raster:bands\":[{\"unit\":\"Kelvin\",\"scale\":0.02,\"data_type\":\"uint16\",\"spatial_resolution\":1000}]},\"View_Time_Day\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Day Time of Observation\",\"raster:bands\":[{\"unit\":\"Hours\",\"scale\":0.1,\"data_type\":\"uint8\",\"spatial_resolution\":1000}]},\"View_Angle_Day\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Day view zenith angle\",\"raster:bands\":[{\"unit\":\"Degree\",\"data_type\":\"uint8\",\"spatial_resolution\":1000}]},\"View_Time_Night\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Night Time of Observation\",\"raster:bands\":[{\"unit\":\"Hours\",\"scale\":0.1,\"data_type\":\"uint8\",\"spatial_resolution\":1000}]},\"View_Angle_Night\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Night view zenith angle\",\"raster:bands\":[{\"unit\":\"Degree\",\"data_type\":\"uint8\",\"spatial_resolution\":1000}]}},\"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/MOD21A2.061\",\"citation\":\"Hulley, G., & Hook, S. (2021). MODIS/Terra Land Surface Temperature/3-Band Emissivity 8-Day L3 Global 1km SIN Grid V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD21A2.061\"},{\"doi\":\"10.5067/MODIS/MYD21A2.061\",\"citation\":\"Hulley, G. (2021). MODIS/Aqua Land Surface Temperature/3-Band Emissivity 8-Day L3 Global 1km SIN Grid V061 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MYD21A2.061\"}],\"msft:storage_account\":\"modiseuwest\",\"msft:short_description\":\"MODIS Land Surface Temperature/3-Band Emissivity 8-Day\",\"msft:region\":\"westeurope\"},{\"id\":\"us-census\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/us-census/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/us-census\"},{\"rel\":\"license\",\"href\":\"https://www.census.gov/about/policies/open-gov/open-data.html\",\"type\":\"text/html\",\"title\":\"U.S. Census Open Data Policy\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/us-census\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"US Census\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/us-census.png\",\"type\":\"image/png\",\"title\":\"US Census\"},\"geoparquet-items\":{\"href\":\"abfs://items/us-census.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-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\":[[\"2021-08-01T00:00:00Z\",\"2021-08-01T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"US Census Bureau\",\"Administrative boundaries\",\"Population\",\"Demographics\"],\"providers\":[{\"url\":\"https://www.census.gov/en.html\",\"name\":\"United States 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 [2020 Census](https://www.census.gov/programs-surveys/decennial-census/decade/2020/2020-census-main.html) counted every person living in the United States and the five U.S. territories. It marked the 24th census in U.S. history and the first time that households were invited to respond to the census online.\\n\\nThe tables included on the Planetary Computer provide information on population and geographic boundaries at various levels of cartographic aggregation.\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Dataset root\",\"table:storage_options\":{\"account_name\":\"ai4edataeuwest\"}}},\"stac_version\":\"1.0.0\",\"table:tables\":[{\"name\":\"American Indian/Alaska Native Areas/Hawaiian Home Lands (AIANNH)\",\"description\":\"This file contains data for legal and statistical [American Indian/Alaska Native Areas/Hawaiian Home Lands (AIANNH)](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1) entities published by the US Census Bureau.\",\"msft:item_name\":\"cb_2020_us_aiannh_500k\"},{\"name\":\"American Indian Tribal Subdivisions (AITSN)\",\"description\":\"This file contains data on [American Indian Tribal Subdivisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1). These areas are the legally defined subdivisions of American Indian Reservations (AIR), Oklahoma Tribal Statistical Areas (OTSA), and Off-Reservation Trust Land (ORTL).\",\"msft:item_name\":\"cb_2020_us_aitsn_500k\"},{\"name\":\"Alaska Native Regional Corporations (ANRC)\",\"description\":\"This file contains data on [Alaska Native Regional Corporations](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_1), which are corporations created according to the Alaska Native Claims Settlement Act. \",\"msft:item_name\":\"cb_2020_02_anrc_500k\"},{\"name\":\"Tribal Block Groups (TBG)\",\"description\":\"This file includes data on [Tribal Block Groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_26), which are subdivisions of Tribal Census Tracts. These block groups can extend over multiple AIRs and ORTLs due to areas not meeting Block Group minimum population thresholds.\",\"msft:item_name\":\"cb_2020_us_tbg_500k\"},{\"name\":\"Tribal Census Tracts (TTRACT)\",\"description\":\"This file includes data on [Tribal Census Tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_27) which are relatively small statistical subdivisions of AIRs and ORTLs defined by federally recognized tribal government officials in partnership with the Census Bureau. Due to population thresholds, the Tracts may consist of multiple non-contiguous areas.\",\"msft:item_name\":\"cb_2020_us_ttract_500k\"},{\"name\":\"Census Block Groups (BG)\",\"description\":\"This file contains data on [Census Block Groups](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_4). These groups are the second smallest geographic grouping. They consist of clusters of blocks within the same census tract that share the same first digit of their 4-character census block number. Census Block Groups generally contain between 600 and 3,000 people and generally cover contiguous areas.\",\"msft:item_name\":\"cb_2020_us_bg_500k\"},{\"name\":\"Census Tracts (TRACT)\",\"description\":\"This file contains data on [Census Tracts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13) which are small and relatively permanent statistical subdivisions of a county or equivalent entity. Tract population size is generally between 1,200 and 8,000 people with an ideal size of 4,000. Boundaries tend to follow visible and identifiable features and are usually contiguous areas.\",\"msft:item_name\":\"cb_2020_us_tract_500k\"},{\"name\":\"Congressional Districts: 116th Congress (CD116)\",\"description\":\"This file contains data on the [Congressional Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_9) for the 116th Congress. \",\"msft:item_name\":\"cb_2020_us_cd116_500k\"},{\"name\":\"Consolidated Cities (CONCITY)\",\"description\":\"This file contains data on [Consolidated Cities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_8). These are areas where one or several other incorporated places in a county or Minor Civil Division are included in a consolidated government but still exist as separate legal entities.\",\"msft:item_name\":\"cb_2020_us_concity_500k\"},{\"name\":\"Counties (COUNTY)\",\"description\":\"This file contains data on [Counties and Equivalent Entities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_12). These are the primary legal divisions of states. Most states use the term \\\"counties,\\\" but other terms such as \\\"Parishes,\\\" \\\"Municipios,\\\" or \\\"Independent Cities\\\" may be used. \",\"msft:item_name\":\"cb_2020_us_county_500k\"},{\"name\":\"Counties within Congressional Districts: 116th Congress (COUNTY_within_CD116)\",\"description\":\"This file contains data on Counties within Congressional Districts.\",\"msft:item_name\":\"cb_2020_us_county_within_cd116_500k\"},{\"name\":\"County Subdivisions (COUSUB)\",\"description\":\"This file contains [County Subdivisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_11), which are the primary divisions of counties and equivalent entities. These divisions vary from state to state and include Barrios, Purchases, Townships, and other types of legal and statistical entities. \",\"msft:item_name\":\"cb_2020_us_cousub_500k\"},{\"name\":\"Divisions (DIVISION)\",\"description\":\"This file contains data on [Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_10) of the US. This file is similar to the Regions file but contains more divisions and encompasses several states per division.\",\"msft:item_name\":\"cb_2020_us_division_500k\"},{\"name\":\"Core Based Statistical Areas (CBSAs)\",\"description\":\"This file contains data on [Core Based Statistical Areas (CBSAs)](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7). This encompasses all metropolitan and micropolitan statistical areas.\",\"msft:item_name\":\"cb_2020_us_cbsa_500k\"},{\"name\":\"Combined Statistical Areas (CSA)\",\"description\":\"This file contains data on [Combined Statistical Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7), which are areas that consist of two or more adjacent CBSAs that have significant employment interchanges.\",\"msft:item_name\":\"cb_2020_us_csa_500k\"},{\"name\":\"Metropolitan Divisions (METDIV)\",\"description\":\"This file contains data on [Metropolitan Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7). These areas are groupings of counties or equivalent entities within a metropolitan statistical area with a core of 2.5 million inhabitants and one or more main counties that represent employment centers, plus adjacent counties with commuting ties.\",\"msft:item_name\":\"cb_2020_us_metdiv_500k\"},{\"name\":\"New England City and Town Areas (NECTA)\",\"description\":\"This file contains [New England City and Town Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7), which encompass metropolitan and micropolitan statistical areas and urban clusters in New England.\",\"msft:item_name\":\"cb_2020_us_necta_500k\"},{\"name\":\"New England City and Town Area Division (NECTADIV)\",\"description\":\"This file contains [New England City and Town Areas Divisions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7), which are smaller groupings of cities and towns in New England that contain a single core of 2.5 million inhabitants. Each division must have a total population of 100,000 or more.\",\"msft:item_name\":\"cb_2020_us_nectadiv_500k\"},{\"name\":\"Combined New England City and Town Areas (CNECTA)\",\"description\":\"This file contains data on [Combined New England City and Town Areas](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_7), consisting of two or more adjacent NECTAs that have significant employment interchanges.\",\"msft:item_name\":\"cb_2020_us_cnecta_500k\"},{\"name\":\"Places (PLACE)\",\"description\":\"This file contains [Places](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_14) which are Incorporated Places (legal entities) and Census Designated Places (CDPs, statistical entities). An incorporated place usually is a city, town, village, or borough but can have other legal descriptions. CDPs are settled concentrations of population that are identifiable by name but are not legally incorporated.\",\"msft:item_name\":\"cb_2020_us_place_500k\"},{\"name\":\"Regions (REGION)\",\"description\":\"This file contains [Regions](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_10) of the US and encompasses several states per division.\",\"msft:item_name\":\"cb_2020_us_region_500k\"},{\"name\":\"School Districts - Elementary (ELSD)\",\"description\":\"This file contains [Elementary School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23), referring to districts with elementary schools.\",\"msft:item_name\":\"cb_2020_us_elsd_500k\"},{\"name\":\"School Districts - Secondary (SCSD)\",\"description\":\"This file contains [Secondary School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23), referring to districts with secondary schools.\",\"msft:item_name\":\"cb_2020_us_scsd_500k\"},{\"name\":\"School Districts - Unified (UNSD)\",\"description\":\"This file contains [Unified School Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_23), referring to districts that provide education to children of all school ages. Unified school districts can have both secondary and elementary schools.\",\"msft:item_name\":\"cb_2020_us_unsd_500k\"},{\"name\":\"State Legislative Districts - Lower Chamber (SLDL)\",\"description\":\"This file 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 Legislative Districts - Upper Chamber (SLDU)\",\"description\":\"This file 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 (STATE)\",\"description\":\"This file contains the [US States and State Equivalent Entities](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_25). Within Census Bureau datasets, the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the US Virgin Islands) are treated as statistical equivalents of states alongside the 50 US states.\",\"msft:item_name\":\"cb_2020_us_state_500k\"},{\"name\":\"Subbarrios (SUBBARRIO)\",\"description\":\"This file contains [Subbarrios](https://www.census.gov/programs-surveys/geography/about/glossary.html#pr), which are legally defined subdivisions of Minor Civil Division in Puerto Rico. They don\\\"t exist within every Minor Civil Division and don\\\"t always cover the entire Minor Civil Division where they do exist.\",\"msft:item_name\":\"cb_2020_72_subbarrio_500k\"},{\"name\":\"United States Outline\",\"description\":\"This file contains the [United States Outline](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_30) shapefile. This contains all 50 US states plus the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the US Virgin Islands). There is only one feature within this dataset.\",\"msft:item_name\":\"cb_2020_us_nation_5m\"},{\"name\":\"Voting Districts (VTD)\",\"description\":\"This file contains all [US Voting Districts](https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_31), which are geographic features established by state, local and tribal governments 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 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 Open Access Policy\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/jrc-gsw\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"JRC 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 Guide\",\"description\":\"Data users guide and description of the JRC GSW datasets\"},\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/jrc-gsw.png\",\"title\":\"European 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 surface water products from the European Commission Joint Research Centre, based on Landsat 5, 7, and 8 imagery. Layers in this collection describe the occurrence, change, and seasonality of surface water from 1984-2020. Complete 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Occurrence Change Intensity\",\"description\":\"Change in water occurrence between the two periods (1984-1999) and (2000-2020)\"},\"extent\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Maximum Water Extent\",\"description\":\"Binary indicator of whether water was ever present (from 1984-2020)\",\"file:values\":[{\"values\":[0],\"summary\":\"Not water\"},{\"values\":[1],\"summary\":\"Water\"}]},\"occurrence\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Occurrence\",\"description\":\"Frequency with which water was present from March 1984 to December 2020\"},\"recurrence\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Recurrence\",\"description\":\"Frequency with which water returns from year to year\"},\"seasonality\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Seasonality\",\"description\":\"Number of months that water was present from January 2020 to December 2020\"},\"transitions\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Transitions\",\"description\":\"Categorical change in surface water status from 1984 to 2020\",\"file:values\":[{\"values\":[1],\"summary\":\"Permanent\"},{\"values\":[2],\"summary\":\"New permanent\"},{\"values\":[3],\"summary\":\"Lost permanent\"},{\"values\":[4],\"summary\":\"Seasonal\"},{\"values\":[5],\"summary\":\"New seasonal\"},{\"values\":[6],\"summary\":\"Lost seasonal\"},{\"values\":[7],\"summary\":\"Seasonal to permanent\"},{\"values\":[8],\"summary\":\"Permanent to seasonal\"},{\"values\":[9],\"summary\":\"Ephemeral permanent\"},{\"values\":[10],\"summary\":\"Ephemeral seasonal\"}]}},\"sci:citation\":\"Jean-Francois Pekel, Andrew Cottam, Noel Gorelick, Alan S. Belward, High-resolution mapping 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 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 Guide\"},{\"rel\":\"license\",\"href\":\"https://cdla.dev/permissive-1-0/\",\"type\":\"text/html\",\"title\":\"Community Data License Agreement - Permissive, Version 1.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/deltares-floods\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Deltares 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 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/) has produced inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level. At the coastline, the model is forced by extreme water levels containing surge and tide from GTSMip6. The water level at the coastline is extended landwards to all areas that are hydrodynamically connected to the coast following a \u2018bathtub\u2019 like approach and calculates the flood depth as the difference between the water level and the topography. Unlike a simple 'bathtub' model, this model attenuates the water level over land with a maximum attenuation factor of 0.5\u2009m\u2009km-1. The attenuation factor simulates the dampening of the flood levels due to the roughness over land.\\n\\nIn its current version, the model does not account for varying roughness over land and permanent water bodies such as rivers and lakes, and it does not account for the compound effects of waves, rainfall, and river discharge on coastal flooding. It also does not include the mitigating effect of coastal flood protection. Flood extents must thus be interpreted as the area that is potentially exposed to 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) for more information.\\n\\n## Digital elevation models (DEMs)\\n\\nThis documentation will refer to three DEMs:\\n\\n* `NASADEM` is the SRTM-derived [NASADEM](https://planetarycomputer.microsoft.com/dataset/nasadem) product.\\n* `MERITDEM` is the [Multi-Error-Removed Improved Terrain DEM](http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/), 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## Global datasets\\n\\nThis collection includes multiple global flood datasets derived from three different DEMs (`NASA`, `MERIT`, and `LIDAR`) and at different resolutions. Not all DEMs have all resolutions:\\n\\n* `NASADEM` and `MERITDEM` are available at `90m` and `1km` resolutions\\n* `LIDAR` is available at `5km` resolution\\n\\n## Historic event datasets\\n\\nThis collection also includes historical storm event data files that follow similar DEM and resolution conventions. Not all storms events are available for each DEM and resolution combination, but generally follow the format of:\\n\\n`events/[DEM]_[resolution]-wm_final/[storm_name]_[event_year]_masked.nc`\\n\\nFor example, a flood map for the MERITDEM-derived 90m flood data for the \\\"Omar\\\" storm in 2008 is available at:\\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 Map\",\"description\":\"Inundation maps of flood depth using a model that takes into account water level attenuation and is forced by sea level.\"},\"index\":{\"type\":\"application/json\",\"roles\":[\"index\"],\"title\":\"Index file\",\"description\":\"Kerchunk index file.\"}},\"stac_version\":\"1.0.0\",\"cube:variables\":{\"inun\":{\"type\":\"data\",\"unit\":\"m\",\"attrs\":{\"units\":\"m\",\"long_name\":\"Coastal flooding\",\"standard_name\":\"water_surface_height_above_reference_datum\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Coastal flooding\"},\"projection\":{\"type\":\"data\",\"attrs\":{\"EPSG_code\":\"EPSG:4326\",\"long_name\":\"wgs84\",\"proj4_params\":\"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs\",\"grid_mapping_name\":\"latitude_longitude\"},\"shape\":[],\"dimensions\":[],\"description\":\"wgs84\"}},\"msft:container\":\"floods\",\"cube:dimensions\":{\"lat\":{\"axis\":\"y\",\"type\":\"spatial\",\"extent\":[-89.97500000000002,89.975],\"description\":\"latitude\",\"reference_system\":{\"name\":\"undefined\",\"type\":\"GeographicCRS\",\"datum\":{\"id\":{\"code\":6326,\"authority\":\"EPSG\"},\"name\":\"World Geodetic System 1984\",\"type\":\"GeodeticReferenceFrame\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"$schema\":\"https://proj.org/schemas/v0.4/projjson.schema.json\",\"coordinate_system\":{\"axis\":[{\"name\":\"Longitude\",\"unit\":\"degree\",\"direction\":\"east\",\"abbreviation\":\"lon\"},{\"name\":\"Latitude\",\"unit\":\"degree\",\"direction\":\"north\",\"abbreviation\":\"lat\"}],\"subtype\":\"ellipsoidal\"}}},\"lon\":{\"axis\":\"x\",\"type\":\"spatial\",\"extent\":[-179.975,179.97500000000005],\"description\":\"longitude\",\"reference_system\":{\"name\":\"undefined\",\"type\":\"GeographicCRS\",\"datum\":{\"id\":{\"code\":6326,\"authority\":\"EPSG\"},\"name\":\"World Geodetic System 1984\",\"type\":\"GeodeticReferenceFrame\",\"ellipsoid\":{\"name\":\"WGS 84\",\"semi_major_axis\":6378137,\"inverse_flattening\":298.257223563}},\"$schema\":\"https://proj.org/schemas/v0.4/projjson.schema.json\",\"coordinate_system\":{\"axis\":[{\"name\":\"Longitude\",\"unit\":\"degree\",\"direction\":\"east\",\"abbreviation\":\"lon\"},{\"name\":\"Latitude\",\"unit\":\"degree\",\"direction\":\"north\",\"abbreviation\":\"lat\"}],\"subtype\":\"ellipsoidal\"}}},\"time\":{\"type\":\"temporal\",\"extent\":[\"2010-01-01T00:00:00Z\",\"2010-01-01T00:00:00Z\"],\"description\":\"time\"}},\"stac_extensions\":[\"https://stac-extensions.github.io/datacube/v2.0.0/schema.json\",\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"deltaresfloodssa\",\"msft:short_description\":\"Global estimates of coastal inundation under various sea level rise conditions and return periods.\",\"msft:region\":\"westeurope\"},{\"id\":\"modis-43A4-061\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/modis-43A4-061/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-43A4-061\"},{\"rel\":\"help\",\"href\":\"https://www.umb.edu/spectralmass/terra_aqua_modis/v006\",\"title\":\"User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/61/MCD43A4\",\"title\":\"MCD43A4 file specification\"},{\"rel\":\"cite-as\",\"href\":\"https://doi.org/10.5067/MODIS/MCD43A4.061\",\"title\":\"LP DAAC - MCD43A4\"},{\"rel\":\"license\",\"href\":\"https://lpdaac.usgs.gov/data/data-citation-and-policies/\",\"title\":\"LP DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/modis-43A4-061\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/modis-43A4-061.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"MODIS Nadir BRDF-Adjusted Reflectance (NBAR) Daily thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/modis-43A4-061.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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\":[[\"2000-02-16T00:00:00Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"NASA\",\"MODIS\",\"Satellite\",\"Imagery\",\"Global\",\"Reflectance\",\"MCD43A4\"],\"providers\":[{\"url\":\"https://lpdaac.usgs.gov/\",\"name\":\"NASA 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 Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. 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format.\",\"msft:partition_info\":{\"is_partitioned\":false},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-141.003,41.6755,-52.6174,83.1139]]},\"temporal\":{\"interval\":[[\"2015-01-01T00:00:00Z\",\"2020-01-01T00:00:00Z\"]]}},\"license\":\"OGL-Canada-2.0\",\"keywords\":[\"Land Cover\",\"Remote Sensing\",\"Landsat\",\"North America\",\"Canada\"],\"providers\":[{\"url\":\"https://www.nrcan.gc.ca/maps-tools-publications/satellite-imagery-air-photos/application-development/land-cover/21755\",\"name\":\"Natural Resources Canada | Ressources naturelles Canada\",\"roles\":[\"licensor\",\"processor\",\"producer\"]},{\"url\":\"https://sparkgeo.com\",\"name\":\"Sparkgeo\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"proj:epsg\":[3978],\"label:type\":[\"raster\"],\"label:tasks\":[\"classification\"],\"label:classes\":[{\"name\":\"\",\"classes\":[\"Temperate or sub-polar needleleaf forest\",\"Sub-polar taiga needleleaf forest\",\"Temperate or sub-polar broadleaf deciduous forest\",\"Mixed forest\",\"Temperate or sub-polar shrubland\",\"Temperate or sub-polar grassland\",\"Sub-polar or polar shrubland-lichen-moss\",\"Sub-polar or polar grassland-lichen-moss\",\"Sub-polar or polar barren-lichen-moss\",\"Wetland\",\"Cropland\",\"Barren lands\",\"Urban\",\"Water\",\"Snow and Ice\"]}]},\"description\":\"Collection of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. This collection of cartographic products offers classified Land Cover of Canada at a 30 metre scale, updated on a 5 year basis.\",\"item_assets\":{\"metadata\":{\"type\":\"application/json\",\"roles\":[\"metadata\"],\"title\":\"Land cover of Canada metadata\"},\"landcover\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\",\"labels\",\"labels-raster\"],\"title\":\"Land cover of Canada COG\",\"proj:epsg\":3978,\"file:values\":[{\"values\":[1],\"summary\":\"Temperate or sub-polar needleleaf forest\"},{\"values\":[2],\"summary\":\"Sub-polar taiga needleleaf forest\"},{\"values\":[5],\"summary\":\"Temperate or sub-polar broadleaf deciduous forest\"},{\"values\":[6],\"summary\":\"Mixed forest\"},{\"values\":[8],\"summary\":\"Temperate or sub-polar shrubland\"},{\"values\":[10],\"summary\":\"Temperate or sub-polar grassland\"},{\"values\":[11],\"summary\":\"Sub-polar or polar shrubland-lichen-moss\"},{\"values\":[12],\"summary\":\"Sub-polar or polar grassland-lichen-moss\"},{\"values\":[13],\"summary\":\"Sub-polar or polar barren-lichen-moss\"},{\"values\":[14],\"summary\":\"Wetland\"},{\"values\":[15],\"summary\":\"Cropland\"},{\"values\":[16],\"summary\":\"Barren lands\"},{\"values\":[17],\"summary\":\"Urban\"},{\"values\":[18],\"summary\":\"Water\"},{\"values\":[19],\"summary\":\"Snow 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 of Land Cover products for Canada as produced by Natural Resources Canada using Landsat satellite imagery. 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A bit flag index is used to track the eight-day snow/no-snow chronology for each 500 m cell.\",\"item_assets\":{\"hdf\":{\"type\":\"application/x-hdf\",\"roles\":[\"data\"],\"title\":\"Source data containing all bands\"},\"metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Federal Geographic Data Committee (FGDC) Metadata\"},\"Maximum_Snow_Extent\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Maximum snow extent over the 8-day period.\",\"raster:bands\":[{\"data_type\":\"uint8\",\"spatial_resolution\":500}],\"classification:classes\":[{\"value\":0,\"description\":\"missing data\"},{\"value\":1,\"description\":\"no decision\"},{\"value\":11,\"description\":\"night\"},{\"value\":25,\"description\":\"no snow\"},{\"value\":37,\"description\":\"lake\"},{\"value\":39,\"description\":\"ocean\"},{\"value\":50,\"description\":\"cloud\"},{\"value\":100,\"description\":\"lake ice\"},{\"value\":200,\"description\":\"snow\"},{\"value\":254,\"description\":\"detector saturated\"}]},\"Eight_Day_Snow_Cover\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Snow chronology 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, D. K., & Riggs, G. A. (2021). MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Version 61 [Data set]. NASA National Snow and Ice Data Center DAAC. https://doi.org/10.5067/MODIS/MOD10A2.061\"},{\"doi\":\"10.5067/MODIS/MYD10A2.061\",\"citation\":\"Hall, D. K., & Riggs, G. A. (2021). MODIS/Aqua Snow Cover 8-Day L3 Global 500m Grid, Version 61 [Data set]. NASA National Snow and Ice Data Center DAAC. https://doi.org/10.5067/MODIS/MYD10A2.061\"}],\"msft:storage_account\":\"modiseuwest\",\"msft:short_description\":\"MODIS 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 license\"},{\"rel\":\"documentation\",\"href\":\"https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time\",\"type\":\"text/html\",\"title\":\"ECMWF Open Data (Real Time) documentation\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/ecmwf-forecast\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ECMWF 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 [ECMWF catalog of real-time products](https://www.ecmwf.int/en/forecasts/datasets/catalogue-ecmwf-real-time-products) offers real-time meterological and oceanographic productions from the ECMWF forecast system. Users should consult the [ECMWF Forecast User Guide](https://confluence.ecmwf.int/display/FUG/1+Introduction) for detailed information on each of the products.\\n\\n## Overview of products\\n\\nThe following diagram shows the publishing schedule of the various products.\\n\\n\\n\\nThe vertical axis shows the various products, defined below, which are grouped by combinations of `stream`, `forecast type`, and `reference time`. The horizontal axis shows *forecast times* in 3-hour intervals out from the reference time. A black square over a particular forecast time, or step, indicates that a forecast is made for that forecast time, for that particular `stream`, `forecast type`, `reference time` combination.\\n\\n* **stream** is the forecasting system that produced the data. The values are available in the `ecmwf:stream` summary of the STAC collection. They are:\\n \ * `enfo`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), atmospheric fields\\n * `mmsf`: [multi-model seasonal forecasts](https://confluence.ecmwf.int/display/FUG/Long-Range+%28Seasonal%29+Forecast) fields from the ECMWF model only.\\n * `oper`: [high-resolution forecast](https://confluence.ecmwf.int/display/FUG/HRES+-+High-Resolution+Forecast), atmospheric fields \\n * `scda`: short cut-off high-resolution forecast, atmospheric fields (also known as \\\"high-frequency products\\\")\\n * `scwv`: short cut-off high-resolution forecast, ocean wave fields (also known as \\\"high-frequency products\\\") and\\n * `waef`: [ensemble forecast](https://confluence.ecmwf.int/display/FUG/ENS+-+Ensemble+Forecasts), ocean wave fields,\\n * `wave`: wave model\\n* **type** is the forecast type. The values are available in the `ecmwf:type` summary of the STAC collection. They are:\\n * `fc`: forecast\\n * `ef`: ensemble forecast\\n * `pf`: ensemble probabilities\\n * `tf`: trajectory forecast for tropical cyclone tracks\\n* **reference time** is the hours after midnight when the model was run. Each stream / type will produce assets for different forecast times (steps from the reference datetime) depending on the reference time.\\n\\nVisit the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) for more details on each of the various products.\\n\\nAssets are available for the previous 30 days.\\n\\n## Asset overview\\n\\nThe data are provided as [GRIB2 files](https://confluence.ecmwf.int/display/CKB/What+are+GRIB+files+and+how+can+I+read+them).\\nAdditionally, [index files](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time#ECMWFOpenDataRealTime-IndexFilesIndexfiles) are provided, which can be used to read subsets of the data from Azure Blob Storage.\\n\\nWithin each `stream`, `forecast type`, `reference time`, the structure of the data are mostly consistent. Each GRIB2 file will have the\\nsame data variables, coordinates (aside from `time` as the *reference time* changes and `step` as the *forecast time* changes). The exception\\nis the `enfo-ep` and `waef-ep` products, which have more `step`s in the 240-hour forecast than in the 360-hour forecast. \\n\\nSee the example notebook for more on how to access the data.\\n\\n## STAC metadata\\n\\nThe Planetary Computer provides a single STAC item per GRIB2 file. Each GRIB2 file is global in extent, so every item has the same\\n`bbox` and `geometry`.\\n\\nA few custom properties are available on each STAC item, which can be used in searches to narrow down the data to items of interest:\\n\\n* `ecmwf:stream`: The forecasting system (see above for definitions). The full set of values is available in the Collection's summaries.\\n* `ecmwf:type`: The forecast type (see above for definitions). The full set of values is available in the Collection's summaries.\\n* `ecmwf:step`: The offset from the reference datetime, expressed as ``, for example `\\\"3h\\\"` means \\\"3 hours from the reference datetime\\\". \\n* `ecmwf:reference_datetime`: The datetime when the model was run. This indicates when the forecast *was made*, rather than when it's valid for.\\n* `ecmwf:forecast_datetime`: The datetime for which the forecast is valid. This is also set as the item's `datetime`.\\n\\nSee the example notebook for more on how to use the STAC metadata to query for particular data.\\n\\n## Attribution\\n\\nThe products listed and described on this page are available to the public and their use is governed by the [Creative Commons CC-4.0-BY license and the ECMWF Terms of Use](https://apps.ecmwf.int/datasets/licences/general/). This means that the data may be redistributed and used commercially, subject to appropriate attribution.\\n\\nThe following wording should be attached to the use of this ECMWF dataset: \\n\\n1. Copyright statement: Copyright \\\"\xA9 [year] European Centre for Medium-Range Weather Forecasts (ECMWF)\\\".\\n2. Source [www.ecmwf.int](http://www.ecmwf.int/)\\n3. License Statement: This data is published under a 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. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications.\\n\\nThe following wording shall be attached to services created with this ECMWF dataset:\\n\\n1. Copyright statement: Copyright \\\"This service is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF)\\\".\\n2. Source www.ecmwf.int\\n3. License Statement: This ECMWF data is published under a 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. Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.\\n5. Where applicable, an indication if the material has been modified and an indication of previous modifications\\n\\n## More information\\n\\nFor more, see the [ECMWF's User Guide](https://confluence.ecmwf.int/display/UDOC/ECMWF+Open+Data+-+Real+Time) and [example notebooks](https://github.com/ecmwf/notebook-examples/tree/master/opencharts).\",\"item_assets\":{\"data\":{\"type\":\"application/wmo-GRIB2\",\"roles\":[\"data\"],\"title\":\"GRIB2 data file\",\"description\":\"The forecast data, as a grib2 file. Subsets of the data can be loaded using information from the associated index file.\"},\"index\":{\"type\":\"application/x-ndjson\",\"roles\":[\"index\"],\"title\":\"Index file\",\"description\":\"The index file contains information on each message within the GRIB2 file.\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:container\":\"ecmwf\",\"stac_extensions\":[\"https://stac-extensions.github.io/item-assets/v1.0.0/schema.json\"],\"msft:storage_account\":\"ai4edataeuwest\",\"msft:short_description\":\"ECMWF Open Data (Real Time) forecasts\"},{\"id\":\"noaa-mrms-qpe-24h-pass2\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/noaa-mrms-qpe-24h-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-24h-pass2\"},{\"rel\":\"license\",\"href\":\"https://www.nssl.noaa.gov/projects/mrms/nmq_data_policy_OGCrevised.pdf\",\"type\":\"application/pdf\",\"title\":\"MRMS Dataset Sharing Policy\"},{\"rel\":\"about\",\"href\":\"https://mrms.nssl.noaa.gov\",\"type\":\"text/html\",\"title\":\"MRMS Homepage\"},{\"rel\":\"about\",\"href\":\"https://vlab.noaa.gov/web/wdtd/-/multi-sensor-qpe-1?selectedFolder=9234881\",\"type\":\"text/html\",\"title\":\"MRMS QPE Technical Product Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-mrms-qpe-24h-pass2\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NOAA MRMS QPE 24-Hour Pass 2\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/noaa-mrms-qpe-24h-pass2-thumb.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"NOAA MRMS QPE 24-Hour Pass 2 Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-mrms-qpe-24h-pass2.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 States\",\"Guam\",\"Caribbean\"],\"providers\":[{\"url\":\"https://www.nssl.noaa.gov/projects/mrms\",\"name\":\"NOAA 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\":[24],\"noaa_mrms_qpe:region\":[\"CONUS\",\"HAWAII\",\"GUAM\",\"ALASKA\",\"CARIB\"]},\"description\":\"The [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\\n\\nMRMS QPE is available as a \\\"Pass 1\\\" or \\\"Pass 2\\\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\\n\\nThis Collection contains the **24-Hour Pass 2** sub-product, i.e., 24-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. 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These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\\n\\nThis dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. 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The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \\n\\nThe dataset is composed of a uniform set of 7 land cover classes. Additional information is available in a [User Guide](https://www.chesapeakeconservancy.org/wp-content/uploads/2020/06/Chesapeake_Conservancy_LandCover101Guide_June2020.pdf). 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The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions, that leads and directs Chesapeake Bay restoration efforts. \\n\\nThe dataset is composed of 13 land cover classes, although not all classes are used in all areas. 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The dataset was 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/) using 13 ancillary datasets including data on zoning, land use, parcel boundaries, landfills, floodplains, and wetlands. The product area encompasses over 250,000 square kilometers in New York, Pennsylvania, Maryland, Delaware, West Virginia, Virginia, and the District of Columbia. The dataset was created by the [Chesapeake Conservancy](https://www.chesapeakeconservancy.org/) [Conservation Innovation Center](https://www.chesapeakeconservancy.org/conservation-innovation-center/) for the [Chesapeake Bay Program](https://www.chesapeakebay.net/), which is a regional partnership of EPA, other federal, state, and local agencies and governments, nonprofits, and academic institutions that leads and directs Chesapeake Bay restoration efforts.\\n\\nThe dataset is composed of 17 land use classes in Virginia and 16 classes in all other jurisdictions. Additional 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) document. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Land Use Classes\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":1}],\"classification:classes\":[{\"value\":1,\"color-hint\":\"000000\",\"description\":\"Impervious Roads\"},{\"value\":2,\"color-hint\":\"730000\",\"description\":\"Impervious Non-Roads\"},{\"value\":3,\"color-hint\":\"55FF00\",\"description\":\"Tree Canopy over Impervious Surfaces\"},{\"value\":4,\"color-hint\":\"0070FF\",\"description\":\"Water\"},{\"value\":5,\"color-hint\":\"00FFC5\",\"description\":\"Tidal Wetlands\"},{\"value\":6,\"color-hint\":\"00E6A9\",\"description\":\"Floodplain Wetlands\"},{\"value\":7,\"color-hint\":\"00E6A9\",\"description\":\"Other 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Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\\n\\nMRMS QPE is available as a \\\"Pass 1\\\" or \\\"Pass 2\\\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\\n\\nThis Collection contains the **1-Hour Pass 1** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 1-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\",\"item_assets\":{\"cog\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Processed Cloud Optimized GeoTIFF file\",\"raster:bands\":[{\"unit\":\"mm\",\"data_type\":\"float64\",\"spatial_resolution\":1000}]},\"grib2\":{\"type\":\"application/wmo-GRIB2\",\"roles\":[\"data\"],\"title\":\"Original 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 multi-sensor cumulative precipation estimate for the past hour with a 1-hour 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 Dataset Sharing Policy\"},{\"rel\":\"about\",\"href\":\"https://mrms.nssl.noaa.gov\",\"type\":\"text/html\",\"title\":\"MRMS Homepage\"},{\"rel\":\"about\",\"href\":\"https://vlab.noaa.gov/web/wdtd/-/multi-sensor-qpe-1?selectedFolder=9234881\",\"type\":\"text/html\",\"title\":\"MRMS QPE Technical Product Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-mrms-qpe-1h-pass2\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NOAA 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 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 States\",\"Guam\",\"Caribbean\"],\"providers\":[{\"url\":\"https://www.nssl.noaa.gov/projects/mrms\",\"name\":\"NOAA 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 [Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation (QPE)](https://www.nssl.noaa.gov/projects/mrms/) products are seamless 1-km mosaics of precipitation accumulation covering the continental United States, Alaska, Hawaii, the Caribbean, and Guam. The products are automatically generated through integration of data from multiple radars and radar networks, surface and satellite observations, numerical weather prediction (NWP) models, and climatology. The products are updated hourly at the top of the hour.\\n\\nMRMS QPE is available as a \\\"Pass 1\\\" or \\\"Pass 2\\\" product. The Pass 1 product is available with a 60-minute latency and includes 60-65% of gauges. The Pass 2 product has a higher latency of 120 minutes, but includes 99% of gauges. The Pass 1 and Pass 2 products are broken into 1-, 3-, 6-, 12-, 24-, 48-, and 72-hour accumulation sub-products.\\n\\nThis Collection contains the **1-Hour Pass 2** sub-product, i.e., 1-hour cumulative precipitation accumulation with a 2-hour latency. The data are available in [Cloud Optimized GeoTIFF](https://www.cogeo.org/) format as well as the original source GRIB2 format files. The GRIB2 files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\",\"item_assets\":{\"cog\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Processed Cloud Optimized GeoTIFF file\",\"raster:bands\":[{\"unit\":\"mm\",\"data_type\":\"float64\",\"spatial_resolution\":1000}]},\"grib2\":{\"type\":\"application/wmo-GRIB2\",\"roles\":[\"data\"],\"title\":\"Original 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 multi-sensor cumulative precipation estimate for the past hour with a 2-hour 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 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 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 Landing Page\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-nclimgrid-monthly\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Monthly 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 NOAA NClimGrid Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/noaa-nclimgrid-monthly.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 States\",\"NOAA\",\"NClimGrid\",\"Climate\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"NOAA National Centers for Environmental Information\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"description\":\"The [NOAA U.S. Climate Gridded Dataset (NClimGrid)](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) consists of four climate variables derived from the [Global Historical Climatology Network daily (GHCNd)](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily) dataset: maximum temperature, minimum temperature, average temperature, and precipitation. The data is provided in 1/24 degree lat/lon (nominal 5x5 kilometer) grids for the Continental United States (CONUS). \\n\\nNClimGrid data is available in monthly and daily temporal intervals, with the daily data further differentiated as \\\"prelim\\\" (preliminary) or \\\"scaled\\\". Preliminary daily data is available within approximately three days of collection. Once a calendar month of preliminary daily data has been collected, it is scaled to match the corresponding monthly value. Monthly data is available from 1895 to the present. Daily preliminary and daily scaled data is available from 1951 to the present. \\n\\nThis Collection contains **Monthly** data. See the journal publication [\\\"Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions\\\"](https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-0248.1.xml) for more information about monthly gridded data.\\n\\nUsers of all NClimGrid data product should be aware that [NOAA advertises](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00332) that:\\n>\\\"On an annual basis, approximately one year of 'final' NClimGrid data is submitted to replace the initially supplied 'preliminary' data for the same time period. Users should be sure to ascertain which level of data is required for their research.\\\"\\n\\nThe source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\\n*Note*: The Planetary Computer currently has STAC metadata for just the monthly collection. We'll have STAC metadata for daily data in our next release. In the meantime, you can access the daily NetCDF data directly from Blob Storage using the storage container at `https://nclimgridwesteurope.blob.core.windows.net/nclimgrid`. See https://planetarycomputer.microsoft.com/docs/concepts/data-catalog/#access-patterns for more.*\\n\",\"item_assets\":{\"prcp\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Monthly Precipitation (mm)\",\"raster:bands\":[{\"unit\":\"mm\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Monthly Average Temperature (degree Celsius)\",\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Monthly Maximmum Temperature (degree Celsius)\",\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Monthly Minimum Temperature (degree Celsius)\",\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]}},\"sci:citation\":\"Vose, Russell S., Applequist, Scott, Squires, Mike, Durre, Imke, Menne, Matthew J., Williams, Claude N. Jr., Fenimore, Chris, Gleason, Karin, and Arndt, Derek (2014): NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid), Version 1. 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, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C., Gleason, K., & Arndt, D. (2014). Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), 1232-1251.\"}],\"msft:storage_account\":\"nclimgridwesteurope\",\"msft:short_description\":\"Gridded surface observations of monthly temperature and precipitation for the continental 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 Landing Page\"},{\"rel\":\"license\",\"href\":\"https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php#Section3_5.0\",\"title\":\"Redistribution information\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/usda-cdl\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USDA 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 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 States\",\"Land Cover\",\"Land Use\",\"Agriculture\"],\"providers\":[{\"url\":\"https://www.nass.usda.gov/\",\"name\":\"United 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 Cropland Data Layer (CDL) is a product of the USDA National Agricultural Statistics Service (NASS) with the mission \\\"to provide timely, accurate and useful statistics in service to U.S. agriculture\\\" (Johnson and Mueller, 2010, p. 1204). The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season.\\n\\nThis collection includes Cropland, Confidence, Cultivated, and Frequency products.\\n\\n- Cropland: Crop-specific land cover data created annually. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat.\\n- Confidence: The predicted confidence associated with an output pixel. A value of zero indicates low confidence, while a value of 100 indicates high confidence.\\n- Cultivated: cultivated and non-cultivated land cover for CONUS based on land cover information derived from the 2017 through 2021 Cropland products.\\n- Frequency: crop specific planting frequency based on land cover information derived from the 2008 through 2021 Cropland 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":255,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"wheat\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":255,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"cotton\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":255,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"cropland\":{\"type\":\"image/tiff; 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 Corn\"},{\"value\":13,\"color_hint\":\"DDA40A\",\"description\":\"Pop or Orn Corn\"},{\"value\":14,\"color_hint\":\"7DD2FF\",\"description\":\"Mint\"},{\"value\":21,\"color_hint\":\"E1007B\",\"description\":\"Barley\"},{\"value\":22,\"color_hint\":\"886153\",\"description\":\"Durum Wheat\"},{\"value\":23,\"color_hint\":\"D7B56B\",\"description\":\"Spring Wheat\"},{\"value\":24,\"color_hint\":\"A46F00\",\"description\":\"Winter Wheat\"},{\"value\":25,\"color_hint\":\"D59EBB\",\"description\":\"Other Small Grains\"},{\"value\":26,\"color_hint\":\"6F6F00\",\"description\":\"Winter Wheat/Soybeans\"},{\"value\":27,\"color_hint\":\"AC007B\",\"description\":\"Rye\"},{\"value\":28,\"color_hint\":\"9F5888\",\"description\":\"Oats\"},{\"value\":29,\"color_hint\":\"6F0048\",\"description\":\"Millet\"},{\"value\":30,\"color_hint\":\"D59EBB\",\"description\":\"Speltz\"},{\"value\":31,\"color_hint\":\"D1FF00\",\"description\":\"Canola\"},{\"value\":32,\"color_hint\":\"7D99FF\",\"description\":\"Flaxseed\"},{\"value\":33,\"color_hint\":\"D5D500\",\"description\":\"Safflower\"},{\"value\":34,\"color_hint\":\"D1FF00\",\"description\":\"Rape Seed\"},{\"value\":35,\"color_hint\":\"00AE4A\",\"description\":\"Mustard\"},{\"value\":36,\"color_hint\":\"FFA4E1\",\"description\":\"Alfalfa\"},{\"value\":37,\"color_hint\":\"A4F18B\",\"description\":\"Other Hay/Non Alfalfa\"},{\"value\":38,\"color_hint\":\"00AE4A\",\"description\":\"Camelina\"},{\"value\":39,\"color_hint\":\"D59EBB\",\"description\":\"Buckwheat\"},{\"value\":41,\"color_hint\":\"A800E3\",\"description\":\"Sugarbeets\"},{\"value\":42,\"color_hint\":\"A40000\",\"description\":\"Dry Beans\"},{\"value\":43,\"color_hint\":\"6F2500\",\"description\":\"Potatoes\"},{\"value\":44,\"color_hint\":\"00AE4A\",\"description\":\"Other Crops\"},{\"value\":45,\"color_hint\":\"B07DFF\",\"description\":\"Sugarcane\"},{\"value\":46,\"color_hint\":\"6F2500\",\"description\":\"Sweet Potatoes\"},{\"value\":47,\"color_hint\":\"FF6666\",\"description\":\"Misc. Vegs & Fruits\"},{\"value\":48,\"color_hint\":\"FF6666\",\"description\":\"Watermelons\"},{\"value\":49,\"color_hint\":\"FFCC66\",\"description\":\"Onions\"},{\"value\":50,\"color_hint\":\"FF6666\",\"description\":\"Cucumbers\"},{\"value\":51,\"color_hint\":\"00AE4A\",\"description\":\"Chick Peas\"},{\"value\":52,\"color_hint\":\"00DDAE\",\"description\":\"Lentils\"},{\"value\":53,\"color_hint\":\"53FF00\",\"description\":\"Peas\"},{\"value\":54,\"color_hint\":\"F1A277\",\"description\":\"Tomatoes\"},{\"value\":55,\"color_hint\":\"FF6666\",\"description\":\"Caneberries\"},{\"value\":56,\"color_hint\":\"00AE4A\",\"description\":\"Hops\"},{\"value\":57,\"color_hint\":\"7DD2FF\",\"description\":\"Herbs\"},{\"value\":58,\"color_hint\":\"E8BEFF\",\"description\":\"Clover/Wildflowers\"},{\"value\":59,\"color_hint\":\"AEFFDD\",\"description\":\"Sod/Grass Seed\"},{\"value\":60,\"color_hint\":\"00AE4A\",\"description\":\"Switchgrass\"},{\"value\":61,\"color_hint\":\"BEBE77\",\"description\":\"Fallow/Idle Cropland\"},{\"value\":63,\"color_hint\":\"92CC92\",\"description\":\"Forest\"},{\"value\":64,\"color_hint\":\"C5D59E\",\"description\":\"Shrubland\"},{\"value\":65,\"color_hint\":\"CCBEA2\",\"description\":\"Barren\"},{\"value\":66,\"color_hint\":\"FF00FF\",\"description\":\"Cherries\"},{\"value\":67,\"color_hint\":\"FF8EAA\",\"description\":\"Peaches\"},{\"value\":68,\"color_hint\":\"B9004F\",\"description\":\"Apples\"},{\"value\":69,\"color_hint\":\"6F4488\",\"description\":\"Grapes\"},{\"value\":70,\"color_hint\":\"007777\",\"description\":\"Christmas Trees\"},{\"value\":71,\"color_hint\":\"B09A6F\",\"description\":\"Other Tree Crops\"},{\"value\":72,\"color_hint\":\"FFFF7D\",\"description\":\"Citrus\"},{\"value\":74,\"color_hint\":\"B56F5B\",\"description\":\"Pecans\"},{\"value\":75,\"color_hint\":\"00A482\",\"description\":\"Almonds\"},{\"value\":76,\"color_hint\":\"E9D5AE\",\"description\":\"Walnuts\"},{\"value\":77,\"color_hint\":\"B09A6F\",\"description\":\"Pears\"},{\"value\":81,\"color_hint\":\"F1F1F1\",\"description\":\"Clouds/No Data\"},{\"value\":82,\"color_hint\":\"9A9A9A\",\"description\":\"Developed\"},{\"value\":83,\"color_hint\":\"4A6fA2\",\"description\":\"Water\"},{\"value\":87,\"color_hint\":\"7DB0B0\",\"description\":\"Wetlands\"},{\"value\":88,\"color_hint\":\"E8FFBE\",\"description\":\"Nonag/Undefined\"},{\"value\":92,\"color_hint\":\"00FFFF\",\"description\":\"Aquaculture\"},{\"value\":111,\"color_hint\":\"4A6FA2\",\"description\":\"Open Water\"},{\"value\":112,\"color_hint\":\"D2E1F8\",\"description\":\"Perennial Ice/Snow\"},{\"value\":121,\"color_hint\":\"9A9A9A\",\"description\":\"Developed/Open Space\"},{\"value\":122,\"color_hint\":\"9A9A9A\",\"description\":\"Developed/Low Intensity\"},{\"value\":123,\"color_hint\":\"9A9A9A\",\"description\":\"Developed/Med Intensity\"},{\"value\":124,\"color_hint\":\"9A9A9A\",\"description\":\"Developed/High Intensity\"},{\"value\":131,\"color_hint\":\"CCBEA2\",\"description\":\"Barren\"},{\"value\":141,\"color_hint\":\"92CC92\",\"description\":\"Deciduous Forest\"},{\"value\":142,\"color_hint\":\"92CC92\",\"description\":\"Evergreen Forest\"},{\"value\":143,\"color_hint\":\"92CC92\",\"description\":\"Mixed Forest\"},{\"value\":152,\"color_hint\":\"C5D59E\",\"description\":\"Shrubland\"},{\"value\":176,\"color_hint\":\"E8FFBE\",\"description\":\"Grassland/Pasture\"},{\"value\":190,\"color_hint\":\"7DB0B0\",\"description\":\"Woody Wetlands\"},{\"value\":195,\"color_hint\":\"7DB0B0\",\"description\":\"Herbaceous Wetlands\"},{\"value\":204,\"color_hint\":\"00FF8B\",\"description\":\"Pistachios\"},{\"value\":205,\"color_hint\":\"D59EBB\",\"description\":\"Triticale\"},{\"value\":206,\"color_hint\":\"FF6666\",\"description\":\"Carrots\"},{\"value\":207,\"color_hint\":\"FF6666\",\"description\":\"Asparagus\"},{\"value\":208,\"color_hint\":\"FF6666\",\"description\":\"Garlic\"},{\"value\":209,\"color_hint\":\"FF6666\",\"description\":\"Cantaloupes\"},{\"value\":210,\"color_hint\":\"FF8EAA\",\"description\":\"Prunes\"},{\"value\":211,\"color_hint\":\"334833\",\"description\":\"Olives\"},{\"value\":212,\"color_hint\":\"E36F25\",\"description\":\"Oranges\"},{\"value\":213,\"color_hint\":\"FF6666\",\"description\":\"Honeydew Melons\"},{\"value\":214,\"color_hint\":\"FF6666\",\"description\":\"Broccoli\"},{\"value\":215,\"color_hint\":\"66994B\",\"description\":\"Avocados\"},{\"value\":216,\"color_hint\":\"FF6666\",\"description\":\"Peppers\"},{\"value\":217,\"color_hint\":\"B09A6F\",\"description\":\"Pomegranates\"},{\"value\":218,\"color_hint\":\"FF8EAA\",\"description\":\"Nectarines\"},{\"value\":219,\"color_hint\":\"FF6666\",\"description\":\"Greens\"},{\"value\":220,\"color_hint\":\"FF8EAA\",\"description\":\"Plums\"},{\"value\":221,\"color_hint\":\"FF6666\",\"description\":\"Strawberreis\"},{\"value\":222,\"color_hint\":\"FF6666\",\"description\":\"Squash\"},{\"value\":223,\"color_hint\":\"FF8EAA\",\"description\":\"Apricots\"},{\"value\":224,\"color_hint\":\"00AE4A\",\"description\":\"Vetch\"},{\"value\":225,\"color_hint\":\"FFD200\",\"description\":\"Winter Wheat/Corn\"},{\"value\":226,\"color_hint\":\"FFD200\",\"description\":\"Oats/Corn\"},{\"value\":227,\"color_hint\":\"FF6666\",\"description\":\"Lettuce\"},{\"value\":228,\"color_hint\":\"FF6666\",\"description\":\"Triticale/Corn\"},{\"value\":229,\"color_hint\":\"FF6666\",\"description\":\"Pumpkins\"},{\"value\":230,\"color_hint\":\"886153\",\"description\":\"Lettuce/Durum Wheat\"},{\"value\":231,\"color_hint\":\"FF6666\",\"description\":\"Lettuce/Cataloupe\"},{\"value\":232,\"color_hint\":\"FF2525\",\"description\":\"Lettuce/Cotton\"},{\"value\":233,\"color_hint\":\"A1007B\",\"description\":\"Lettuce/Barley\"},{\"value\":234,\"color_hint\":\"FF9E0A\",\"description\":\"Durum Wheat/Sorghum\"},{\"value\":235,\"color_hint\":\"FF9E0A\",\"description\":\"Barley/Sorghum\"},{\"value\":236,\"color_hint\":\"A46F00\",\"description\":\"Winter Wheat/Sorghum\"},{\"value\":237,\"color_hint\":\"FFD200\",\"description\":\"Barley/Corn\"},{\"value\":238,\"color_hint\":\"A46F00\",\"description\":\"Winter Wheat/Cotton\"},{\"value\":239,\"color_hint\":\"256F00\",\"description\":\"Soybeans/Cotton\"},{\"value\":240,\"color_hint\":\"256F00\",\"description\":\"Soybeans/Oats \"},{\"value\":241,\"color_hint\":\"FFD200\",\"description\":\"Corn/Soybeans\"},{\"value\":242,\"color_hint\":\"000099\",\"description\":\"Blueberries\"},{\"value\":243,\"color_hint\":\"FF6666\",\"description\":\"Cabbage\"},{\"value\":244,\"color_hint\":\"FF6666\",\"description\":\"Cauliflower\"},{\"value\":245,\"color_hint\":\"FF6666\",\"description\":\"Celery\"},{\"value\":246,\"color_hint\":\"FF6666\",\"description\":\"Radishes\"},{\"value\":247,\"color_hint\":\"FF6666\",\"description\":\"Turnips\"},{\"value\":248,\"color_hint\":\"FF6666\",\"description\":\"Eggplants\"},{\"value\":249,\"color_hint\":\"FF6666\",\"description\":\"Gourds\"},{\"value\":250,\"color_hint\":\"FF6666\",\"description\":\"Cranberries\"},{\"value\":254,\"color_hint\":\"256F00\",\"description\":\"Barley/Soybeans\"}]},\"soybeans\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":255,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"confidence\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":0,\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"cultivated\":{\"type\":\"image/tiff; application=geotiff; 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 UDA Cropland Data Layer is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural 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 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: An End-to-End Platform for Low-Cost, Hyperlocal Environment Sensing in Cities\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/eclipse\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Urban Innovation Eclipse Sensor Data\",\"assets\":{\"data\":{\"href\":\"abfs://eclipse/Chicago/\",\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Full 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 Innovation Chicago Sensors\"},\"geoparquet-items\":{\"href\":\"abfs://items/eclipse.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 pollution\"],\"providers\":[{\"url\":\"https://www.microsoft.com/en-us/research/urban-innovation-research/\",\"name\":\"Urban Innovation\",\"roles\":[\"producer\",\"licensor\",\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"description\":\"The [Project Eclipse](https://www.microsoft.com/en-us/research/project/project-eclipse/) Network is a low-cost air quality sensing network for cities and a research project led by the [Urban Innovation Group]( https://www.microsoft.com/en-us/research/urban-innovation-research/) at Microsoft Research.\\n\\nProject Eclipse currently includes over 100 locations in Chicago, Illinois, USA.\\n\\nThis network was deployed starting in July, 2021, through a collaboration with the City of Chicago, the Array of Things Project, JCDecaux Chicago, and the Environmental Law and Policy Center as well as local environmental justice organizations in the city. [This talk]( https://www.microsoft.com/en-us/research/video/technology-demo-project-eclipse-hyperlocal-air-quality-monitoring-for-cities/) documents the network design and data calibration strategy.\\n\\n## Storage resources\\n\\nData are stored in [Parquet](https://parquet.apache.org/) files in Azure Blob Storage in the West Europe Azure region, in the following blob container:\\n\\n`https://ai4edataeuwest.blob.core.windows.net/eclipse`\\n\\nWithin that container, the periodic occurrence snapshots are stored in `Chicago/YYYY-MM-DD`, where `YYYY-MM-DD` corresponds to the date of the snapshot.\\nEach snapshot contains a sensor readings from the next 7-days in Parquet format starting with date on the folder name YYYY-MM-DD.\\nTherefore, the data files for the first snapshot are at\\n\\n`https://ai4edataeuwest.blob.core.windows.net/eclipse/chicago/2022-01-01/data_*.parquet\\n\\nThe Parquet file schema is as described below. \\n\\n## Additional Documentation\\n\\nFor 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## License and attribution\\nPlease cite: Daepp, Cabral, Ranganathan et al. (2022) [Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing 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## Contact\\n\\nFor questions about this dataset, contact [`msrurbanops@microsoft.com`](mailto:msrurbanops@microsoft.com?subject=eclipse%20question) \\n\\n\\n## Learn more\\n\\nThe [Eclipse Project](https://www.microsoft.com/en-us/research/urban-innovation-research/) contains an overview of the Project Eclipse at Microsoft Research.\\n\\n\",\"item_assets\":{\"data\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Weekly dataset\",\"table:storage_options\":{\"account_name\":\"ai4edataeuwest\"}}},\"sci:citation\":\"Daepp, Cabral, Ranganathan et al. (2022) Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities. ACM/IEEE Information Processing in Sensor Networks. Milan, Italy. 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The land cover classes are defined using the United Nations 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/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \\n\\nThe data in this Collection have been converted from the [original NetCDF data](https://planetarycomputer.microsoft.com/dataset/esa-cci-lc-netcdf) to a set of tiled [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs).\\n\",\"item_assets\":{\"lccs_class\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Land Cover Class Defined in the Land Cover Classification System\",\"description\":\"Land cover class per pixel, defined using the Land Cover Classification System developed by the United Nations Food and Agriculture Organization.\",\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":300}],\"classification:classes\":[{\"name\":\"no-data\",\"value\":0,\"no_data\":true,\"color_hint\":\"000000\",\"description\":\"No 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(v2.1).\"},{\"url\":\"http://esa-landcover-cci.org\",\"name\":\"ESA Climate Change Initiative\",\"roles\":[\"licensor\"],\"description\":\"The ESA Climate Change Initiative (CCI) is leading the product creation.\"},{\"url\":\"https://copernicus.eu\",\"name\":\"Copernicus\",\"roles\":[\"licensor\"],\"description\":\"Hosts 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 ESA Climate Change Initiative (CCI) [Land Cover dataset](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview) provides consistent global annual land cover maps at 300m spatial resolution from 1992 to 2020. The land cover classes are defined using the United Nations 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/) (LCCS). In addition to the land cover maps, four quality flags are produced to document the reliability of the classification and change detection. \\n\\nThe data in this Collection are the original NetCDF files accessed from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/#!/home). We recommend users use the [`esa-cci-lc` Collection](planetarycomputer.microsoft.com/dataset/esa-cci-lc), which provides the data as Cloud Optimized GeoTIFFs.\",\"item_assets\":{\"netcdf\":{\"type\":\"application/netcdf\",\"roles\":[\"data\",\"quality\"],\"title\":\"ESA 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 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 metadata\"},{\"rel\":\"about\",\"href\":\"https://www.fws.gov/sites/default/files/documents/national-wetlands-inventory-fact-sheet.pdf\",\"type\":\"application/pdf\",\"title\":\"Project Fact Sheet\"},{\"rel\":\"about\",\"href\":\"https://www.fws.gov/program/national-wetlands-inventory\",\"type\":\"text/html\",\"title\":\"Project Landing Page\"},{\"rel\":\"license\",\"href\":\"http://www.usa.gov/publicdomain/label/1.0/\",\"type\":\"text/html\",\"title\":\"US Public Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/fws-nwi\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"FWS 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 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 States\"],\"providers\":[{\"url\":\"https://www.fws.gov\",\"name\":\"U.S. Fish and Wildlife Service\",\"email\":\"wetlands_team@fws.gov\",\"roles\":[\"producer\",\"licensor\"],\"description\":\"The U.S. Fish and Wildlife Service is the principal federal agency tasked with providing information to the public on the extent and status of the nation's wetland and deepwater habitats, as well as changes to these habitats over time.\"},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"fws_nwi:state\":[\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\"Connecticut\",\"Delaware\",\"District of Columbia\",\"Florida\",\"Georgia\",\"Hawaii\",\"Idaho\",\"Illinois\",\"Indiana\",\"Iowa\",\"Kansas\",\"Kentucky\",\"Louisiana\",\"Maine\",\"Maryland\",\"Massachusetts\",\"Michigan\",\"Minnesota\",\"Mississippi\",\"Missouri\",\"Montana\",\"Nebraska\",\"Nevada\",\"New Hampshire\",\"New Jersey\",\"New Mexico\",\"New York\",\"North Carolina\",\"North Dakota\",\"Ohio\",\"Oklahoma\",\"Oregon\",\"Pacific Trust Islands\",\"Pennsylvania\",\"Puerto Rico and Virgin Islands\",\"Rhode Island\",\"South Carolina\",\"South Dakota\",\"Tennessee\",\"Texas\",\"Utah\",\"Vermont\",\"Virginia\",\"Washington\",\"West 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 Wetlands Data Layer is the product of over 45 years of work by the National Wetlands Inventory (NWI) and its collaborators and currently contains more than 35 million wetland and deepwater features. This dataset, covering the conterminous United States, Hawaii, Puerto Rico, the Virgin Islands, Guam, the major Northern Mariana Islands and Alaska, continues to grow at a rate of 50 to 100 million acres annually as data are updated.\\n\\n**NOTE:** Due to the variation in use and analysis of this data by the end user, each state's wetlands data extends beyond the state boundary. Each state includes wetlands data that intersect the 1:24,000 quadrangles that contain part of that state (1:2,000,000 source data). This allows the user to clip the data to their specific analysis datasets. Beware that two adjacent states will contain some of the same data along their borders.\\n\\nFor more information, visit the National Wetlands Inventory [homepage](https://www.fws.gov/program/national-wetlands-inventory).\\n\\n## STAC Metadata\\n\\nIn addition to the `zip` asset in every STAC item, each item has its own assets unique to its wetlands. In general, each item will have several assets, each linking to a [geoparquet](https://github.com/opengeospatial/geoparquet) asset with data for the entire region or a sub-region within that state. Use the `cloud-optimized` [role](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-roles) 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 dataset containing wetlands boundaries and identification across the United 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 CONUS Science Products\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products\",\"type\":\"text/html\",\"title\":\"Proprietary, Unrestricted\"},{\"rel\":\"source\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/lcmap-data-access\",\"type\":\"text/html\",\"title\":\"USGS 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 readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 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 LCMAP CONUS 1.3 Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/usgs-lcmap-conus-v13.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Cover\",\"Land Cover Change\",\"CONUS\"],\"providers\":[{\"url\":\"https://www.usgs.gov/special-topics/lcmap\",\"name\":\"United 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 [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \\n\\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP CONUS Collection 1.3](https://www.usgs.gov/special-topics/lcmap/collection-13-conus-science-products), which was released in August 2022 for years 1985-2021. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \\\"nodata\\\" values where appropriate, and an updated projection definition.\\n\",\"item_assets\":{\"dates\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"Landsat Observation Dates\",\"description\":\"Landsat observation dates used as input to the CCDC algorithm.\"},\"lcpri\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Primary Land Cover\",\"description\":\"Land cover classification consisting of eight general land cover types. The most 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 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 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Secondary Land Cover\",\"description\":\"Land cover classification consisting of eight general land cover types. The second most 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 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 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Change Magnitude\",\"description\":\"The spectral strength or intensity of a time series model 'break' when spectral observations have diverged from the model predictions.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"float32\",\"spatial_resolution\":30}]},\"scmqa\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Model Quality\",\"description\":\"Information regarding the type of time series 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 model established for July 1st of current year.\"},{\"name\":\"simple_model\",\"value\":4,\"description\":\"A partial, 4-coefficient harmonic model.\"},{\"name\":\"advanced_model\",\"value\":6,\"description\":\"A partial, 6-coefficient harmonic model.\"},{\"name\":\"full_model\",\"value\":8,\"description\":\"A full, 8-coefficient harmonic model.\"},{\"name\":\"start_fit\",\"value\":14,\"description\":\"A simple model at the beginning of a time series where sparse and/or highly variable spectral measurements prevent establishment of a harmonic model.\"},{\"name\":\"end_fit\",\"value\":24,\"description\":\"A simple model at the end of a time series where there are insufficient observations and/or time to establish a new harmonic model following a model break.\"},{\"name\":\"insufficient_clear\",\"value\":44,\"description\":\"A simple model for the entire time series in cases where fewer than 25% of input observations are labeled as 'Clear' or 'Water' by the U.S. Landsat ARD per-pixel quality band (PIXELQA).\"},{\"name\":\"persistent_snow\",\"value\":54,\"description\":\"A simple model for the entire time series in cases where 75% or more of input observations are labeled as 'Snow' by the U.S. Landsat ARD per-pixel quality band (PIXELQA).\"}]},\"browse\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"USGS Browse Image\",\"description\":\"Image 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual Land Cover Change\",\"description\":\"Synthesis of Primary Land Cover of current 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 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 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 change from Developed to Cropland\"},{\"name\":\"1_to_3\",\"value\":13,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Grass/Shrub\"},{\"name\":\"1_to_4\",\"value\":14,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Tree Cover\"},{\"name\":\"1_to_5\",\"value\":15,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Water\"},{\"name\":\"1_to_6\",\"value\":16,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Wetlands\"},{\"name\":\"1_to_7\",\"value\":17,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Snow/Ice\"},{\"name\":\"1_to_8\",\"value\":18,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Barren\"},{\"name\":\"2_to_1\",\"value\":21,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Developed\"},{\"name\":\"2_to_3\",\"value\":23,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Grass/Shrub\"},{\"name\":\"2_to_4\",\"value\":24,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Tree Cover\"},{\"name\":\"2_to_5\",\"value\":25,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Water\"},{\"name\":\"2_to_6\",\"value\":26,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Wetlands\"},{\"name\":\"2_to_7\",\"value\":27,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Snow/Ice\"},{\"name\":\"2_to_8\",\"value\":28,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Barren\"},{\"name\":\"3_to_1\",\"value\":31,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Developed\"},{\"name\":\"3_to_2\",\"value\":32,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Cropland\"},{\"name\":\"3_to_4\",\"value\":34,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Tree Cover\"},{\"name\":\"3_to_5\",\"value\":35,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Water\"},{\"name\":\"3_to_6\",\"value\":36,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Wetlands\"},{\"name\":\"3_to_7\",\"value\":37,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Snow/Ice\"},{\"name\":\"3_to_8\",\"value\":38,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Barren\"},{\"name\":\"4_to_1\",\"value\":41,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Tree Cover to Developed\"},{\"name\":\"4_to_2\",\"value\":42,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Tree Cover to Cropland\"},{\"name\":\"4_to_3\",\"value\":43,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Tree Cover to Grass/Shrub\"},{\"name\":\"4_to_5\",\"value\":45,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Tree Cover to Water\"},{\"name\":\"4_to_6\",\"value\":46,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Tree Cover to Wetlands\"},{\"name\":\"4_to_7\",\"value\":47,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Tree Cover to Snow/Ice\"},{\"name\":\"4_to_8\",\"value\":48,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Tree Cover to Barren\"},{\"name\":\"5_to_1\",\"value\":51,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Developed\"},{\"name\":\"5_to_2\",\"value\":52,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Cropland\"},{\"name\":\"5_to_3\",\"value\":53,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Grass/Shrub\"},{\"name\":\"5_to_4\",\"value\":54,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Tree Cover\"},{\"name\":\"5_to_6\",\"value\":56,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Wetlands\"},{\"name\":\"5_to_7\",\"value\":57,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Snow/Ice\"},{\"name\":\"5_to_8\",\"value\":58,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Barren\"},{\"name\":\"6_to_1\",\"value\":61,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to Developed\"},{\"name\":\"6_to_2\",\"value\":62,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to Cropland\"},{\"name\":\"6_to_3\",\"value\":63,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to Grass/Shrub\"},{\"name\":\"6_to_4\",\"value\":64,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to Tree Cover\"},{\"name\":\"6_to_5\",\"value\":65,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to Water\"},{\"name\":\"6_to_7\",\"value\":67,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to Snow/Ice\"},{\"name\":\"6_to_8\",\"value\":68,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to Barren\"},{\"name\":\"7_to_1\",\"value\":71,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Developed\"},{\"name\":\"7_to_2\",\"value\":72,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Cropland\"},{\"name\":\"7_to_3\",\"value\":73,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Grass/Shrub\"},{\"name\":\"7_to_4\",\"value\":74,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Tree Cover\"},{\"name\":\"7_to_5\",\"value\":75,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Water\"},{\"name\":\"7_to_6\",\"value\":76,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Wetlands\"},{\"name\":\"7_to_8\",\"value\":78,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Barren\"},{\"name\":\"8_to_1\",\"value\":81,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Developed\"},{\"name\":\"8_to_2\",\"value\":82,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Cropland\"},{\"name\":\"8_to_3\",\"value\":83,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Grass/Shrub\"},{\"name\":\"8_to_4\",\"value\":84,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Tree Cover\"},{\"name\":\"8_to_5\",\"value\":85,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Water\"},{\"name\":\"8_to_6\",\"value\":86,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Wetlands\"},{\"name\":\"8_to_7\",\"value\":87,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Snow/Ice\"}]},\"sclast\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Time Since Last Change\",\"description\":\"Time, in days, since the last identified Spectral Change (SCTIME).\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"scstab\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Spectral Stability Period\",\"description\":\"Measure of the amount of time in days that a pixel has been in its current spectral state as of July 1st. Current spectral state can refer to both during stable time series segments or a period outside of stable time series segments.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"sctime\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Time of Spectral Change\",\"description\":\"Represents the timing of a spectral 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Primary Land Cover Confidence\",\"description\":\"Provides provenance tracking and a measure of confidence that the Primary Land Cover label matches the training 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 of confidence that the Primary Land Cover label matches the training data.\"},{\"values\":[151],\"summary\":\"Time series model identified as transition from a Grass/Shrub class to a Tree Cover class. Primary Land Cover class assignment based on secondary analysis.\"},{\"values\":[152],\"summary\":\"Time series model identified as transition from a Tree Cover class to a Grass/Shrub class. Primary Land cover class assignment based on secondary analysis.\"},{\"values\":[201],\"summary\":\"No stable time series models were produced for this location. Primary Land Cover was assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP Level 1 classification schema, see LCMAP Science Product Guide for more information).\"},{\"values\":[202],\"summary\":\"Insufficient data available to extend most recent time series model past July 1st of current year. Land cover assigned the last identified cover class from earlier year.\"},{\"values\":[211],\"summary\":\"July 1st falls in a gap between two stable time series models of the same land cover class. Primary Land Cover assigned the primary land cover class of those before/after models.\"},{\"values\":[212],\"summary\":\"July 1st falls in a gap between two stable time series models of differing land cover class. If July 1st is before the 'break date' of the earlier model, Primary Land Cover is assigned the primary land cover class of that earlier model. Otherwise, Primary Land Cover is assigned the primary land cover class of the subsequent, later model.\"},{\"values\":[213],\"summary\":\"Insufficient data available to establish a stable time series model at the beginning of the time series prior to July 1st of the current year. Primary Land Cover assigned the primary land cover class of 1st subsequent model.\"},{\"values\":[214],\"summary\":\"Insufficient data available to establish a new stable time series model following a break near the end of the time series prior to July 1st of the current year. Primary Land Cover assigned the last identified primary land cover class from earlier year.\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcsconf\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Secondary Land Cover Confidence\",\"description\":\"Provides provenance tracking and a measure of confidence that the Secondary Land Cover label matches the training 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 of confidence that the Secondary Land Cover label matches the training data.\"},{\"values\":[151],\"summary\":\"Time series model identified as transition from a Grass/Shrub class to a Tree Cover class in Primary Land Cover. Primary Land Cover class assignment based on secondary analysis and Secondary Land Cover class assigned logical opposite of Primary.\"},{\"values\":[152],\"summary\":\"Time series model identified as transition from a Tree Cover class to a Grass/Shrub class in Primary Land Cover. Primary Land Cover class assignment based on secondary analysis and Secondary Land Cover class assigned logical opposite of Primary.\"},{\"values\":[201],\"summary\":\"No stable time series models were produced for this location. Secondary Land Cover assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP classification schema, see LCMAP Science Product Guide for more information).\"},{\"values\":[202],\"summary\":\"Insufficient data available to extend most recent time series model past July 1st of current year. Secondary Land Cover assigned the last identified secondary cover class from earlier year.\"},{\"values\":[211],\"summary\":\"July 1st falls in a gap between two stable time series models of the same secondary land cover class. Secondary Land Cover assigned the land cover class of those before/after models.\"},{\"values\":[212],\"summary\":\"July 1st falls in a gap between two stable time series models of differing secondary land cover classes. If July 1st is before the 'break date' of the earlier model, Secondary Land Cover is assigned the secondary land cover class of the earlier model. Otherwise, Secondary Land Cover is assigned the secondary land cover class of the subsequent model.\"},{\"values\":[213],\"summary\":\"Insufficient data available to establish a stable time series model at the beginning of the time series prior to July 1st of the current year. Secondary Land Cover assigned the secondary land cover class of 1st subsequent model.\"},{\"values\":[214],\"summary\":\"Insufficient data available to establish a new stable time series model following a break near the end of the time series prior to July 1st of the current year. Secondary Land Cover assigned the last identified secondary cover class from earlier year.\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcpri_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Primary Land Cover Metadata\",\"description\":\"Primary Land Cover product metadata\"},\"lcsec_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Secondary Land Cover Metadata\",\"description\":\"Secondary Land Cover product metadata\"},\"scmag_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Change Magnitude Metadata\",\"description\":\"Change Magnitude product metadata\"},\"scmqa_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Model Quality Metadata\",\"description\":\"Model Quality product metadata\"},\"lcachg_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Annual Land Cover Change Metadata\",\"description\":\"Annual Land Cover Change product metadata\"},\"sclast_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Time Since Last Change Metadata\",\"description\":\"Time Since Last Change product metadata\"},\"scstab_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Spectral Stability Period Metadata\",\"description\":\"Spectral Stability Period product metadata\"},\"sctime_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Time of Spectral Change Metadata\",\"description\":\"Time of Spectral Change product metadata\"},\"lcpconf_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Primary Land Cover Confidence Metadata\",\"description\":\"Primary Land Cover Confidence product metadata\"},\"lcsconf_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Secondary Land Cover Confidence Metadata\",\"description\":\"Secondary Land Cover Confidence product metadata\"}},\"sci:citation\":\"U.S. Geological Survey (USGS), 2022, Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3 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, J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E., Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith, K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant, A.L., Zelenak, D., Reker, R.R., and Rover, J., 2020, Lessons learned implementing an operational continuous United States national land change monitoring capability-The Land Change Monitoring, Assessment, and Projection (LCMAP) approach: Remote Sensing of Environment, v. 238, article 111356\"},{\"doi\":\"10.1016/j.rse.2014.01.011\",\"citation\":\"Zhu, Z., and Woodcock, C.E., 2014, Continuous change detection and classification of land cover using all available Landsat data: Remote Sensing of Environment, v. 144, p. 152-171\"}],\"msft:storage_account\":\"landcoverdata\",\"msft:short_description\":\"USGS Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.3 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 Hawaii Science Products\"},{\"rel\":\"license\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products\",\"type\":\"text/html\",\"title\":\"Proprietary, Unrestricted\"},{\"rel\":\"source\",\"href\":\"https://www.usgs.gov/special-topics/lcmap/lcmap-data-access\",\"type\":\"text/html\",\"title\":\"USGS 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 readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"USGS 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 LCMAP Hawaii 1.0 Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/usgs-lcmap-hawaii-v10.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Cover\",\"Land Cover Change\",\"Hawaii\"],\"providers\":[{\"url\":\"https://www.usgs.gov/special-topics/lcmap\",\"name\":\"United 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 [Land Change Monitoring, Assessment, and Projection](https://www.usgs.gov/special-topics/lcmap) (LCMAP) product provides land cover mapping and change monitoring from the U.S. Geological Survey's [Earth Resources Observation and Science](https://www.usgs.gov/centers/eros) (EROS) Center. LCMAP's Science Products are developed by applying time-series modeling on a per-pixel basis to [Landsat Analysis Ready Data](https://www.usgs.gov/landsat-missions/landsat-us-analysis-ready-data) (ARD) using an implementation of the [Continuous Change Detection and Classification](https://doi.org/10.1016/j.rse.2014.01.011) (CCDC) algorithm. All available clear (non-cloudy) U.S. Landsat ARD observations are fit to a harmonic model to predict future Landsat-like surface reflectance. Where Landsat surface reflectance observations differ significantly from those predictions, a change is identified. Attributes of the resulting model sequences (e.g., start/end dates, residuals, model coefficients) are then used to produce a set of land surface change products and as inputs to the subsequent classification to thematic land cover. \\n\\nThis [STAC](https://stacspec.org/en) Collection contains [LCMAP Hawaii Collection 1.0](https://www.usgs.gov/special-topics/lcmap/collection-1-hawaii-science-products), which was released in January 2022 for years 2000-2020. The data are tiled according to the Landsat ARD tile grid and consist of [Cloud Optimized GeoTIFFs](https://www.cogeo.org/) (COGs) and corresponding metadata files. Note that the provided COGs differ slightly from those in the USGS source data. They have been reprocessed to add overviews, \\\"nodata\\\" values where appropriate, and an updated projection definition.\\n\",\"item_assets\":{\"dates\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"Landsat Observation Dates\",\"description\":\"Landsat observation dates used as input to the CCDC algorithm.\"},\"lcpri\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Primary Land Cover\",\"description\":\"Land cover classification consisting of eight general land cover types. The most 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 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 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Secondary Land Cover\",\"description\":\"Land cover classification consisting of eight general land cover types. The second most 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 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 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Change Magnitude\",\"description\":\"The spectral strength or intensity of a time series model 'break' when spectral observations have diverged from the model predictions.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"float32\",\"spatial_resolution\":30}]},\"scmqa\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Model Quality\",\"description\":\"Information regarding the type of time series 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 model established for July 1st of current year.\"},{\"name\":\"simple_model\",\"value\":4,\"description\":\"A partial, 4-coefficient harmonic model.\"},{\"name\":\"advanced_model\",\"value\":6,\"description\":\"A partial, 6-coefficient harmonic model.\"},{\"name\":\"full_model\",\"value\":8,\"description\":\"A full, 8-coefficient harmonic model.\"},{\"name\":\"start_fit\",\"value\":14,\"description\":\"A simple model at the beginning of a time series where sparse and/or highly variable spectral measurements prevent establishment of a harmonic model.\"},{\"name\":\"end_fit\",\"value\":24,\"description\":\"A simple model at the end of a time series where there are insufficient observations and/or time to establish a new harmonic model following a model break.\"},{\"name\":\"insufficient_clear\",\"value\":44,\"description\":\"A simple model for the entire time series in cases where fewer than 25% of input observations are labeled as 'Clear' or 'Water' by the U.S. Landsat ARD per-pixel quality band (PIXELQA).\"},{\"name\":\"persistent_snow\",\"value\":54,\"description\":\"A simple model for the entire time series in cases where 75% or more of input observations are labeled as 'Snow' by the U.S. Landsat ARD per-pixel quality band (PIXELQA).\"}]},\"browse\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"USGS Browse Image\",\"description\":\"Image 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Annual Land Cover Change\",\"description\":\"Synthesis of Primary Land Cover of current 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 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 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 change from Developed to Cropland\"},{\"name\":\"1_to_3\",\"value\":13,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Grass/Shrub\"},{\"name\":\"1_to_4\",\"value\":14,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Tree Cover\"},{\"name\":\"1_to_5\",\"value\":15,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Water\"},{\"name\":\"1_to_6\",\"value\":16,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Wetlands\"},{\"name\":\"1_to_7\",\"value\":17,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Snow/Ice\"},{\"name\":\"1_to_8\",\"value\":18,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Developed to Barren\"},{\"name\":\"2_to_1\",\"value\":21,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Developed\"},{\"name\":\"2_to_3\",\"value\":23,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Grass/Shrub\"},{\"name\":\"2_to_4\",\"value\":24,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Tree Cover\"},{\"name\":\"2_to_5\",\"value\":25,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Water\"},{\"name\":\"2_to_6\",\"value\":26,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Wetlands\"},{\"name\":\"2_to_7\",\"value\":27,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Snow/Ice\"},{\"name\":\"2_to_8\",\"value\":28,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Cropland to Barren\"},{\"name\":\"3_to_1\",\"value\":31,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Developed\"},{\"name\":\"3_to_2\",\"value\":32,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Cropland\"},{\"name\":\"3_to_4\",\"value\":34,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Tree Cover\"},{\"name\":\"3_to_5\",\"value\":35,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Water\"},{\"name\":\"3_to_6\",\"value\":36,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Wetlands\"},{\"name\":\"3_to_7\",\"value\":37,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to Snow/Ice\"},{\"name\":\"3_to_8\",\"value\":38,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Grass/Shrub to 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Developed\"},{\"name\":\"5_to_2\",\"value\":52,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Cropland\"},{\"name\":\"5_to_3\",\"value\":53,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Grass/Shrub\"},{\"name\":\"5_to_4\",\"value\":54,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Tree Cover\"},{\"name\":\"5_to_6\",\"value\":56,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Wetlands\"},{\"name\":\"5_to_7\",\"value\":57,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Snow/Ice\"},{\"name\":\"5_to_8\",\"value\":58,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Water to Barren\"},{\"name\":\"6_to_1\",\"value\":61,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to Developed\"},{\"name\":\"6_to_2\",\"value\":62,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Wetlands to 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Grass/Shrub\"},{\"name\":\"7_to_4\",\"value\":74,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Tree Cover\"},{\"name\":\"7_to_5\",\"value\":75,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Water\"},{\"name\":\"7_to_6\",\"value\":76,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Wetlands\"},{\"name\":\"7_to_8\",\"value\":78,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Snow/Ice to Barren\"},{\"name\":\"8_to_1\",\"value\":81,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Developed\"},{\"name\":\"8_to_2\",\"value\":82,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Cropland\"},{\"name\":\"8_to_3\",\"value\":83,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Grass/Shrub\"},{\"name\":\"8_to_4\",\"value\":84,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Tree Cover\"},{\"name\":\"8_to_5\",\"value\":85,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Water\"},{\"name\":\"8_to_6\",\"value\":86,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Wetlands\"},{\"name\":\"8_to_7\",\"value\":87,\"color_hint\":\"ab00d6\",\"description\":\"Cover change from Barren to Snow/Ice\"}]},\"sclast\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Time Since Last Change\",\"description\":\"Time, in days, since the last identified Spectral Change (SCTIME).\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"scstab\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Spectral Stability Period\",\"description\":\"Measure of the amount of time in days that a pixel has been in its current spectral state as of July 1st. Current spectral state can refer to both during stable time series segments or a period outside of stable time series segments.\",\"raster:bands\":[{\"sampling\":\"area\",\"data_type\":\"uint16\",\"spatial_resolution\":30}]},\"sctime\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Time of Spectral Change\",\"description\":\"Represents the timing of a spectral 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Primary Land Cover Confidence\",\"description\":\"Provides provenance tracking and a measure of confidence that the Primary Land Cover label matches the training 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 of confidence that the Primary Land Cover label matches the training data.\"},{\"values\":[151],\"summary\":\"Time series model identified as transition from a Grass/Shrub class to a Tree Cover class. Primary Land Cover class assignment based on secondary analysis.\"},{\"values\":[152],\"summary\":\"Time series model identified as transition from a Tree Cover class to a Grass/Shrub class. Primary Land cover class assignment based on secondary analysis.\"},{\"values\":[201],\"summary\":\"No stable time series models were produced for this location. Primary Land Cover was assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP Level 1 classification schema, see LCMAP Science Product Guide for more information).\"},{\"values\":[202],\"summary\":\"Insufficient data available to extend most recent time series model past July 1st of current year. Land cover assigned the last identified cover class from earlier year.\"},{\"values\":[211],\"summary\":\"July 1st falls in a gap between two stable time series models of the same land cover class. Primary Land Cover assigned the primary land cover class of those before/after models.\"},{\"values\":[212],\"summary\":\"July 1st falls in a gap between two stable time series models of differing land cover class. If July 1st is before the 'break date' of the earlier model, Primary Land Cover is assigned the primary land cover class of that earlier model. Otherwise, Primary Land Cover is assigned the primary land cover class of the subsequent, later model.\"},{\"values\":[213],\"summary\":\"Insufficient data available to establish a stable time series model at the beginning of the time series prior to July 1st of the current year. Primary Land Cover assigned the primary land cover class of 1st subsequent model.\"},{\"values\":[214],\"summary\":\"Insufficient data available to establish a new stable time series model following a break near the end of the time series prior to July 1st of the current year. Primary Land Cover assigned the last identified primary land cover class from earlier year.\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcsconf\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Secondary Land Cover Confidence\",\"description\":\"Provides provenance tracking and a measure of confidence that the Secondary Land Cover label matches the training 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 of confidence that the Secondary Land Cover label matches the training data.\"},{\"values\":[151],\"summary\":\"Time series model identified as transition from a Grass/Shrub class to a Tree Cover class in Primary Land Cover. Primary Land Cover class assignment based on secondary analysis and Secondary Land Cover class assigned logical opposite of Primary.\"},{\"values\":[152],\"summary\":\"Time series model identified as transition from a Tree Cover class to a Grass/Shrub class in Primary Land Cover. Primary Land Cover class assignment based on secondary analysis and Secondary Land Cover class assigned logical opposite of Primary.\"},{\"values\":[201],\"summary\":\"No stable time series models were produced for this location. Secondary Land Cover assigned the land cover class present in NLCD-2001 (cross-walked to LCMAP classification schema, see LCMAP Science Product Guide for more information).\"},{\"values\":[202],\"summary\":\"Insufficient data available to extend most recent time series model past July 1st of current year. Secondary Land Cover assigned the last identified secondary cover class from earlier year.\"},{\"values\":[211],\"summary\":\"July 1st falls in a gap between two stable time series models of the same secondary land cover class. Secondary Land Cover assigned the land cover class of those before/after models.\"},{\"values\":[212],\"summary\":\"July 1st falls in a gap between two stable time series models of differing secondary land cover classes. If July 1st is before the 'break date' of the earlier model, Secondary Land Cover is assigned the secondary land cover class of the earlier model. Otherwise, Secondary Land Cover is assigned the secondary land cover class of the subsequent model.\"},{\"values\":[213],\"summary\":\"Insufficient data available to establish a stable time series model at the beginning of the time series prior to July 1st of the current year. Secondary Land Cover assigned the secondary land cover class of 1st subsequent model.\"},{\"values\":[214],\"summary\":\"Insufficient data available to establish a new stable time series model following a break near the end of the time series prior to July 1st of the current year. Secondary Land Cover assigned the last identified secondary cover class from earlier year.\"}],\"raster:bands\":[{\"nodata\":0,\"sampling\":\"area\",\"data_type\":\"uint8\",\"spatial_resolution\":30}]},\"lcpri_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Primary Land Cover Metadata\",\"description\":\"Primary Land Cover product metadata\"},\"lcsec_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Secondary Land Cover Metadata\",\"description\":\"Secondary Land Cover product metadata\"},\"scmag_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Change Magnitude Metadata\",\"description\":\"Change Magnitude product metadata\"},\"scmqa_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Model Quality Metadata\",\"description\":\"Model Quality product metadata\"},\"lcachg_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Annual Land Cover Change Metadata\",\"description\":\"Annual Land Cover Change product metadata\"},\"sclast_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Time Since Last Change Metadata\",\"description\":\"Time Since Last Change product metadata\"},\"scstab_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Spectral Stability Period Metadata\",\"description\":\"Spectral Stability Period product metadata\"},\"sctime_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Time of Spectral Change Metadata\",\"description\":\"Time of Spectral Change product metadata\"},\"lcpconf_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Primary Land Cover Confidence Metadata\",\"description\":\"Primary Land Cover Confidence product metadata\"},\"lcsconf_metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Secondary Land Cover Confidence Metadata\",\"description\":\"Secondary Land Cover Confidence product metadata\"}},\"sci:citation\":\"U.S. Geological Survey (USGS), 2022, Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 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, C., Xian, G., Wellington, D., Smith, K., Horton, J., & Zhou, Q., 2022, Development of the LCMAP annual land cover product across Hawaii: International Journal of Applied Earth Observation and Geoinformation, v. 113, article 103015.\"},{\"doi\":\"10.1016/j.rse.2019.111356\",\"citation\":\"Brown, J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E., Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith, K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant, A.L., Zelenak, D., Reker, R.R., and Rover, J., 2020, Lessons learned implementing an operational continuous United States national land change monitoring capability-The Land Change Monitoring, Assessment, and Projection (LCMAP) approach: Remote Sensing of Environment, v. 238, article 111356\"}],\"msft:storage_account\":\"landcoverdata\",\"msft:short_description\":\"USGS Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 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 U.S. Climate Normals Landing Page\"},{\"rel\":\"license\",\"href\":\"https://www.noaa.gov/information-technology/open-data-dissemination\",\"type\":\"text/html\",\"title\":\"NOAA Open Data Dissemination\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-tabular\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NOAA 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 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Normals\",\"Weather\",\"Surface Observations\",\"Climatology\",\"CONUS\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"NOAA 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 [NOAA United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals) provide information about typical climate conditions for thousands of weather station locations across the United States. Normals act both as a ruler to compare current weather and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables for each weather station. \\n\\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \\n\\nThis Collection contains tabular weather variable data at weather station locations in GeoParquet format, converted from the source CSV files. The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\\nData are provided for annual/seasonal, monthly, daily, and hourly frequencies for the following time periods:\\n\\n- Legacy 30-year normals (1981\u20132010)\\n- Supplemental 15-year normals (2006\u20132020)\\n\",\"item_assets\":{\"geoparquet\":{\"type\":\"application/x-parquet\",\"roles\":[\"data\"],\"title\":\"Dataset root\",\"table:storage_options\":{\"account_name\":\"noaanormals\"}}},\"msft:region\":\"eastus\",\"stac_version\":\"1.0.0\",\"table:tables\":[{\"name\":\"1981_2010-hourly\",\"description\":\"Hourly Climate Normals for Period 1981-2010\"},{\"name\":\"1981_2010-daily\",\"description\":\"Daily Climate Normals for Period 1981-2010\"},{\"name\":\"1981_2010-monthly\",\"description\":\"Monthly Climate Normals for Period 1981-2010\"},{\"name\":\"1981_2010-annualseasonal\",\"description\":\"Annual/Seasonal Climate Normals for Period 1981-2010\"},{\"name\":\"1991_2020-hourly\",\"description\":\"Hourly Climate Normals for Period 1991-2020\"},{\"name\":\"1991_2020-daily\",\"description\":\"Daily Climate Normals for Period 1991-2020\"},{\"name\":\"1991_2020-monthly\",\"description\":\"Monthly Climate Normals for Period 1991-2020\"},{\"name\":\"1991_2020-annualseasonal\",\"description\":\"Annual/Seasonal Climate Normals for Period 1991-2020\"},{\"name\":\"2006_2020-hourly\",\"description\":\"Hourly Climate Normals for Period 2006-2020\"},{\"name\":\"2006_2020-daily\",\"description\":\"Daily Climate Normals for Period 2006-2020\"},{\"name\":\"2006_2020-monthly\",\"description\":\"Monthly Climate Normals for Period 2006-2020\"},{\"name\":\"2006_2020-annualseasonal\",\"description\":\"Annual/Seasonal 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, A., I. Durre, S. Applequist, R. Vose, M. Squires, X. Yin, R. Heim, and T. Owen, 2012: NOAA's 1981-2010 climate normals: An overview. Bull. Amer. Meteor. Soc., 93, 1687-1697. DOI: 10.1175/BAMS-D-11-00197.1.\"},{\"doi\":\"10.1175/BAMS-D-11-00173.1\",\"citation\":\"Applequist, S., A. Arguez, I. Durre, M. Squires, R. Vose, and X. Yin, 2012: 1981-2010 U.S. Hourly Normals. Bulletin of the American Meteorological Society, 93, 1637-1640. DOI: 10.1175/BAMS-D-11-00173.1.\"}],\"msft:storage_account\":\"noaanormals\",\"msft:short_description\":\"Tabular Climate Normal data for over 15,000 United States weather station locations 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 U.S. Climate Normals Landing Page\"},{\"rel\":\"license\",\"href\":\"https://www.noaa.gov/information-technology/open-data-dissemination\",\"type\":\"text/html\",\"title\":\"NOAA 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 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 for Monthly Gridded Normals\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NOAA 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 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Normals\",\"Weather\",\"Surface Observations\",\"Climatology\",\"CONUS\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"NOAA 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 [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\\n\\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \\n\\nThe data in this Collection are the original NetCDF files provided by NOAA's National Centers for Environmental Information. This Collection contains gridded data for the following frequencies and time periods:\\n\\n- Annual, seasonal, and monthly normals\\n - 100-year (1901\u20132000)\\n - 30-year (1991\u20132020)\\n \ - 15-year (2006\u20132020)\\n- Daily normals\\n - 30-year (1991\u20132020)\\n \ - 15-year (2006\u20132020)\\n\\nFor most use-cases, we recommend using the [`noaa-climate-normals-gridded`](https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded) collection, which contains the same data in Cloud Optimized GeoTIFF format. The NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (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 Climate Normals for the contiguous United States in the original NetCDF format 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 Landing Page\"},{\"rel\":\"about\",\"href\":\"https://www.goes-r.gov/users/docs/PUG-main-vol1.pdf\",\"type\":\"application/pdf\",\"title\":\"Product 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 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 readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"GOES-R 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 GLM Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/goes-glm.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 [Geostationary Lightning Mapper (GLM)](https://www.goes-r.gov/spacesegment/glm.html) is a single-channel, near-infrared optical transient detector that can detect the momentary changes in an optical scene, indicating the presence of lightning. GLM measures total lightning (in-cloud, cloud-to-cloud and cloud-to-ground) activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. GLM collects information such as the frequency, location and extent of lightning discharges to identify intensifying thunderstorms and tropical cyclones. Trends in total lightning available from the GLM provide critical information to forecasters, allowing them to focus on developing severe storms much earlier and before these storms produce damaging winds, hail or even tornadoes.\\n\\nThe GLM data product consists of a hierarchy of earth-located lightning radiant energy measures including events, groups, and flashes:\\n\\n- Lightning events are detected by the instrument.\\n- Lightning groups are a collection of one or more lightning events that satisfy temporal and spatial coincidence thresholds.\\n- Similarly, lightning flashes are a collection of one or more lightning groups that satisfy temporal and spatial coincidence thresholds.\\n\\nThe product includes the relationship among lightning events, groups, and flashes, and the area coverage of lightning groups and flashes. The product also includes processing and data quality metadata, and satellite state and location information. \\n\\nThe NetCDF files are delivered 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 NetCDF4 file\"}},\"msft:region\":\"westeurope\",\"sci:citation\":\"GOES-R Algorithm Working Group and GOES-R Series Program, (2018): NOAA GOES-R Series Geostationary Lightning Mapper (GLM) Level 2 Lightning Detection: Events, Groups, and Flashes. NOAA National Centers for Environmental Information. 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 lightning detection over the Western Hemisphere from the Geostationary Lightning 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 Sentinel data terms\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel 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 1 GRD\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-1-grd.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) mission is a constellation of two polar-orbiting satellites, operating day and night performing C-band synthetic aperture radar imaging. The Level-1 Ground Range Detected (GRD) products in this Collection consist of focused SAR data that has been detected, multi-looked and projected to ground range using the Earth ellipsoid model WGS84. The ellipsoid projection of the GRD products is corrected using the terrain height specified in the product general annotation. The terrain height used varies in azimuth but is constant in range (but can be different for each IW/EW sub-swath).\\n\\nGround range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth. Pixel values represent detected amplitude. Phase information is lost. The resulting product has approximately square resolution pixels and square pixel spacing with reduced speckle at a cost of reduced spatial resolution.\\n\\nFor the IW and EW GRD products, multi-looking is performed on each burst individually. All bursts in all sub-swaths are then seamlessly merged to form a single, contiguous, ground range, detected image per polarization.\\n\\nFor more information see the [ESA documentation](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-types-processing-levels/level-1)\\n\\n### Terrain Correction\\n\\nUsers might want to geometrically or radiometrically terrain correct the Sentinel-1 GRD data from this collection. The [Sentinel-1-RTC Collection](https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc) collection is a global radiometrically terrain corrected dataset derived from Sentinel-1 GRD. Additionally, users can terrain-correct on the fly using [any DEM available on the Planetary Computer](https://planetarycomputer.microsoft.com/catalog?tags=DEM). See [Customizable radiometric terrain correction](https://planetarycomputer.microsoft.com/docs/tutorials/customizable-rtc-sentinel1/) for more.\",\"item_assets\":{\"hh\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"HH: horizontal transmit, horizontal receive\",\"description\":\"Amplitude of signal transmitted with horizontal polarization and received with horizontal polarization with radiometric terrain correction applied.\"},\"hv\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"HV: horizontal transmit, vertical receive\",\"description\":\"Amplitude of signal transmitted with horizontal polarization and received with vertical polarization with radiometric terrain correction applied.\"},\"vh\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VH: vertical transmit, horizontal receive\",\"description\":\"Amplitude of signal transmitted with vertical polarization and received with horizontal polarization with radiometric terrain correction applied.\"},\"vv\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VV: vertical transmit, vertical receive\",\"description\":\"Amplitude of signal transmitted with vertical polarization and received with vertical polarization with radiometric terrain correction applied.\"},\"thumbnail\":{\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"Preview Image\",\"description\":\"An averaged, decimated preview image in PNG format. Single polarisation products are represented with a grey scale image. Dual polarisation products are represented by a single composite colour image in RGB with the red channel (R) representing the co-polarisation VV or HH), the green channel (G) represents the cross-polarisation (VH or HV) and the blue channel (B) represents the ratio of the cross an co-polarisations.\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Manifest File\",\"description\":\"General product metadata in XML format. Contains a high-level textual description of the product and references to all of product's components, the product metadata, including the product identification and the resource references, and references to the physical location of each component file contained in the product.\"},\"schema-noise-hh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Noise Schema\",\"description\":\"Estimated thermal noise look-up tables\"},\"schema-noise-hv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Noise Schema\",\"description\":\"Estimated thermal noise look-up tables\"},\"schema-noise-vh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Noise Schema\",\"description\":\"Estimated thermal noise look-up tables\"},\"schema-noise-vv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Noise Schema\",\"description\":\"Estimated thermal noise look-up tables\"},\"schema-product-hh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product Schema\",\"description\":\"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc.\"},\"schema-product-hv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product Schema\",\"description\":\"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc.\"},\"schema-product-vh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product Schema\",\"description\":\"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc.\"},\"schema-product-vv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Product Schema\",\"description\":\"Describes the main characteristics corresponding to the band: state of the platform during acquisition, image properties, Doppler information, geographic location, etc.\"},\"schema-calibration-hh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Calibration Schema\",\"description\":\"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration.\"},\"schema-calibration-hv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Calibration Schema\",\"description\":\"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration.\"},\"schema-calibration-vh\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Calibration Schema\",\"description\":\"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables that can be used for absolute product calibration.\"},\"schema-calibration-vv\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"title\":\"Calibration Schema\",\"description\":\"Calibration metadata including calibration information and the beta nought, sigma nought, gamma and digital number look-up tables 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 Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth 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 U.S. Climate Normals Landing Page\"},{\"rel\":\"license\",\"href\":\"https://www.noaa.gov/information-technology/open-data-dissemination\",\"type\":\"text/html\",\"title\":\"NOAA 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 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 for Monthly Gridded Normals\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/noaa-climate-normals-gridded\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NOAA 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 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Normals\",\"Weather\",\"Surface Observations\",\"Climatology\",\"CONUS\"],\"providers\":[{\"url\":\"https://www.ncei.noaa.gov/\",\"name\":\"NOAA 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 [NOAA Gridded United States Climate Normals](https://www.ncei.noaa.gov/products/land-based-station/us-climate-normals#tab-1027) provide a continuous grid of temperature and precipitation data across the contiguous United States (CONUS). The grids are derived from NOAA's [NClimGrid dataset](https://planetarycomputer.microsoft.com/dataset/group/noaa-nclimgrid), and resolutions (nominal 5x5 kilometer) and spatial extents (CONUS) therefore match that of NClimGrid. Monthly, seasonal, and annual gridded normals are computed from simple averages of the NClimGrid data and are provided for three time-periods: 1901\u20132020, 1991\u20132020, and 2006\u20132020. Daily gridded normals are smoothed for a smooth transition from one day to another and are provided for two time-periods: 1991\u20132020, and 2006\u20132020.\\n\\nNOAA produces Climate Normals in accordance with the [World Meteorological Organization](https://public.wmo.int/en) (WMO), of which the United States is a member. The WMO requires each member nation to compute 30-year meteorological quantity averages at least every 30 years, and recommends an update each decade, in part to incorporate newer weather stations. The 1991\u20132020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. \\n\\nThis Collection contains gridded data for the following frequencies and time periods:\\n\\n- Annual, seasonal, and monthly normals\\n - 100-year (1901\u20132000)\\n \ - 30-year (1991\u20132020)\\n - 15-year (2006\u20132020)\\n- Daily normals\\n - 30-year (1991\u20132020)\\n - 15-year (2006\u20132020)\\n\\nThe data in this Collection have been converted from the original NetCDF format to Cloud Optimized GeoTIFFs (COGs). The source NetCDF files are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\n\\n## STAC Metadata\\n\\nThe STAC items in this collection contain several custom fields that can be used to further filter the data.\\n\\n* `noaa_climate_normals:period`: Climate normal time period. This can be \\\"1901-2000\\\", \\\"1991-2020\\\", or \\\"2006-2020\\\".\\n* `noaa_climate_normals:frequency`: Climate normal temporal interval (frequency). This can be \\\"daily\\\", \\\"monthly\\\", \\\"seasonal\\\" , or \\\"annual\\\"\\n* `noaa_climate_normals:time_index`: Time step index, e.g., month of year (1-12).\\n\\nThe `description` field of the assets varies by frequency. Using `prcp_norm` as an example, the descriptions are\\n\\n* annual: \\\"Annual precipitation normals from monthly precipitation normal values\\\"\\n* seasonal: \\\"Seasonal precipitation normals (WSSF) from monthly normals\\\"\\n* monthly: \\\"Monthly precipitation normals from monthly precipitation values\\\"\\n* daily: \\\"Precipitation normals from daily averages\\\"\\n\\nCheck the assets on individual items for the appropriate description.\\n\\nThe STAC keys for most assets consist of two abbreviations. A \\\"variable\\\":\\n\\n\\n| Abbreviation | Description |\\n| ------------ | ---------------------------------------- |\\n| prcp | Precipitation over the time period |\\n| tavg | Mean temperature over the time period |\\n| tmax | Maximum temperature over the time period |\\n| tmin | Minimum temperature over the time period |\\n\\nAnd an \\\"aggregation\\\":\\n\\n| Abbreviation | Description \ |\\n| ------------ | ------------------------------------------------------------------------------ |\\n| max | Maximum of the variable over the time period |\\n| min | Minimum of the variable over the time period |\\n| std | Standard deviation of the value over the time period |\\n| flag | An count of the number of inputs (months, years, etc.) to calculate the normal |\\n| norm | The normal for the variable over the time period |\\n\\nSo, for example, `prcp_max` for monthly data is the \\\"Maximum values of all input monthly precipitation normal values\\\".\\n\",\"item_assets\":{\"prcp_max\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"prcp_min\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"prcp_std\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_max\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_min\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_std\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_max\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_min\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_std\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_max\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_min\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_std\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"prcp_flag\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"months\",\"nodata\":0,\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"prcp_norm\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_flag\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"months\",\"nodata\":0,\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tavg_norm\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_flag\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"months\",\"nodata\":0,\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmax_norm\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_flag\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"months\",\"nodata\":0,\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"tmin_norm\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"m2dprcp_norm\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"description\":\"Month-To-Day Precipitation normals\",\"raster:bands\":[{\"unit\":\"millimeter\",\"nodata\":\"nan\",\"data_type\":\"float32\",\"spatial_resolution\":5000}]},\"y2dprcp_norm\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"description\":\"Year-To-Day 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 Climate Normals for the contiguous United States in Cloud Optimized GeoTIFF (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 Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/aster-l1t\",\"type\":\"text/html\",\"title\":\"Human readable dataset overview and reference\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/aster-l1t\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"ASTER L1T\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/aster.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"ASTER L1T\"},\"geoparquet-items\":{\"href\":\"abfs://items/aster-l1t.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 yellow/green\",\"center_wavelength\":0.56,\"full_width_half_max\":0.08},{\"gsd\":15,\"name\":\"VNIR_Band2\",\"common_name\":\"red\",\"description\":\"visible red\",\"center_wavelength\":0.66,\"full_width_half_max\":0.06},{\"gsd\":15,\"name\":\"VNIR_Band3N\",\"common_name\":\"nir08\",\"description\":\"near infrared\",\"center_wavelength\":0.82,\"full_width_half_max\":0.08},{\"gsd\":30,\"name\":\"SWIR_Band4\",\"common_name\":\"swir16\",\"description\":\"short-wave infrared\",\"center_wavelength\":1.65,\"full_width_half_max\":0.1},{\"gsd\":30,\"name\":\"SWIR_Band5\",\"common_name\":\"swir22\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.165,\"full_width_half_max\":0.04},{\"gsd\":30,\"name\":\"SWIR_Band6\",\"common_name\":\"swir22\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.205,\"full_width_half_max\":0.04},{\"gsd\":30,\"name\":\"SWIR_Band7\",\"common_name\":\"swir22\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.26,\"full_width_half_max\":0.05},{\"gsd\":30,\"name\":\"SWIR_Band8\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.339,\"full_width_half_max\":0.07},{\"gsd\":30,\"name\":\"SWIR_Band9\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.395,\"full_width_half_max\":0.07},{\"gsd\":90,\"name\":\"TIR_Band10\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":8.3,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band11\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":8.65,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band12\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":9.11,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band13\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":10.6,\"full_width_half_max\":0.7},{\"gsd\":90,\"name\":\"TIR_Band14\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":11.3,\"full_width_half_max\":0.7}],\"platform\":[\"terra\"],\"instruments\":[\"aster\"]},\"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\",\"item_assets\":{\"TIR\":{\"roles\":[\"data\"],\"title\":\"TIR Swath data\",\"eo:bands\":[{\"gsd\":90,\"name\":\"TIR_Band10\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":8.3,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band11\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":8.65,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band12\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":9.11,\"full_width_half_max\":0.35},{\"gsd\":90,\"name\":\"TIR_Band13\",\"common_name\":\"lwir\",\"description\":\"thermal infrared\",\"center_wavelength\":10.6,\"full_width_half_max\":0.7},{\"gsd\":90,\"name\":\"TIR_Band14\",\"common_name\":\"lwir\",\"description\":\"thermal 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 metadata\"},\"SWIR\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SWIR Swath data\",\"eo:bands\":[{\"gsd\":30,\"name\":\"SWIR_Band4\",\"common_name\":\"swir\",\"description\":\"short-wave infrared\",\"center_wavelength\":1.65,\"full_width_half_max\":0.1},{\"gsd\":30,\"name\":\"SWIR_Band5\",\"common_name\":\"swir\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.165,\"full_width_half_max\":0.04},{\"gsd\":30,\"name\":\"SWIR_Band6\",\"common_name\":\"swir\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.205,\"full_width_half_max\":0.04},{\"gsd\":30,\"name\":\"SWIR_Band7\",\"common_name\":\"swir\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.26,\"full_width_half_max\":0.05},{\"gsd\":30,\"name\":\"SWIR_Band8\",\"common_name\":\"swir\",\"description\":\"short-wave infrared\",\"center_wavelength\":2.339,\"full_width_half_max\":0.07},{\"gsd\":30,\"name\":\"SWIR_Band9\",\"common_name\":\"swir\",\"description\":\"short-wave 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VNIR Swath data\",\"eo:bands\":[{\"gsd\":15,\"name\":\"VNIR_Band1\",\"common_name\":\"yellow/green\",\"description\":\"visible yellow/green\",\"center_wavelength\":0.56,\"full_width_half_max\":0.08},{\"gsd\":15,\"name\":\"VNIR_Band2\",\"common_name\":\"red\",\"description\":\"visible red\",\"center_wavelength\":0.66,\"full_width_half_max\":0.06},{\"gsd\":15,\"name\":\"VNIR_Band3N\",\"common_name\":\"near 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 browse file\",\"description\":\"Geometric quality assessment report.\"},\"qa-browse\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"title\":\"QA browse file\",\"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.\"},\"tir-browse\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"title\":\"Standalone reduced resolution TIR\"},\"vnir-browse\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"title\":\"VNIR 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 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.\",\"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 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 homepage\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"CIL 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 Impact Lab\",\"Rhodium Group\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://impactlab.org/\",\"name\":\"Climate 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 World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\\n\\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\\n\\n## Accessing the data\\n\\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\n* [Attribution-ShareAlike (CC BY SA 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa)\\n\\nEach modeling center with bias corrected and downscaled data in this collection 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) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by-sa#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\\n\\n## Data format & contents\\n\\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 179MB in memory.\\n\\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\\n\\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201Clon\u201D coordinate extends from -179.875 to 179.875, and the \u201Clat\u201D coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\n\\n## Available institutions, models, and scenarios by license collection\\n\\n| Modeling institution | Source model | Available experiments \ | License collection |\\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 \ | Public domain datasets |\\n| INM | INM-CM4-8 \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| BCC | BCC-CSM2-MR \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \ | CC-BY-40] |\\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40] |\\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40] |\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40] |\\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] \ |\\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40] \ |\\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] \ |\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40] |\\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] \ |\\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\n| NOAA-GFDL | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40] \ |\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40] |\\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40] \ |\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\\n| EC-Earth-Consortium \ | EC-Earth3-AerChem | ssp370 | CC-BY-40] \ |\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40] |\\n| EC-Earth-Consortium \ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] \ |\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40] |\\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-SA-40] \ |\\n\\n*Notes:*\\n\\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\\n\\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\\n\\n## Project methods\\n\\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\\n\\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\\n\\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\\n\\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\\n\\n## Citing, licensing, and using data produced by this project\\n\\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\\n\\n### CIL GDPCIR\\n\\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\\n\\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \\n\\nThe code repository may be cited using the following:\\n\\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\n\\n### ERA5\\n\\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, 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> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\n>\\n> Mu\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n>\\n> Mu\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n\\n### GCM-specific citations & licenses\\n\\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\\n\\n#### Public Domain Datasets\\n\\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\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 \ CMIP Citation:\\n\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\\n\\n ScenarioMIP Citation:\\n\\n \ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\n\\n\\n* **INM-CM5-0**\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\n\\n\\n#### CC-BY-4.0\\n\\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\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 \ CMIP Citation:\\n\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\n\\n ScenarioMIP Citation:\\n\\n \ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\\n\\n\\n* **ACCESS-ESM1-5**\\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 \ CMIP Citation:\\n\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\n\\n ScenarioMIP Citation:\\n\\n \ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\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 \ CMIP Citation:\\n\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\n\\n \ ScenarioMIP Citation:\\n\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\n\\n ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\n\\n \ ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\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 \ CMIP Citation:\\n\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\\n\\n ScenarioMIP Citation:\\n\\n \ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\n\\n\\n* **GFDL-ESM4**\\n\\n \ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\n\\n \ CMIP Citation:\\n\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\n\\n ScenarioMIP Citation:\\n\\n \ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\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 \ CMIP Citation:\\n\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\\n\\n ScenarioMIP Citation:\\n\\n \ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\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 \ CMIP Citation:\\n\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\n\\n \ ScenarioMIP Citation:\\n\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\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 \ CMIP Citation:\\n\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\n\\n ScenarioMIP Citation:\\n\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\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 \ CMIP Citation:\\n\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\n\\n \ ScenarioMIP Citation:\\n\\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\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 \ CMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\n\\n \ ScenarioMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\n\\n\\n* **NESM3**\\n\\n \ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\n\\n \ CMIP Citation:\\n\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\\n\\n ScenarioMIP Citation:\\n\\n \ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\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 \ CMIP Citation:\\n\\n > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\\n\\n ScenarioMIP Citation:\\n\\n \ > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\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 \ CMIP Citation:\\n\\n > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\\n\\n ScenarioMIP Citation:\\n\\n \ > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\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 \ CMIP Citation:\\n\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\n\\n \ ScenarioMIP Citation:\\n\\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\n\\n\\n#### CC-BY-SA-4.0\\n\\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/). Note that this license requires citation of the source model output (included here) and requires that derived works be shared under the same license. Please see https://creativecommons.org/licenses/by-sa/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by-sa.\\n\\n* **CanESM5**\\n\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt)\\n\\n \ CMIP Citation:\\n\\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\\n\\n ScenarioMIP Citation:\\n\\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\n\\n## Acknowledgements\\n\\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\\n\\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\\n\\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\\n\\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\n\\n## Financial support\\n\\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\\n\\n## Additional links:\\n\\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\n* Climate 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 Maximum Near-Surface Air Temperature\",\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily Minimum Near-Surface Air Temperature\",\"description\":\"Daily Minimum Near-Surface Air Temperature\"}},\"stac_version\":\"1.0.0\",\"msft:group_id\":\"cil-gdpcir\",\"cube:variables\":{\"pr\":{\"type\":\"data\",\"unit\":\"mm day-1\",\"attrs\":{\"units\":\"mm day-1\"},\"dimensions\":[\"time\",\"lat\",\"lon\"]},\"tasmax\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')\",\"long_name\":\"Daily Maximum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area: mean time: maximum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMX\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily Maximum Near-Surface Air 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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 Impact Lab Global Downscaled Projections for Climate Impacts Research (CC-BY-SA-4.0)\",\"msft:region\":\"westeurope\"},{\"id\":\"naip\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip/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/naip\"},{\"rel\":\"license\",\"href\":\"https://www.fsa.usda.gov/help/policies-and-links/\",\"title\":\"Public Domain\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/naip\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"NAIP: National Agriculture Imagery Program\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/naip.png\",\"type\":\"image/png\",\"roles\":[\"thumbnail\"],\"title\":\"NAIP thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/naip.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-124.784,24.744,-66.951,49.346],[-156.003,19.059,-154.809,20.127],[-67.316,17.871,-65.596,18.565],[-64.94,17.622,-64.56,17.814]]},\"temporal\":{\"interval\":[[\"2010-01-01T00:00:00Z\",\"2023-12-31T00:00:00Z\"]]}},\"license\":\"proprietary\",\"keywords\":[\"NAIP\",\"Aerial\",\"Imagery\",\"USDA\",\"AFPO\",\"Agriculture\",\"United States\"],\"providers\":[{\"url\":\"https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/\",\"name\":\"USDA Farm Service Agency\",\"roles\":[\"producer\",\"licensor\"]},{\"url\":\"https://www.esri.com/\",\"name\":\"Esri\",\"roles\":[\"processor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\",\"processor\"]}],\"summaries\":{\"gsd\":[0.3,0.6,1.0],\"eo:bands\":[{\"name\":\"Red\",\"common_name\":\"red\",\"description\":\"visible red\"},{\"name\":\"Green\",\"common_name\":\"green\",\"description\":\"visible green\"},{\"name\":\"Blue\",\"common_name\":\"blue\",\"description\":\"visible blue\"},{\"name\":\"NIR\",\"common_name\":\"nir\",\"description\":\"near-infrared\"}]},\"description\":\"The [National Agriculture Imagery Program](https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/) (NAIP) \\nprovides U.S.-wide, high-resolution aerial imagery, with four spectral bands (R, G, B, IR). \\nNAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) \\nwithin the [US Department of Agriculture](https://www.usda.gov/) (USDA). \\nData are captured at least once every three years for each state. \\nThis dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\\nYou can visualize the coverage of current and past collections [here](https://naip-usdaonline.hub.arcgis.com/). \\n\",\"item_assets\":{\"image\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"RGBIR COG tile\",\"eo:bands\":[{\"name\":\"Red\",\"common_name\":\"red\"},{\"name\":\"Green\",\"common_name\":\"green\"},{\"name\":\"Blue\",\"common_name\":\"blue\"},{\"name\":\"NIR\",\"common_name\":\"nir\",\"description\":\"near-infrared\"}]},\"metadata\":{\"type\":\"text/plain\",\"roles\":[\"metadata\"],\"title\":\"FGDC Metdata\"},\"thumbnail\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"title\":\"Thumbnail\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:container\":\"naip\",\"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\":\"naipeuwest\",\"msft:short_description\":\"NAIP provides US-wide, high-resolution aerial imagery. This dataset includes NAIP images from 2010 to the present.\"},{\"id\":\"io-lulc-9-class\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/io-lulc-9-class/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-9-class\"},{\"rel\":\"related\",\"href\":\"https://livingatlas.arcgis.com/landcover/\"},{\"rel\":\"license\",\"href\":\"https://creativecommons.org/licenses/by/4.0/\",\"type\":\"text/html\",\"title\":\"CC BY 4.0\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"10m Annual Land Use Land Cover (9-class) V1\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-lulc-9-class.png\",\"title\":\"10m Annual Land Use Land Cover (9-class)\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/io-lulc-9-class.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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\",\"2023-01-01T00:00:00Z\"]]}},\"license\":\"CC-BY-4.0\",\"keywords\":[\"Global\",\"Land Cover\",\"Land Use\",\"Sentinel\"],\"providers\":[{\"url\":\"https://www.esri.com/\",\"name\":\"Esri\",\"roles\":[\"licensor\"]},{\"url\":\"https://www.impactobservatory.com/\",\"name\":\"Impact Observatory\",\"roles\":[\"processor\",\"producer\",\"licensor\"]},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"host\"]}],\"summaries\":{\"raster:bands\":[{\"nodata\":0,\"spatial_resolution\":10}]},\"description\":\"__Note__: _A new version of this item is available for your use. This mature version of the map remains available for use in existing applications. This item will be retired in December 2024. There is 2023 data available in the newer [9-class v2 dataset](https://planetarycomputer.microsoft.com/dataset/io-lulc-annual-v02)._\\n\\nTime series of annual global maps of land use and land cover (LULC). It currently has data from 2017-2022. The maps are derived from ESA Sentinel-2 imagery at 10m resolution. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year.\\n\\nThis dataset was generated by [Impact Observatory](http://impactobservatory.com/), who used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. The global map was produced by applying this model to the Sentinel-2 annual scene collections on the Planetary Computer. Each of the maps has an assessed average accuracy of over 75%.\\n\\nThis map uses an updated model from the [10-class model](https://planetarycomputer.microsoft.com/dataset/io-lulc) and combines Grass(formerly class 3) and Scrub (formerly class 6) into a single Rangeland class (class 11). The original Esri 2020 Land Cover collection uses 10 classes (Grass and Scrub separate) and an older version of the underlying deep learning model. The Esri 2020 Land Cover map was also produced by Impact Observatory. The map remains available for use in existing applications. New applications should use the updated version of 2020 once it is available in this collection, especially when using data from multiple years of this time series, to ensure consistent classification.\\n\\nAll years are available under a Creative Commons BY-4.0.\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Global land cover data\",\"file:values\":[{\"values\":[0],\"summary\":\"No Data\"},{\"values\":[1],\"summary\":\"Water\"},{\"values\":[2],\"summary\":\"Trees\"},{\"values\":[4],\"summary\":\"Flooded vegetation\"},{\"values\":[5],\"summary\":\"Crops\"},{\"values\":[7],\"summary\":\"Built 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 land cover information with 9 classes for 2017-2022 at 10m 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 BY 4.0\"},{\"rel\":\"about\",\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf\",\"type\":\"application/pdf\",\"title\":\"Technical White Paper\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/io-biodiversity\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Biodiversity Intactness\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/io-biodiversity-thumb.png\",\"title\":\"Biodiversity Intactness\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/io-biodiversity.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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\",\"2020-12-31T23:59:59Z\"]]}},\"license\":\"CC-BY-4.0\",\"keywords\":[\"Global\",\"Biodiversity\"],\"providers\":[{\"url\":\"https://www.impactobservatory.com/\",\"name\":\"Impact 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 by [Impact Observatory](https://www.impactobservatory.com/), in collaboration with [Vizzuality](https://www.vizzuality.com/), these datasets estimate terrestrial Biodiversity Intactness as 100-meter gridded maps for the years 2017-2020.\\n\\nMaps depicting the intactness of global biodiversity have become a critical tool for spatial planning and management, monitoring the extent of biodiversity across Earth, and identifying critical remaining intact habitat. Yet, these maps are often years out of date by the time they are available to scientists and policy-makers. The datasets in this STAC Collection build on past studies that map Biodiversity Intactness using the [PREDICTS database](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.2579) of spatially referenced observations of biodiversity across 32,000 sites from over 750 studies. The approach differs from previous work by modeling the relationship between observed biodiversity metrics and contemporary, global, geospatial layers of human pressures, with the intention of providing a high resolution monitoring product into the future.\\n\\nBiodiversity intactness is estimated as a combination of two metrics: Abundance, the quantity of individuals, and Compositional Similarity, how similar the composition of species is to an intact baseline. Linear mixed effects models are fit to estimate the predictive capacity of spatial datasets of human pressures on each of these metrics and project results spatially across the globe. These methods, as well as comparisons to other leading datasets and guidance on interpreting results, are further explained in a methods [white paper](https://ai4edatasetspublicassets.blob.core.windows.net/assets/pdfs/io-biodiversity/Biodiversity_Intactness_whitepaper.pdf) entitled \u201CGlobal 100m Projections of Biodiversity Intactness for the years 2017-2020.\u201D\\n\\nAll years are available under a Creative Commons BY-4.0 license.\\n\",\"item_assets\":{\"data\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Biodiversity Intactness\",\"description\":\"Terrestrial 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 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 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 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 readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sea 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 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Centers for Environmental Information\",\"roles\":[\"producer\",\"processor\",\"licensor\"],\"description\":\"NCEI is the Nation's leading authority for environmental data, and manage one of the largest archives of atmospheric, coastal, geophysical, and oceanic research in the world. NCEI contributes to the NESDIS mission by developing new products and services that span the science disciplines and enable better data discovery.\"},{\"url\":\"https://planetarycomputer.microsoft.com\",\"name\":\"Microsoft\",\"roles\":[\"processor\",\"host\"]}],\"description\":\"The Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with AVHRR SST observations. The final record is output to a 3-hourly 0.25\xB0 resolution grid over the global ice-free oceans from January 1988\u2014present.\\n\\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\nFor the NetCDF files, see collection `noaa-cdr-sea-surface-temperature-whoi-netcdf`.\\n\",\"item_assets\":{\"fill_missing_qc\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"sea_surface_temperature\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"raster:bands\":[{\"unit\":\"degree Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]}},\"msft:region\":\"eastus\",\"sci:citation\":\"Clayson, Carol Anne; Brown, Jeremiah; and NOAA CDR Program (2016). NOAA Climate Data Record (CDR) of Sea Surface Temperature - WHOI, Version 2. NOAA National Climatic 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 Sea Surface Temperature-Woods Hole Oceanographic Institution (WHOI) Climate Data Record (CDR) is one of three CDRs which combine to form the NOAA Ocean Surface Bundle (OSB) CDR. The resultant sea surface temperature (SST) data are produced through modeling the diurnal variability in combination with 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 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 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 readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Global 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 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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Centers for Environmental Information\",\"roles\":[\"producer\",\"processor\",\"licensor\"],\"description\":\"NCEI is the Nation's leading authority for environmental data, and manage one of the largest archives of atmospheric, coastal, geophysical, and oceanic research in the world. NCEI contributes to the NESDIS mission by developing new products 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 Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (five-yearly) scales. This CDR quantifies ocean heat content change over time, which is an essential metric for understanding climate change and the Earth's energy budget. It provides time-series for multiple depth ranges in the global ocean and each of the major basins (Atlantic, Pacific, and Indian) divided by hemisphere (Northern, Southern).\\n\\nThese Cloud Optimized GeoTIFFs (COGs) were created from NetCDF files which are delivered to Azure as part of the [NOAA Open Data Dissemination (NODD) Program](https://www.noaa.gov/information-technology/open-data-dissemination).\\nFor the NetCDF files, see collection `noaa-cdr-ocean-heat-content-netcdf`.\\n\",\"item_assets\":{\"heat_content\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Ocean Heat Content anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"10^18 joules\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_salinity\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean salinity anomalies from WOA09\",\"raster:bands\":[{\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_temperature\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean temperature anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"degrees Celsius\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_halosteric_sea_level\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean halosteric sea level anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"millimeters\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_thermosteric_sea_level\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean thermosteric sea level anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"millimeters\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]},\"mean_total_steric_sea_level\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Mean total steric sea level anomalies from WOA09\",\"raster:bands\":[{\"unit\":\"millimeters\",\"nodata\":\"nan\",\"data_type\":\"float32\"}]}},\"msft:region\":\"eastus\",\"sci:citation\":\"Levitus, Sydney; Antonov, John I.; Boyer, Tim P.; Baranova, Olga K.; Garc\xEDa, Hern\xE1n E.; Locarnini, Ricardo A.; Mishonov, Alexey V.; Reagan, James R.; [Seidov, Dan; Yarosh, Evgeney; Zweng, Melissa M. (2017). NCEI ocean heat content, temperature anomalies, salinity anomalies, thermosteric sea level anomalies, halosteric sea level anomalies, and total steric sea level anomalies from 1955 to present calculated from in situ oceanographic subsurface profile data (NCEI Accession 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 Ocean Heat Content Climate Data Record (CDR) is a set of ocean heat content anomaly (OHCA) time-series for 1955-present on 3-monthly, yearly, and pentadal (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 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 homepage\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"CIL 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 Impact Lab\",\"Rhodium Group\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://impactlab.org/\",\"name\":\"Climate 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 World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\\n\\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\\n\\n## Accessing the data\\n\\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\n\\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc0#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc0#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\\n\\n## Data format & contents\\n\\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\\n\\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\\n\\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201Clon\u201D coordinate extends from -179.875 to 179.875, and the \u201Clat\u201D coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\n\\n## Available institutions, models, and scenarios by license collection\\n\\n| Modeling institution | Source model | Available experiments \ | License collection |\\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 \ | Public domain datasets |\\n| INM | INM-CM4-8 \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| BCC | BCC-CSM2-MR \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \ | CC-BY-40 |\\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 |\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 \ |\\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 |\\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| NOAA-GFDL \ | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 |\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\n| EC-Earth-Consortium \ | EC-Earth3-AerChem | ssp370 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 |\\n| EC-Earth-Consortium \ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] \ |\\n\\n*Notes:*\\n\\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\\n\\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\\n\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \\n\\n## Project methods\\n\\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\\n\\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\\n\\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\\n\\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\\n\\n\\n## Citing, licensing, and using data produced by this project\\n\\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\\n\\n### CIL GDPCIR\\n\\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\\n\\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \\n\\nThe code repository may be cited using the following:\\n\\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\n\\n### ERA5\\n\\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, 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> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\n>\\n> Mu\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n>\\n> Mu\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n\\n### GCM-specific citations & licenses\\n\\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\\n\\n#### Public Domain Datasets\\n\\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\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 \ CMIP Citation:\\n\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\\n\\n ScenarioMIP Citation:\\n\\n \ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\n\\n\\n* **INM-CM5-0**\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\n\\n\\n#### CC-BY-4.0\\n\\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\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 \ CMIP Citation:\\n\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\n\\n ScenarioMIP Citation:\\n\\n \ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\\n\\n\\n* **ACCESS-ESM1-5**\\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 \ CMIP Citation:\\n\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\n\\n ScenarioMIP Citation:\\n\\n \ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\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 \ CMIP Citation:\\n\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\n\\n \ ScenarioMIP Citation:\\n\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\n\\n ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\n\\n \ ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\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 \ CMIP Citation:\\n\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\\n\\n ScenarioMIP Citation:\\n\\n \ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\n\\n\\n* **GFDL-ESM4**\\n\\n \ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\n\\n \ CMIP Citation:\\n\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\n\\n ScenarioMIP Citation:\\n\\n \ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\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 \ CMIP Citation:\\n\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\\n\\n ScenarioMIP Citation:\\n\\n \ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\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 \ CMIP Citation:\\n\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\n\\n \ ScenarioMIP Citation:\\n\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\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 \ CMIP Citation:\\n\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\n\\n ScenarioMIP Citation:\\n\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\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 \ CMIP Citation:\\n\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\n\\n \ ScenarioMIP Citation:\\n\\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\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 \ CMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\n\\n \ ScenarioMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\n\\n\\n* **NESM3**\\n\\n \ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\n\\n \ CMIP Citation:\\n\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\\n\\n ScenarioMIP Citation:\\n\\n \ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\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 \ CMIP Citation:\\n\\n > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\\n\\n ScenarioMIP Citation:\\n\\n \ > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\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 \ CMIP Citation:\\n\\n > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\\n\\n ScenarioMIP Citation:\\n\\n \ > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\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 \ CMIP Citation:\\n\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\n\\n \ ScenarioMIP Citation:\\n\\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; 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). Note: this dataset was previously licensed\\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\\n\\n CMIP Citation:\\n\\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\\n\\n ScenarioMIP Citation:\\n\\n \ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\n\\n## Acknowledgements\\n\\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\\n\\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\\n\\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\\n\\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\n\\n## Financial support\\n\\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\\n\\n## Additional links:\\n\\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\n* Climate 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 Maximum Near-Surface Air Temperature\",\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily Minimum Near-Surface Air Temperature\",\"description\":\"Daily Minimum Near-Surface Air Temperature\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"cil-gdpcir\",\"cube:variables\":{\"pr\":{\"type\":\"data\",\"unit\":\"mm day-1\",\"attrs\":{\"units\":\"mm day-1\"},\"dimensions\":[\"time\",\"lat\",\"lon\"]},\"tasmax\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')\",\"long_name\":\"Daily Maximum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area: mean time: maximum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMX\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')\",\"long_name\":\"Daily Minimum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area: mean time: minimum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMN\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily 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 Impact Lab Global Downscaled Projections for Climate Impacts Research (CC0-1.0)\"},{\"id\":\"cil-gdpcir-cc-by\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by/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\"},{\"rel\":\"license\",\"href\":\"https://spdx.org/licenses/CC-BY-4.0.html\",\"type\":\"text/html\",\"title\":\"Creative Commons Attribution 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 homepage\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"CIL Global Downscaled Projections for Climate Impacts Research (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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Impact Lab\",\"Rhodium Group\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://impactlab.org/\",\"name\":\"Climate 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 World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\\n\\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\\n\\n## Accessing the data\\n\\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\n\\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\\n\\n## Data format & contents\\n\\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\\n\\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\\n\\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201Clon\u201D coordinate extends from -179.875 to 179.875, and the \u201Clat\u201D coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\n\\n## Available institutions, models, and scenarios by license collection\\n\\n| Modeling institution | Source model | Available experiments \ | License collection |\\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 \ | Public domain datasets |\\n| INM | INM-CM4-8 \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| BCC | BCC-CSM2-MR \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \ | CC-BY-40 |\\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 |\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 \ |\\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 |\\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| NOAA-GFDL \ | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 |\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\n| EC-Earth-Consortium \ | EC-Earth3-AerChem | ssp370 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 |\\n| EC-Earth-Consortium \ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] \ |\\n\\n*Notes:*\\n\\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\\n\\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\\n\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \\n\\n## Project methods\\n\\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\\n\\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\\n\\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\\n\\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\\n\\n## Citing, licensing, and using data produced by this project\\n\\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\\n\\n### CIL GDPCIR\\n\\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\\n\\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \\n\\nThe code repository may be cited using the following:\\n\\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\n\\n### ERA5\\n\\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, 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> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\n>\\n> Mu\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n>\\n> Mu\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n\\n### GCM-specific citations & licenses\\n\\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\\n\\n#### Public Domain Datasets\\n\\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\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 \ CMIP Citation:\\n\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\\n\\n ScenarioMIP Citation:\\n\\n \ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\n\\n\\n* **INM-CM5-0**\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\n\\n\\n#### CC-BY-4.0\\n\\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\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 \ CMIP Citation:\\n\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\n\\n ScenarioMIP Citation:\\n\\n \ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\\n\\n\\n* **ACCESS-ESM1-5**\\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 \ CMIP Citation:\\n\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\n\\n ScenarioMIP Citation:\\n\\n \ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\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 \ CMIP Citation:\\n\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\n\\n \ ScenarioMIP Citation:\\n\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\n\\n ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\n\\n \ ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\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 \ CMIP Citation:\\n\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\\n\\n ScenarioMIP Citation:\\n\\n \ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\n\\n\\n* **GFDL-ESM4**\\n\\n \ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\n\\n \ CMIP Citation:\\n\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\n\\n ScenarioMIP Citation:\\n\\n \ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\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 \ CMIP Citation:\\n\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\\n\\n ScenarioMIP Citation:\\n\\n \ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\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 \ CMIP Citation:\\n\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\n\\n \ ScenarioMIP Citation:\\n\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\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 \ CMIP Citation:\\n\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\n\\n ScenarioMIP Citation:\\n\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\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 \ CMIP Citation:\\n\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\n\\n \ ScenarioMIP Citation:\\n\\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\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 \ CMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\n\\n \ ScenarioMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\n\\n\\n* **NESM3**\\n\\n \ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\n\\n \ CMIP Citation:\\n\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\\n\\n ScenarioMIP Citation:\\n\\n \ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\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 \ CMIP Citation:\\n\\n > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\\n\\n ScenarioMIP Citation:\\n\\n \ > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\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 \ CMIP Citation:\\n\\n > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\\n\\n ScenarioMIP Citation:\\n\\n \ > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\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 \ CMIP Citation:\\n\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\n\\n \ ScenarioMIP Citation:\\n\\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\n\\n* **CanESM5**\\n\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\\n\\n CMIP Citation:\\n\\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\\n\\n ScenarioMIP Citation:\\n\\n \ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\n\\n## Acknowledgements\\n\\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\\n\\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\\n\\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\\n\\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\n\\n## Financial support\\n\\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\\n\\n## Additional links:\\n\\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\n* Climate 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 Maximum Near-Surface Air Temperature\",\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily Minimum Near-Surface Air Temperature\",\"description\":\"Daily Minimum Near-Surface Air Temperature\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"cil-gdpcir\",\"cube:variables\":{\"pr\":{\"type\":\"data\",\"unit\":\"mm day-1\",\"attrs\":{\"units\":\"mm day-1\"},\"dimensions\":[\"time\",\"lat\",\"lon\"]},\"tasmax\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')\",\"long_name\":\"Daily Maximum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area: mean time: maximum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMX\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')\",\"long_name\":\"Daily Minimum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area: mean time: minimum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMN\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily 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 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 CDR Sea Surface Temperature - 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The mission consists of one satellite carrying the [TROPOspheric Monitoring Instrument](http://www.tropomi.eu/) (TROPOMI). The satellite flies in loose formation with NASA's [Suomi NPP](https://www.nasa.gov/mission_pages/NPP/main/index.html) spacecraft, allowing utilization of co-located cloud mask data provided by the [Visible Infrared Imaging Radiometer Suite](https://www.nesdis.noaa.gov/current-satellite-missions/currently-flying/joint-polar-satellite-system/visible-infrared-imaging) (VIIRS) instrument onboard Suomi NPP during processing of the TROPOMI methane product.\\n\\nThe Sentinel-5 Precursor mission aims to reduce the global atmospheric data gap between the retired [ENVISAT](https://earth.esa.int/eogateway/missions/envisat) and [AURA](https://www.nasa.gov/mission_pages/aura/main/index.html) missions and the future [Sentinel-5](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-5) mission. Sentinel-5 Precursor [Level 2 data](http://www.tropomi.eu/data-products/level-2-products) provide total columns of ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide and formaldehyde, tropospheric columns of ozone, vertical profiles of ozone and cloud & aerosol information. These measurements are used for improving air quality forecasts and monitoring the concentrations of atmospheric constituents.\\n\\nThis STAC Collection provides Sentinel-5 Precursor Level 2 data, in NetCDF format, since April 2018 for the following products:\\n\\n* [`L2__AER_AI`](http://www.tropomi.eu/data-products/uv-aerosol-index): Ultraviolet aerosol index\\n* [`L2__AER_LH`](http://www.tropomi.eu/data-products/aerosol-layer-height): Aerosol layer height\\n* [`L2__CH4___`](http://www.tropomi.eu/data-products/methane): Methane (CH4) total column\\n* [`L2__CLOUD_`](http://www.tropomi.eu/data-products/cloud): Cloud fraction, albedo, and top pressure\\n* [`L2__CO____`](http://www.tropomi.eu/data-products/carbon-monoxide): Carbon monoxide (CO) total column\\n* [`L2__HCHO__`](http://www.tropomi.eu/data-products/formaldehyde): Formaldehyde (HCHO) total column\\n* [`L2__NO2___`](http://www.tropomi.eu/data-products/nitrogen-dioxide): Nitrogen dioxide (NO2) total column\\n* 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(turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band 4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band 5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band 6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band 7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band 9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band 10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band 11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band 12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa16\",\"description\":\"Band 16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band 18 - Water vapour absorption reference. Common reference band with SLSTR. Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band 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 Collection provides Sentinel-3 Full Resolution [OLCI Level-2 Water][olci-l2] products containing data on water-leaving reflectance, ocean color, and more.\\n\\n## Data files\\n\\nThis dataset includes data on:\\n\\n- Surface directional reflectance\\n- Chlorophyll-a concentration\\n- Suspended matter concentration\\n- Energy flux\\n- Aerosol load\\n- Integrated water vapor column\\n\\nEach variable is contained within NetCDF files. Error estimates are available for each product.\\n\\n## Processing overview\\n\\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the 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) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\\n\\nThis Collection contains Level-2 data in NetCDF files from November 2017 to present.\\n\\n[olci-l2]: 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 18 - Water vapour absorption reference. Common reference band with SLSTR. 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The dataset provides both retrieved and diagnostic global aerosol parameters at super-pixel (4.5 km x 4.5 km) resolution in a single NetCDF file for all regions over land and ocean free of snow/ice cover, excluding high cloud fraction data. The retrieved and derived aerosol parameters are:\\n\\n- Aerosol Optical Depth (AOD) at 440, 550, 670, 985, 1600 and 2250 nm\\n- Error estimates (i.e. standard deviation) in AOD at 440, 550, 670, 985, 1600 and 2250 nm\\n- Single Scattering Albedo (SSA) at 440, 550, 670, 985, 1600 and 2250 nm\\n- Fine-mode AOD at 550nm\\n- Aerosol Angstrom parameter between 550 and 865nm\\n- Dust AOD at 550nm\\n- Aerosol absorption optical depth at 550nm\\n\\nAtmospherically corrected nadir surface directional reflectances at 440, 550, 670, 985, 1600 and 2250 nm at super-pixel (4.5 km x 4.5 km) resolution are also provided. More information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/level-2-aod) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/products-algorithms/level-2-aod-algorithms-and-products).\\n\\nThis 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 channel Oa03\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"SYN_550\",\"description\":\"SLSTR nadir and oblique channel S1\",\"center_wavelength\":0.55,\"full_width_half_max\":0.02},{\"name\":\"SYN_670\",\"description\":\"SLSTR nadir and oblique channel S2\",\"center_wavelength\":0.659,\"full_width_half_max\":0.02},{\"name\":\"SYN_865\",\"description\":\"OLCI channel Oa17, SLSTR nadir and oblique channel S2\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"SYN_1600\",\"description\":\"SLSTR nadir and oblique channel S5\",\"center_wavelength\":1.61,\"full_width_half_max\":0.06},{\"name\":\"SYN_2250\",\"description\":\"SLSTR nadir and oblique channel S6\",\"center_wavelength\":2.25,\"full_width_half_max\":0.05}],\"description\":\"Global aerosol parameters\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE 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 global aerosol and surface reflectance at super-pixel (4.5km) resolution (SYNERGY AOD).\"},{\"id\":\"sentinel-3-synergy-v10-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-v10-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-v10-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel Data 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Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02},{\"name\":\"B2\",\"description\":\"OLCI channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035},{\"name\":\"B3\",\"description\":\"OLCI channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055},{\"name\":\"MIR\",\"description\":\"SLSTR 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 Collection provides the Sentinel-3 [Synergy Level-2 10-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from ground reflectance during a 10-day window, and four surface reflectance bands:\\n\\n- B0 (Blue, 450nm)\\n- B2 (Red, 645nm)\\n- B3 (NIR, 835nm)\\n- MIR (SWIR, 1665nm)\\n\\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\\n\\n## Data files\\n\\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\\n\\nEach 10-day product is delivered as a set of 10 rectangular scenes:\\n\\n- AFRICA\\n- NORTH_AMERICA\\n- SOUTH_AMERICA\\n- CENTRAL_AMERICA\\n- NORTH_ASIA\\n- WEST_ASIA\\n- SOUTH_EAST_ASIA\\n- ASIAN_ISLANDS\\n- AUSTRALASIA\\n- EUROPE\\n\\nMore information 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-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/v10-product).\\n\\nThis 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 optical 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(LRR and LFR) Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-olci-lfr-l2-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3 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 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 STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported 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 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa10\",\"description\":\"Band 10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band 11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band 12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa17\",\"description\":\"Band 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band 18 - Water vapour absorption reference. Common reference band with SLSTR. Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band 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 collection provides Sentinel-3 Full Resolution [OLCI Level-2 Land][olci-l2] products containing data on global vegetation, chlorophyll, and water vapor.\\n\\n## Data files\\n\\nThis dataset includes data on three primary variables:\\n\\n* OLCI global vegetation index file\\n* terrestrial Chlorophyll index file\\n* integrated water vapor over water file.\\n\\nEach variable is contained within a separate NetCDF file, and is cataloged as an asset in each Item.\\n\\nSeveral associated variables are also provided in the annotations data files:\\n\\n* rectified reflectance for red and NIR channels (RC681 and RC865)\\n* classification, quality and science flags (LQSF)\\n* common data such as the ortho-geolocation of land pixels, solar and satellite angles, atmospheric and meteorological data, time stamp or instrument information. These variables are inherited from Level-1B products.\\n\\nThis full resolution product offers a spatial sampling of approximately 300 m.\\n\\n## Processing overview\\n\\nThe values in the data files have been converted from Top of Atmosphere radiance to reflectance, and include various corrections for gaseous absorption and pixel classification. More information about the 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-land) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-olci/level-2/processing).\\n\\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\\n\\n[olci-l2]: 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 18 - Water vapour absorption reference. Common reference band with SLSTR. Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01}],\"description\":\"Integrated water vapour column\"},\"lqsf\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Land quality and science flags\"},\"ogvi\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa03\",\"description\":\"Band 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa10\",\"description\":\"Band 10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa17\",\"description\":\"Band 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"OLCI Global Vegetation Index (OGVI)\"},\"otci\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"Oa10\",\"description\":\"Band 10 - 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Each product contains three NetCDF files:\\n\\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\\n\\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\\n\\nThis 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 product manifest\"},\"reduced-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Reduced measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.32,\"description\":\"Band Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]},\"enhanced-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Enhanced measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.29,\"description\":\"Band C - Ionospheric correction\",\"frequency_band\":\"C\",\"center_frequency\":5.409999872},{\"band_width\":0.32,\"description\":\"Band Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]},\"standard-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Standard measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.29,\"description\":\"Band C - Ionospheric correction\",\"frequency_band\":\"C\",\"center_frequency\":5.409999872},{\"band_width\":0.32,\"description\":\"Band 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 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 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 Land Surface Temperature Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-lst-l2-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3 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 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 STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported 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 8 - SST, LST, Active fire\",\"center_wavelength\":10.854,\"full_width_half_max\":0.776},{\"name\":\"S9\",\"description\":\"Band 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 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) products containing data on land surface temperature measurements on a 1km grid. Radiance is measured in two channels to determine the temperature of the Earth's surface skin in the instrument field of view, where the term \\\"skin\\\" refers to the top surface of bare soil or the effective emitting temperature of vegetation canopies as viewed from above.\\n\\n## Data files\\n\\nThe dataset includes data on the primary measurement variable, land surface temperature, in a single NetCDF file, `LST_in.nc`. A second file, `LST_ancillary.nc`, contains several ancillary variables:\\n\\n- Normalized Difference Vegetation Index\\n- Surface biome classification\\n- Fractional vegetation cover\\n- Total water vapor column\\n\\nIn addition to the primary and ancillary data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags. More information 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) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing).\\n\\nThis Collection contains Level-2 data in NetCDF files from April 2016 to present.\\n\\n## STAC Item geometries\\n\\nThe Collection contains small \\\"chips\\\" and long \\\"stripes\\\" of data collected along the satellite direction of travel. Approximately five percent of the STAC Items describing long stripes of data contain geometries that encompass a larger area than an exact concave hull of the data extents. This may require additional filtering when searching 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 8 - SST, LST, Active fire\",\"center_wavelength\":10.854,\"full_width_half_max\":0.776},{\"name\":\"S9\",\"description\":\"Band 9 - SST, LST\",\"center_wavelength\":12.0225,\"full_width_half_max\":0.905}],\"description\":\"Land Surface Temperature (LST) values\"},\"slstr-met-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Meteorological parameters regridded onto the 16km tie points\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE product manifest\"},\"slstr-time-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Time annotations for the 1km 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view\"},\"slstr-cartesian-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full resolution cartesian coordinates for the 1km TIR grid, nadir view\"},\"slstr-cartesian-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km 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 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 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 Sea Surface Temperature Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-wst-l2-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3 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 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 STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported 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 7 - SST, LST, Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398},{\"name\":\"S8\",\"description\":\"Band 8 - SST, LST, Active fire\",\"center_wavelength\":10.854,\"full_width_half_max\":0.776},{\"name\":\"S9\",\"description\":\"Band 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 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) products containing data on sea surface temperature measurements on a 1km grid. Each product consists of a single NetCDF file containing all data variables:\\n\\n- Sea Surface Temperature (SST) value\\n- SST total uncertainty\\n- Latitude and longitude coordinates\\n- SST time deviation\\n- Single Sensor Error Statistic (SSES) bias and standard deviation estimate\\n- Contextual parameters such as wind speed at 10 m and fractional sea-ice contamination\\n- Quality flag\\n- Satellite zenith angle\\n- Top Of Atmosphere (TOA) Brightness Temperature (BT)\\n- TOA noise equivalent BT\\n\\nMore information 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-wst) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/sst-processing).\\n\\nThis 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 7 - SST, LST, Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398},{\"name\":\"S8\",\"description\":\"Band 8 - SST, LST, Active fire\",\"center_wavelength\":10.854,\"full_width_half_max\":0.776},{\"name\":\"S9\",\"description\":\"Band 9 - SST, LST\",\"center_wavelength\":12.0225,\"full_width_half_max\":0.905}],\"description\":\"Skin Sea Surface Temperature (SST) values\"},\"browse-jpg\":{\"type\":\"image/jpeg\",\"roles\":[\"thumbnail\"],\"description\":\"Preview image produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)\"},\"eop-metadata\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"Metadata produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE 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 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 Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/product-types\",\"type\":\"text/html\",\"title\":\"Sentinel-3 Ocean Radar Altimetry Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-sral-wat-l2-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3 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 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 STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Collection provides Sentinel-3 [SRAL Level-2 Ocean Altimetry](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry/level-2-algorithms-products) products, which contain data on ocean radar altimetry measurements. Each product contains three NetCDF files:\\n\\n- A reduced data file containing a subset of the 1 Hz Ku-band parameters.\\n- A standard data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters.\\n- An enhanced data file containing the standard 1 Hz and 20 Hz Ku- and C-band parameters along with the waveforms and parameters necessary to reprocess the data.\\n\\nMore information about the product and data processing can be found in the [User Guide](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-altimetry/overview) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-altimetry).\\n\\nThis 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 metadata file produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE product manifest\"},\"reduced-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Reduced measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.32,\"description\":\"Band Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]},\"enhanced-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Enhanced measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.29,\"description\":\"Band C - Ionospheric correction\",\"frequency_band\":\"C\",\"center_frequency\":5.409999872},{\"band_width\":0.32,\"description\":\"Band Ku - Range measurements\",\"frequency_band\":\"Ku\",\"center_frequency\":13.575000064}]},\"standard-measurement\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Standard measurement data file\",\"s3:altimetry_bands\":[{\"band_width\":0.29,\"description\":\"Band C - Ionospheric correction\",\"frequency_band\":\"C\",\"center_frequency\":5.409999872},{\"band_width\":0.32,\"description\":\"Band 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 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 DAAC - Data Citation and Policies\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/hls2-s30\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Harmonized 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 Sentinel Collection Thumbnail\"},\"geoparquet-items\":{\"href\":\"abfs://items/hls2-s30.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 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 Landsat Sentinel-2 (HLS) Version 2.0 provides consistent surface reflectance (SR)\\nand top of atmosphere (TOA) brightness data from the Operational Land Imager (OLI) aboard the\\njoint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI)\\naboard the ESA (European Space Agency) Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites.\\nThe combined measurement enables global observations of the land every 2-3 days at 30 meter\\n(m) spatial resolution. The HLS-S30 and HLS-L30 products are gridded to the same resolution and\\nMilitary Grid Reference System (MGRS) tiling system and are \\\"stackable\\\" for time series analysis.\\nThis dataset is in the form of cloud-optimized GeoTIFFs. The HLS v2.0 data is generated by NASA's\\nIMPACT team at Marshall Space Flight Center. The product latency is 1.7 days, from the satellite\\noverpass to the HLS data availability at NASA's Land Processes Distributed Active Archive 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 collection contains HLS data collected from Sentinel-2A, Sentinel-2B, and Sentinel-2C.\\nHLS data generated from Landsat can be found in a separate collection.\\n\",\"item_assets\":{\"B01\":{\"type\":\"image/tiff; application=geotiff; 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; 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; 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; 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Red-Edge 1\",\"eo:bands\":[{\"name\":\"B05\",\"center_wavelength\":0.7039,\"full_width_half_max\":0.019}]},\"B06\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Red-Edge 2\",\"eo:bands\":[{\"name\":\"B06\",\"center_wavelength\":0.7402,\"full_width_half_max\":0.018}]},\"B07\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Red-Edge 3\",\"eo:bands\":[{\"name\":\"B07\",\"center_wavelength\":0.7825,\"full_width_half_max\":0.028}]},\"B08\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"NIR Broad\",\"eo:bands\":[{\"name\":\"B08\",\"common_name\":\"nir\",\"center_wavelength\":0.8351,\"full_width_half_max\":0.145}]},\"B09\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"Water Vapor\",\"eo:bands\":[{\"name\":\"B09\",\"center_wavelength\":0.945,\"full_width_half_max\":0.026}]},\"B10\":{\"type\":\"image/tiff; 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; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SWIR 1\",\"eo:bands\":[{\"name\":\"B11\",\"common_name\":\"swir16\",\"center_wavelength\":1.6137,\"full_width_half_max\":0.143}]},\"B12\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SWIR 2\",\"eo:bands\":[{\"name\":\"B12\",\"common_name\":\"swir22\",\"center_wavelength\":2.22024,\"full_width_half_max\":0.242}]},\"B8A\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"NIR Narrow\",\"eo:bands\":[{\"name\":\"B8A\",\"center_wavelength\":0.8648,\"full_width_half_max\":0.033}]},\"SAA\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SAA\",\"eo:bands\":[{\"name\":\"SAA\"}]},\"SZA\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"SZA\",\"eo:bands\":[{\"name\":\"SZA\"}]},\"VAA\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VAA\",\"eo:bands\":[{\"name\":\"VAA\"}]},\"VZA\":{\"type\":\"image/tiff; application=geotiff; profile=cloud-optimized\",\"roles\":[\"data\"],\"title\":\"VZA\",\"eo:bands\":[{\"name\":\"VZA\"}]},\"Fmask\":{\"type\":\"image/tiff; 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 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 License\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/ms-buildings\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Microsoft Building Footprints\",\"assets\":{\"delta\":{\"href\":\"az://footprints/delta/2023-04-25/ml-buildings.parquet/\",\"title\":\"Delta table\",\"description\":\"Delta table with latest buildings footprints partitioned 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 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 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 Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases.\\n\\nFor more information, see the [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints/) repository on GitHub.\\n\\n## Building footprint creation\\n\\nThe building extraction is done in two stages:\\n\\n1. Semantic Segmentation \u2013 Recognizing building pixels on an aerial image using deep neural networks (DNNs)\\n2. Polygonization \u2013 Converting building pixel detections into polygons\\n\\n**Stage 1: Semantic Segmentation**\\n\\n![Semantic segmentation](https://raw.githubusercontent.com/microsoft/GlobalMLBuildingFootprints/main/images/segmentation.jpg)\\n\\n**Stage 2: Polygonization**\\n\\n![Polygonization](https://github.com/microsoft/GlobalMLBuildingFootprints/raw/main/images/polygonization.jpg)\\n\\n## Data assets\\n\\nThe building footprints are provided as a set of [geoparquet](https://github.com/opengeospatial/geoparquet) datasets in [Delta][delta] table format.\\nThe data are partitioned by\\n\\n1. Region\\n2. quadkey at [Bing Map Tiles][tiles] level 9\\n\\nEach `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area.\\n\\nNote that older items in this dataset are *not* spatially partitioned. We recommend using data with a processing date\\nof 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata\\nfor each item (see below).\\n\\n## Delta Format\\n\\nThe collection-level asset under the `delta` key gives you the fsspec-style URL\\nto the Delta table. This can be used to efficiently query for matching partitions\\nby `Region` and `quadkey`. See the notebook for an example using Python.\\n\\n## STAC metadata\\n\\nThis STAC collection has one STAC item per region. The `msbuildings:region`\\nproperty can be used to filter items to a specific region, and the `msbuildings:quadkey`\\nproperty can be used to filter items to a specific quadkey (though you can also search\\nby the `geometry`).\\n\\nNote that older STAC items are not spatially partitioned. We recommend filtering on\\nitems with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection\\nsummary for `msbuildings:processing-date` 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 Footprints\",\"description\":\"Parquet dataset with the building footprints 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 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 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 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 Fire Radiative Power Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-slstr-frp-l2-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3 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 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 STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported 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 10 - Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398},{\"name\":\"S5\",\"description\":\"Band 5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band 6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"S7\",\"description\":\"Band 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 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) (FRP) products containing data on fires detected over land and ocean.\\n\\n## Data files\\n\\nThe primary measurement data is contained in the `FRP_in.nc` file and provides FRP and uncertainties, projected onto a 1km grid, for fires detected in the thermal infrared (TIR) spectrum over land. Since February 2022, FRP and uncertainties are also provided for fires detected in the short wave infrared (SWIR) spectrum over both land and ocean, with the delivered data projected onto a 500m grid. The latter SWIR-detected fire data is only available for night-time measurements and is contained in the `FRP_an.nc` or `FRP_bn.nc` files.\\n\\nIn addition to the measurement data files, a standard set of annotation data files provide meteorological information, geolocation and time coordinates, geometry information, and quality flags.\\n\\n## Processing\\n\\nThe TIR fire detection is based on measurements from the S7 and F1 bands of the [SLSTR instrument](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-3-slstr/instrument); SWIR fire detection is based on the S5 and S6 bands. More information about 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) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/frp-processing).\\n\\nThis 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 5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band 6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"S7\",\"description\":\"Band 7 - SST, LST, Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398},{\"name\":\"F1\",\"description\":\"Band 10 - Active fire\",\"center_wavelength\":3.742,\"full_width_half_max\":0.398}],\"description\":\"Fire Radiative Power (FRP) dataset\"},\"slstr-met-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Meteorological parameters regridded onto the 16km tie points\"},\"safe-manifest\":{\"type\":\"application/xml\",\"roles\":[\"metadata\"],\"description\":\"SAFE product manifest\"},\"slstr-time-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Time annotations for the 1 KM grid\"},\"slstr-flags-fn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Global flags for the 1km F1 grid, nadir view\"},\"slstr-flags-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Global flags for the 1km TIR grid, nadir view\"},\"slstr-indices-fn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Scan, pixel and detector annotations for the 1km F1 grid, nadir view\"},\"slstr-indices-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Scan, pixel and detector annotations for the 1km TIR grid, nadir view\"},\"slstr-geodetic-fn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full resolution geodetic coordinates for the 1km F1 grid, nadir view\"},\"slstr-geodetic-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full resolution geodetic coordinates for the 1km TIR grid, nadir view\"},\"slstr-geodetic-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km geodetic coordinates\"},\"slstr-geometry-tn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km solar and satellite geometry annotations, nadir view\"},\"slstr-cartesian-fn\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full resolution cartesian coordinates for the 1km F1 grid, nadir view\"},\"slstr-cartesian-in\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Full resolution cartesian coordinates for the 1km TIR grid, nadir view\"},\"slstr-cartesian-tx\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"16km 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 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 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 Land Surface Reflectance and Aerosol Product User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-syn-l2-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3 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 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 STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported 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 1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band 2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band 4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band 5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band 6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band 7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band 9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band 10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band 11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band 12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa13\",\"description\":\"Band 13 - O2 absorption / aerosol correction\",\"center_wavelength\":0.76125,\"full_width_half_max\":0.0025},{\"name\":\"Oa16\",\"description\":\"Band 16 - Atmospheric / aerosol correction\",\"center_wavelength\":0.77875,\"full_width_half_max\":0.015},{\"name\":\"Oa17\",\"description\":\"Band 17 - Atmospheric / aerosol correction, clouds, pixel co-registration\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"Oa18\",\"description\":\"Band 18 - Water vapour absorption reference. Common reference band with SLSTR. Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band 21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04},{\"name\":\"S1\",\"description\":\"Band 1 - Cloud screening, vegetation monitoring, aerosol\",\"center_wavelength\":0.55427,\"full_width_half_max\":0.01926},{\"name\":\"S2\",\"description\":\"Band 2 - NDVI, vegetation monitoring, aerosol\",\"center_wavelength\":0.65947,\"full_width_half_max\":0.01925},{\"name\":\"S3\",\"description\":\"Band 3 - NDVI, cloud flagging, pixel co-registration\",\"center_wavelength\":0.868,\"full_width_half_max\":0.0206},{\"name\":\"S4\",\"description\":\"Band 4 - Cirrus detection over land\",\"center_wavelength\":1.3748,\"full_width_half_max\":0.0208},{\"name\":\"S5\",\"description\":\"Band 5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band 6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"SYN01\",\"description\":\"OLCI channel Oa01\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"SYN02\",\"description\":\"OLCI channel Oa02\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"SYN03\",\"description\":\"OLCI channel Oa03\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"SYN04\",\"description\":\"OLCI channel Oa04\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"SYN05\",\"description\":\"OLCI channel Oa05\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"SYN06\",\"description\":\"OLCI channel Oa06\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"SYN07\",\"description\":\"OLCI channel Oa07\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"SYN08\",\"description\":\"OLCI channel Oa08\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"SYN09\",\"description\":\"OLCI channel Oa09\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"SYN10\",\"description\":\"OLCI channel Oa10\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"SYN11\",\"description\":\"OLCI channel Oa11\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"SYN12\",\"description\":\"OLCI channel Oa12\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"SYN13\",\"description\":\"OLCI channel Oa16\",\"center_wavelength\":0.7785,\"full_width_half_max\":0.015},{\"name\":\"SYN14\",\"description\":\"OLCI channel Oa17\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"SYN15\",\"description\":\"OLCI channel Oa18\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"SYN16\",\"description\":\"OLCI channel Oa21\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04},{\"name\":\"SYN17\",\"description\":\"SLSTR nadir channel S1\",\"center_wavelength\":0.555,\"full_width_half_max\":0.02},{\"name\":\"SYN18\",\"description\":\"SLSTR nadir channel S2\",\"center_wavelength\":0.659,\"full_width_half_max\":0.02},{\"name\":\"SYN19\",\"description\":\"SLSTR nadir channel S3\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"SYN20\",\"description\":\"SLSTR nadir channel S5\",\"center_wavelength\":1.61,\"full_width_half_max\":0.06},{\"name\":\"SYN21\",\"description\":\"SLSTR nadir channel S6\",\"center_wavelength\":2.25,\"full_width_half_max\":0.05},{\"name\":\"SYN22\",\"description\":\"SLSTR oblique channel S1\",\"center_wavelength\":0.555,\"full_width_half_max\":0.02},{\"name\":\"SYN23\",\"description\":\"SLSTR oblique channel S2\",\"center_wavelength\":0.659,\"full_width_half_max\":0.02},{\"name\":\"SYN24\",\"description\":\"SLSTR oblique channel S3\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02},{\"name\":\"SYN25\",\"description\":\"SLSTR oblique channel S5\",\"center_wavelength\":1.61,\"full_width_half_max\":0.06},{\"name\":\"SYN26\",\"description\":\"SLSTR 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 Collection provides the Sentinel-3 [Synergy Level-2 Land Surface Reflectance and Aerosol](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-syn) product, which contains data on Surface Directional Reflectance, Aerosol Optical Thickness, and an Angstrom coefficient estimate over land.\\n\\n## Data Files\\n\\nIndividual NetCDF files for the following variables:\\n\\n- Surface Directional Reflectance (SDR) with their associated error estimates for the sun-reflective [SLSTR](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-slstr) channels (S1 to S6 for both nadir and oblique views, except S4) and for all [OLCI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-olci) channels, except for the oxygen absorption bands Oa13, Oa14, Oa15, and the water vapor bands Oa19 and Oa20.\\n- Aerosol optical thickness at 550nm with error estimates.\\n- Angstrom coefficient at 550nm.\\n\\nMore information 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) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/syn-level-2-product).\\n\\nThis 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 stamps annotation\"},\"syn-amin\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"L2 Aerosol model index number data\"},\"syn-flags\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Classification and quality Flags associated with OLCI, SLSTR and SYNERGY products\"},\"syn-ato550\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"S1\",\"description\":\"Band 1 - Cloud screening, vegetation monitoring, aerosol\",\"center_wavelength\":0.55427,\"full_width_half_max\":0.01926},{\"name\":\"S2\",\"description\":\"Band 2 - NDVI, vegetation monitoring, aerosol\",\"center_wavelength\":0.65947,\"full_width_half_max\":0.01925},{\"name\":\"S3\",\"description\":\"Band 3 - NDVI, cloud flagging, pixel co-registration\",\"center_wavelength\":0.868,\"full_width_half_max\":0.0206},{\"name\":\"S5\",\"description\":\"Band 5 - Cloud clearing, ice, snow, vegetation monitoring\",\"center_wavelength\":1.6134,\"full_width_half_max\":0.06068},{\"name\":\"S6\",\"description\":\"Band 6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"Oa01\",\"description\":\"Band 1 - Aerosol correction, improved water constituent retrieval\",\"center_wavelength\":0.4,\"full_width_half_max\":0.015},{\"name\":\"Oa02\",\"description\":\"Band 2 - Yellow substance and detrital pigments (turbidity)\",\"center_wavelength\":0.4125,\"full_width_half_max\":0.01},{\"name\":\"Oa03\",\"description\":\"Band 3 - Chlorophyll absorption maximum, biogeochemistry, vegetation\",\"center_wavelength\":0.4425,\"full_width_half_max\":0.01},{\"name\":\"Oa04\",\"description\":\"Band 4 - Chlorophyll\",\"center_wavelength\":0.49,\"full_width_half_max\":0.01},{\"name\":\"Oa05\",\"description\":\"Band 5 - Chlorophyll, sediment, turbidity, red tide\",\"center_wavelength\":0.51,\"full_width_half_max\":0.01},{\"name\":\"Oa06\",\"description\":\"Band 6 - Chlorophyll reference (minimum)\",\"center_wavelength\":0.56,\"full_width_half_max\":0.01},{\"name\":\"Oa07\",\"description\":\"Band 7 - Sediment loading\",\"center_wavelength\":0.62,\"full_width_half_max\":0.01},{\"name\":\"Oa08\",\"description\":\"Band 8 - 2nd Chlorophyll absorption maximum, sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band 9 - Improved 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10\"},\"syn-oa11-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN11\",\"description\":\"OLCI channel Oa11\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01}],\"description\":\"Surface directional reflectance associated with OLCI channel 11\"},\"syn-oa12-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN12\",\"description\":\"OLCI channel Oa12\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075}],\"description\":\"Surface directional reflectance associated with OLCI channel 12\"},\"syn-oa16-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN13\",\"description\":\"OLCI channel Oa16\",\"center_wavelength\":0.7785,\"full_width_half_max\":0.015}],\"description\":\"Surface directional reflectance associated with OLCI channel 16\"},\"syn-oa17-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN14\",\"description\":\"OLCI channel Oa17\",\"center_wavelength\":0.865,\"full_width_half_max\":0.02}],\"description\":\"Surface directional reflectance associated with OLCI channel 17\"},\"syn-oa18-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN15\",\"description\":\"OLCI channel Oa18\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01}],\"description\":\"Surface directional reflectance associated with OLCI channel 18\"},\"syn-oa21-reflectance\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"SYN16\",\"description\":\"OLCI channel Oa21\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Surface directional reflectance associated with OLCI channel 21\"},\"syn-sdr-removed-pixels\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"S1\",\"description\":\"Band 1 - Cloud screening, vegetation monitoring, aerosol\",\"center_wavelength\":0.55427,\"full_width_half_max\":0.01926},{\"name\":\"S2\",\"description\":\"Band 2 - NDVI, vegetation monitoring, aerosol\",\"center_wavelength\":0.65947,\"full_width_half_max\":0.01925},{\"name\":\"S3\",\"description\":\"Band 3 - NDVI, cloud flagging, pixel co-registration\",\"center_wavelength\":0.868,\"full_width_half_max\":0.0206},{\"name\":\"S4\",\"description\":\"Band 4 - Cirrus detection over land\",\"center_wavelength\":1.3748,\"full_width_half_max\":0.0208},{\"name\":\"S6\",\"description\":\"Band 6 - Vegetation state and cloud clearing\",\"center_wavelength\":2.2557,\"full_width_half_max\":0.05015},{\"name\":\"Oa01\",\"description\":\"Band 1 - Aerosol correction, improved water constituent 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sediment, yellow substance / vegetation\",\"center_wavelength\":0.665,\"full_width_half_max\":0.01},{\"name\":\"Oa09\",\"description\":\"Band 9 - Improved fluorescence retrieval\",\"center_wavelength\":0.67375,\"full_width_half_max\":0.0075},{\"name\":\"Oa10\",\"description\":\"Band 10 - Chlorophyll fluorescence peak, red edge\",\"center_wavelength\":0.68125,\"full_width_half_max\":0.0075},{\"name\":\"Oa11\",\"description\":\"Band 11 - Chlorophyll fluorescence baseline, red edge transition\",\"center_wavelength\":0.70875,\"full_width_half_max\":0.01},{\"name\":\"Oa12\",\"description\":\"Band 12 - O2 absorption / clouds, vegetation\",\"center_wavelength\":0.75375,\"full_width_half_max\":0.0075},{\"name\":\"Oa13\",\"description\":\"Band 13 - O2 absorption / aerosol correction\",\"center_wavelength\":0.76125,\"full_width_half_max\":0.0025},{\"name\":\"Oa16\",\"description\":\"Band 16 - Atmospheric / aerosol 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Common reference band with SLSTR. Vegetation monitoring\",\"center_wavelength\":0.885,\"full_width_half_max\":0.01},{\"name\":\"Oa19\",\"description\":\"Band 19 - Water vapour absorption, vegetation monitoring (maximum REFLECTANCE)\",\"center_wavelength\":0.9,\"full_width_half_max\":0.01},{\"name\":\"Oa21\",\"description\":\"Band 21 - Water vapour absorption, atmospheric / aerosol correction\",\"center_wavelength\":1.02,\"full_width_half_max\":0.04}],\"description\":\"Surface directional reflectance and aerosol parameters associated with removed pixel\"},\"annotations-removed-pixels\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"description\":\"Annotations parameters associated with removed pixel\"}},\"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 surface reflectance and aerosol over land (SYNERGY SYN).\"},{\"id\":\"sentinel-3-synergy-vgp-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-vgp-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-vgp-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp\",\"type\":\"text/html\",\"title\":\"Sentinel-3 Top of Atmosphere Reflectance Product (SPOT VEGETATION) User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-vgp-l2-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-synergy-vgp-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3 Top of Atmosphere Reflectance (SPOT VEGETATION) Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-synergy-vgp-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"QS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180,-90,180,90]]},\"temporal\":{\"interval\":[[\"2018-10-08T08:09:40.491227Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Reflectance\"],\"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\":\"B0\",\"description\":\"OLCI channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02},{\"name\":\"B2\",\"description\":\"OLCI channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035},{\"name\":\"B3\",\"description\":\"OLCI channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055},{\"name\":\"MIR\",\"description\":\"SLSTR 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-vgp\"],\"s3:product_type\":[\"SY_2_VGP___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This Collection provides the Sentinel-3 [Synergy Level-2 Top of Atmosphere Reflectance](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vgp) product, which is a SPOT VEGETATION Continuity Product containing measurement data similar to that obtained by the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboad the SPOT-3 and SPOT-4 satellites. The primary variables are four top of atmosphere reflectance bands:\\n\\n- B0 (Blue, 450nm)\\n- B2 (Red, 645nm)\\n- B3 (NIR, 835nm)\\n- MIR (SWIR, 1665nm)\\n\\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument and have been adapted for scientific applications requiring highly accurate physical measurements through correction for systematic errors and re-sampling to predefined geographic projections. The pixel brightness count is the ground area's apparent reflectance as seen at the top of atmosphere.\\n\\n## Data files\\n\\nNetCDF files are provided for the four reflectance bands. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\\n\\nMore information 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-vgp) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/level-2/vgt-p-product).\\n\\nThis 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 optical thickness data\"},\"b0\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B0\",\"description\":\"OLCI channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02}],\"description\":\"Top of 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Product).\"},{\"id\":\"sentinel-3-synergy-vg1-l2-netcdf\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-3-synergy-vg1-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-vg1-l2-netcdf\"},{\"rel\":\"license\",\"href\":\"https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice\",\"type\":\"application/pdf\",\"title\":\"Sentinel Data License\"},{\"rel\":\"about\",\"href\":\"https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10\",\"type\":\"text/html\",\"title\":\"Sentinel-3 1-Day Surface Reflectance and NDVI Product (SPOT VEGETATION) User Guide\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/sentinel-3-synergy-vg1-l2-netcdf\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION)\",\"assets\":{\"thumbnail\":{\"href\":\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/sentinel-3-synergy-vg1-l2-netcdf-thumb.png\",\"title\":\"Sentinel-3 1-Day Surface Reflectance and NDVI (SPOT VEGETATION) Thumbnail\",\"media_type\":\"image/png\"},\"geoparquet-items\":{\"href\":\"abfs://items/sentinel-3-synergy-vg1-l2-netcdf.parquet\",\"type\":\"application/x-parquet\",\"roles\":[\"stac-items\"],\"title\":\"GeoParquet STAC Items\",\"description\":\"Snapshot of the collection's STAC items exported to GeoParquet format.\",\"msft:partition_info\":{\"is_partitioned\":true,\"partition_frequency\":\"AS\"},\"table:storage_options\":{\"account_name\":\"pcstacitems\"}}},\"extent\":{\"spatial\":{\"bbox\":[[-180.0,-56.0,180.0,75.0]]},\"temporal\":{\"interval\":[[\"2018-10-04T23:17:21Z\",null]]}},\"license\":\"proprietary\",\"keywords\":[\"Sentinel\",\"Copernicus\",\"ESA\",\"Satellite\",\"Reflectance\",\"NDVI\"],\"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\":\"B0\",\"description\":\"OLCI channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02},{\"name\":\"B2\",\"description\":\"OLCI channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035},{\"name\":\"B3\",\"description\":\"OLCI channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055},{\"name\":\"MIR\",\"description\":\"SLSTR 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-vg1\"],\"s3:product_type\":[\"SY_2_VG1___\"],\"sat:orbit_state\":[\"ascending\",\"descending\"],\"s3:processing_timeliness\":[\"NT\"],\"sat:platform_international_designator\":[\"2016-011A\",\"2018-039A\"]},\"description\":\"This Collection provides the Sentinel-3 [Synergy Level-2 1-Day Surface Reflectance and NDVI](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-3-synergy/product-types/level-2-vg1-v10) products, which are SPOT VEGETATION Continuity Products similar to those obtained from the [VEGETATION instrument](https://docs.terrascope.be/#/Satellites/SPOT-VGT/MissionInstruments) onboard the SPOT-4 and SPOT-5 satellites. The primary variables are a maximum Normalized Difference Vegetation Index (NDVI) composite, which is derived from daily ground reflecrtance, and four surface reflectance bands:\\n\\n- B0 (Blue, 450nm)\\n- B2 (Red, 645nm)\\n- B3 (NIR, 835nm)\\n- MIR (SWIR, 1665nm)\\n\\nThe four reflectance bands have center wavelengths matching those on the original SPOT VEGETATION instrument. The NDVI variable, which is an indicator of the amount of vegetation, is derived from the B3 and B2 bands.\\n\\n## Data files\\n\\nThe four reflectance bands and NDVI values are each contained in dedicated NetCDF files. Additional metadata are delivered in annotation NetCDF files, each containing a single variable, including the geometric viewing and illumination conditions, the total water vapour and ozone columns, and the aerosol optical depth.\\n\\nEach 1-day product is delivered as a set of 10 rectangular scenes:\\n\\n- AFRICA\\n- NORTH_AMERICA\\n- SOUTH_AMERICA\\n- CENTRAL_AMERICA\\n- NORTH_ASIA\\n- WEST_ASIA\\n- SOUTH_EAST_ASIA\\n- ASIAN_ISLANDS\\n- AUSTRALASIA\\n- EUROPE\\n\\nMore information 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-vg1-v10) and [Technical Guide](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-synergy/vgt-s/vg1-product-surface-reflectance).\\n\\nThis 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 optical thickness data\"},\"b0\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B0\",\"description\":\"OLCI channels Oa02, Oa03\",\"center_wavelength\":0.45,\"full_width_half_max\":0.02}],\"description\":\"Surface Reflectance Data Set associated with VGT-B0 channel\"},\"b2\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B2\",\"description\":\"OLCI channels Oa06, Oa07, Oa08, Oa09, Oa10\",\"center_wavelength\":0.645,\"full_width_half_max\":0.035}],\"description\":\"Surface Reflectance Data Set associated with VGT-B2 channel\"},\"b3\":{\"type\":\"application/x-netcdf\",\"roles\":[\"data\"],\"eo:bands\":[{\"name\":\"B3\",\"description\":\"OLCI channels Oa16, Oa17, Oa18, Oa21\",\"center_wavelength\":0.835,\"full_width_half_max\":0.055}],\"description\":\"Surface Reflectance Data Set associated with 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The discrete classification maps provide 11 classes defined using the Land Cover Classification System (LCCS) developed by the United Nations (UN) Food and Agriculture Organization (FAO). The map images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\\n\\nThe WorldCover product is developed by a consortium of European service providers and research organizations. [VITO](https://remotesensing.vito.be/) (Belgium) is the prime contractor of the WorldCover consortium together with [Brockmann Consult](https://www.brockmann-consult.de/) (Germany), [CS SI](https://www.c-s.fr/) (France), [Gamma Remote Sensing AG](https://www.gamma-rs.ch/) (Switzerland), [International Institute for Applied Systems Analysis](https://www.iiasa.ac.at/) (Austria), and [Wageningen University](https://www.wur.nl/nl/Wageningen-University.htm) (The Netherlands).\\n\\nTwo versions of the WorldCover product are available:\\n\\n- WorldCover 2020 produced using v100 of the algorithm\\n - [WorldCover 2020 v100 User Manual](https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf)\\n \ - [WorldCover 2020 v100 Validation Report]()\\n\\n- WorldCover 2021 produced using v200 of the algorithm\\n - [WorldCover 2021 v200 User Manual]()\\n \ - [WorldCover 2021 v200 Validaton Report]()\\n\\nSince the WorldCover maps for 2020 and 2021 were generated with 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The 3DEP program provides raster elevation data for the conterminous United States, Alaska, Hawaii, and the island territories, at a variety of spatial resolutions. The seamless DEM layers produced by the 3DEP program are updated frequently to integrate newly available, improved elevation source data. \n\nDEM layers are available nationally at grid spacings of 1 arc-second (approximately 30 meters) for the conterminous United States, and at approximately 1, 3, and 9 meters for parts of the United States. Most seamless DEM data for Alaska is available at a resolution of approximately 60 meters, where only lower resolution source data exist.\n","item_assets":{"data":{"type":"image/tiff; 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 digital elevation data at horizontal resolutions ranging from one to sixty meters","msft:region":"westeurope"}' headers: Accept-Ranges: - bytes Access-Control-Allow-Credentials: - 'true' Access-Control-Allow-Headers: - X-PC-Request-Entity,DNT,Keep-Alive,User-Agent,X-Requested-With,If-Modified-Since,Cache-Control,Content-Type,Authorization Access-Control-Allow-Methods: - PUT, GET, POST, OPTIONS Access-Control-Allow-Origin: - '*' Access-Control-Max-Age: - '1728000' Connection: - keep-alive Content-Length: - '1451' Content-Type: - application/json Date: - Thu, 31 Jul 2025 15:07:32 GMT Strict-Transport-Security: - max-age=31536000; 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includeSubDomains X-Cache: - CONFIG_NOCACHE content-encoding: - gzip vary: - Accept-Encoding x-azure-ref: - 20250731T150732Z-r17d779659cp5mbvhC1DEN7mgn00000003pg000000002xw3 status: code: 200 message: OK - request: body: null headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - python-requests/2.32.4 method: GET uri: https://planetarycomputer.microsoft.com/api/stac/v1/collections/fia response: body: string: '{"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 Open Access Data Use Agreement"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/fia","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Forest 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 Description and User Guide"},"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/fia.png","type":"image/gif","title":"Forest Inventory and Analysis"},"geoparquet-items":{"href":"abfs://items/fia.parquet","type":"application/x-parquet","roles":["stac-items"],"title":"GeoParquet STAC items","description":"Snapshot of the collection''s STAC items exported 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 Service"],"providers":[{"url":"https://www.fia.fs.fed.us/","name":"Forest Inventory & Analysis","roles":["producer","licensor"]},{"url":"https://carbonplan.org/","name":"CarbonPlan","roles":["processor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host"]}],"description":"Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service''s [Forest Inventory and Analysis](https://www.fia.fs.fed.us/) (FIA) program.\n\nThe Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA''s primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation''s forest land.\n\nDomain: continental U.S., 1928-2018\n\nResolution: plot-level (irregular polygon)\n\nThis dataset was curated and brought to Azure by [CarbonPlan](https://carbonplan.org/).\n","item_assets":{"data":{"type":"application/x-parquet","roles":["data"],"title":"Dataset root","table:storage_options":{"account_name":"cpdataeuwest"}}},"stac_version":"1.0.0","table:tables":[{"name":"Survey Table","description":"Survey table. This table contains one record for each year an inventory is conducted in a State for annual inventory or one record for each periodic inventory.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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 World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\\n\\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\\n\\n## Accessing the data\\n\\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\n\\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\\n\\n## Data format & contents\\n\\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\\n\\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\\n\\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201Clon\u201D coordinate extends from -179.875 to 179.875, and the \u201Clat\u201D coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\n\\n## Available institutions, models, and scenarios by license collection\\n\\n| Modeling institution | Source model | Available experiments \ | License collection |\\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 \ | Public domain datasets |\\n| INM | INM-CM4-8 \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| BCC | BCC-CSM2-MR \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \ | CC-BY-40 |\\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 |\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 \ |\\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 |\\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| NOAA-GFDL \ | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 |\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\n| EC-Earth-Consortium \ | EC-Earth3-AerChem | ssp370 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 |\\n| EC-Earth-Consortium \ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] \ |\\n\\n*Notes:*\\n\\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\\n\\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\\n\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \\n\\n## Project methods\\n\\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\\n\\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\\n\\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\\n\\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\\n\\n## Citing, licensing, and using data produced by this project\\n\\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\\n\\n### CIL GDPCIR\\n\\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\\n\\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \\n\\nThe code repository may be cited using the following:\\n\\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\n\\n### ERA5\\n\\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, 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> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\n>\\n> Mu\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n>\\n> Mu\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n\\n### GCM-specific citations & licenses\\n\\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\\n\\n#### Public Domain Datasets\\n\\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\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 \ CMIP Citation:\\n\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\\n\\n ScenarioMIP Citation:\\n\\n \ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\n\\n\\n* **INM-CM5-0**\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\n\\n\\n#### CC-BY-4.0\\n\\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\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 \ CMIP Citation:\\n\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\n\\n ScenarioMIP Citation:\\n\\n \ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\\n\\n\\n* **ACCESS-ESM1-5**\\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 \ CMIP Citation:\\n\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\n\\n ScenarioMIP Citation:\\n\\n \ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\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 \ CMIP Citation:\\n\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\n\\n \ ScenarioMIP Citation:\\n\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\n\\n ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\n\\n \ ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\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 \ CMIP Citation:\\n\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\\n\\n ScenarioMIP Citation:\\n\\n \ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\n\\n\\n* **GFDL-ESM4**\\n\\n \ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\n\\n \ CMIP Citation:\\n\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\n\\n ScenarioMIP Citation:\\n\\n \ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\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 \ CMIP Citation:\\n\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\\n\\n ScenarioMIP Citation:\\n\\n \ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\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 \ CMIP Citation:\\n\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\n\\n \ ScenarioMIP Citation:\\n\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\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 \ CMIP Citation:\\n\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\n\\n ScenarioMIP Citation:\\n\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\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 \ CMIP Citation:\\n\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\n\\n \ ScenarioMIP Citation:\\n\\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\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 \ CMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\n\\n \ ScenarioMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\n\\n\\n* **NESM3**\\n\\n \ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\n\\n \ CMIP Citation:\\n\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\\n\\n ScenarioMIP Citation:\\n\\n \ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\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 \ CMIP Citation:\\n\\n > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\\n\\n ScenarioMIP Citation:\\n\\n \ > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\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 \ CMIP Citation:\\n\\n > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\\n\\n ScenarioMIP Citation:\\n\\n \ > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\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 \ CMIP Citation:\\n\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\n\\n \ ScenarioMIP Citation:\\n\\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\n\\n* **CanESM5**\\n\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\\n\\n CMIP Citation:\\n\\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\\n\\n ScenarioMIP Citation:\\n\\n \ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\n\\n## Acknowledgements\\n\\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\\n\\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\\n\\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\\n\\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. For method advice and useful conversations, we would like to thank Keith Dixon, Dennis Adams-Smith, and [Joe Hamman](https://github.com/jhamman).\\n\\n## Financial support\\n\\nThis research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.\\n\\n## Additional links:\\n\\n* CIL GDPCIR project homepage: [github.com/ClimateImpactLab/downscaleCMIP6](https://github.com/ClimateImpactLab/downscaleCMIP6)\\n* Project listing on zenodo: https://doi.org/10.5281/zenodo.6403794\\n* Climate 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 Maximum Near-Surface Air Temperature\",\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"application/vnd+zarr\",\"roles\":[\"data\"],\"title\":\"Daily Minimum Near-Surface Air Temperature\",\"description\":\"Daily Minimum Near-Surface Air Temperature\"}},\"msft:region\":\"westeurope\",\"stac_version\":\"1.0.0\",\"msft:group_id\":\"cil-gdpcir\",\"cube:variables\":{\"pr\":{\"type\":\"data\",\"unit\":\"mm day-1\",\"attrs\":{\"units\":\"mm day-1\"},\"dimensions\":[\"time\",\"lat\",\"lon\"]},\"tasmax\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')\",\"long_name\":\"Daily Maximum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area: mean time: maximum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMX\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily Maximum Near-Surface Air Temperature\"},\"tasmin\":{\"type\":\"data\",\"unit\":\"K\",\"attrs\":{\"units\":\"K\",\"comment\":\"minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')\",\"long_name\":\"Daily Minimum Near-Surface Air Temperature\",\"coordinates\":\"height\",\"cell_methods\":\"area: mean time: minimum (interval: 5 minutes)\",\"cell_measures\":\"area: areacella\",\"original_name\":\"TREFHTMN\",\"standard_name\":\"air_temperature\"},\"dimensions\":[\"time\",\"lat\",\"lon\"],\"description\":\"Daily 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 Impact Lab Global Downscaled Projections for Climate Impacts Research (CC-BY-4.0)\"}" headers: Accept-Ranges: - bytes Access-Control-Allow-Credentials: - 'true' Access-Control-Allow-Headers: - X-PC-Request-Entity,DNT,Keep-Alive,User-Agent,X-Requested-With,If-Modified-Since,Cache-Control,Content-Type,Authorization Access-Control-Allow-Methods: - PUT, GET, POST, OPTIONS Access-Control-Allow-Origin: - '*' Access-Control-Max-Age: - '1728000' Connection: - keep-alive Content-Length: - '11061' Content-Type: - application/json Date: - Thu, 31 Jul 2025 15:07:36 GMT Strict-Transport-Security: - max-age=31536000; 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includeSubDomains X-Cache: - CONFIG_NOCACHE content-encoding: - gzip vary: - Accept-Encoding x-azure-ref: - 20250731T150736Z-r17d779659cgv7lqhC1DENzvbg00000003mg000000005q74 status: code: 200 message: OK - request: body: null headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - python-requests/2.32.4 method: GET uri: https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by response: body: string: "{\"id\":\"cil-gdpcir-cc-by\",\"type\":\"Collection\",\"links\":[{\"rel\":\"items\",\"type\":\"application/geo+json\",\"href\":\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by/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\"},{\"rel\":\"license\",\"href\":\"https://spdx.org/licenses/CC-BY-4.0.html\",\"type\":\"text/html\",\"title\":\"Creative Commons Attribution 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 homepage\"},{\"rel\":\"describedby\",\"href\":\"https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by\",\"title\":\"Human readable dataset overview and reference\",\"type\":\"text/html\"}],\"title\":\"CIL Global Downscaled Projections for Climate Impacts Research (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 STAC items\",\"description\":\"Snapshot of the collection's STAC items exported 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 Impact Lab\",\"Rhodium Group\",\"Precipitation\",\"Temperature\"],\"providers\":[{\"url\":\"https://impactlab.org/\",\"name\":\"Climate 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 World Climate Research Programme's [6th Coupled Model Intercomparison Project (CMIP6)](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) represents an enormous advance in the quality, detail, and scope of climate modeling.\\n\\nThe [Global Downscaled Projections for Climate Impacts Research](https://github.com/ClimateImpactLab/downscaleCMIP6) dataset makes this modeling more applicable to understanding the impacts of changes in the climate on humans and society with two key developments: trend-preserving bias correction and downscaling. In this dataset, the [Climate Impact Lab](https://impactlab.org) provides global, daily minimum and maximum air temperature at the surface (`tasmin` and `tasmax`) and daily cumulative surface precipitation (`pr`) corresponding to the CMIP6 historical, ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 scenarios for 25 global climate models on a 1/4-degree regular global grid.\\n\\n## Accessing the data\\n\\nGDPCIR data can be accessed on the Microsoft Planetary Computer. The dataset is made of of three collections, distinguished by data license:\\n* [Public domain (CC0-1.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0)\\n* [Attribution (CC BY 4.0) collection](https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by)\\n\\nEach modeling center with bias corrected and downscaled data in this collection falls into one of these license categories - see the [table below](/dataset/cil-gdpcir-cc-by#available-institutions-models-and-scenarios-by-license-collection) to see which model is in each collection, and see the section below on [Citing, Licensing, and using data produced by this project](/dataset/cil-gdpcir-cc-by#citing-licensing-and-using-data-produced-by-this-project) for citations and additional information about each license.\\n\\n## Data format & contents\\n\\nThe data is stored as partitioned zarr stores (see [https://zarr.readthedocs.io](https://zarr.readthedocs.io)), each of which includes thousands of data and metadata files covering the full time span of the experiment. Historical zarr stores contain just over 50 GB, while SSP zarr stores contain nearly 70GB. Each store is stored as a 32-bit float, with dimensions time (daily datetime), lat (float latitude), and lon (float longitude). The data is chunked at each interval of 365 days and 90 degree interval of latitude and longitude. Therefore, each chunk is `(365, 360, 360)`, with each chunk occupying approximately 180MB in memory.\\n\\nHistorical data is daily, excluding leap days, from Jan 1, 1950 to Dec 31, 2014; SSP data is daily, excluding leap days, from Jan 1, 2015 to either Dec 31, 2099 or Dec 31, 2100, depending on data availability in the source GCM.\\n\\nThe spatial domain covers all 0.25-degree grid cells, indexed by the grid center, with grid edges on the quarter-degree, using a -180 to 180 longitude convention. Thus, the \u201Clon\u201D coordinate extends from -179.875 to 179.875, and the \u201Clat\u201D coordinate extends from -89.875 to 89.875, with intermediate values at each 0.25-degree increment between (e.g. -179.875, -179.625, -179.375, etc).\\n\\n## Available institutions, models, and scenarios by license collection\\n\\n| Modeling institution | Source model | Available experiments \ | License collection |\\n| -------------------- | ----------------- | ------------------------------------------ | ---------------------- |\\n| CAS | FGOALS-g3 [^1] | SSP2-4.5, SSP3-7.0, and SSP5-8.5 \ | Public domain datasets |\\n| INM | INM-CM4-8 \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| INM | INM-CM5-0 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | Public domain datasets |\\n| BCC | BCC-CSM2-MR \ | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| CMCC | CMCC-CM2-SR5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 \ | CC-BY-40 |\\n| CMCC | CMCC-ESM2 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40 |\\n| CSIRO-ARCCSS | ACCESS-CM2 | SSP2-4.5 and SSP3-7.0 | CC-BY-40 |\\n| CSIRO | ACCESS-ESM1-5 | SSP1-2.6, SSP2-4.5, and SSP3-7.0 | CC-BY-40 |\\n| MIROC | MIROC-ES2L | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| MIROC | MIROC6 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| MOHC | HadGEM3-GC31-LL | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 \ |\\n| MOHC | UKESM1-0-LL | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| MPI-M | MPI-ESM1-2-LR | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| MPI-M/DKRZ [^2] | MPI-ESM1-2-HR | SSP1-2.6 and SSP5-8.5 | CC-BY-40 |\\n| NCC | NorESM2-LM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 \ |\\n| NCC | NorESM2-MM | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| NOAA-GFDL \ | GFDL-CM4 | SSP2-4.5 and SSP5-8.5 | CC-BY-40 |\\n| NOAA-GFDL | GFDL-ESM4 | SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 | CC-BY-40 |\\n| NUIST | NESM3 | SSP1-2.6, SSP2-4.5, and SSP5-8.5 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3 | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\n| EC-Earth-Consortium \ | EC-Earth3-AerChem | ssp370 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3-CC | ssp245 and ssp585 | CC-BY-40 |\\n| EC-Earth-Consortium \ | EC-Earth3-Veg | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 \ |\\n| EC-Earth-Consortium | EC-Earth3-Veg-LR | ssp1-2.6, ssp2-4.5, ssp3-7.0, and ssp5-8.5 | CC-BY-40 |\\n| CCCma | CanESM5 | ssp1-2.6, ssp2-4.5, ssp3-7.0, ssp5-8.5 | CC-BY-40[^3] \ |\\n\\n*Notes:*\\n\\n[^1]: At the time of running, no ssp1-2.6 precipitation data was available. Therefore, we provide `tasmin` and `tamax` for this model and experiment, but not `pr`. All other model/experiment combinations in the above table include all three variables.\\n\\n[^2]: The institution which ran MPI-ESM1-2-HR\u2019s historical (CMIP) simulations is `MPI-M`, while the future (ScenarioMIP) simulations were run by `DKRZ`. Therefore, the institution component of `MPI-ESM1-2-HR` filepaths differ between `historical` and `SSP` scenarios.\\n\\n[^3]: This dataset was previously licensed as [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), but was relicensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0) in March, 2023. \\n\\n## Project methods\\n\\nThis project makes use of statistical bias correction and downscaling algorithms, which are specifically designed to accurately represent changes in the extremes. For this reason, we selected Quantile Delta Mapping (QDM), following the method introduced by [Cannon et al. (2015)](https://doi.org/10.1175/JCLI-D-14-00754.1), which preserves quantile-specific trends from the GCM while fitting the full distribution for a given day-of-year to a reference dataset (ERA5).\\n\\nWe then introduce a similar method tailored to increase spatial resolution while preserving extreme behavior, Quantile-Preserving Localized-Analog Downscaling (QPLAD).\\n\\nTogether, these methods provide a robust means to handle both the central and tail behavior seen in climate model output, while aligning the full distribution to a state-of-the-art reanalysis dataset and providing the spatial granularity needed to study surface impacts.\\n\\nFor further documentation, see [Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts](https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1513/) (EGUsphere, 2022 [preprint]).\\n\\n## Citing, licensing, and using data produced by this project\\n\\nProjects making use of the data produced as part of the Climate Impact Lab Global Downscaled Projections for Climate Impacts Research (CIL GDPCIR) project are requested to cite both this project and the source datasets from which these results are derived. Additionally, the use of data derived from some GCMs *requires* citations, and some modeling centers impose licensing restrictions & requirements on derived works. See each GCM's license info in the links below for more information.\\n\\n### CIL GDPCIR\\n\\nUsers are requested to cite this project in derived works. Our method documentation paper may be cited using the following:\\n\\n> Gergel, D. R., Malevich, S. B., McCusker, K. E., Tenezakis, E., Delgado, M. T., Fish, M. A., and Kopp, R. E.: Global downscaled projections for climate impacts research (GDPCIR): preserving extremes for modeling future climate impacts, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1513, 2023. \\n\\nThe code repository may be cited using the following:\\n\\n> Diana Gergel, Kelly McCusker, Brewster Malevich, Emile Tenezakis, Meredith Fish, Michael Delgado (2022). ClimateImpactLab/downscaleCMIP6: (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6403794\\n\\n### ERA5\\n\\nAdditionally, we request you cite the historical dataset used in bias correction and downscaling, 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> Hersbach, H, et al. The ERA5 global reanalysis. Q J R Meteorol Soc.2020; 146: 1999\u20132049. DOI: [10.1002/qj.3803](https://doi.org/10.1002/qj.3803)\\n>\\n> Mu\xF1oz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n>\\n> Mu\xF1oz Sabater, J., (2021): ERA5-Land hourly data from 1950 to 1980. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on June 4, 2021), DOI: [10.24381/cds.e2161bac](https://doi.org/10.24381/cds.e2161bac)\\n\\n### GCM-specific citations & licenses\\n\\nThe CMIP6 simulation data made available through the Earth System Grid Federation (ESGF) are subject to Creative Commons [BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) or [BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) licenses. The Climate Impact Lab has reached out to each of the modeling institutions to request waivers from these terms so the outputs of this project may be used with fewer restrictions, and has been granted permission to release the data using the licenses listed here.\\n\\n#### Public Domain Datasets\\n\\nThe following bias corrected and downscaled model simulations are available in the public domain using a [CC0 1.0 Universal Public Domain Declaration](https://creativecommons.org/publicdomain/zero/1.0/). Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc0.\\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 \ CMIP Citation:\\n\\n > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 CMIP*. Version 20190826. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1783\\n\\n ScenarioMIP Citation:\\n\\n \ > Li, Lijuan **(2019)**. *CAS FGOALS-g3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190818; SSP2-4.5 version 20190818; SSP3-7.0 version 20190820; SSP5-8.5 tasmax version 20190819; SSP5-8.5 tasmin version 20190819; SSP5-8.5 pr version 20190818. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2056\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 CMIP*. Version 20190530. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1422\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM4-8 model output prepared for CMIP6 ScenarioMIP*. Version 20190603. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12321\\n\\n\\n* **INM-CM5-0**\\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 \ CMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 CMIP*. Version 20190610. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1423\\n\\n ScenarioMIP Citation:\\n\\n > Volodin, Evgeny; Mortikov, Evgeny; Gritsun, Andrey; Lykossov, Vasily; Galin, Vener; Diansky, Nikolay; Gusev, Anatoly; Kostrykin, Sergey; Iakovlev, Nikolay; Shestakova, Anna; Emelina, Svetlana **(2019)**. *INM INM-CM5-0 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190619; SSP2-4.5 version 20190619; SSP3-7.0 version 20190618; SSP5-8.5 version 20190724. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.12322\\n\\n\\n#### CC-BY-4.0\\n\\nThe following bias corrected and downscaled model simulations are licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). Note that this license requires citation of the source model output (included here). Please see https://creativecommons.org/licenses/by/4.0/ for more information. Access the collection on Planetary Computer at https://planetarycomputer.microsoft.com/dataset/cil-gdpcir-cc-by.\\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 \ CMIP Citation:\\n\\n > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2281\\n\\n ScenarioMIP Citation:\\n\\n \ > Dix, Martin; Bi, Doahua; Dobrohotoff, Peter; Fiedler, Russell; Harman, Ian; Law, Rachel; Mackallah, Chloe; Marsland, Simon; O'Farrell, Siobhan; Rashid, Harun; Srbinovsky, Jhan; Sullivan, Arnold; Trenham, Claire; Vohralik, Peter; Watterson, Ian; Williams, Gareth; Woodhouse, Matthew; Bodman, Roger; Dias, Fabio Boeira; Domingues, Catia; Hannah, Nicholas; Heerdegen, Aidan; Savita, Abhishek; Wales, Scott; Allen, Chris; Druken, Kelsey; Evans, Ben; Richards, Clare; Ridzwan, Syazwan Mohamed; Roberts, Dale; Smillie, Jon; Snow, Kate; Ward, Marshall; Yang, Rui **(2019)**. *CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2285\\n\\n\\n* **ACCESS-ESM1-5**\\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 \ CMIP Citation:\\n\\n > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2288\\n\\n ScenarioMIP Citation:\\n\\n \ > Ziehn, Tilo; Chamberlain, Matthew; Lenton, Andrew; Law, Rachel; Bodman, Roger; Dix, Martin; Wang, Yingping; Dobrohotoff, Peter; Srbinovsky, Jhan; Stevens, Lauren; Vohralik, Peter; Mackallah, Chloe; Sullivan, Arnold; O'Farrell, Siobhan; Druken, Kelsey **(2019)**. *CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP*. Version 20191115. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2291\\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 \ CMIP Citation:\\n\\n > Xin, Xiaoge; Zhang, Jie; Zhang, Fang; Wu, Tongwen; Shi, Xueli; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2018)**. *BCC BCC-CSM2MR model output prepared for CMIP6 CMIP*. Version 20181126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1725\\n\\n \ ScenarioMIP Citation:\\n\\n > Xin, Xiaoge; Wu, Tongwen; Shi, Xueli; Zhang, Fang; Li, Jianglong; Chu, Min; Liu, Qianxia; Yan, Jinghui; Ma, Qiang; Wei, Min **(2019)**. *BCC BCC-CSM2MR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190315; SSP2-4.5 version 20190318; SSP3-7.0 version 20190318; SSP5-8.5 version 20190318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1732\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP*. Version 20200616. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1362\\n\\n ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele **(2020)**. *CMCC CMCC-CM2-SR5 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200717; SSP2-4.5 version 20200617; SSP3-7.0 version 20200622; SSP5-8.5 version 20200622. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1365\\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 \ CMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 CMIP*. Version 20210114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13164\\n\\n \ ScenarioMIP Citation:\\n\\n > Lovato, Tomas; Peano, Daniele; Butensch\xF6n, Momme **(2021)**. *CMCC CMCC-ESM2 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20210126; SSP2-4.5 version 20210129; SSP3-7.0 version 20210202; SSP5-8.5 version 20210126. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.13168\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 CMIP*. Version 20200624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.639\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 ScenarioMIP*. Version 20200827. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.724\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.640\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2021)**. *EC-Earth-Consortium EC-Earth3-CC model output prepared for CMIP6 ScenarioMIP*. Version 20210113. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.15327\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 CMIP*. Version 20200217. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.643\\n\\n \ ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2020)**. *EC-Earth-Consortium EC-Earth3-Veg-LR model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20201201; SSP2-4.5 version 20201123; SSP3-7.0 version 20201123; SSP5-8.5 version 20201201. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.728\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 CMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.642\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP*. Version 20200225. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.727\\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 \ CMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.181\\n\\n ScenarioMIP Citation:\\n\\n > EC-Earth Consortium (EC-Earth) **(2019)**. *EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 ScenarioMIP*. Version 20200310. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.251\\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 \ CMIP Citation:\\n\\n > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Bushuk, Mitchell; Dunne, Krista A.; Dussin, Raphael; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, P.C.D; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1402\\n\\n ScenarioMIP Citation:\\n\\n \ > Guo, Huan; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Schwarzkopf, Daniel M; Seman, Charles J; Shao, Andrew; Silvers, Levi; Wyman, Bruce; Yan, Xiaoqin; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Held, Isaac M; Krasting, John P.; Horowitz, Larry W.; Milly, Chris; Shevliakova, Elena; Winton, Michael; Zhao, Ming; Zhang, Rong **(2018)**. *NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.9242\\n\\n\\n* **GFDL-ESM4**\\n\\n \ License description: [data_licenses/GFDL-ESM4.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/GFDL-ESM4.txt)\\n\\n \ CMIP Citation:\\n\\n > Krasting, John P.; John, Jasmin G; Blanton, Chris; McHugh, Colleen; Nikonov, Serguei; Radhakrishnan, Aparna; Rand, Kristopher; Zadeh, Niki T.; Balaji, V; Durachta, Jeff; Dupuis, Christopher; Menzel, Raymond; Robinson, Thomas; Underwood, Seth; Vahlenkamp, Hans; Dunne, Krista A.; Gauthier, Paul PG; Ginoux, Paul; Griffies, Stephen M.; Hallberg, Robert; Harrison, Matthew; Hurlin, William; Malyshev, Sergey; Naik, Vaishali; Paulot, Fabien; Paynter, David J; Ploshay, Jeffrey; Reichl, Brandon G; Schwarzkopf, Daniel M; Seman, Charles J; Silvers, Levi; Wyman, Bruce; Zeng, Yujin; Adcroft, Alistair; Dunne, John P.; Dussin, Raphael; Guo, Huan; He, Jian; Held, Isaac M; Horowitz, Larry W.; Lin, Pu; Milly, P.C.D; Shevliakova, Elena; Stock, Charles; Winton, Michael; Wittenberg, Andrew T.; Xie, Yuanyu; Zhao, Ming **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP*. Version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1407\\n\\n ScenarioMIP Citation:\\n\\n \ > John, Jasmin G; Blanton, Chris; McHugh, Colleen; Radhakrishnan, Aparna; Rand, Kristopher; Vahlenkamp, Hans; Wilson, Chandin; Zadeh, Niki T.; Dunne, John P.; Dussin, Raphael; Horowitz, Larry W.; Krasting, John P.; Lin, Pu; Malyshev, Sergey; Naik, Vaishali; Ploshay, Jeffrey; Shevliakova, Elena; Silvers, Levi; Stock, Charles; Winton, Michael; Zeng, Yujin **(2018)**. *NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 ScenarioMIP*. Version 20180701. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1414\\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 \ CMIP Citation:\\n\\n > Ridley, Jeff; Menary, Matthew; Kuhlbrodt, Till; Andrews, Martin; Andrews, Tim **(2018)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP*. Version 20190624. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.419\\n\\n ScenarioMIP Citation:\\n\\n \ > Good, Peter **(2019)**. *MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20200114; SSP2-4.5 version 20190908; SSP5-8.5 version 20200114. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.10845\\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 \ CMIP Citation:\\n\\n > Hajima, Tomohiro; Abe, Manabu; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogura, Tomoo; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio; Tachiiri, Kaoru **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 CMIP*. Version 20191129. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.902\\n\\n \ ScenarioMIP Citation:\\n\\n > Tachiiri, Kaoru; Abe, Manabu; Hajima, Tomohiro; Arakawa, Osamu; Suzuki, Tatsuo; Komuro, Yoshiki; Ogochi, Koji; Watanabe, Michio; Yamamoto, Akitomo; Tatebe, Hiroaki; Noguchi, Maki A.; Ohgaito, Rumi; Ito, Akinori; Yamazaki, Dai; Ito, Akihiko; Takata, Kumiko; Watanabe, Shingo; Kawamiya, Michio **(2019)**. *MIROC MIROC-ES2L model output prepared for CMIP6 ScenarioMIP*. Version 20200318. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.936\\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 \ CMIP Citation:\\n\\n > Tatebe, Hiroaki; Watanabe, Masahiro **(2018)**. *MIROC MIROC6 model output prepared for CMIP6 CMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.881\\n\\n ScenarioMIP Citation:\\n\\n > Shiogama, Hideo; Abe, Manabu; Tatebe, Hiroaki **(2019)**. *MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP*. Version 20191016. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.898\\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 \ CMIP Citation:\\n\\n > Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann; Wachsmann, Fabian; Schupfner, Martin; Legutke, Stephanie; Giorgetta, Marco; Reick, Christian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Esch, Monika; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.741\\n\\n \ ScenarioMIP Citation:\\n\\n > Schupfner, Martin; Wieners, Karl-Hermann; Wachsmann, Fabian; Steger, Christian; Bittner, Matthias; Jungclaus, Johann; Fr\xFCh, Barbara; Pankatz, Klaus; Giorgetta, Marco; Reick, Christian; Legutke, Stephanie; Esch, Monika; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2450\\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 \ CMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Legutke, Stephanie; Schupfner, Martin; Wachsmann, Fabian; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 CMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.742\\n\\n \ ScenarioMIP Citation:\\n\\n > Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann; Reick, Christian; Esch, Monika; Bittner, Matthias; Gayler, Veronika; Haak, Helmuth; de Vrese, Philipp; Raddatz, Thomas; Mauritsen, Thorsten; von Storch, Jin-Song; Behrens, J\xF6rg; Brovkin, Victor; Claussen, Martin; Crueger, Traute; Fast, Irina; Fiedler, Stephanie; Hagemann, Stefan; Hohenegger, Cathy; Jahns, Thomas; Kloster, Silvia; Kinne, Stefan; Lasslop, Gitta; Kornblueh, Luis; Marotzke, Jochem; Matei, Daniela; Meraner, Katharina; Mikolajewicz, Uwe; Modali, Kameswarrao; M\xFCller, Wolfgang; Nabel, Julia; Notz, Dirk; Peters-von Gehlen, Karsten; Pincus, Robert; Pohlmann, Holger; Pongratz, Julia; Rast, Sebastian; Schmidt, Hauke; Schnur, Reiner; Schulzweida, Uwe; Six, Katharina; Stevens, Bjorn; Voigt, Aiko; Roeckner, Erich **(2019)**. *MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP*. Version 20190710. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.793\\n\\n\\n* **NESM3**\\n\\n \ License description: [data_licenses/NESM3.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/NESM3.txt)\\n\\n \ CMIP Citation:\\n\\n > Cao, Jian; Wang, Bin **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 CMIP*. Version 20190812. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2021\\n\\n ScenarioMIP Citation:\\n\\n \ > Cao, Jian **(2019)**. *NUIST NESMv3 model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190806; SSP2-4.5 version 20190805; SSP5-8.5 version 20190811. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.2027\\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 \ CMIP Citation:\\n\\n > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 CMIP*. Version 20190815. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.502\\n\\n ScenarioMIP Citation:\\n\\n \ > Seland, \xD8yvind; Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-LM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.604\\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 \ CMIP Citation:\\n\\n > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 CMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.506\\n\\n ScenarioMIP Citation:\\n\\n \ > Bentsen, Mats; Olivi\xE8, Dirk Jan Leo; Seland, \xD8yvind; Toniazzo, Thomas; Gjermundsen, Ada; Graff, Lise Seland; Debernard, Jens Boldingh; Gupta, Alok Kumar; He, Yanchun; Kirkev\xE5g, Alf; Schwinger, J\xF6rg; Tjiputra, Jerry; Aas, Kjetil Schanke; Bethke, Ingo; Fan, Yuanchao; Griesfeller, Jan; Grini, Alf; Guo, Chuncheng; Ilicak, Mehmet; Karset, Inger Helene Hafsahl; Landgren, Oskar Andreas; Liakka, Johan; Moseid, Kine Onsum; Nummelin, Aleksi; Spensberger, Clemens; Tang, Hui; Zhang, Zhongshi; Heinze, Christoph; Iversen, Trond; Schulz, Michael **(2019)**. *NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP*. Version 20191108. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.608\\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 \ CMIP Citation:\\n\\n > Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Mulcahy, Jane; Sellar, Alistair; Walton, Jeremy; Jones, Colin **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP*. Version 20190627. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1569\\n\\n \ ScenarioMIP Citation:\\n\\n > Good, Peter; Sellar, Alistair; Tang, Yongming; Rumbold, Steve; Ellis, Rich; Kelley, Douglas; Kuhlbrodt, Till; Walton, Jeremy **(2019)**. *MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP*. SSP1-2.6 version 20190708; SSP2-4.5 version 20190715; SSP3-7.0 version 20190726; SSP5-8.5 version 20190726. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1567\\n\\n* **CanESM5**\\n\\n License description: [data_licenses/CanESM5.txt](https://raw.githubusercontent.com/ClimateImpactLab/downscaleCMIP6/master/data_licenses/CanESM5.txt). Note: this dataset was previously licensed\\n under CC BY-SA 4.0, but was relicensed as CC BY 4.0 in March, 2023.\\n\\n CMIP Citation:\\n\\n > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 CMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1303\\n\\n ScenarioMIP Citation:\\n\\n \ > Swart, Neil Cameron; Cole, Jason N.S.; Kharin, Viatcheslav V.; Lazare, Mike; Scinocca, John F.; Gillett, Nathan P.; Anstey, James; Arora, Vivek; Christian, James R.; Jiao, Yanjun; Lee, Warren G.; Majaess, Fouad; Saenko, Oleg A.; Seiler, Christian; Seinen, Clint; Shao, Andrew; Solheim, Larry; von Salzen, Knut; Yang, Duo; Winter, Barbara; Sigmond, Michael **(2019)**. *CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP*. Version 20190429. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.1317\\n\\n## Acknowledgements\\n\\nThis work is the result of many years worth of work by members of the [Climate Impact Lab](https://impactlab.org), but would not have been possible without many contributions from across the wider scientific and computing communities.\\n\\nSpecifically, we would like to acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we would like to thank the climate modeling groups for producing and making their model output available. We would particularly like to thank the modeling institutions whose results are included as an input to this repository (listed above) for their contributions to the CMIP6 project and for responding to and granting our requests for license waivers.\\n\\nWe would also like to thank Lamont-Doherty Earth Observatory, the [Pangeo Consortium](https://github.com/pangeo-data) (and especially the [ESGF Cloud Data Working Group](https://pangeo-data.github.io/pangeo-cmip6-cloud/#)) and Google Cloud and the Google Public Datasets program for making the [CMIP6 Google Cloud collection](https://console.cloud.google.com/marketplace/details/noaa-public/cmip6) possible. In particular we're extremely grateful to [Ryan Abernathey](https://github.com/rabernat), [Naomi Henderson](https://github.com/naomi-henderson), [Charles Blackmon-Luca](https://github.com/charlesbluca), [Aparna Radhakrishnan](https://github.com/aradhakrishnanGFDL), [Julius Busecke](https://github.com/jbusecke), and [Charles Stern](https://github.com/cisaacstern) for the huge amount of work they've done to translate the ESGF CMIP6 netCDF archives into consistently-formattted, analysis-ready zarr stores on Google Cloud.\\n\\nWe're also grateful to the [xclim developers](https://github.com/Ouranosinc/xclim/graphs/contributors) ([DOI: 10.5281/zenodo.2795043](https://doi.org/10.5281/zenodo.2795043)), in particular [Pascal Bourgault](https://github.com/aulemahal), [David Huard](https://github.com/huard), and [Travis Logan](https://github.com/tlogan2000), for implementing the QDM bias correction method in the xclim python package, supporting our QPLAD implementation into the package, and ongoing support in integrating dask into downscaling workflows. 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\"image/tiff\",\n \"title\": \"Band 7 (swir22)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/015/033/LC08_L1TP_015033_20180708_20180717_01_T1/LC08_L1TP_015033_20180708_20180717_01_T1_B7.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B7\",\n \"full_width_half_max\" : 0.22,\n \"center_wavelength\" : 2.2,\n \"common_name\" : \"swir22\"\n }\n ]\n },\n \ \"B8\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 8 (pan)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/015/033/LC08_L1TP_015033_20180708_20180717_01_T1/LC08_L1TP_015033_20180708_20180717_01_T1_B8.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B8\",\n \"full_width_half_max\" : 0.18,\n \"center_wavelength\" : 0.59,\n \"common_name\" : \"pan\"\n }\n ]\n },\n \ \"B9\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 9 (cirrus)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/015/033/LC08_L1TP_015033_20180708_20180717_01_T1/LC08_L1TP_015033_20180708_20180717_01_T1_B9.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B9\",\n \"full_width_half_max\" : 0.02,\n \"center_wavelength\" : 1.37,\n \"common_name\" : \"cirrus\"\n }\n ]\n },\n \ \"B10\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 10 (lwir)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/015/033/LC08_L1TP_015033_20180708_20180717_01_T1/LC08_L1TP_015033_20180708_20180717_01_T1_B10.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B10\",\n \"full_width_half_max\" : 0.8,\n \"center_wavelength\" : 10.9,\n \"common_name\" : \"lwir11\"\n }\n ]\n },\n \ \"B11\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 11 (lwir)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/015/033/LC08_L1TP_015033_20180708_20180717_01_T1/LC08_L1TP_015033_20180708_20180717_01_T1_B11.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B11\",\n \"full_width_half_max\" : 1,\n \"center_wavelength\" : 12,\n \"common_name\" : \"lwir2\"\n }\n ]\n },\n \ \"ANG\": {\n \"title\": \"Angle coefficients file\",\n \"type\": \"text/plain\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/015/033/LC08_L1TP_015033_20180708_20180717_01_T1/LC08_L1TP_015033_20180708_20180717_01_T1_ANG.txt\",\n \ \"roles\": []\n },\n \"MTL\": {\n \"title\": \"original metadata file\",\n \"type\": \"text/plain\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/015/033/LC08_L1TP_015033_20180708_20180717_01_T1/LC08_L1TP_015033_20180708_20180717_01_T1_MTL.txt\",\n \ \"roles\": []\n },\n \"BQA\": {\n \"title\": \"Band quality data\",\n \"type\": \"image/tiff\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/015/033/LC08_L1TP_015033_20180708_20180717_01_T1/LC08_L1TP_015033_20180708_20180717_01_T1_BQA.TIF\",\n \ \"roles\": []\n }\n },\n \"links\": [\n {\n \"rel\": \"self\",\n \ \"href\": \"./LC80150332018189LGN00.json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\"\n },\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../catalog.json\"\n }\n ]\n}\n" headers: Accept-Ranges: - bytes Access-Control-Allow-Origin: - '*' Cache-Control: - max-age=300 Connection: - keep-alive Content-Encoding: - gzip Content-Length: - '1375' Content-Security-Policy: - default-src 'none'; style-src 'unsafe-inline'; sandbox Content-Type: - text/plain; charset=utf-8 Cross-Origin-Resource-Policy: - cross-origin Date: - Thu, 31 Jul 2025 15:07:39 GMT ETag: - W/"f1956bfe0994b5049095141c760f2d24f565019a6e81e1aec1816740157df998" Expires: - Thu, 31 Jul 2025 15:12:39 GMT Source-Age: - '0' Strict-Transport-Security: - max-age=31536000 Vary: - Authorization,Accept-Encoding Via: - 1.1 varnish X-Cache: - MISS X-Cache-Hits: - '0' X-Content-Type-Options: - nosniff X-Fastly-Request-ID: - 87edae353c9a8817346824d46ba4d79ca5151404 X-Frame-Options: - deny X-GitHub-Request-Id: - B01D:2CF9F0:436DB5:553CE8:688B86B8 X-Served-By: - cache-den-kden1300097-DEN X-Timer: - S1753974460.579919,VS0,VE98 X-XSS-Protection: - 1; mode=block status: code: 200 message: OK - request: body: null headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - python-requests/2.32.4 method: GET uri: https://raw.githubusercontent.com/stac-utils/pystac/v1.9.0/docs/example-catalog/landsat-8-l1/2018-06/LC80300332018166LGN00.json response: body: string: "{\n \"type\": \"Feature\",\n \"stac_version\" : \"1.0.0\",\n \"stac_extensions\" : [\n \"https://stac-extensions.github.io/eo/v1.1.0/schema.json\",\n \"https://stac-extensions.github.io/view/v1.0.0/schema.json\",\n \ \"https://stac-extensions.github.io/projection/v1.1.0/schema.json\"\n \ ],\n \"id\": \"LC80300332018166LGN00\",\n \"bbox\": [\n -101.40793,\n \ 37.81084,\n -98.6721,\n 39.97469\n ],\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.84368079413701,\n \ 39.97210491033466\n ],\n [\n -98.67492641719046,\n \ 39.54833037653145\n ],\n [\n -99.23946071016417,\n \ 37.81370881408165\n ],\n [\n -101.40560438472555,\n \ 38.24476872678675\n ],\n [\n -100.84368079413701,\n \ 39.97210491033466\n ]\n ]\n ]\n },\n \"collection\": \"landsat-8-l1\",\n \"properties\": {\n \"collection\": \"landsat-8-l1\",\n \ \"datetime\": \"2018-06-15T17:18:03Z\",\n \"view:sun_azimuth\": 125.5799919,\n \ \"view:sun_elevation\": 66.54407242,\n \"eo:cloud_cover\": 0,\n \"instruments\": [\"OLI_TIRS\"],\n \"view:off_nadir\": 0,\n \"platform\": \"landsat-8\",\n \ \"gsd\": 30,\n \"proj:epsg\": 32614,\n \"proj:transform\": [294285.0, 30.0, 0.0, 4425015.0, 0.0, -30],\n \"proj:geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [294285.0, 4187385.0],\n \ [294285.0, 4425015.0],\n [528015.0, 4425015.0],\n [528015.0, 4187385.0],\n [294285.0, 4187385.0]\n ]\n ]\n },\n \ \"proj:shape\": [7791, 7921]\n },\n \"assets\": {\n \"index\": {\n \ \"type\": \"text/html\",\n \"title\": \"HTML index page\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/index.html\",\n \ \"roles\" : []\n },\n \"thumbnail\": {\n \"title\": \"Thumbnail image\",\n \"type\": \"image/jpeg\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_thumb_large.jpg\",\n \ \"roles\" : [\n \"thumbnail\"\n ]\n },\n \"B1\": {\n \ \"type\": \"image/tiff\",\n \"title\": \"Band 1 (coastal)\",\n \ \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B1.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B1\",\n \"full_width_half_max\" : 0.02,\n \"center_wavelength\" : 0.44,\n \"common_name\" : \"coastal\"\n }\n ]\n },\n \ \"B2\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 2 (blue)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B2.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B2\",\n \"full_width_half_max\" : 0.06,\n \"center_wavelength\" : 0.48,\n \"common_name\" : \"blue\"\n }\n ]\n },\n \ \"B3\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 3 (green)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B3.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B3\",\n \"full_width_half_max\" : 0.06,\n \"center_wavelength\" : 0.56,\n \"common_name\" : \"green\"\n }\n ]\n },\n \ \"B4\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 4 (red)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B4.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B4\",\n \"full_width_half_max\" : 0.04,\n \"center_wavelength\" : 0.65,\n \"common_name\" : \"red\"\n }\n ]\n },\n \ \"B5\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 5 (nir)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B5.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B5\",\n \"full_width_half_max\" : 0.03,\n \"center_wavelength\" : 0.86,\n \"common_name\" : \"nir\"\n }\n ]\n },\n \ \"B6\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 6 (swir16)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B6.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B6\",\n \"full_width_half_max\" : 0.08,\n \"center_wavelength\" : 1.6,\n \"common_name\" : \"swir16\"\n }\n ]\n },\n \ \"B7\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 7 (swir22)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B7.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B7\",\n \"full_width_half_max\" : 0.22,\n \"center_wavelength\" : 2.2,\n \"common_name\" : \"swir22\"\n }\n ]\n },\n \ \"B8\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 8 (pan)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B8.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B8\",\n \"full_width_half_max\" : 0.18,\n \"center_wavelength\" : 0.59,\n \"common_name\" : \"pan\"\n }\n ]\n },\n \ \"B9\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 9 (cirrus)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B9.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B9\",\n \"full_width_half_max\" : 0.02,\n \"center_wavelength\" : 1.37,\n \"common_name\" : \"cirrus\"\n }\n ]\n },\n \ \"B10\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 10 (lwir)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B10.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B10\",\n \"full_width_half_max\" : 0.8,\n \"center_wavelength\" : 10.9,\n \"common_name\" : \"lwir11\"\n }\n ]\n },\n \ \"B11\": {\n \"type\": \"image/tiff\",\n \"title\": \"Band 11 (lwir)\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_B11.TIF\",\n \ \"roles\": [],\n \"eo:bands\": [\n {\n \"name\" : \"B11\",\n \"full_width_half_max\" : 1,\n \"center_wavelength\" : 12,\n \"common_name\" : \"lwir2\"\n }\n ]\n },\n \ \"ANG\": {\n \"title\": \"Angle coefficients file\",\n \"type\": \"text/plain\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_ANG.txt\",\n \ \"roles\": []\n },\n \"MTL\": {\n \"title\": \"original metadata file\",\n \"type\": \"text/plain\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_MTL.txt\",\n \ \"roles\": []\n },\n \"BQA\": {\n \"title\": \"Band quality data\",\n \"type\": \"image/tiff\",\n \"href\": \"https://s3-us-west-2.amazonaws.com/landsat-pds/c1/L8/030/033/LC08_L1TP_030033_20180615_20180703_01_T1/LC08_L1TP_030033_20180615_20180703_01_T1_BQA.TIF\",\n \ \"roles\": []\n }\n },\n \"links\": [\n {\n \"rel\": \"self\",\n \ \"href\": \"./LC80300332018166LGN00.json\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"../collection.json\"\n },\n {\n \"rel\": \"collection\",\n \"href\": \"../collection.json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"../../catalog.json\"\n }\n ]\n}\n" headers: Accept-Ranges: - bytes Access-Control-Allow-Origin: - '*' Cache-Control: - max-age=300 Connection: - keep-alive Content-Encoding: - gzip Content-Length: - '1384' Content-Security-Policy: - default-src 'none'; style-src 'unsafe-inline'; sandbox Content-Type: - text/plain; charset=utf-8 Cross-Origin-Resource-Policy: - cross-origin Date: - Thu, 31 Jul 2025 15:07:39 GMT ETag: - W/"6da3b66dc885889ceff60c1a0618fcc959b22734a7a4d9646fe0f55f0869ca5f" Expires: - Thu, 31 Jul 2025 15:12:39 GMT Source-Age: - '0' Strict-Transport-Security: - max-age=31536000 Vary: - Authorization,Accept-Encoding Via: - 1.1 varnish X-Cache: - MISS X-Cache-Hits: - '0' X-Content-Type-Options: - nosniff X-Fastly-Request-ID: - 644b3f6a0cc1868ac4c1d8db5eb862be5f6656f4 X-Frame-Options: - deny X-GitHub-Request-Id: - 1AD7:1EA0F5:74C855:963BCF:688B86B8 X-Served-By: - cache-den-kden1300066-DEN X-Timer: - S1753974460.713381,VS0,VE111 X-XSS-Protection: - 1; mode=block status: code: 200 message: OK version: 1 ================================================ FILE: tests/cassettes/test_client/test_query_string_in_collections_url.yaml ================================================ interactions: - request: body: null headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - python-requests/2.32.4 method: GET uri: https://paituli.csc.fi/geoserver/ogc/stac/v1 response: body: string: '{"title":"Paituli STAC","description":"Paituli STAC with Finnish data. More info: https://paituli.csc.fi/stac.html","links":[{"href":"https://paituli.csc.fi/geoserver/ogc/stac/v1/?f=application%2Fjson","rel":"self","type":"application/json","title":"This document"},{"href":"https://paituli.csc.fi/geoserver/ogc/stac/v1/?f=application%2Fyaml","rel":"alternate","type":"application/yaml","title":"This document as application/yaml"},{"href":"https://paituli.csc.fi/geoserver/ogc/stac/v1/?f=text%2Fhtml","rel":"alternate","type":"text/html","title":"This document as text/html"},{"href":"https://paituli.csc.fi/geoserver/ogc/stac/v1/openapi?f=application%2Fvnd.oai.openapi%2Bjson%3Bversion%3D3.0","rel":"service-desc","type":"application/vnd.oai.openapi+json;version=3.0","title":"API definition for this endpoint as application/vnd.oai.openapi+json;version=3.0"},{"href":"https://paituli.csc.fi/geoserver/ogc/stac/v1/openapi?f=application%2Fyaml","rel":"service-desc","type":"application/yaml","title":"API definition for this endpoint as 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These SST use the ARC SST retrieval and cloud screening which differ from the methods used to produce the Gridded Surface Temperature (ATS_NR__2P) products. In addition to SST, the L2P products contain the ATSR Saharan Dust Index (ASDI) and the clear-sky probability estimated by the ARC cloud detection algorithm. The L2P processor also generates L3U products; these are the L2P products averaged onto a regular grid at 0.1 degree resolution (they are therefore similar to the AR / Meteo Envisat-format products). \ The L2P and L3U products are provided in NetCDF-4 format following GHRSST Data Specifications (GDS) v2. 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\"https://earth.esa.int/eogateway/documents/20142/37627/ENVISAT-1%20PRODUCTS%20SPECIFICATIONS%20-%20VOLUME%207-%20AATSR%20PRODUCTS%20SPECIFICATIONS\",\n \ \"type\": \"application/pdf\",\n \"title\": \"AATSR Products Specifications - Product Specifications\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/AATSR-product-handbook.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"AATSR Product Handbook - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/instruments/aatsr/products-information\",\n \ \"type\": \"text/html\",\n \"title\": \"AATSR Product Information - Product Information\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/L2P-Product-Description.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"(A)ATSR SST L2P/L3U Product User Guide - Product Specifications\"\n },\n {\n \ \"rel\": \"describedby\",\n 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\"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Envisat\",\n \ \"title\": \"More collections for Envisat platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/702277a0-8a84-5af3-a8f4-31d712ff17c3\",\n \ \"title\": \"More collections for AATSR instrument\"\n }\n \ ],\n \"id\": \"ENVISAT.UPA-L2P-L3U\",\n \"updated\": \"2022-11-11T00:00:00.00Z\",\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"AATSR\"],\n \ \"platform\": [\"Envisat\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n \ 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"2021-10-09T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"FedEO\",\n \"SCIHUB\",\n \"DIF10\",\n \ \"Sea Surface Topography\",\n \"EARTH SCIENCE > OCEANS > SEA SURFACE TOPOGRAPHY\",\n \"Radar Altimeters\",\n \"Radiometers\",\n \ \"Sentinel-6\",\n \"POSEIDON-4\",\n \"AMR-C\"\n ],\n \ \"created\": \"2024-01-02T00:00:00.00Z\",\n \"description\": \"Copernicus Sentinel-6 Michael Freilich is an Earth Observation satellite mission developed to provide enhanced continuity to the very stable time series of mean sea level measurements and ocean sea state that started in 1992, with the TOPEX/Poseidon mission, then continued by the Jason-1, Jason-2 and Jason-3 satellite missions.\",\n \ \"type\": \"Collection\",\n \"title\": \"Sentinel-6 Products\",\n \ 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\"title\": \"More collections for TROPOMI instrument\"\n }\n \ ],\n \"id\": \"EOP:ESA:Sentinel-5P\",\n \"updated\": \"2017-04-30T00:00:00.00Z\",\n \ \"stac_extensions\": [\"https://stac-extensions.github.io/processing/v1.2.0/schema.json\"],\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\"\n },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n }\n ],\n \"summaries\": {\n \"instruments\": [\"TROPOMI\"],\n \"processing:lineage\": [\"The Sentinel-5 Precursor objectives are to provide operational space-borne observations in support to the operational monitoring of Air Quality, Climate, Ozone and Surface UV. It will provide measurements of Ozone, NO_2, SO_2, Formaldehyde, Aerosol, Carbonmonoxide, Methane, Clouds.\"],\n \"platform\": [\"Sentinel-5P\"]\n \ }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n 90\n ]]},\n \ \"temporal\": {\"interval\": [[\n \"2016-06-22T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"Planet\",\n \"Land Surface\",\n \"Mapping and Cartography\",\n \"Natural Hazards and Disaster Risk\",\n \"EARTH SCIENCE > LAND SURFACE\",\n \"EARTH SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS\",\n \"VIS (0.40 - 0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"Sun-synchronous\",\n \ \"Very High Resolution - VHR (0 - 5m)\",\n \"475 km (reference)\",\n \ \"25 km\",\n \"Imaging Spectrometers/Radiometers\",\n \"PlanetScope\",\n \ \"PlanetScope Camera\"\n ],\n \"created\": \"2019-08-09T00:00:00.00Z\",\n \ \"description\": \"The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components:\\t - Image File (GeoTIFF format) - Metadata File (XML format) - Rational Polynomial Coefficients (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance:\\t - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components:\\t - Image File (GeoTIFF format) - Metadata File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 3-band natural color (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance:\\t - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and color-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. \ PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/ can be accessed (Planet registration requested) to discover and check the data readiness. It is worth to mention that the data distribution is based on Data Voucher, corresponding to maximum amount of square kilometers can be ordered and downloaded by the project in a maximum period of 15 moths (this duration cannot be extended) starting from the project proposal acceptance date. Each Date Voucher includes PlanetScope tile view streaming access for a total of 20,000 tiles per calendar month during the project period. All details about the data provision, data access conditions and quota assignment 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 available in Resources section.\",\n \"type\": \"Collection\",\n \"title\": \"PlanetScope Full Archive\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://www.planet.com/products/#satellite-imagery\",\n \ \"type\": \"text/html\",\n \"title\": \"Planet products - Catalogue\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Planet-combined-imagery-product-specs-2020.pdf?text=Combined-Imagery-Product-Spec.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Imagery Product Specifications - Product Specifications\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://developers.planet.com/docs/apps/explorer/\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Explorer User Guide - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://www.planet.com/explorer/\",\n \"type\": \"text/html\",\n \"title\": \"Planet Explorer Catalogue - Catalogue\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Terms of Applicability - Access Guide\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? 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- ESA Earth Observation User Services Portal\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/atom%2Bxml\",\n \ \"type\": \"application/atom+xml\",\n \"title\": \"Atom format\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/xml\",\n \ \"type\": \"application/xml\",\n \"title\": \"Dublin Core metadata\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/ERS-1\",\n \ \"title\": \"More collections for ERS-1 platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/ERS-2\",\n \ \"title\": \"More collections for ERS-2 platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/93343b90-3e9f-5830-abe7-151cc69401c1\",\n \ \"title\": \"More collections for ATSR-1 instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/f3ef7078-65ef-5dee-8c5c-61f9706a34ad\",\n \ \"title\": \"More collections for ATSR-2 instrument\"\n }\n \ ],\n \"id\": \"ERS.AT_NR__2P\",\n \"updated\": \"2023-06-13T13:23:39Z\",\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\n \ \"ATSR-1\",\n \"ATSR-2\"\n ],\n \"platform\": [\n \"ERS-1\",\n \"ERS-2\"\n ]\n }\n },\n \ {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n \ -90,\n 180,\n 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"1995-07-01T00:00:00.000Z\",\n \"2015-12-31T23:59:59.999Z\"\n \ ]]}\n },\n \"stac_version\": \"1.0.0\",\n \"sci:doi\": \"10.57780/esa-1268efe\",\n \"keywords\": [\n \"World\",\n \"Atmosphere\",\n \ \"Atmospheric Chemistry\",\n \"Atmospheric Radiation\",\n \"Atmospheric Temperature\",\n \"Climate\",\n \"EARTH SCIENCE > ATMOSPHERE\",\n \ \"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY\",\n \"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION\",\n \"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE\",\n \"EARTH SCIENCE > CLIMATE INDICATORS\",\n \"Sun-synchronous\",\n \"GDP 5.0\",\n \"782 to 785 km\",\n \"5 km\",\n \"Spectrometers\",\n \"ERS-2\",\n \ \"GOME\"\n ],\n \"created\": \"2019-05-29T00:00:00.00Z\",\n \ \"description\": \"The GOME Total Column Water Vapour (TCWV) Climate product was generated by the Max Planck Institute for Chemistry (MPIC), and the German Aerospace Center (DLR) within the ESA GOME-Evolution project. It is a Level 3 type product containing homogenized time-series of the global distribution of TCWV spanning over more than two decades (1995-2015). The data is provided as single netCDF file, containing monthly mean TCWV (units kg/m2) with 1-degree resolution, and is based on measurements from the satellite instruments ERS-2 GOME, Envisat SCIAMACHY, and MetOp-A GOME-2. Details are available in the paper by Beirle et al, 2018,. Please also consult the GOME TCWV Product Quality Readme file before using the data. 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Product Specifications\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/GOME-Level1-to-2-Algorithms-Description-DLFE-841-GDP-L12.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"GOME Level 1 to 2 Algorithms Description - Technical Note\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Algorithm-Theoretical-Basis-Document-for_GOME-Total-Column-Densities-of-Ozone-And-Nitrogen-Dioxid.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Algorithm Theoretical Basis Document for GOME Total Column Densities of Ozone and Nitrogen Dioxide - Technical Note\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/GOME-Disclaimer-2004-DLFE-640.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Disclaimer for GOME Level-1 and Level-2 Data Products - Technical Note\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/GOME-Software-Databases-For-Level1-to-2-Processing-DLFE-843-GDP-DB.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"GOME Software Databases for Level 1 to 2 Processing - Technical Note\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Data-Validation-Report-for-ERS-2-GOME-Data-Processor-upgrade-to-version4.0-DLFE-641.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Delta Validation Report for ERS-2 GOME Data Processor upgrade toversion 4.0 - Technical Note\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/GOME-Data-Processor-DLFE-656-DLR-GOME-SUM-gdp01-ex-4.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"GOME Data Processor Extraction Software - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/search?category=Document+library&filter=gome\",\n \ \"type\": \"text/html\",\n \"title\": \"More GOME Documents - Other\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/instruments/gome/products-information\",\n \ \"type\": \"text/html\",\n \"title\": \"Documentation related to GOME data quality - 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0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \ \"Sun-synchronous\",\n \"Very High Resolution - VHR (0 - 5m)\",\n \ \"High Resolution - HR (5 - 20 m)\",\n \"450 km\",\n \"16.5 km\",\n \"Cameras\",\n \"QuickBird-2\",\n \"BGI\"\n ],\n \ \"created\": \"2019-05-22T00:00:00.00Z\",\n \"description\": \"QuickBird high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, QuickBird offers archive panchromatic products up to 0.60m GSD resolution and 4-Bands Multispectral products up to 2.4m GSD resolution \\u2022 Panchromatic and 4-bands - Levels: STANDARD (2A) / VIEW READY STANDARD (OR2A), VIEW READY STEREO, MAP-READY (ORTHO) 1:12.000 Orthorectified, MAP-READY (ORTHO) 1:50.000 Orthorectified - Resolutions: 0.30 m, 0.40 m, 0.50 m. 0.60 m \\u2022 8-Bands - Levels: STANDARD (2A) / VIEW READY STANDARD (OR2A), VIEW READY STEREO, MAP-READY (ORTHO) 1:12.000 Orthorectified - Resolutions: 0.30 m, 0.40 m, 0.50 m. 0.60 m 4-Bands being an optional from: \\u2022\\u2022 4-Band Multispectral (BLUE, GREEN, RED, NIR1) \\u2022 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) \\u2022 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) \\u2022 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) \\u2022 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) \\u2022 Natural Colour / Coloured Infrared (3-Band pan-sharpened) The list of available archived data can be retrieved using the Image Library (https://www.euspaceimaging.com/image-library/) catalogue.\",\n \"type\": \"Collection\",\n \"title\": \"QuickBird full archive\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Map-Ready-imagery-data-sheet.pdf/83b9a034-5548-1fc7-4744-8d0fdb922f52\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Map Ready data imagery sheet - Product Sepcification\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/DigitalGlobe-GeoStereo.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"GeoStereo product details - Product Sepcification\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/DigitalGlobe-Stereo-Imagery.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Stereo product details - Product Sepcification\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/view-ready-imagery-data-sheet.pdf/e1476ac6-51cc-6052-c138-844dcc0200b8\",\n \ \"type\": \"application/pdf\",\n \"title\": \"View Ready imagery data sheet - Product Sepcification\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/WorldView-GeoEye-QuickBird-Terms-Of-Applicability.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Indian Data Terms of Applicability - 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0.4 \xB5m)\",\n \"VIS (0.40 - 0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"SWIR (1.3 - 3.0 \xB5m)\",\n \"MWIR (3.0 - 6.0 \xB5m)\",\n \"4.0\",\n \"Very Low Resolution - VLR (> 1200 m)\",\n \"Imaging Spectrometers/Radiometers\",\n \"Scatterometers\",\n \ \"TERRA\",\n \"AQUA\",\n \"QuikSCAT\",\n \"OrbView-2\",\n \ \"MODIS\",\n \"SeaWinds\",\n \"SeaWiFS\"\n ],\n \ \"created\": \"2019-11-21T00:00:00.00Z\",\n \"description\": \"ADAM enables generating typical monthly variations of the global Earth surface reflectance at 0.1\xB0 spatial resolution (Plate Carree projection) and over the spectral range 240-4000nm. The ADAM product is made of gridded monthly mean climatologies over land and ocean surfaces, and of a companion API toolkit that enables the calculation of hyperspectral (at 1 nm resolution over the whole 240-4000 nm spectral range) and multidirectional reflectances (i.e. in any illumination/viewing geometry) depending on user choices. The ADAM climatologies that feed the ADAM calculation tools are: For ocean: monthly chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is used to compute the water column reflectance (which shows large spectral variations in the visible, but is insignificant in the near and mid infrared). monthly wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate the ocean glint reflectance. For land: monthly normalized surface reflectances in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain of MOD09A1 products (derived from Aqua and Terra observations), on which relies the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow) reflectance. uncertainty variance-covariance matrix for the 7 spectral bands associated to the normalized surface reflectance. 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0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"Sun-synchronous\",\n \ \"Very High Resolution - VHR (0 - 5m)\",\n \"475 km (reference)\",\n \ \"25 km\",\n \"Imaging Spectrometers/Radiometers\",\n \"PlanetScope\",\n \ \"PlanetScope Camera\"\n ],\n \"created\": \"2019-08-09T00:00:00.00Z\",\n \ \"description\": \"The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components:\\t - Image File (GeoTIFF format) - Metadata File (XML format) - Rational Polynomial Coefficients (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance:\\t - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components:\\t - Image File (GeoTIFF format) - Metadata File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 3-band natural color (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance:\\t - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and color-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. \ PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/ can be accessed (Planet registration requested) to discover and check the data readiness. It is worth to mention that the data distribution is based on Data Voucher, corresponding to maximum amount of square kilometers can be ordered and downloaded by the project in a maximum period of 15 moths (this duration cannot be extended) starting from the project proposal acceptance date. Each Date Voucher includes PlanetScope tile view streaming access for a total of 20,000 tiles per calendar month during the project period. 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The SkySat Basic Scene product is uncalibrated and in a raw digital number format, not corrected for any geometric distortions inherent in the imaging process. Rational Polynomial Coefficients (RPCs) is provided to enable orthorectification by the user. Basic Scene Product Components and Format Processing levels\\t/\\t- Analytic (unorthorectified, radiometrically corrected, multispectral BGRN); - Analytic DN (unorthorectified, multispectral BGRN); - Panchromatic DN (unorthorectified, panchromatic)\\t// Product Components and Format\\t/\\t- Image File (GeoTIFF format); - Metadata File (JSON format); - Rational Polynomial Coefficients (Text File); - UDM File (GeoTIFF format)\\t// Image configuration\\t/\\t- 4-band Analytic DN Image (Blue, Green, Red, NIR); - 1-band Panchromatic DN Image (Pan)\\t// Ground Sampling Distance\\t/\\t3.7 m at nadir (average at reference altitude 475 km)\\t// Ground Sampling Distance (nadir)\\t/\\t- Panchromatic 0.86m and Multispectral 1.0m for SkySat-1&2; - Panchromatic 0.65m and Multispectral 0.8m for SkySat-3 to 13 (0.72 m and 1.0m for data acquired before 30/06/2020)\\t// Accuracy\\t/\\t<50 m RMSE\\t// \ The SkySat Ortho Scene is sensor- and geometrically-corrected (by using DEMs with a post spacing of between 30 and 90 meters) and is projected to a cartographic map projection; the accuracy of the product will vary from region to region based on available GCPs. Different products are available: \ - The SkySat Visual Ortho Scene product is orthorectified, pansharpened, and color-corrected (using a color curve) 3-band RGB Imagery - The SkySat Pansharpened Multispectral Scene product is orthorectified, pansharpened 4-band BGRN Imagery - The SkySat Analytic DN Ortho Scene product is orthorectified, multispectral BGRN, uncalibrated, digital number imagery product. The product has been processed to remove distortions caused by terrain; It eliminates the perspective effect on the ground (not on buildings), restoring the geometry of a vertical shot. Transformation to at-sensor radiance is not included - The SkySat Panchromatic DN Ortho Scene product is orthorectified, panchromatic, uncalibrated, digital number imagery product. It has a finer GSD than the Analytic Product. Transformation to at-sensor radiance is not included. - The SkySat Analytic Ortho Scene are calibrated multispectral imagery products with radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. - The SkySat Consolidated Product are Ortho Collect product created by composing ~60 SkySat Ortho Scenes (Visual, Pansharpened Multispectral, Analytic DN, Panchromatic DN) along an imaging strip into segments. Ortho Scene Product Components and Format \\t/\\tVisual Ortho\\t/\\tPansharpened Multispectral\\t/\\tAnalytic DN Ortho\\t/\\tPanchromatic DN Ortho\\t/\\tAnalytic Ortho\\t// Product Components and Format\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format);\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t// Image configuration\\t/\\t3-band Pansharpened Image (PS Red, PS Green, PS Blue)\\t/\\t4-band Pansharpened Image (PS Blue, PS Green, PS Red, PS NIR)\\t/\\t4-band Analytic DN Image (B, G, R, N)\\t/\\t1-band Panchromatic Image\\t/\\t4-band Analytic Image (B, G, R, N)\\t// Ground Sampling Distance\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t// Projection\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t// Accuracy\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\tradiometric accuracy: +/- 5% Relative accuracy at < 10 degrees off-nadir angle\\t// The products are available as part of the Planet provision from RapidEye, Skysat and PlanetScope constellations. SkySat collection has worldwide coverage: The Planet Explorer Catalogue can be accessed (Planet registration requested) to discover and check the data readiness. All details about the data provision, data access conditions and 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 \ \"type\": \"Collection\",\n \"title\": \"SkySat Full Archive and New Tasking\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking\",\n \ \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \"title\": \"FedEO Clearinghouse\"\n },\n \ {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \ \"title\": \"collections\",\n \"type\": \"application/json\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://www.planet.com/products/#satellite-imagery\",\n \"type\": \"text/html\",\n \"title\": \"Planet products - Web site\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Planet-combined-imagery-product-specs-2020.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Imagery Product Specifications - Product Specifications\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://developers.planet.com/docs/apps/explorer/\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Explorer User Guide - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/SkySat-tasking-request-form.pdf/075f845e-13bd-36e2-4743-3aeb2053c61c\",\n \ \"type\": \"application/pdf\",\n \"title\": \"SkySat Tasking Request Form - Access guide\"\n },\n {\n \"rel\": \"describedby\",\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 \ \"type\": \"application/pdf\",\n \"title\": \"Planet Terms of Applicability - Access guide\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? 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Geopositioned product based on sensor model. Coefficients derived from satellite orientation parameters coming from telemetry and appended to metadata\\rL1T (L1R orthorectified): Orthorectified to sub-pixel accuracy (10 metres RMS error approximately) with respect to Landsat ETM+ reference data and hole-filled seamless SRTM DEM data V3, 2006 (90 m)\\rGEOSAT-1 products are provided in DIMAP format. The image products are delivered in the TIFF and GeoTIFF image formats by default. 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\"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/GEOSAT-1\",\n \ \"title\": \"More collections for GEOSAT-1 platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/5cc6eaab-0ddc-572f-a1b7-afa52ea5333d\",\n \ \"title\": \"More collections for SLIM6 instrument\"\n }\n \ ],\n \"id\": \"Geosat-1.Full.archive.and.tasking\",\n \"updated\": \"2023-06-13T13:23:40Z\",\n \"providers\": [\n {\n \"roles\": [\"producer\"],\n \"name\": \"ESA/ESRIN\",\n \"url\": \"https://earth.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"SLIM6\"],\n \ \"platform\": [\"GEOSAT-1\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n \ 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"2016-06-01T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"GNSS\",\n \"AIS\",\n \ \"ADS-B\",\n \"Atmosphere\",\n \"Ionosphere and Magnetosphere Dynamics\",\n \"Weather Events\",\n \"Oceans\",\n \"EARTH SCIENCE > ATMOSPHERE\",\n \"EARTH SCIENCE > SUN-EARTH INTERACTIONS > IONOSPHERE/MAGNETOSPHERE DYNAMICS\",\n \"EARTH SCIENCE > ATMOSPHERE > WEATHER EVENTS\",\n \"EARTH SCIENCE > OCEANS\",\n \"Sun-synchronous\",\n \ \"500 km\",\n \"Imaging Spectrometers/Radiometers\",\n \"Spire\",\n \ \"AIRSAFE\",\n \"SENSE\",\n \"STRATOS\"\n ],\n \ \"created\": \"2020-09-04T00:00:00.00Z\",\n \"description\": \"The data collected by Spire from it's 110 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested: ADS-B Data Stream Global ADS-B satellite data observed by Spire satellites and processed through the ground stations network. Historical ADS-B data older than 6 months can be delivered as data cuts containing CSV file(s) accessible through a Web Service or Cloud storage solutions. Live ADS-B data is available through a streaming API, and recent historical data can be accessed through a REST API. Data is distributed as a monthly subscription: historical data can be requested starting from 3 December 2008, the time period for live data starts from a user-defined date and continues for 30 days. AIS AIS messages include satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations (up to 40 million messages per day). Historical AIS data are delivered as a cvs file with availability back to June 2016 or via Historical API from December 2018; live AIS data are pushed to end users via TCP or through Messages API. Data is distributed as a monthly subscription, from a user-defined date and continues for a 30 day period. GNSS-Radio Occultation GNSS Radio Occultation (GNSS-RO) measurements are collected globally on a continuous basis, generating profiles of the Earth\\u2019s atmosphere. Derived Level 1 and Level 2 products include both atmospheric and ionospheric products. Historical data for most of the GNSS-RO products are available from December 2018 to the present. Near real-time (within 90 minutes or less latency from collection to delivery) GNSS-RO profiles are also available upon request. GNSS Reflectometry GNSS Reflectometry (GNSS-R) is a technique to measure Earth\\u2019s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: conventional, near-nadir incidence LHCP reflections collected by the Spire GNSS-R satellites (e.g., Spire GNSS-R \\u201cBatch-1\\u201d satellites) and grazing angle (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while conventional GNSS-R data are available from December 2020 to the present. \ Name: Automatic Identification System (AIS) Description: The automatic identification system (AIS) is an automatic tracking system that uses transponders on ships and is used by vessel traffic services. Spire data includes satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations. Data format and content: .parquet.gz files The AIS files contain time-series data on received AIS messages, both the raw NMEA message and added post-processing data for each message. Application: Supply chain analysis, commodity trading, identification of illegal fishing or dark targets, ship route and fuel use optimization, analysis of global trade patterns, anti-piracy, autonomous vessel software, ocean currents. Name: Automatic Dependent Surveillance-Broadcast (ADS-B) Description: Spire AirSafe ADS-B products give access to satellite and terrestrial ADS-B data from captured aircrafts. Data format and content: .csv.gz files The decompressed csv file contains a list of hexadecimal representations of ADS-B messages associated with the timestamp they were received on the satellite. Application: Fleet management, ICAO regulatory compliance, route optimization, predictive maintenance, global airspace, domain awareness. Name: Global Navigation Satellite System Radio Occultation (GNSS-RO) Description: GNSS atmospheric radio occultation (GNSS-RO) relies on the detection of a change in a radio signal as it passes through a planet's atmosphere, i.e. as it is refracted by the atmosphere. This data set contains precise orbit determination (POD) solutions, satellite attitude information, high-rate occultation observations, excess phase, and derived atmospheric dry temperature profiles. Data format and content: podObs*.rnx This file contains raw pseudorange, carrier phase, Doppler frequency, and signal-to-noise measurements for each observed GPS signal from a single Spire satellite which allow to estimate the positions and velocities of each Spire satellite and also used to derive ionospheric total electron content data. leoOrb*.sp3 This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file leoAtt*.log It contains 1 Hz rate quaternion information measured from a single Spire satellite describing the satellite orientation. opnGns*ro.bin, opnGns*rst.bin these files contain raw measurements from the occulting GNSS satellite (one for each signal frequency) and raw phase data from one or more reference GNSS satellites. atmPhs* The file contains occultation excess phase delay. Also contains SNR values, ransmitter and receiver positions and open loop model information. atmPrf*.nc The file contains profiles of atmospheric dry pressure, dry temperature and neutral refractivity as a function of altitude produced from full processing of one occultation event. bfrPrf*.bufr The file contains derived profiles of dry pressure, dry temperature, refractivity and bending angle for each occultation. Application:\\tAtmospheric profiles of pressure, dry temperature, bending angle, and refractivity used in numerical weather prediction data assimilation and climate change studies. Name: Raw IF samples from GNSS-RO satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of GNSS-RO satellites. Data format and content: rocRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-RO studies, GNSS RFI and jamming monitoring, research. Name: Raw IF samples from GNSS-R satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of conventional GNSS-R satellites. Data format and content: gbrRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-R studies, GNSS RFI and jamming monitoring, research, etc. Name: Grazing angle GNSS-R observations Description: During grazing angle GNSS-R events, signal reflection at two frequencies is observed through the limb-facing antenna and is trackedusing an open-loop tracking technique thatrelies on a model topredict the propagationdelay and Doppler of thereflected signal. Simultaneous open-looptracking of the signaldirectly along theline-of-sight from thetransmitter to thereceiver is alsoperformed to provideadditional data that maybenecessary for signalcalibration. The mainoutput of the open-looptracking are in-phase (I)and quadrature (Q)accumulation samples(nominally at 50 Hz),which represent the residual Doppler (phase) from the model. Data format and content: grzObs*.nc L1A filecontains rawopen loopcarrier phasemeasurementsat 50 Hzsampling forgrazingangleGNSS-Rreflectionscaptured in the GNSS-RO RHC Pantennas, (bothdirect andreflectedsignals) on GNSS-RO satellites. Application: Sea surface and sea ice height extent, and classification. \ Name: Georeferenced grazing angle GNSS-R observations Description: The low-levelobservations of the high-rate grazing angle GNSS-R observationsbut withthegeoreferenced bistatic radar parameters of the satellite receiver,specular reflection, and GNSS transmitter included. Data format and content: grzRfl*.nc L1B file contains the georeferenced grazing angle GNSS-R data collected by Spire GNSS-RO satellites, including the low-level observables and bistatic radar geometries (e.g., receiver, specular reflection, and the transmitter locations). Application: Sea surface and sea ice height extent, and classification Name: GNSS-R calibrated bistatic radar reflectivities Description: Higher level product used to derive land-surface reflectivity. Data format and content: gbrRfl*.nc L1A along-track calibrated relative power between reflected and direct signals (e.g., bistatic radar reflectivities) measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, soil moisture, ocean wind, and sea ice applications Name: GNSS-R calibrated bistatic radar cross-sections Description: Higher level product used to derive ocean surface roughness products. Data format and content: gbrRCS*.nc L1B along-track calibrated and normalized bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, ocean wind and sea ice applications Name: Combined Surface Soil Moisture Description: Combined CYGNSS and SMAP soil moisture data are provided as a combined surface soil moisture (COMB-SSM) product in two data level formats: L2U1 and L3U1. 6 x 6 km grid cell. L-band measurements of surface soil moisture benefit from better vegetation penetration in comparison to traditional C-band measurements. Data format and content: COMB-SSM.nc This file contains the combined data product containing measurements from both CYGNSS and SMAP reported on a 6 km global Equi7Grid grid. Application: Agriculture, crop insurance, farming solutions, climatology, terrain awareness, peatlands and wetlands monitoring etc. Name: Ionosphere total electron content Description: Spire routinely collects and processes a large volume of total electron content (TEC) data, representing the line-of-sight integration of electron density between a Spire satellite and a GNSS satellite. Each file contains line-of-sight ionospheric total electron content (TEC) estimates derived for a \\u2018single viewing arc\\u2019 contained in the POD observation file. Viewing arcs are at least 10 minutes in duration. Data format and content: podTec*.nc This file contains the line-of-sight total electron content with associated orbital information. Application: Space weather research, tsunamigenic earthquakes, weather applications, space situational awareness (SSA), autonomous vehicles etc Name: Ionosphere scintillation Description: The scintillation index for each GNSS frequency is computed onboard the spacecraft. This index provides a measure of the fluctuations of the GNSS signal over the course of 10 seconds caused by propagation of the radio signals through electron density irregularities in the ionosphere. After the raw indices are downlinked to the ground, they are packaged along with associated metadata such as orbit position to create the final scintillation data product. Data format and content: scnLv1*.nc This file contains on-board computed scintillation data (S4 only) with associated orbital information Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications etc. Name: Electron density profile Description: Electron density profiles are retrieved as a function of altitude. Electron density profiles are processed from podTec netcdf files, which span a sufficient elevation angle range. A standard Abel inversion algorithm is applied to retrieve the profiles. Data format and content: ionPrf*.nc This file contains electron density profile retrieved from podTec files spanning appropriate elevation angle range Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications. The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639).\",\n \ \"type\": \"Collection\",\n \"title\": \"Spire live and historical data\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n 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\"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Spire\",\n \ \"title\": \"More collections for Spire platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/1c3bd49f-bdc9-5c33-a273-2f0f7a5b3f88\",\n \ \"title\": \"More collections for STRATOS instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/e5735a20-bdb8-5cff-b385-c16c84c855f4\",\n \ \"title\": \"More collections for SENSE instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/11f58510-1770-53a7-9b13-5a5564b4e9c2\",\n \ \"title\": \"More collections for AIRSAFE instrument\"\n }\n \ ],\n \"id\": \"Spire.live.and.historical.data\",\n \"updated\": \"2023-06-13T13:23:42Z\",\n \"providers\": [\n {\n \"roles\": [\"producer\"],\n \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ 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SCIENCE\",\n \"EARTH SCIENCE > LAND SURFACE > LANDSCAPE\",\n \"EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY\",\n \"EARTH SCIENCE > ATMOSPHERE > WEATHER EVENTS\",\n \"VIS (0.40 - 0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"SWIR (1.3 - 3.0 \xB5m)\",\n \"Sun-synchronous\",\n \ \"Very High Resolution - VHR (0 - 5m)\",\n \"High Resolution - HR (5 - 20 m)\",\n \"832 km for SPOT-1 to 5, 694 km for SPOT-6 and 7\",\n \"60 km\",\n \"Cameras\",\n \"SPOT 6\",\n \"SPOT 7\",\n \"NAOMI\"\n ],\n \"created\": \"2019-07-16T00:00:00.00Z\",\n \ \"description\": \"The SPOT 6 and 7 satellites ensure data continuity with the no longer operational SPOT 5 satellite and provide an archive of very high resolution optical acquisition as well as the possibility to task the satellites for new acquisitions. 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CryoSat also carries three star trackers for measuring the orientation of the baseline. In addition, a radio receiver called Doppler Orbit and Radio Positioning Integration by Satellite (DORIS) and a small laser retroreflector ensures that CryoSat's position will be accurately tracked. More detailed information on CryoSat instruments is available on the CryoSat mission page. The following CryoSat datasets are available and distributed to registered users: Level 1B and L2 Ice products: FDM, LRM, SAR and SARIn Consolidated Level 2 (GDR): (LRM+SAR+SARIN) consolidated ice products over an orbit Intermediate Level 2 Ice products: LRM, SAR and SARIn L1b and L2 Ocean Products: GOP and IOP CryoTEMPO EOLIS Point Products CryoTEMPO EOLIS Gridded Products Detailed information concerning each of the above datasets is available in the CryoSat Products Overview (https://earth.esa.int/eogateway/missions/cryosat/products) and in the news item: CryoSat Ocean Products now open to scientific community (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). \ CryoSat Level 1B altimetric products contain time and geo-location information as well as SIRAL measurements in engineering units. Calibration corrections are included and have been applied to the window delay computations. In Offline products, geophysical corrections are computed from Analysis Auxiliary Data Files (ADFs), whereas in FDM products corrections are computed for Forecast ADFs. All corrections are included in the data products and therefore the range can be calculated by taking into account the surface type. The Offline Level 2 LRM, SAR and SARIn ice altimetric products are generated 30 days after data acquisition and are principally dedicated to glaciologists working on sea-ice and land-ice areas. The Level 2 FDM products are near-real time ocean products, generated 2-3 hours after data acquisition, and fulfill the needs of some ocean operational services. Level 2 products contain the time of measurement, the geo-location and the height of the surface. IOP and GOP are outputs of the CryoSat Ocean Processor. These products are dedicated to the study of ocean surfaces, and provided specifically for the needs of the oceanographic community. IOP are generated 2-3 days after data sensing acquisition and use the DORIS Preliminary Orbit. GOP are typically generated 30 days after data sensing acquisition and use the DORIS Precise Orbit. Geophysical corrections are computed from the Analysis ADFs, however following the oceanographic convention the corrections are available but not directly applied to the range (as for FDM). The CryoSat ThEMatic PrOducts (Cryo-TEMPO) projects aim to deliver a new paradigm of simplified, harmonized, and agile CryoSat-2 products, that are easily accessible to new communities of non-altimeter experts and end users. The Cryo-TEMPO datasets include dedicated products over five thematic areas, covering Sea Ice, Land Ice, Polar Ocean, Coastal Ocean and Inland Water, together with a novel SWATH product (CryoTEMPO-EOLIS) that exploits CryoSat's SARIn mode over ice sheet margins. The standard Cryo-TEMPO products include fully-traceable uncertainties and use rapidly evolving, state-of-the-art processing dedicated to each thematic area. Throughout the project, the products will be constantly evolved, and validated by a group of Thematic Users, thus ensuring optimal relevance and impact for the intended target communities. More information on the Cryo-TEMPO products can be found on the Project Website (http://cryosat.mssl.ucl.ac.uk/tempo/index.html). The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users. The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users.The dataset consists of systematic reprocessing of the entire CryoSat archive to generate new L2-Swath products, increasing data sampling by 1 to 2 orders of magnitude compared with the operational L2 ESA product. In addition, the EOLIS dataset is joined with the ESA L2 Point-Of-Closest-Approach to generate monthly DEM (Digital Elevation Model) products. 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The dataset regularly grows as ESA collects new SkySat products. Two different product types are offered, Ground Sampling Distance at nadir up to 65 cm for PAN and up to 0.8m for MS EO-SIP Product Type Product description Content SSC_DEF_SC Basic and Ortho scene * Level 1B 4-bands Analytic /DN Basic scene * Level 1B 4-bands Panchromatic /DN Basic scene * Level 1A 1-band Panchromatic DN Pre Sup resolution Basic scene * Level 3B 3-bands Visual Ortho Scene * Level 3B 4-bands Pansharpened Multispectral Ortho Scene * Level 3B 4-bands Analytic/DN/SR Ortho Scene * Level 3B 1-band Panchromatic /DN Ortho Scene SSC_DEF_CO Ortho Collect * Visual 3-band Pansharpened Image * Multispectral 4-band Pansharpened Image * Multispectral 4-band Analytic/DN/SR Image (B, G, R,N) * 1-band Panchromatic Image The Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere or for any geometric distortions inherent in the imaging process \\u2022Analytic - unorthorectified, radiometrically corrected, multispectral BGRN \\u2022Analytic DN - unorthorectified, multispectral BGRN \\u2022Panchromatic - unorthorectified, radiometrically corrected, panchromatic (PAN) \\u2022Panchromatic DN - unorthorectified, panchromatic (PAN) \\u2022L1A Panchromatic DN - unorthorectified, pre-super resolution, panchromatic (PAN) The Ortho Scene product is sensor and geometrically corrected, and is projected to a cartographic map projection. \\u2022Visual - orthorectified, pansharpened, and color-corrected (using a color curve) 3-band RGB Imagery \\u2022Pansharpened Multispectral - orthorectified, pansharpened 4-band BGRN Imagery \\u2022Analytic SR - orthorectified, multispectral BGRN. 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The level 1 products generated can be either L1TP or L1GT: \\u2022 L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement. The highest quality Level-1 products suitable for pixel-level time series analysis. GCPs used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000) data set. \\u2022 L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products: L1GT data products consist of L0 product data with systematic radiometric, geometric and terrain corrections applied and resampled for registration to a cartographic projection, referenced to the WGS84, G873, or current version. The dissemination server contains three different classes of Level1 products \\u2022 Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed upon downlink but use an initial TIRS line-of-sight model parameters; the data is made available in less than 12 hours (4-6 hours typically). Once the data have been reprocessed with the refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier (in 14-16 days). \\u2022 Tier 1 (T1): Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP) corrected data that have well-characterized radiometry and are inter-calibrated across the different Landsat instruments. The georegistration of Tier 1 scenes is consistent and within prescribed image-to-image tolerances of \u2266 12-meter radial root mean square error (RMSE). \\u2022 Tier 2 (T2): Landsat scenes not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do not meet the Tier 1 geometry specification due to less accurate orbital information (specific to older Landsat sensors), significant cloud cover, insufficient ground control, or other factors. This includes Systematic Terrain (L1GT) and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. The data are available a couple of days after the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: \\u2022 L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients file, and Quality Assessment (QA) Bands. \\u2022 L2SR - Level 2 Surface Reflectance (L2SR) products: include Surface Reflectance (SR), an angle coefficients file, and Quality Assessment (QA) Bands; it is generated if ST could not be generated \ Two different categories of Level 1 products are offered: LC with Optical, Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal not available). 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Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\\t\\tOCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. 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0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"Sun-synchronous\",\n \ \"Very High Resolution - VHR (0 - 5m)\",\n \"475 km (reference)\",\n \ \"25 km\",\n \"Imaging Spectrometers/Radiometers\",\n \"PlanetScope\",\n \ \"PlanetScope Camera\"\n ],\n \"created\": \"2019-08-09T00:00:00.00Z\",\n \ \"description\": \"The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components:\\t - Image File (GeoTIFF format) - Metadata File (XML format) - Rational Polynomial Coefficients (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance:\\t - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components:\\t - Image File (GeoTIFF format) - Metadata File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 3-band natural color (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance:\\t - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and color-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. \ PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/ can be accessed (Planet registration requested) to discover and check the data readiness. It is worth to mention that the data distribution is based on Data Voucher, corresponding to maximum amount of square kilometers can be ordered and downloaded by the project in a maximum period of 15 moths (this duration cannot be extended) starting from the project proposal acceptance date. Each Date Voucher includes PlanetScope tile view streaming access for a total of 20,000 tiles per calendar month during the project period. 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Catalogue\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Planet-combined-imagery-product-specs-2020.pdf?text=Combined-Imagery-Product-Spec.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Imagery Product Specifications - Product Specifications\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://developers.planet.com/docs/apps/explorer/\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Explorer User Guide - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://www.planet.com/explorer/\",\n \"type\": \"text/html\",\n \"title\": \"Planet Explorer Catalogue - Catalogue\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Terms of Applicability - Access Guide\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? - 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76.9 cm)\",\n \"Sun-synchronous\",\n \ \"High Resolution - HR (5 - 20 m)\",\n \"Medium Resolution - MR (20 - 500 m)\",\n \"620 km\",\n \"14-66 km (beam dependent) for stripmap 108-235 km (beam dependent) for topsar\",\n \"Imaging Radars\",\n \"SAOCOM-1A\",\n \"SAOCOM-1B\",\n \"SAOCOM-SAR\"\n \ ],\n \"created\": \"2021-10-27T00:00:00.00Z\",\n \"description\": \"This collection provides access to the SAOCOM products acquired in the ASI Zone of Exclusivity, that correspond mainly to the European territory plus the international waters in front of North Africa and the Middle East, archived and catalogued in the ASI/CONAE dissemination system.\",\n \"type\": \"Collection\",\n \"title\": \"SAOCOM data products\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \ \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/SAOCOM.data.products\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/SAOCOM.data.products?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SAOCOM.data.products?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SAOCOM.data.products?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SAOCOM.data.products?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SAOCOM.data.products?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SAOCOM.data.products?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/SAOCOM.data.products\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://www.asi.it/en/earth-science/saocom/\",\n \ \"type\": \"text/html\",\n \"title\": \"ASI SAOCOM portal - 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0.4 \xB5m)\",\n \"Sun-synchronous\",\n \"Horizontal resolution at observation scale for Rayleigh/Mie: 87/10 km 250-2000 m vertical resolution\",\n \ \"ALD_U_N_2C\",\n \"320 km\",\n \"Lidar/Laser Sounders\",\n \ \"Aeolus\",\n \"ALADIN\"\n ],\n \"created\": \"2023-02-17T00:00:00.00Z\",\n \ \"description\": \"The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds.\",\n \"type\": \"Collection\",\n \"title\": \"Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Level2-Algorithm-Theoretical-Basis-Document.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Aeolus L2B Algorithm Theoretical Baseline Document - Level 2B processor algorithm description\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-L2B-2C-Input-Output-DD-ICD.pdf/2321b6a0-27d0-b628-771e-fcca4928bc32\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Aeolus L2B/2C Processor Input/Output Data Definitions Interface Control Document - Level 2B/2C data definition\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Sensor-and-Product-Description.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Aeolus Sensor and Product Description - Aeolus Sensor and Product Description\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? - ESA Earth Observation User Services Portal\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/atom%2Bxml\",\n \ \"type\": \"application/atom+xml\",\n \"title\": \"Atom format\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/xml\",\n \ \"type\": \"application/xml\",\n \"title\": \"Dublin Core metadata\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\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 \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\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 \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\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 \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Aeolus\",\n \ \"title\": \"More collections for Aeolus platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/a032cde6-16e3-5940-988e-f6d79870d14c\",\n \ \"title\": \"More collections for ALADIN instrument\"\n }\n \ ],\n \"id\": \"L2C_Wind_products\",\n \"updated\": \"2023-06-13T13:23:39Z\",\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"ALADIN\"],\n \ \"platform\": [\"Aeolus\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -84,\n 180,\n \ 84\n ]]},\n \"temporal\": {\"interval\": [[\n \"2013-11-13T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"Planet\",\n \"Land Surface\",\n \"Mapping and Cartography\",\n \"Natural Hazards and Disaster Risk\",\n \"EARTH SCIENCE > LAND SURFACE\",\n \"EARTH SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS\",\n \"VIS (0.40 - 0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"Sun-synchronous\",\n \ \"Very High Resolution - VHR (0 - 5m)\",\n \"475-575 km\",\n \ \"8 km at nadir for SkySat-1 to -2, 6.6 km at nadir for SkySat-3 to -13\",\n \"Imaging Spectrometers/Radiometers\",\n \"SkySat\",\n \ \"SkySat Camera\"\n ],\n \"created\": \"2019-08-09T00:00:00.00Z\",\n \ \"description\": \"The SkySat Level 2B Basic Scene, Level 3B Ortho Scene and Level 3B Consolidated full archive and new tasking products are available as part of Planet imagery offer. The SkySat Basic Scene product is uncalibrated and in a raw digital number format, not corrected for any geometric distortions inherent in the imaging process. Rational Polynomial Coefficients (RPCs) is provided to enable orthorectification by the user. Basic Scene Product Components and Format Processing levels\\t/\\t- Analytic (unorthorectified, radiometrically corrected, multispectral BGRN); - Analytic DN (unorthorectified, multispectral BGRN); - Panchromatic DN (unorthorectified, panchromatic)\\t// Product Components and Format\\t/\\t- Image File (GeoTIFF format); - Metadata File (JSON format); - Rational Polynomial Coefficients (Text File); - UDM File (GeoTIFF format)\\t// Image configuration\\t/\\t- 4-band Analytic DN Image (Blue, Green, Red, NIR); - 1-band Panchromatic DN Image (Pan)\\t// Ground Sampling Distance\\t/\\t3.7 m at nadir (average at reference altitude 475 km)\\t// Ground Sampling Distance (nadir)\\t/\\t- Panchromatic 0.86m and Multispectral 1.0m for SkySat-1&2; - Panchromatic 0.65m and Multispectral 0.8m for SkySat-3 to 13 (0.72 m and 1.0m for data acquired before 30/06/2020)\\t// Accuracy\\t/\\t<50 m RMSE\\t// \ The SkySat Ortho Scene is sensor- and geometrically-corrected (by using DEMs with a post spacing of between 30 and 90 meters) and is projected to a cartographic map projection; the accuracy of the product will vary from region to region based on available GCPs. Different products are available: \ - The SkySat Visual Ortho Scene product is orthorectified, pansharpened, and color-corrected (using a color curve) 3-band RGB Imagery - The SkySat Pansharpened Multispectral Scene product is orthorectified, pansharpened 4-band BGRN Imagery - The SkySat Analytic DN Ortho Scene product is orthorectified, multispectral BGRN, uncalibrated, digital number imagery product. The product has been processed to remove distortions caused by terrain; It eliminates the perspective effect on the ground (not on buildings), restoring the geometry of a vertical shot. Transformation to at-sensor radiance is not included - The SkySat Panchromatic DN Ortho Scene product is orthorectified, panchromatic, uncalibrated, digital number imagery product. It has a finer GSD than the Analytic Product. Transformation to at-sensor radiance is not included. - The SkySat Analytic Ortho Scene are calibrated multispectral imagery products with radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. - The SkySat Consolidated Product are Ortho Collect product created by composing ~60 SkySat Ortho Scenes (Visual, Pansharpened Multispectral, Analytic DN, Panchromatic DN) along an imaging strip into segments. Ortho Scene Product Components and Format \\t/\\tVisual Ortho\\t/\\tPansharpened Multispectral\\t/\\tAnalytic DN Ortho\\t/\\tPanchromatic DN Ortho\\t/\\tAnalytic Ortho\\t// Product Components and Format\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format);\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t// Image configuration\\t/\\t3-band Pansharpened Image (PS Red, PS Green, PS Blue)\\t/\\t4-band Pansharpened Image (PS Blue, PS Green, PS Red, PS NIR)\\t/\\t4-band Analytic DN Image (B, G, R, N)\\t/\\t1-band Panchromatic Image\\t/\\t4-band Analytic Image (B, G, R, N)\\t// Ground Sampling Distance\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t// Projection\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t// Accuracy\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\tradiometric accuracy: +/- 5% Relative accuracy at < 10 degrees off-nadir angle\\t// The products are available as part of the Planet provision from RapidEye, Skysat and PlanetScope constellations. SkySat collection has worldwide coverage: The Planet Explorer Catalogue can be accessed (Planet registration requested) to discover and check the data readiness. All details about the data provision, data access conditions and 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 \ \"type\": \"Collection\",\n \"title\": \"SkySat Full Archive and New Tasking\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking\",\n \ \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \"title\": \"FedEO Clearinghouse\"\n },\n \ {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \ \"title\": \"collections\",\n \"type\": \"application/json\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://www.planet.com/products/#satellite-imagery\",\n \"type\": \"text/html\",\n \"title\": \"Planet products - Web site\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Planet-combined-imagery-product-specs-2020.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Imagery Product Specifications - Product Specifications\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://developers.planet.com/docs/apps/explorer/\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Explorer User Guide - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/SkySat-tasking-request-form.pdf/075f845e-13bd-36e2-4743-3aeb2053c61c\",\n \ \"type\": \"application/pdf\",\n \"title\": \"SkySat Tasking Request Form - Access guide\"\n },\n {\n \"rel\": \"describedby\",\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 \ \"type\": \"application/pdf\",\n \"title\": \"Planet Terms of Applicability - Access guide\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? - ESA Earth Observation User Services Portal\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/atom%2Bxml\",\n \ \"type\": \"application/atom+xml\",\n \"title\": \"Atom format\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/xml\",\n \ \"type\": \"application/xml\",\n \"title\": \"Dublin Core metadata\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/SkySat.Full.Archive.and.New.Tasking?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/SkySat\",\n \ \"title\": \"More collections for SkySat platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/429a0634-46aa-518a-ac92-b5bcec0315d4\",\n \ \"title\": \"More collections for SkySat Camera instrument\"\n }\n \ ],\n \"id\": \"SkySat.Full.Archive.and.New.Tasking\",\n \"updated\": \"2023-06-13T13:23:42Z\",\n \"providers\": [\n {\n \"roles\": [\"producer\"],\n \"name\": \"ESA/ESRIN\",\n \"url\": \"https://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"SkySat Camera\"],\n \"platform\": [\"SkySat\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n \ 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"2009-08-01T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"GEOSAT\",\n \"DIMAP\",\n \ \"TIFF\",\n \"Vegetation\",\n \"Agriculture\",\n \"Forestry\",\n \ \"EARTH SCIENCE > BIOSPHERE > VEGETATION\",\n \"EARTH SCIENCE > AGRICULTURE\",\n \"EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > TERRESTRIAL ECOSYSTEMS > FORESTS\",\n \"VIS (0.40 - 0.75 \xC2\xB5m)\",\n \"NIR (0.75 - 1.30 \xC2\xB5m)\",\n \"Sun-synchronous\",\n \"Medium Resolution - MR (20 - 500 m)\",\n \"663 km\",\n \"625 km\",\n \ \"Cameras\",\n \"GEOSAT-1\",\n \"SLIM6\"\n ],\n \ \"created\": \"2019-05-22T00:00:00.00Z\",\n \"description\": \"GEOSAT-1 full archive and new tasking products are available at 22 m resolution in two processing levels.\\r\\rL1R (Basic Geopositioned): All 3 spectral channels combined into a band-registered image. Geopositioned product based on sensor model. Coefficients derived from satellite orientation parameters coming from telemetry and appended to metadata\\rL1T (L1R orthorectified): Orthorectified to sub-pixel accuracy (10 metres RMS error approximately) with respect to Landsat ETM+ reference data and hole-filled seamless SRTM DEM data V3, 2006 (90 m)\\rGEOSAT-1 products are provided in DIMAP format. The image products are delivered in the TIFF and GeoTIFF image formats by default. All products can be provided in False Colours (R,G,NIR) or Natural Colours (R, G, Synthetic Blue).\\r\\rAll details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.\\r\\rThe list of available archived data can be retrieved using the GEOSAT catalogue (https://catalogue.geosat.space/cscda/extcat/)\\rAll details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. \\r\\rThe list of available archived data can be retrieved using the Deimos catalogue (http://www.deimos-imaging.com/catalogue).\",\n \"type\": \"Collection\",\n \"title\": \"GEOSAT-1 full archive and tasking\",\n \ \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking\",\n \ \"type\": 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{\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/GEOSAT-1\",\n \ \"title\": \"More collections for GEOSAT-1 platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/5cc6eaab-0ddc-572f-a1b7-afa52ea5333d\",\n \ \"title\": \"More collections for SLIM6 instrument\"\n }\n \ ],\n \"id\": \"Geosat-1.Full.archive.and.tasking\",\n \"updated\": \"2023-06-13T13:23:40Z\",\n \"providers\": [\n {\n \"roles\": [\"producer\"],\n \"name\": \"ESA/ESRIN\",\n \"url\": \"https://earth.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"SLIM6\"],\n \ \"platform\": [\"GEOSAT-1\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n \ 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"2016-06-01T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"GNSS\",\n \"AIS\",\n \ \"ADS-B\",\n \"Atmosphere\",\n \"Ionosphere and Magnetosphere Dynamics\",\n \"Weather Events\",\n \"Oceans\",\n \"EARTH SCIENCE > ATMOSPHERE\",\n \"EARTH SCIENCE > SUN-EARTH INTERACTIONS > IONOSPHERE/MAGNETOSPHERE DYNAMICS\",\n \"EARTH SCIENCE > ATMOSPHERE > WEATHER EVENTS\",\n \"EARTH SCIENCE > OCEANS\",\n \"Sun-synchronous\",\n \ \"500 km\",\n \"Imaging Spectrometers/Radiometers\",\n \"Spire\",\n \ \"AIRSAFE\",\n \"SENSE\",\n \"STRATOS\"\n ],\n \ \"created\": \"2020-09-04T00:00:00.00Z\",\n \"description\": \"The data collected by Spire from it's 110 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested: ADS-B Data Stream Global ADS-B satellite data observed by Spire satellites and processed through the ground stations network. Historical ADS-B data older than 6 months can be delivered as data cuts containing CSV file(s) accessible through a Web Service or Cloud storage solutions. Live ADS-B data is available through a streaming API, and recent historical data can be accessed through a REST API. Data is distributed as a monthly subscription: historical data can be requested starting from 3 December 2008, the time period for live data starts from a user-defined date and continues for 30 days. AIS AIS messages include satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations (up to 40 million messages per day). Historical AIS data are delivered as a cvs file with availability back to June 2016 or via Historical API from December 2018; live AIS data are pushed to end users via TCP or through Messages API. Data is distributed as a monthly subscription, from a user-defined date and continues for a 30 day period. GNSS-Radio Occultation GNSS Radio Occultation (GNSS-RO) measurements are collected globally on a continuous basis, generating profiles of the Earth\\u2019s atmosphere. Derived Level 1 and Level 2 products include both atmospheric and ionospheric products. Historical data for most of the GNSS-RO products are available from December 2018 to the present. Near real-time (within 90 minutes or less latency from collection to delivery) GNSS-RO profiles are also available upon request. GNSS Reflectometry GNSS Reflectometry (GNSS-R) is a technique to measure Earth\\u2019s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: conventional, near-nadir incidence LHCP reflections collected by the Spire GNSS-R satellites (e.g., Spire GNSS-R \\u201cBatch-1\\u201d satellites) and grazing angle (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while conventional GNSS-R data are available from December 2020 to the present. \ Name: Automatic Identification System (AIS) Description: The automatic identification system (AIS) is an automatic tracking system that uses transponders on ships and is used by vessel traffic services. Spire data includes satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations. Data format and content: .parquet.gz files The AIS files contain time-series data on received AIS messages, both the raw NMEA message and added post-processing data for each message. Application: Supply chain analysis, commodity trading, identification of illegal fishing or dark targets, ship route and fuel use optimization, analysis of global trade patterns, anti-piracy, autonomous vessel software, ocean currents. Name: Automatic Dependent Surveillance-Broadcast (ADS-B) Description: Spire AirSafe ADS-B products give access to satellite and terrestrial ADS-B data from captured aircrafts. Data format and content: .csv.gz files The decompressed csv file contains a list of hexadecimal representations of ADS-B messages associated with the timestamp they were received on the satellite. Application: Fleet management, ICAO regulatory compliance, route optimization, predictive maintenance, global airspace, domain awareness. Name: Global Navigation Satellite System Radio Occultation (GNSS-RO) Description: GNSS atmospheric radio occultation (GNSS-RO) relies on the detection of a change in a radio signal as it passes through a planet's atmosphere, i.e. as it is refracted by the atmosphere. This data set contains precise orbit determination (POD) solutions, satellite attitude information, high-rate occultation observations, excess phase, and derived atmospheric dry temperature profiles. Data format and content: podObs*.rnx This file contains raw pseudorange, carrier phase, Doppler frequency, and signal-to-noise measurements for each observed GPS signal from a single Spire satellite which allow to estimate the positions and velocities of each Spire satellite and also used to derive ionospheric total electron content data. leoOrb*.sp3 This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file leoAtt*.log It contains 1 Hz rate quaternion information measured from a single Spire satellite describing the satellite orientation. opnGns*ro.bin, opnGns*rst.bin these files contain raw measurements from the occulting GNSS satellite (one for each signal frequency) and raw phase data from one or more reference GNSS satellites. atmPhs* The file contains occultation excess phase delay. Also contains SNR values, ransmitter and receiver positions and open loop model information. atmPrf*.nc The file contains profiles of atmospheric dry pressure, dry temperature and neutral refractivity as a function of altitude produced from full processing of one occultation event. bfrPrf*.bufr The file contains derived profiles of dry pressure, dry temperature, refractivity and bending angle for each occultation. Application:\\tAtmospheric profiles of pressure, dry temperature, bending angle, and refractivity used in numerical weather prediction data assimilation and climate change studies. Name: Raw IF samples from GNSS-RO satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of GNSS-RO satellites. Data format and content: rocRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-RO studies, GNSS RFI and jamming monitoring, research. Name: Raw IF samples from GNSS-R satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of conventional GNSS-R satellites. Data format and content: gbrRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-R studies, GNSS RFI and jamming monitoring, research, etc. Name: Grazing angle GNSS-R observations Description: During grazing angle GNSS-R events, signal reflection at two frequencies is observed through the limb-facing antenna and is trackedusing an open-loop tracking technique thatrelies on a model topredict the propagationdelay and Doppler of thereflected signal. Simultaneous open-looptracking of the signaldirectly along theline-of-sight from thetransmitter to thereceiver is alsoperformed to provideadditional data that maybenecessary for signalcalibration. The mainoutput of the open-looptracking are in-phase (I)and quadrature (Q)accumulation samples(nominally at 50 Hz),which represent the residual Doppler (phase) from the model. Data format and content: grzObs*.nc L1A filecontains rawopen loopcarrier phasemeasurementsat 50 Hzsampling forgrazingangleGNSS-Rreflectionscaptured in the GNSS-RO RHC Pantennas, (bothdirect andreflectedsignals) on GNSS-RO satellites. Application: Sea surface and sea ice height extent, and classification. \ Name: Georeferenced grazing angle GNSS-R observations Description: The low-levelobservations of the high-rate grazing angle GNSS-R observationsbut withthegeoreferenced bistatic radar parameters of the satellite receiver,specular reflection, and GNSS transmitter included. Data format and content: grzRfl*.nc L1B file contains the georeferenced grazing angle GNSS-R data collected by Spire GNSS-RO satellites, including the low-level observables and bistatic radar geometries (e.g., receiver, specular reflection, and the transmitter locations). Application: Sea surface and sea ice height extent, and classification Name: GNSS-R calibrated bistatic radar reflectivities Description: Higher level product used to derive land-surface reflectivity. Data format and content: gbrRfl*.nc L1A along-track calibrated relative power between reflected and direct signals (e.g., bistatic radar reflectivities) measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, soil moisture, ocean wind, and sea ice applications Name: GNSS-R calibrated bistatic radar cross-sections Description: Higher level product used to derive ocean surface roughness products. Data format and content: gbrRCS*.nc L1B along-track calibrated and normalized bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, ocean wind and sea ice applications Name: Combined Surface Soil Moisture Description: Combined CYGNSS and SMAP soil moisture data are provided as a combined surface soil moisture (COMB-SSM) product in two data level formats: L2U1 and L3U1. 6 x 6 km grid cell. L-band measurements of surface soil moisture benefit from better vegetation penetration in comparison to traditional C-band measurements. Data format and content: COMB-SSM.nc This file contains the combined data product containing measurements from both CYGNSS and SMAP reported on a 6 km global Equi7Grid grid. Application: Agriculture, crop insurance, farming solutions, climatology, terrain awareness, peatlands and wetlands monitoring etc. Name: Ionosphere total electron content Description: Spire routinely collects and processes a large volume of total electron content (TEC) data, representing the line-of-sight integration of electron density between a Spire satellite and a GNSS satellite. Each file contains line-of-sight ionospheric total electron content (TEC) estimates derived for a \\u2018single viewing arc\\u2019 contained in the POD observation file. Viewing arcs are at least 10 minutes in duration. Data format and content: podTec*.nc This file contains the line-of-sight total electron content with associated orbital information. Application: Space weather research, tsunamigenic earthquakes, weather applications, space situational awareness (SSA), autonomous vehicles etc Name: Ionosphere scintillation Description: The scintillation index for each GNSS frequency is computed onboard the spacecraft. This index provides a measure of the fluctuations of the GNSS signal over the course of 10 seconds caused by propagation of the radio signals through electron density irregularities in the ionosphere. After the raw indices are downlinked to the ground, they are packaged along with associated metadata such as orbit position to create the final scintillation data product. Data format and content: scnLv1*.nc This file contains on-board computed scintillation data (S4 only) with associated orbital information Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications etc. Name: Electron density profile Description: Electron density profiles are retrieved as a function of altitude. Electron density profiles are processed from podTec netcdf files, which span a sufficient elevation angle range. A standard Abel inversion algorithm is applied to retrieve the profiles. Data format and content: ionPrf*.nc This file contains electron density profile retrieved from podTec files spanning appropriate elevation angle range Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications. The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639).\",\n \ \"type\": \"Collection\",\n \"title\": \"Spire live and historical data\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n 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{\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Spire Terms of Applicability - Access Guide\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? - ESA Earth Observation User Services Portal\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/atom%2Bxml\",\n \ \"type\": \"application/atom+xml\",\n \"title\": \"Atom format\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/xml\",\n \ \"type\": \"application/xml\",\n \"title\": \"Dublin Core metadata\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": 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collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Spire\",\n \ \"title\": \"More collections for Spire platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/1c3bd49f-bdc9-5c33-a273-2f0f7a5b3f88\",\n \ \"title\": \"More collections for STRATOS instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/e5735a20-bdb8-5cff-b385-c16c84c855f4\",\n \ \"title\": \"More collections for SENSE instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/11f58510-1770-53a7-9b13-5a5564b4e9c2\",\n \ \"title\": \"More collections for AIRSAFE instrument\"\n }\n \ ],\n \"id\": \"Spire.live.and.historical.data\",\n \"updated\": \"2023-06-13T13:23:42Z\",\n \"providers\": [\n {\n \"roles\": [\"producer\"],\n \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ 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- ESA Earth Observation User Services Portal\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L1B_Wind_Products?httpAccept=application/atom%2Bxml\",\n \ \"type\": \"application/atom+xml\",\n \"title\": \"Atom format\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L1B_Wind_Products?httpAccept=application/xml\",\n \ \"type\": \"application/xml\",\n \"title\": \"Dublin Core metadata\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L1B_Wind_Products?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L1B_Wind_Products?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L1B_Wind_Products?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L1B_Wind_Products?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L1B_Wind_Products?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/L1B_Wind_Products?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Aeolus\",\n \ \"title\": \"More collections for Aeolus platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/a032cde6-16e3-5940-988e-f6d79870d14c\",\n \ \"title\": \"More collections for ALADIN instrument\"\n }\n \ ],\n \"id\": \"L1B_Wind_Products\",\n \"updated\": \"2023-06-15T11:50:19Z\",\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"ALADIN\"],\n \ \"platform\": [\"Aeolus\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n \ 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"2010-04-08T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"sci:doi\": \"10.5270/CR2-120cf4c\",\n \"keywords\": [\n \"World\",\n \ \"DIF10\",\n \"Ice Product\",\n \"ice-sheet elevation\",\n \ \"sea-ice freeboard\",\n \"altimeter\",\n \"Oceans\",\n \ \"EARTH SCIENCE > OCEANS\",\n \"Snow and Ice\",\n \"EARTH SCIENCE > CRYOSPHERE > SNOW/ICE\",\n \"EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SNOW/ICE\",\n \"Inclined, non-sun-synchronous\",\n \"FDM\",\n \ \"LRM\",\n \"SAR\",\n \"SARIn\",\n \"GRD\",\n \ \"GOP\",\n \"IOP\",\n \"Radar Altimeters\",\n \"CryoSat-2\",\n \ \"SIRAL\"\n ],\n \"created\": \"2019-06-04T00:00:00.000Z\",\n \ \"description\": \"CryoSat's primary payload is the SAR/Interferometric Radar Altimeter (SIRAL) (https://earth.esa.int/eogateway/instruments/siral) which has extended capabilities to meet the measurement requirements for ice-sheet elevation and sea-ice freeboard. CryoSat also carries three star trackers for measuring the orientation of the baseline. In addition, a radio receiver called Doppler Orbit and Radio Positioning Integration by Satellite (DORIS) and a small laser retroreflector ensures that CryoSat's position will be accurately tracked. More detailed information on CryoSat instruments is available on the CryoSat mission page. The following CryoSat datasets are available and distributed to registered users: Level 1B and L2 Ice products: FDM, LRM, SAR and SARIn Consolidated Level 2 (GDR): (LRM+SAR+SARIN) consolidated ice products over an orbit Intermediate Level 2 Ice products: LRM, SAR and SARIn L1b and L2 Ocean Products: GOP and IOP CryoTEMPO EOLIS Point Products CryoTEMPO EOLIS Gridded Products Detailed information concerning each of the above datasets is available in the CryoSat Products Overview (https://earth.esa.int/eogateway/missions/cryosat/products) and in the news item: CryoSat Ocean Products now open to scientific community (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). \ CryoSat Level 1B altimetric products contain time and geo-location information as well as SIRAL measurements in engineering units. Calibration corrections are included and have been applied to the window delay computations. In Offline products, geophysical corrections are computed from Analysis Auxiliary Data Files (ADFs), whereas in FDM products corrections are computed for Forecast ADFs. All corrections are included in the data products and therefore the range can be calculated by taking into account the surface type. The Offline Level 2 LRM, SAR and SARIn ice altimetric products are generated 30 days after data acquisition and are principally dedicated to glaciologists working on sea-ice and land-ice areas. The Level 2 FDM products are near-real time ocean products, generated 2-3 hours after data acquisition, and fulfill the needs of some ocean operational services. Level 2 products contain the time of measurement, the geo-location and the height of the surface. IOP and GOP are outputs of the CryoSat Ocean Processor. These products are dedicated to the study of ocean surfaces, and provided specifically for the needs of the oceanographic community. IOP are generated 2-3 days after data sensing acquisition and use the DORIS Preliminary Orbit. GOP are typically generated 30 days after data sensing acquisition and use the DORIS Precise Orbit. Geophysical corrections are computed from the Analysis ADFs, however following the oceanographic convention the corrections are available but not directly applied to the range (as for FDM). The CryoSat ThEMatic PrOducts (Cryo-TEMPO) projects aim to deliver a new paradigm of simplified, harmonized, and agile CryoSat-2 products, that are easily accessible to new communities of non-altimeter experts and end users. The Cryo-TEMPO datasets include dedicated products over five thematic areas, covering Sea Ice, Land Ice, Polar Ocean, Coastal Ocean and Inland Water, together with a novel SWATH product (CryoTEMPO-EOLIS) that exploits CryoSat's SARIn mode over ice sheet margins. The standard Cryo-TEMPO products include fully-traceable uncertainties and use rapidly evolving, state-of-the-art processing dedicated to each thematic area. Throughout the project, the products will be constantly evolved, and validated by a group of Thematic Users, thus ensuring optimal relevance and impact for the intended target communities. More information on the Cryo-TEMPO products can be found on the Project Website (http://cryosat.mssl.ucl.ac.uk/tempo/index.html). The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users. The CryoTEMPO-EOLIS swath product exploits CryoSat's SARIn mode and the novel Swath processing technique to deliver increased spatial and temporal coverage of time-dependent elevation over land ice, a critical metric for tracking ice mass trends in support to a wide variety of end-users.The dataset consists of systematic reprocessing of the entire CryoSat archive to generate new L2-Swath products, increasing data sampling by 1 to 2 orders of magnitude compared with the operational L2 ESA product. In addition, the EOLIS dataset is joined with the ESA L2 Point-Of-Closest-Approach to generate monthly DEM (Digital Elevation Model) products. 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The dataset regularly grows as ESA collects new SkySat products. Two different product types are offered, Ground Sampling Distance at nadir up to 65 cm for PAN and up to 0.8m for MS EO-SIP Product Type Product description Content SSC_DEF_SC Basic and Ortho scene * Level 1B 4-bands Analytic /DN Basic scene * Level 1B 4-bands Panchromatic /DN Basic scene * Level 1A 1-band Panchromatic DN Pre Sup resolution Basic scene * Level 3B 3-bands Visual Ortho Scene * Level 3B 4-bands Pansharpened Multispectral Ortho Scene * Level 3B 4-bands Analytic/DN/SR Ortho Scene * Level 3B 1-band Panchromatic /DN Ortho Scene SSC_DEF_CO Ortho Collect * Visual 3-band Pansharpened Image * Multispectral 4-band Pansharpened Image * Multispectral 4-band Analytic/DN/SR Image (B, G, R,N) * 1-band Panchromatic Image The Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere or for any geometric distortions inherent in the imaging process \\u2022Analytic - unorthorectified, radiometrically corrected, multispectral BGRN \\u2022Analytic DN - unorthorectified, multispectral BGRN \\u2022Panchromatic - unorthorectified, radiometrically corrected, panchromatic (PAN) \\u2022Panchromatic DN - unorthorectified, panchromatic (PAN) \\u2022L1A Panchromatic DN - unorthorectified, pre-super resolution, panchromatic (PAN) The Ortho Scene product is sensor and geometrically corrected, and is projected to a cartographic map projection. \\u2022Visual - orthorectified, pansharpened, and color-corrected (using a color curve) 3-band RGB Imagery \\u2022Pansharpened Multispectral - orthorectified, pansharpened 4-band BGRN Imagery \\u2022Analytic SR - orthorectified, multispectral BGRN. Atmospherically corrected Surface Reflectance product. \\u2022Analytic - orthorectified, multispectral BGRN. Radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance \\u2022Analytic DN - orthorectified, multispectral BGRN, uncalibrated digital number imagery product Radiometric corrections applied to correct for any sensor artifacts \\u2022Panchromatic - orthorectified, radiometrically correct, panchromatic (PAN) \\u2022Panchromatic DN - orthorectified, panchromatic (PAN), uncalibrated digital number imagery product The Ortho Collect product is created by composing SkySat Ortho Scenes along an imaging strip. The product may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes. 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The level 1 products generated can be either L1TP or L1GT: \\u2022 L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement. The highest quality Level-1 products suitable for pixel-level time series analysis. GCPs used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000) data set. \\u2022 L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products: L1GT data products consist of L0 product data with systematic radiometric, geometric and terrain corrections applied and resampled for registration to a cartographic projection, referenced to the WGS84, G873, or current version. The dissemination server contains three different classes of Level1 products \\u2022 Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed upon downlink but use an initial TIRS line-of-sight model parameters; the data is made available in less than 12 hours (4-6 hours typically). Once the data have been reprocessed with the refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier (in 14-16 days). \\u2022 Tier 1 (T1): Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP) corrected data that have well-characterized radiometry and are inter-calibrated across the different Landsat instruments. The georegistration of Tier 1 scenes is consistent and within prescribed image-to-image tolerances of \u2266 12-meter radial root mean square error (RMSE). \\u2022 Tier 2 (T2): Landsat scenes not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do not meet the Tier 1 geometry specification due to less accurate orbital information (specific to older Landsat sensors), significant cloud cover, insufficient ground control, or other factors. This includes Systematic Terrain (L1GT) and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. The data are available a couple of days after the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: \\u2022 L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients file, and Quality Assessment (QA) Bands. \\u2022 L2SR - Level 2 Surface Reflectance (L2SR) products: include Surface Reflectance (SR), an angle coefficients file, and Quality Assessment (QA) Bands; it is generated if ST could not be generated \ Two different categories of Level 1 products are offered: LC with Optical, Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal not available). 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Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing.\\t\\tOCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud optical thickness is computed using libRadtran [Mayer and Kylling (2005)] radiative transfer simulations taking as input the cloud-top albedo retrieved with ROCINN. 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collections for Metop-A platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Metop-B\",\n \ \"title\": \"More collections for Metop-B platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Metop-C\",\n \ \"title\": \"More collections for Metop-C platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/eaa1390e-6453-5b90-9ed2-90c3f5096d83\",\n \ \"title\": \"More collections for GOME-2 instrument\"\n }\n \ ],\n \"id\": \"3dd6bbdd-5dca-411e-b251-cdc325d703c4\",\n \"updated\": \"2024-06-20T13:07:47Z\",\n \"stac_extensions\": [\"https://stac-extensions.github.io/processing/v1.2.0/schema.json\"],\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"DE/DLR\"\n },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n }\n ],\n \"summaries\": {\n \"instruments\": 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\"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-6?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-6?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-6?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": 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\"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"items\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P/items?httpAccept=application/geo%2Bjson;profile=https://stacspec.org\",\n \ \"type\": \"application/geo+json\",\n \"title\": \"Datasets search for the series 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\"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Sentinel-5P\",\n \ \"title\": \"More collections for Sentinel-5P platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/09f91afd-c0d2-52a9-9b50-b895a60917fa\",\n \ 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This FMR follows the first Envisat Altimetry reprocessing Version (V2.1) completed in 2012. The GDR and S-GDR data products were reprocessed for all cycles from 6 to 113 (May 2002 to April 2012) into a homogeneous standard in NetCDF format (close to Sentinel-3). The Sensor Data Record (SGDR) Product from RA-2/MWR includes the data in the GDR product (https://earth.esa.int/eogateway/catalog/envisat-ra-2-geophysical-data-record-gdr-ra2_gdr__2p-) (RA-2 geophysical data, MWR data) and also RA-2 averaged waveforms (18Hz) and RA-2 individual waveforms (1800Hz). This product is a continuation of ERS RA data. This data product has a coverage of 1 pass, pole-pole, a spatial sampling of about 390 m along track and a size of 31 to 40 MB, depending on presence of individual waveforms. 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The in-band reference irradiances for the 15 MERIS bands are computed by averaging the in-band solar irradiance of each pixel. The in-band solar irradiance of each pixel is computed by integrating the reference solar spectrum with the band-pass of each pixel. The MERIS RR Level 1 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. Each measurement and annotation data file is in NetCDF 4. The Level 1 product is composed of 22 measurements data files: 15 files containing radiances at each band (one band per file), accompanied by the associated error estimates, and 7 annotation data files. The band-pass of each pixel is derived from on-ground and in-flight characterisation via an instrument model. The values \\\"Band wavelength\\\" and \\\"Bandwidth\\\" provided in the Manifest file of the Level 1 products are the averaged band-pass of each pixel over the instrument field of view. The Auxiliary data used are listed in the Manifest file associated to each product. MERIS was operating continuously on the day side of the Envisat orbit (descending track). 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0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \ \"SWIR (1.3 - 3.0 \xB5m)\",\n \"TIR (6.0 - 15.0 \xB5nm)\",\n \ \"DPAS 5.0\",\n \"Medium Resolution - MR (20 - 500 m)\",\n \ \"OAT_GEO_1P\",\n \"OAT_GTC_1P\",\n \"OAT_SP__2P\",\n \ \"OAT_SR__2P\",\n \"705 km\",\n \"185 km\",\n \"Imaging Spectrometers/Radiometers\",\n \"Landsat-8\",\n \"OLI\",\n \"TIRS\"\n \ ],\n \"created\": \"2022-01-14T00:00:00.000Z\",\n \"description\": \"This dataset contains the European Coverage of Landsat 8 Collection 2 data, both Level 1 and Level 2, since the beginning of the mission. Landsat 8 Collection 2 is the result of reprocessing effort on the archive and on fresh products with significant improvement with respect to Collection 1 on data quality, obtained by means of advancements in data processing, algorithm development. The primary characteristic is a relevant improvement in the absolute geolocation accuracy (now re-baselined to the European Space Agency Copernicus Sentinel-2 Global Reference Image, GRI) but includes also updated digital elevation modelling sources, improved Radiometric Calibration (even correction for the TIRS striping effect), enhanced Quality Assessment Bands, updated and consistent metadata files, usage of Cloud Optimized Georeferenced (COG) Tagged Image File Format. \ Landsat 8 level 1 products combine data from the 2 Landsat instruments, OLI and TIRS. The level 1 products generated can be either L1TP or L1GT: \\u2022 L1TP - Level 1 Precision Terrain (Corrected) (L1T) products: Radiometrically calibrated and orthorectified using ground control points (GCPs) and digital elevation model (DEM) data to correct for relief displacement. The highest quality Level-1 products suitable for pixel-level time series analysis. GCPs used for L1TP correction are derived from the Global Land Survey 2000 (GLS2000) data set. \\u2022 L1GT - Level 1 Systematic Terrain (Corrected) (L1GT) products: L1GT data products consist of L0 product data with systematic radiometric, geometric and terrain corrections applied and resampled for registration to a cartographic projection, referenced to the WGS84, G873, or current version. The dissemination server contains three different classes of Level1 products \\u2022 Real Time (RT): Newly acquired Landsat 8 OLI/TIRS data are processed upon downlink but use an initial TIRS line-of-sight model parameters; the data is made available in less than 12 hours (4-6 hours typically). Once the data have been reprocessed with the refined TIRS parameters, the products are transitioned to either Tier 1 or Tier 2 and removed from the Real-Time tier (in 14-16 days). \\u2022 Tier 1 (T1): Landsat scenes with the highest available data quality are placed into Tier 1 and are considered suitable for time-series analysis. Tier 1 includes Level-1 Precision and Terrain (L1TP) corrected data that have well-characterized radiometry and are inter-calibrated across the different Landsat instruments. The georegistration of Tier 1 scenes is consistent and within prescribed image-to-image tolerances of \u2266 12-meter radial root mean square error (RMSE). \\u2022 Tier 2 (T2): Landsat scenes not meeting Tier 1 criteria during processing are assigned to Tier 2. Tier 2 scenes adhere to the same radiometric standard as Tier 1 scenes, but do not meet the Tier 1 geometry specification due to less accurate orbital information (specific to older Landsat sensors), significant cloud cover, insufficient ground control, or other factors. This includes Systematic Terrain (L1GT) and Systematic (L1GS) processed data. Landsat 8 level 2 products are generated from L1GT and L1TP Level 1 products that meet the <76 degrees Solar Zenith Angle constraint and include the required auxiliary data inputs to generate a scientifically viable product. The data are available a couple of days after the Level1 T1/T2. The level 2 products generated can be L2SP or L2SR: \\u2022 L2SP - Level 2 Science Products (L2SP) products: include Surface Reflectance (SR), Surface Temperature (ST), ST intermediate bands, an angle coefficients file, and Quality Assessment (QA) Bands. \\u2022 L2SR - Level 2 Surface Reflectance (L2SR) products: include Surface Reflectance (SR), an angle coefficients file, and Quality Assessment (QA) Bands; it is generated if ST could not be generated \ Two different categories of Level 1 products are offered: LC with Optical, Thermal and Quality Map images, LO with Optical and Quality Map images (Thermal not available). 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It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 \xC2\xB5m spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - June 2024. The WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.These data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.When citing this dataset, please also cite the following peer-reviewed publication: Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669\xE2\\u0080\\u0093694, https://doi.org/10.5194/amt-16-669-2023, 2023.\",\n \"type\": \"Collection\",\n \"title\": \"ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8, November 2017 - June 2024\",\n \"license\": \"various\",\n \"assets\": {\n \"search\": {\n \"roles\": [\"search\"],\n \"href\": \"https://fedeo-client.ceos.org?url=https://emc.spacebel.be/api?httpAccept=application/opensearchdescription%252Bxml&uid=2b8c6a8f1abd40a6b0ce07c40b1c57ff\",\n \ \"type\": \"text/html\",\n \"title\": \"Search client\"\n \ },\n \"enclosure\": {\n \"roles\": [\"data\"],\n \"href\": \"http://data.ceda.ac.uk/neodc/esacci/ghg/data/cci_plus/CH4_S5P_WFMD/v1.8_extended_june2024\",\n \ \"type\": \"application/x-binary\",\n \"title\": \"DOWNLOAD - Download Data\"\n },\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"cite-as\",\n \ \"href\": \"https://doi.org/https://doi.org/10.5194/amt-16-669-2023\",\n \ \"type\": \"text/html\",\n \"title\": \"Landing page\"\n \ },\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff\",\n \ \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \"title\": \"FedEO Clearinghouse\"\n },\n \ {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \ \"title\": \"collections\",\n \"type\": \"application/json\"\n \ },\n {\n \"rel\": \"items\",\n \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff/items?httpAccept=application/geo%2Bjson;profile=https://stacspec.org\",\n \ \"type\": \"application/geo+json\",\n \"title\": \"Datasets search for the series 2b8c6a8f1abd40a6b0ce07c40b1c57ff\"\n },\n {\n \ \"rel\": \"http://www.opengis.net/def/rel/ogc/1.0/queryables\",\n \ \"href\": \"https://emc.spacebel.be/collections/2b8c6a8f1abd40a6b0ce07c40b1c57ff/queryables\",\n \ \"type\": \"application/schema+json\",\n \"title\": \"Queryables for 2b8c6a8f1abd40a6b0ce07c40b1c57ff\"\n },\n {\n \"rel\": \"search\",\n \"href\": \"https://emc.spacebel.be/collections/series/items/2b8c6a8f1abd40a6b0ce07c40b1c57ff/api\",\n \ \"type\": \"application/opensearchdescription+xml\",\n \"title\": \"OpenSearch Description Document\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://catalogue.ceda.ac.uk/uuid/2b8c6a8f1abd40a6b0ce07c40b1c57ff\",\n \ \"title\": \"CEDA Data Catalogue Page - Detail and access information for the resource\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://www.atmos-meas-tech.net/12/6771/2019/\",\n \"title\": \"A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor - No further details.\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://doi.org/10.5194/amt-16-669-2023\",\n \ \"title\": \"Schneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. 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The Sentinel-3 SYN Fire_cci v1.1 pixel product is distributed as 6 continental tiles and is based upon surface reflectance data from the OLCI and SLSTR instruments (combined as the Synergy (SYN) product) onboard the Sentinel-3 A&B satellites. This information is complemented by VIIRS thermal information. This product, called FireCCIS311 for short, is available for the years 2019 to 2022.The FireCCIS311 Pixel product described here includes maps at 0.002777-degree (approx. 300m) resolution. Burned area (BA) information includes 3 individual files, packed in a compressed tar.gz file: date of BA detection (labelled JD), the confidence level (CL, a probability value estimating the confidence that a pixel is actually burned), and the land cover (LC) information as defined in the Copernicus Climate Change Service (C3S) Land Cover v2.1.1 product. An unpacked version of the data is also available. 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This is version 1.1 of the dataset.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and \ functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:\xE2\\u0080\xA2\\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\xE2\\u0080\xA2\\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\xE2\\u0080\xA2\\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \xE2\\u0080\xA2\\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \xE2\\u0080\xA2\\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. 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Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\"],\n \"platform\": [\n \"AQUA\",\n \"Envisat\",\n \ \"ERS-2\",\n \"Jason-1\",\n \"JASON-3\",\n \"Landsat-4\",\n \ \"Landsat-5\",\n \"Landsat-7\",\n \"Landsat-8\",\n \ \"Metop-A\",\n \"Metop-B\",\n \"OrbView-2\",\n \ \"SARAL\",\n \"Sentinel-1A\",\n \"Sentinel-3A\",\n \ \"Sentinel-3B\",\n \"TERRA\",\n \"TOPEX/POSEIDON\"\n \ ]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n 90\n \ ]]},\n \"temporal\": {\"interval\": [[\n \"1992-09-26T00:00:00Z\",\n \ \"2020-12-31T23:59:59Z\"\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \"keywords\": [\n \"ESA\",\n \"CCI\",\n \"Lakes\",\n \ \"ECV\",\n \"orthoimagery\",\n \"EARTH SCIENCE>BIOSPHERE>ECOSYSTEMS>FRESHWATER ECOSYSTEMS>LAKE/POND>MONTANE LAKE\"\n ],\n \"description\": \"This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2020, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. This is version 2.0.2 of the dataset. The five thematic climate variables included in this dataset are:\xE2\\u0080\xA2 Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\xE2\\u0080\xA2 Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\xE2\\u0080\xA2 Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\xE2\\u0080\xA2 Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\xE2\\u0080\xA2 Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat OLI, ERS, MODIS Terra/Aqua and Metop.Detailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Cr\xC3\xA9taux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. 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Measurement data sets at resolutions of 0.5 by 0.5 degrees and 10 by 10 arcmin with respect to a latitude/longitude grid provide continuity with existing ATSR products. Other data sets contain data averaged over equal area cells of 50 by 50 km and 17 by 17 km aligned with the satellite ground track. Both top-of-atmosphere and surface data sets are provided. The surface temperature data sets provide, for sea cells, nadir and dual view sea surface temperatures, and for land cells, land surface temperature and NDVI. Cloud data is also included. No ADS are included in the AST product; auxiliary data is contained within the MDS. The data sets of the AST product are arranged by surface type and resolution. This product is a continuation of ERS ATSR data. The 3rd reprocessing of AATSR data was performed in 2013; the processing updates that have been put in place and the scientific improvements are outlined in full in the User Summary Note for the Third Envisat AATSR Reprocessing https://earth.esa.int/eogateway/documents/20142/37627/IDEAS-VEG-OQC-REP-2148-%28A%29ATSR-Third+Reprocessing-Dataset-User-Summary-v1-0.pdf\",\n \ \"type\": \"Collection\",\n \"title\": \"Envisat AATSR Averaged Surface Temperature (AST) Product [ATS_AR__2P]\",\n \"license\": \"various\",\n \ \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.ATS.AR__2P\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.ATS.AR__2P?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/ENVISAT.ATS.AR__2P?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/ENVISAT.ATS.AR__2P?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/ENVISAT.ATS.AR__2P?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/ENVISAT.ATS.AR__2P?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/ENVISAT.ATS.AR__2P?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.ATS.AR__2P\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/ENVISAT-1%20PRODUCTS%20SPECIFICATIONS%20-%20VOLUME%207-%20AATSR%20PRODUCTS%20SPECIFICATIONS\",\n \ \"type\": \"application/pdf\",\n \"title\": \"AATSR Product Specification - Product Specifications\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/instruments/aatsr/auxiliary-data\",\n \ \"type\": \"text/html\",\n \"title\": \"AATSR auxiliary data - Auxiliary Data\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/instruments/aatsr/products-information\",\n \ \"type\": \"text/html\",\n \"title\": \"AATSR Product Information - Product Information\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/AATSR-product-handbook.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"AATSR Product Handbook - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/AATSR-Level-2-ATS_AR__2P-Product-Quality-Readme-Information.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"ATS_AR__2P Product Quality Readme Information - Product Specifications\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/search?text=&category=Document%20library&filter=envisat,aatsr\",\n \ \"type\": \"text/html\",\n \"title\": \"More AATSR Documents - Product Specifications\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/search?text=&category=Tools%20and%20toolboxes&filter=aatsr\",\n \ \"type\": \"text/html\",\n \"title\": \"(A)ATSR Software Tools - Tools\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? 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These SST use the ARC SST retrieval and cloud screening which differ from the methods used to produce the Gridded Surface Temperature (ATS_NR__2P) products. In addition to SST, the L2P products contain the ATSR Saharan Dust Index (ASDI) and the clear-sky probability estimated by the ARC cloud detection algorithm. The L2P processor also generates L3U products; these are the L2P products averaged onto a regular grid at 0.1 degree resolution (they are therefore similar to the AR / Meteo Envisat-format products). \ The L2P and L3U products are provided in NetCDF-4 format following GHRSST Data Specifications (GDS) v2. 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\"https://earth.esa.int/eogateway/documents/20142/37627/ENVISAT-1%20PRODUCTS%20SPECIFICATIONS%20-%20VOLUME%207-%20AATSR%20PRODUCTS%20SPECIFICATIONS\",\n \ \"type\": \"application/pdf\",\n \"title\": \"AATSR Products Specifications - Product Specifications\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/AATSR-product-handbook.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"AATSR Product Handbook - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/instruments/aatsr/products-information\",\n \ \"type\": \"text/html\",\n \"title\": \"AATSR Product Information - Product Information\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/L2P-Product-Description.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"(A)ATSR SST L2P/L3U Product User Guide - Product Specifications\"\n },\n {\n \ \"rel\": \"describedby\",\n 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\"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/EOP:ESA:Sentinel-5P?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Sentinel-5P\",\n \ \"title\": \"More collections for Sentinel-5P platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/09f91afd-c0d2-52a9-9b50-b895a60917fa\",\n \ \"title\": \"More collections for TROPOMI instrument\"\n }\n \ ],\n \"id\": \"EOP:ESA:Sentinel-5P\",\n \"updated\": \"2017-04-30T00:00:00.00Z\",\n \ \"stac_extensions\": [\"https://stac-extensions.github.io/processing/v1.2.0/schema.json\"],\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\"\n },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n }\n ],\n \"summaries\": {\n \"instruments\": [\"TROPOMI\"],\n \"processing:lineage\": [\"The Sentinel-5 Precursor objectives are to provide operational space-borne observations in support to the operational monitoring of Air Quality, Climate, Ozone and Surface UV. It will provide measurements of Ozone, NO_2, SO_2, Formaldehyde, Aerosol, Carbonmonoxide, Methane, Clouds.\"],\n \"platform\": [\"Sentinel-5P\"]\n \ }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n 90\n ]]},\n \ \"temporal\": {\"interval\": [[\n \"2016-06-22T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"Planet\",\n \"Land Surface\",\n \"Mapping and Cartography\",\n \"Natural Hazards and Disaster Risk\",\n \"EARTH SCIENCE > LAND SURFACE\",\n \"EARTH SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS\",\n \"VIS (0.40 - 0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"Sun-synchronous\",\n \ \"Very High Resolution - VHR (0 - 5m)\",\n \"475 km (reference)\",\n \ \"25 km\",\n \"Imaging Spectrometers/Radiometers\",\n \"PlanetScope\",\n \ \"PlanetScope Camera\"\n ],\n \"created\": \"2019-08-09T00:00:00.00Z\",\n \ \"description\": \"The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components:\\t - Image File (GeoTIFF format) - Metadata File (XML format) - Rational Polynomial Coefficients (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance:\\t - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Accuracy: <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components:\\t - Image File (GeoTIFF format) - Metadata File (XML format) - Thumbnail File (GeoTIFF format) - Unusable Data Mask UDM File (GeoTIFF format) - Usable Data Mask UDM2 File (GeoTIFF format) Bands: 3-band natural color (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (costal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance:\\t - Approximate, satellite altitude dependent - Dove-C: 3.0 m-4.1 m - Dove-R: 3.0 m-4.1 m - SuperDove: 3.7 m-4.2 m Projection: UTM WGS84 Accuracy: <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and color-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. \ PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. _$$Planet Explorer Catalogue$$ https://www.planet.com/explorer/ can be accessed (Planet registration requested) to discover and check the data readiness. It is worth to mention that the data distribution is based on Data Voucher, corresponding to maximum amount of square kilometers can be ordered and downloaded by the project in a maximum period of 15 moths (this duration cannot be extended) starting from the project proposal acceptance date. Each Date Voucher includes PlanetScope tile view streaming access for a total of 20,000 tiles per calendar month during the project period. All details about the data provision, data access conditions and quota assignment 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 available in Resources section.\",\n \"type\": \"Collection\",\n \"title\": \"PlanetScope Full Archive\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/PlanetScope.Full.Archive\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://www.planet.com/products/#satellite-imagery\",\n \ \"type\": \"text/html\",\n \"title\": \"Planet products - Catalogue\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Planet-combined-imagery-product-specs-2020.pdf?text=Combined-Imagery-Product-Spec.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Imagery Product Specifications - Product Specifications\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://developers.planet.com/docs/apps/explorer/\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Explorer User Guide - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://www.planet.com/explorer/\",\n \"type\": \"text/html\",\n \"title\": \"Planet Explorer Catalogue - Catalogue\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Planet Terms of Applicability - Access Guide\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? 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- ESA Earth Observation User Services Portal\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/atom%2Bxml\",\n \ \"type\": \"application/atom+xml\",\n \"title\": \"Atom format\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/xml\",\n \ \"type\": \"application/xml\",\n \"title\": \"Dublin Core metadata\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ERS.AT_NR__2P?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/ERS-1\",\n \ \"title\": \"More collections for ERS-1 platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/ERS-2\",\n \ \"title\": \"More collections for ERS-2 platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/93343b90-3e9f-5830-abe7-151cc69401c1\",\n \ \"title\": \"More collections for ATSR-1 instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/f3ef7078-65ef-5dee-8c5c-61f9706a34ad\",\n \ \"title\": \"More collections for ATSR-2 instrument\"\n }\n \ ],\n \"id\": \"ERS.AT_NR__2P\",\n \"updated\": \"2023-06-13T13:23:39Z\",\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\n \ \"ATSR-1\",\n \"ATSR-2\"\n ],\n \"platform\": [\n \"ERS-1\",\n \"ERS-2\"\n ]\n }\n },\n \ {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n \ -90,\n 180,\n 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"1995-07-01T00:00:00.000Z\",\n \"2015-12-31T23:59:59.999Z\"\n \ ]]}\n },\n \"stac_version\": \"1.0.0\",\n \"sci:doi\": \"10.57780/esa-1268efe\",\n \"keywords\": [\n \"World\",\n \"Atmosphere\",\n \ \"Atmospheric Chemistry\",\n \"Atmospheric Radiation\",\n \"Atmospheric Temperature\",\n \"Climate\",\n \"EARTH SCIENCE > ATMOSPHERE\",\n \ \"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY\",\n \"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION\",\n \"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE\",\n \"EARTH SCIENCE > CLIMATE INDICATORS\",\n \"Sun-synchronous\",\n \"GDP 5.0\",\n \"782 to 785 km\",\n \"5 km\",\n \"Spectrometers\",\n \"ERS-2\",\n \ \"GOME\"\n ],\n \"created\": \"2019-05-29T00:00:00.00Z\",\n \ \"description\": \"The GOME Total Column Water Vapour (TCWV) Climate product was generated by the Max Planck Institute for Chemistry (MPIC), and the German Aerospace Center (DLR) within the ESA GOME-Evolution project. It is a Level 3 type product containing homogenized time-series of the global distribution of TCWV spanning over more than two decades (1995-2015). The data is provided as single netCDF file, containing monthly mean TCWV (units kg/m2) with 1-degree resolution, and is based on measurements from the satellite instruments ERS-2 GOME, Envisat SCIAMACHY, and MetOp-A GOME-2. Details are available in the paper by Beirle et al, 2018,. Please also consult the GOME TCWV Product Quality Readme file before using the data. 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Product Specifications\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/GOME-Level1-to-2-Algorithms-Description-DLFE-841-GDP-L12.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"GOME Level 1 to 2 Algorithms Description - Technical Note\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Algorithm-Theoretical-Basis-Document-for_GOME-Total-Column-Densities-of-Ozone-And-Nitrogen-Dioxid.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Algorithm Theoretical Basis Document for GOME Total Column Densities of Ozone and Nitrogen Dioxide - Technical Note\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/GOME-Disclaimer-2004-DLFE-640.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Disclaimer for GOME Level-1 and Level-2 Data Products - Technical Note\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/GOME-Software-Databases-For-Level1-to-2-Processing-DLFE-843-GDP-DB.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"GOME Software Databases for Level 1 to 2 Processing - Technical Note\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Data-Validation-Report-for-ERS-2-GOME-Data-Processor-upgrade-to-version4.0-DLFE-641.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Delta Validation Report for ERS-2 GOME Data Processor upgrade toversion 4.0 - Technical Note\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/GOME-Data-Processor-DLFE-656-DLR-GOME-SUM-gdp01-ex-4.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"GOME Data Processor Extraction Software - User Manual\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/search?category=Document+library&filter=gome\",\n \ \"type\": \"text/html\",\n \"title\": \"More GOME Documents - Other\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/instruments/gome/products-information\",\n \ \"type\": \"text/html\",\n \"title\": \"Documentation related to GOME data quality - 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0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \ \"Sun-synchronous\",\n \"Very High Resolution - VHR (0 - 5m)\",\n \ \"High Resolution - HR (5 - 20 m)\",\n \"450 km\",\n \"16.5 km\",\n \"Cameras\",\n \"QuickBird-2\",\n \"BGI\"\n ],\n \ \"created\": \"2019-05-22T00:00:00.00Z\",\n \"description\": \"QuickBird high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, QuickBird offers archive panchromatic products up to 0.60m GSD resolution and 4-Bands Multispectral products up to 2.4m GSD resolution \\u2022 Panchromatic and 4-bands - Levels: STANDARD (2A) / VIEW READY STANDARD (OR2A), VIEW READY STEREO, MAP-READY (ORTHO) 1:12.000 Orthorectified, MAP-READY (ORTHO) 1:50.000 Orthorectified - Resolutions: 0.30 m, 0.40 m, 0.50 m. 0.60 m \\u2022 8-Bands - Levels: STANDARD (2A) / VIEW READY STANDARD (OR2A), VIEW READY STEREO, MAP-READY (ORTHO) 1:12.000 Orthorectified - Resolutions: 0.30 m, 0.40 m, 0.50 m. 0.60 m 4-Bands being an optional from: \\u2022\\u2022 4-Band Multispectral (BLUE, GREEN, RED, NIR1) \\u2022 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) \\u2022 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) \\u2022 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) \\u2022 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) \\u2022 Natural Colour / Coloured Infrared (3-Band pan-sharpened) The list of available archived data can be retrieved using the Image Library (https://www.euspaceimaging.com/image-library/) catalogue.\",\n \"type\": \"Collection\",\n \"title\": \"QuickBird full archive\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/QuickBird.full.archive\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Map-Ready-imagery-data-sheet.pdf/83b9a034-5548-1fc7-4744-8d0fdb922f52\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Map Ready data imagery sheet - Product Sepcification\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/DigitalGlobe-GeoStereo.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"GeoStereo product details - Product Sepcification\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/DigitalGlobe-Stereo-Imagery.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Stereo product details - Product Sepcification\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/view-ready-imagery-data-sheet.pdf/e1476ac6-51cc-6052-c138-844dcc0200b8\",\n \ \"type\": \"application/pdf\",\n \"title\": \"View Ready imagery data sheet - Product Sepcification\"\n },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/WorldView-GeoEye-QuickBird-Terms-Of-Applicability.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Indian Data Terms of Applicability - 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0.4 \xB5m)\",\n \"VIS (0.40 - 0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"SWIR (1.3 - 3.0 \xB5m)\",\n \"MWIR (3.0 - 6.0 \xB5m)\",\n \"4.0\",\n \"Very Low Resolution - VLR (> 1200 m)\",\n \"Imaging Spectrometers/Radiometers\",\n \"Scatterometers\",\n \ \"TERRA\",\n \"AQUA\",\n \"QuikSCAT\",\n \"OrbView-2\",\n \ \"MODIS\",\n \"SeaWinds\",\n \"SeaWiFS\"\n ],\n \ \"created\": \"2019-11-21T00:00:00.00Z\",\n \"description\": \"ADAM enables generating typical monthly variations of the global Earth surface reflectance at 0.1\xB0 spatial resolution (Plate Carree projection) and over the spectral range 240-4000nm. The ADAM product is made of gridded monthly mean climatologies over land and ocean surfaces, and of a companion API toolkit that enables the calculation of hyperspectral (at 1 nm resolution over the whole 240-4000 nm spectral range) and multidirectional reflectances (i.e. in any illumination/viewing geometry) depending on user choices. The ADAM climatologies that feed the ADAM calculation tools are: For ocean: monthly chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is used to compute the water column reflectance (which shows large spectral variations in the visible, but is insignificant in the near and mid infrared). monthly wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate the ocean glint reflectance. For land: monthly normalized surface reflectances in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain of MOD09A1 products (derived from Aqua and Terra observations), on which relies the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow) reflectance. uncertainty variance-covariance matrix for the 7 spectral bands associated to the normalized surface reflectance. 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\"https://emc.spacebel.be/series/concepts/instruments/ada8f84c-48ef-50a6-b2ba-635a1bdb7d14\",\n \ \"title\": \"More collections for MODIS instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/3a0e9379-9541-569c-8492-3636d0ef2a27\",\n \ \"title\": \"More collections for SeaWinds instrument\"\n }\n \ ],\n \"id\": \"ADAM.Surface.Reflectance.Database\",\n \"updated\": \"2023-06-13T13:23:39Z\",\n \"stac_extensions\": [\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\"],\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\n \ \"SeaWiFS\",\n \"MODIS\",\n \"SeaWinds\"\n ],\n \ \"platform\": [\n \"TERRA\",\n \"AQUA\",\n \"QuikSCAT\",\n \ \"OrbView-2\"\n ]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -82,\n 180,\n \ 82\n ]]},\n \"temporal\": {\"interval\": [[\n \"1991-08-01T00:00:00.000Z\",\n \ \"2011-07-04T23:59:59.999Z\"\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \"sci:doi\": \"10.57780/ers-af99f2b\",\n \"keywords\": [\n \"World\",\n \"Natural Hazards and Disaster Risk\",\n \"Atmosphere\",\n \ \"Atmospheric Winds\",\n \"EARTH SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS\",\n \"EARTH SCIENCE > ATMOSPHERE\",\n \"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS\",\n \"Sun-synchronous\",\n \ \"ASPS 10.04\",\n \"Very Low Resolution - VLR (> 1200 m)\",\n \ \"782 to 785 km\",\n \"5 km\",\n \"Scatterometers\",\n \ \"ERS-1\",\n \"ERS-2\",\n \"AMI/Scatterometer\"\n ],\n \ \"created\": \"2019-05-29T00:00:00.00Z\",\n \"description\": \"The ERS data reprocessed with the ASPS facility is also available in the UWI format to maintain the compatibility with the FD (Fast Delivery) products. 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Please refer to the _$$MOS Product Format Specification$$ https://earth.esa.int/eogateway/documents/d/earth-online/mos-product-format-specification for further details. \\rThe collection consists of data products of the following type: \\r\\rMES_GEC_1P: Geocoded Ellipsoid GCP Corrected Level 1 MOS-1/1B MESSR products which are the default products generated by the MOS MESSR processor in all cases (where possible), with usage of the latest set of LANDSAT improved GCP (Ground Control Points). 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\"https://emc.spacebel.be/collections/ESA_Orthorectified_Map_oriented_Level1_products?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ESA_Orthorectified_Map_oriented_Level1_products?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ESA_Orthorectified_Map_oriented_Level1_products?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ESA_Orthorectified_Map_oriented_Level1_products?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ESA_Orthorectified_Map_oriented_Level1_products?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ESA_Orthorectified_Map_oriented_Level1_products?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/MOS-1\",\n \ \"title\": \"More collections for MOS-1 platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/MOS-1B\",\n \ \"title\": \"More collections for MOS-1B platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/512f0f9f-5eff-5a9b-bc5f-2616eb03d875\",\n \ \"title\": \"More collections for MESSR instrument\"\n }\n \ ],\n \"id\": \"ESA_Orthorectified_Map_oriented_Level1_products\",\n \ \"updated\": \"2024-01-11T15:24:06Z\",\n \"stac_extensions\": [\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\"],\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"https://earth.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"MESSR\"],\n \ \"platform\": [\n \"MOS-1\",\n \"MOS-1B\"\n ]\n \ }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n 90\n ]]},\n \ \"temporal\": {\"interval\": [[\n \"2020-07-09T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"Level 2C assisted wind fields assimilation processing\",\n \"Atmosphere\",\n \"Atmospheric Indicators\",\n \ \"Atmospheric Winds\",\n \"Climate\",\n \"EARTH SCIENCE > ATMOSPHERE\",\n \"EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WINDS\",\n \ \"EARTH SCIENCE > CLIMATE INDICATORS\",\n \"UV (0.01 - 0.4 \xB5m)\",\n \"Sun-synchronous\",\n \"Horizontal resolution at observation scale for Rayleigh/Mie: 87/10 km 250-2000 m vertical resolution\",\n \ \"ALD_U_N_2C\",\n \"320 km\",\n \"Lidar/Laser Sounders\",\n \ \"Aeolus\",\n \"ALADIN\"\n ],\n \"created\": \"2023-02-17T00:00:00.00Z\",\n \ \"description\": \"The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds.\",\n \"type\": \"Collection\",\n \"title\": \"Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19139-2%2Bxml\",\n \ \"title\": \"ISO 19139-2 metadata\",\n \"type\": \"application/vnd.iso.19139-2+xml\"\n \ },\n \"metadata_dif_10\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/dif10%2Bxml\",\n \ \"title\": \"DIF-10 metadata\",\n \"type\": \"application/dif10+xml\"\n \ },\n \"metadata_iso_19115_3\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=application/vnd.iso.19115-3%2Bxml\",\n \ \"title\": \"ISO 19115-3 metadata\",\n \"type\": \"application/vnd.iso.19115-3+xml\"\n \ },\n \"metadata_ogc_17_084r1\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?mode=owc\",\n \ \"title\": \"OGC 17-084r1 metadata\",\n \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/eoc-geojson/1.0\\\"\"\n \ },\n \"metadata_html\": {\n \"roles\": [\"metadata\"],\n \ \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products?httpAccept=text/html\",\n \ \"title\": \"HTML\",\n \"type\": \"text/html\"\n }\n \ },\n \"links\": [\n {\n \"rel\": \"self\",\n \"href\": \"https://emc.spacebel.be/collections/L2C_Wind_products\",\n \"type\": \"application/json\"\n },\n {\n \"rel\": \"root\",\n \ \"href\": \"https://emc.spacebel.be\",\n \"type\": \"application/json\",\n \ \"title\": \"FedEO Clearinghouse\"\n },\n {\n \"rel\": \"parent\",\n \"href\": \"https://emc.spacebel.be\",\n \"title\": \"collections\",\n \"type\": \"application/json\"\n },\n {\n \ \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Level2-Algorithm-Theoretical-Basis-Document.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Aeolus L2B Algorithm Theoretical Baseline Document - Level 2B processor algorithm description\"\n \ },\n {\n \"rel\": \"describedby\",\n \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-L2B-2C-Input-Output-DD-ICD.pdf/2321b6a0-27d0-b628-771e-fcca4928bc32\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Aeolus L2B/2C Processor Input/Output Data Definitions Interface Control Document - Level 2B/2C data definition\"\n },\n {\n \"rel\": \"describedby\",\n \ \"href\": \"https://earth.esa.int/eogateway/documents/20142/37627/Aeolus-Sensor-and-Product-Description.pdf\",\n \ \"type\": \"application/pdf\",\n \"title\": \"Aeolus Sensor and Product Description - Aeolus Sensor and Product Description\"\n },\n \ {\n \"rel\": \"describedby\",\n \"href\": \"https://esatellus.service-now.com/csp?id=esa_simple_request\",\n \ \"type\": \"text/html\",\n \"title\": \"Get Help? 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- ESA Earth Observation User Services Portal\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/GOCE_Level_2?httpAccept=application/atom%2Bxml\",\n \ \"type\": \"application/atom+xml\",\n \"title\": \"Atom format\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/GOCE_Level_2?httpAccept=application/xml\",\n \ \"type\": \"application/xml\",\n \"title\": \"Dublin Core metadata\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/GOCE_Level_2?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/GOCE_Level_2?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/GOCE_Level_2?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/GOCE_Level_2?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/GOCE_Level_2?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/GOCE_Level_2?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/GOCE\",\n \ \"title\": \"More collections for GOCE platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/d199ff14-9d4f-50eb-909e-98d020e9cf9b\",\n \ \"title\": \"More collections for STR instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/bc5654d6-8472-5509-8aa8-550a078dadd6\",\n \ \"title\": \"More collections for EGG instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/03a1a96e-daa1-52e4-ab3f-508cbe9b5cf9\",\n \ \"title\": \"More collections for SSTI instrument\"\n }\n \ ],\n \"id\": \"GOCE_Level_2\",\n \"updated\": \"2023-06-13T13:23:40Z\",\n \ \"stac_extensions\": [\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\"],\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\n \ \"STR\",\n \"EGG\",\n \"SSTI\"\n ],\n \"platform\": [\"GOCE\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -84,\n 180,\n 84\n \ ]]},\n \"temporal\": {\"interval\": [[\n \"2013-11-13T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"Planet\",\n \"Land Surface\",\n \"Mapping and Cartography\",\n \"Natural Hazards and Disaster Risk\",\n \"EARTH SCIENCE > LAND SURFACE\",\n \"EARTH SCIENCE > HUMAN DIMENSIONS > NATURAL HAZARDS\",\n \"VIS (0.40 - 0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"Sun-synchronous\",\n \ \"Very High Resolution - VHR (0 - 5m)\",\n \"475-575 km\",\n \ \"8 km at nadir for SkySat-1 to -2, 6.6 km at nadir for SkySat-3 to -13\",\n \"Imaging Spectrometers/Radiometers\",\n \"SkySat\",\n \ \"SkySat Camera\"\n ],\n \"created\": \"2019-08-09T00:00:00.00Z\",\n \ \"description\": \"The SkySat Level 2B Basic Scene, Level 3B Ortho Scene and Level 3B Consolidated full archive and new tasking products are available as part of Planet imagery offer. The SkySat Basic Scene product is uncalibrated and in a raw digital number format, not corrected for any geometric distortions inherent in the imaging process. Rational Polynomial Coefficients (RPCs) is provided to enable orthorectification by the user. Basic Scene Product Components and Format Processing levels\\t/\\t- Analytic (unorthorectified, radiometrically corrected, multispectral BGRN); - Analytic DN (unorthorectified, multispectral BGRN); - Panchromatic DN (unorthorectified, panchromatic)\\t// Product Components and Format\\t/\\t- Image File (GeoTIFF format); - Metadata File (JSON format); - Rational Polynomial Coefficients (Text File); - UDM File (GeoTIFF format)\\t// Image configuration\\t/\\t- 4-band Analytic DN Image (Blue, Green, Red, NIR); - 1-band Panchromatic DN Image (Pan)\\t// Ground Sampling Distance\\t/\\t3.7 m at nadir (average at reference altitude 475 km)\\t// Ground Sampling Distance (nadir)\\t/\\t- Panchromatic 0.86m and Multispectral 1.0m for SkySat-1&2; - Panchromatic 0.65m and Multispectral 0.8m for SkySat-3 to 13 (0.72 m and 1.0m for data acquired before 30/06/2020)\\t// Accuracy\\t/\\t<50 m RMSE\\t// \ The SkySat Ortho Scene is sensor- and geometrically-corrected (by using DEMs with a post spacing of between 30 and 90 meters) and is projected to a cartographic map projection; the accuracy of the product will vary from region to region based on available GCPs. Different products are available: \ - The SkySat Visual Ortho Scene product is orthorectified, pansharpened, and color-corrected (using a color curve) 3-band RGB Imagery - The SkySat Pansharpened Multispectral Scene product is orthorectified, pansharpened 4-band BGRN Imagery - The SkySat Analytic DN Ortho Scene product is orthorectified, multispectral BGRN, uncalibrated, digital number imagery product. The product has been processed to remove distortions caused by terrain; It eliminates the perspective effect on the ground (not on buildings), restoring the geometry of a vertical shot. Transformation to at-sensor radiance is not included - The SkySat Panchromatic DN Ortho Scene product is orthorectified, panchromatic, uncalibrated, digital number imagery product. It has a finer GSD than the Analytic Product. Transformation to at-sensor radiance is not included. - The SkySat Analytic Ortho Scene are calibrated multispectral imagery products with radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. - The SkySat Consolidated Product are Ortho Collect product created by composing ~60 SkySat Ortho Scenes (Visual, Pansharpened Multispectral, Analytic DN, Panchromatic DN) along an imaging strip into segments. Ortho Scene Product Components and Format \\t/\\tVisual Ortho\\t/\\tPansharpened Multispectral\\t/\\tAnalytic DN Ortho\\t/\\tPanchromatic DN Ortho\\t/\\tAnalytic Ortho\\t// Product Components and Format\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format);\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t/\\tImage File (GeoTIFF format); Metadata File (JSON format); Rational Polynomial Coefficients (Text File); UDM File (GeoTIFF format)\\t// Image configuration\\t/\\t3-band Pansharpened Image (PS Red, PS Green, PS Blue)\\t/\\t4-band Pansharpened Image (PS Blue, PS Green, PS Red, PS NIR)\\t/\\t4-band Analytic DN Image (B, G, R, N)\\t/\\t1-band Panchromatic Image\\t/\\t4-band Analytic Image (B, G, R, N)\\t// Ground Sampling Distance\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t/\\t50 cm\\t// Projection\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t/\\tUTM WGS84\\t// Accuracy\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\t<10 m RMSE\\t/\\tradiometric accuracy: +/- 5% Relative accuracy at < 10 degrees off-nadir angle\\t// The products are available as part of the Planet provision from RapidEye, Skysat and PlanetScope constellations. SkySat collection has worldwide coverage: The Planet Explorer Catalogue can be accessed (Planet registration requested) to discover and check the data readiness. 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This FMR follows the first Envisat Altimetry reprocessing Version (V2.1) completed in 2012. The GDR and S-GDR data products were reprocessed for all cycles from 6 to 113 (May 2002 to April 2012) into a homogeneous standard in NetCDF format (close to Sentinel-3). The Sensor Data Record (SGDR) Product from RA-2/MWR includes the data in the GDR product (https://earth.esa.int/eogateway/catalog/envisat-ra-2-geophysical-data-record-gdr-ra2_gdr__2p-) (RA-2 geophysical data, MWR data) and also RA-2 averaged waveforms (18Hz) and RA-2 individual waveforms (1800Hz). This product is a continuation of ERS RA data. This data product has a coverage of 1 pass, pole-pole, a spatial sampling of about 390 m along track and a size of 31 to 40 MB, depending on presence of individual waveforms. The radiometric accuracy is 0.2 dB and auxiliary data include: Orbit state vectors (DORIS, FOS), RA2 and MWR characterisation data, Platform attitude, Gain calibration, USO frequency, ECMWF data, time relation, leap second, Ionospheric corrections, geoid, mean sea surface, slope data, and tide model (ocean, earth, loading, pole). 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- ESA Earth Observation User Services Portal\"\n },\n {\n \"rel\": \"alternate\",\n \ \"href\": \"https://emc.spacebel.be/collections/ENVISAT.RA2.MWS_2P?httpAccept=application/atom%2Bxml\",\n \ \"type\": \"application/atom+xml\",\n \"title\": \"Atom format\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.RA2.MWS_2P?httpAccept=application/xml\",\n \ \"type\": \"application/xml\",\n \"title\": \"Dublin Core metadata\"\n },\n {\n \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.RA2.MWS_2P?httpAccept=application/ld%2Bjson;profile=https://schema.org\",\n \ \"type\": \"application/ld+json;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"JSON-LD (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.RA2.MWS_2P?httpAccept=application/ld%2Bjson;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/ld+json;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"JSON-LD (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.RA2.MWS_2P?httpAccept=application/rdf%2Bxml;profile=https://schema.org\",\n \ \"type\": \"application/rdf+xml;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"RDF/XML (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.RA2.MWS_2P?httpAccept=application/rdf%2Bxml;profile=http://data.europa.eu/930/\",\n \ \"type\": \"application/rdf+xml;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"RDF/XML (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.RA2.MWS_2P?httpAccept=text/turtle;profile=https://schema.org\",\n \ \"type\": \"text/turtle;profile=\\\"https://schema.org\\\"\",\n \ \"title\": \"Turtle (schema.org) metadata\"\n },\n {\n \ \"rel\": \"alternate\",\n \"href\": \"https://emc.spacebel.be/collections/ENVISAT.RA2.MWS_2P?httpAccept=text/turtle;profile=http://data.europa.eu/930/\",\n \ \"type\": \"text/turtle;profile=\\\"http://data.europa.eu/930/\\\"\",\n \ \"title\": \"Turtle (GeoDCAT-AP) metadata\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:organisationName/ESA@ESRIN\",\n \ \"title\": \"More collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Envisat\",\n \ \"title\": \"More collections for Envisat platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/2ebd3948-3f88-5944-8e3c-7e60099ec941\",\n \ \"title\": \"More collections for RA-2 instrument\"\n }\n \ ],\n \"id\": \"ENVISAT.RA2.MWS_2P\",\n \"updated\": \"2023-06-13T13:23:41Z\",\n \ \"stac_extensions\": [\"https://stac-extensions.github.io/scientific/v1.0.0/schema.json\"],\n \ \"providers\": [\n {\n \"roles\": [\"producer\"],\n \ \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"RA-2\"],\n \ \"platform\": [\"Envisat\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n \ 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"2009-08-01T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"GEOSAT\",\n \"DIMAP\",\n \ \"TIFF\",\n \"Vegetation\",\n \"Agriculture\",\n \"Forestry\",\n \ \"EARTH SCIENCE > BIOSPHERE > VEGETATION\",\n \"EARTH SCIENCE > AGRICULTURE\",\n \"EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > TERRESTRIAL ECOSYSTEMS > FORESTS\",\n \"VIS (0.40 - 0.75 \xC2\xB5m)\",\n \"NIR (0.75 - 1.30 \xC2\xB5m)\",\n \"Sun-synchronous\",\n \"Medium Resolution - MR (20 - 500 m)\",\n \"663 km\",\n \"625 km\",\n \ \"Cameras\",\n \"GEOSAT-1\",\n \"SLIM6\"\n ],\n \ \"created\": \"2019-05-22T00:00:00.00Z\",\n \"description\": \"GEOSAT-1 full archive and new tasking products are available at 22 m resolution in two processing levels.\\r\\rL1R (Basic Geopositioned): All 3 spectral channels combined into a band-registered image. Geopositioned product based on sensor model. Coefficients derived from satellite orientation parameters coming from telemetry and appended to metadata\\rL1T (L1R orthorectified): Orthorectified to sub-pixel accuracy (10 metres RMS error approximately) with respect to Landsat ETM+ reference data and hole-filled seamless SRTM DEM data V3, 2006 (90 m)\\rGEOSAT-1 products are provided in DIMAP format. The image products are delivered in the TIFF and GeoTIFF image formats by default. All products can be provided in False Colours (R,G,NIR) or Natural Colours (R, G, Synthetic Blue).\\r\\rAll details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.\\r\\rThe list of available archived data can be retrieved using the GEOSAT catalogue (https://catalogue.geosat.space/cscda/extcat/)\\rAll details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. \\r\\rThe list of available archived data can be retrieved using the Deimos catalogue (http://www.deimos-imaging.com/catalogue).\",\n \"type\": \"Collection\",\n \"title\": \"GEOSAT-1 full archive and tasking\",\n \ \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/Geosat-1.Full.archive.and.tasking\",\n \ \"type\": 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\"https://emc.spacebel.be/series/concepts/instruments/5cc6eaab-0ddc-572f-a1b7-afa52ea5333d\",\n \ \"title\": \"More collections for SLIM6 instrument\"\n }\n \ ],\n \"id\": \"Geosat-1.Full.archive.and.tasking\",\n \"updated\": \"2023-06-13T13:23:40Z\",\n \"providers\": [\n {\n \"roles\": [\"producer\"],\n \"name\": \"ESA/ESRIN\",\n \"url\": \"https://earth.esa.int\"\n \ },\n {\n \"roles\": [\"host\"],\n \"name\": \"FedEO Clearinghouse\",\n \"url\": \"https://emc.spacebel.be/readme.html\"\n \ }\n ],\n \"summaries\": {\n \"instruments\": [\"SLIM6\"],\n \ \"platform\": [\"GEOSAT-1\"]\n }\n },\n {\n \"extent\": {\n \"spatial\": {\"bbox\": [[\n -180,\n -90,\n 180,\n \ 90\n ]]},\n \"temporal\": {\"interval\": [[\n \"2016-06-01T00:00:00.000Z\",\n \ null\n ]]}\n },\n \"stac_version\": \"1.0.0\",\n \ \"keywords\": [\n \"World\",\n \"GNSS\",\n \"AIS\",\n \ \"ADS-B\",\n \"Atmosphere\",\n \"Ionosphere and Magnetosphere Dynamics\",\n \"Weather Events\",\n \"Oceans\",\n \"EARTH SCIENCE > ATMOSPHERE\",\n \"EARTH SCIENCE > SUN-EARTH INTERACTIONS > IONOSPHERE/MAGNETOSPHERE DYNAMICS\",\n \"EARTH SCIENCE > ATMOSPHERE > WEATHER EVENTS\",\n \"EARTH SCIENCE > OCEANS\",\n \"Sun-synchronous\",\n \ \"500 km\",\n \"Imaging Spectrometers/Radiometers\",\n \"Spire\",\n \ \"AIRSAFE\",\n \"SENSE\",\n \"STRATOS\"\n ],\n \ \"created\": \"2020-09-04T00:00:00.00Z\",\n \"description\": \"The data collected by Spire from it's 110 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested: ADS-B Data Stream Global ADS-B satellite data observed by Spire satellites and processed through the ground stations network. Historical ADS-B data older than 6 months can be delivered as data cuts containing CSV file(s) accessible through a Web Service or Cloud storage solutions. Live ADS-B data is available through a streaming API, and recent historical data can be accessed through a REST API. Data is distributed as a monthly subscription: historical data can be requested starting from 3 December 2008, the time period for live data starts from a user-defined date and continues for 30 days. AIS AIS messages include satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations (up to 40 million messages per day). Historical AIS data are delivered as a cvs file with availability back to June 2016 or via Historical API from December 2018; live AIS data are pushed to end users via TCP or through Messages API. Data is distributed as a monthly subscription, from a user-defined date and continues for a 30 day period. GNSS-Radio Occultation GNSS Radio Occultation (GNSS-RO) measurements are collected globally on a continuous basis, generating profiles of the Earth\\u2019s atmosphere. Derived Level 1 and Level 2 products include both atmospheric and ionospheric products. Historical data for most of the GNSS-RO products are available from December 2018 to the present. Near real-time (within 90 minutes or less latency from collection to delivery) GNSS-RO profiles are also available upon request. GNSS Reflectometry GNSS Reflectometry (GNSS-R) is a technique to measure Earth\\u2019s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: conventional, near-nadir incidence LHCP reflections collected by the Spire GNSS-R satellites (e.g., Spire GNSS-R \\u201cBatch-1\\u201d satellites) and grazing angle (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while conventional GNSS-R data are available from December 2020 to the present. \ Name: Automatic Identification System (AIS) Description: The automatic identification system (AIS) is an automatic tracking system that uses transponders on ships and is used by vessel traffic services. Spire data includes satellite AIS (S-AIS) as observed by Spire satellites and terrestrial AIS (T-AIS) from third party sensor stations. Data format and content: .parquet.gz files The AIS files contain time-series data on received AIS messages, both the raw NMEA message and added post-processing data for each message. Application: Supply chain analysis, commodity trading, identification of illegal fishing or dark targets, ship route and fuel use optimization, analysis of global trade patterns, anti-piracy, autonomous vessel software, ocean currents. Name: Automatic Dependent Surveillance-Broadcast (ADS-B) Description: Spire AirSafe ADS-B products give access to satellite and terrestrial ADS-B data from captured aircrafts. Data format and content: .csv.gz files The decompressed csv file contains a list of hexadecimal representations of ADS-B messages associated with the timestamp they were received on the satellite. Application: Fleet management, ICAO regulatory compliance, route optimization, predictive maintenance, global airspace, domain awareness. Name: Global Navigation Satellite System Radio Occultation (GNSS-RO) Description: GNSS atmospheric radio occultation (GNSS-RO) relies on the detection of a change in a radio signal as it passes through a planet's atmosphere, i.e. as it is refracted by the atmosphere. This data set contains precise orbit determination (POD) solutions, satellite attitude information, high-rate occultation observations, excess phase, and derived atmospheric dry temperature profiles. Data format and content: podObs*.rnx This file contains raw pseudorange, carrier phase, Doppler frequency, and signal-to-noise measurements for each observed GPS signal from a single Spire satellite which allow to estimate the positions and velocities of each Spire satellite and also used to derive ionospheric total electron content data. leoOrb*.sp3 This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file leoAtt*.log It contains 1 Hz rate quaternion information measured from a single Spire satellite describing the satellite orientation. opnGns*ro.bin, opnGns*rst.bin these files contain raw measurements from the occulting GNSS satellite (one for each signal frequency) and raw phase data from one or more reference GNSS satellites. atmPhs* The file contains occultation excess phase delay. Also contains SNR values, ransmitter and receiver positions and open loop model information. atmPrf*.nc The file contains profiles of atmospheric dry pressure, dry temperature and neutral refractivity as a function of altitude produced from full processing of one occultation event. bfrPrf*.bufr The file contains derived profiles of dry pressure, dry temperature, refractivity and bending angle for each occultation. Application:\\tAtmospheric profiles of pressure, dry temperature, bending angle, and refractivity used in numerical weather prediction data assimilation and climate change studies. Name: Raw IF samples from GNSS-RO satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of GNSS-RO satellites. Data format and content: rocRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-RO studies, GNSS RFI and jamming monitoring, research. Name: Raw IF samples from GNSS-R satellites Description: Raw intermediate frequency (IF) sampled data (I/Q) from the GNSS receiver front-end of conventional GNSS-R satellites. Data format and content: gbrRIF*.zip Binary raw IF data and associated ancillary data (e.g., POD data) in a zip archive per collection event. Application: GNSS-R studies, GNSS RFI and jamming monitoring, research, etc. Name: Grazing angle GNSS-R observations Description: During grazing angle GNSS-R events, signal reflection at two frequencies is observed through the limb-facing antenna and is trackedusing an open-loop tracking technique thatrelies on a model topredict the propagationdelay and Doppler of thereflected signal. Simultaneous open-looptracking of the signaldirectly along theline-of-sight from thetransmitter to thereceiver is alsoperformed to provideadditional data that maybenecessary for signalcalibration. The mainoutput of the open-looptracking are in-phase (I)and quadrature (Q)accumulation samples(nominally at 50 Hz),which represent the residual Doppler (phase) from the model. Data format and content: grzObs*.nc L1A filecontains rawopen loopcarrier phasemeasurementsat 50 Hzsampling forgrazingangleGNSS-Rreflectionscaptured in the GNSS-RO RHC Pantennas, (bothdirect andreflectedsignals) on GNSS-RO satellites. Application: Sea surface and sea ice height extent, and classification. \ Name: Georeferenced grazing angle GNSS-R observations Description: The low-levelobservations of the high-rate grazing angle GNSS-R observationsbut withthegeoreferenced bistatic radar parameters of the satellite receiver,specular reflection, and GNSS transmitter included. Data format and content: grzRfl*.nc L1B file contains the georeferenced grazing angle GNSS-R data collected by Spire GNSS-RO satellites, including the low-level observables and bistatic radar geometries (e.g., receiver, specular reflection, and the transmitter locations). Application: Sea surface and sea ice height extent, and classification Name: GNSS-R calibrated bistatic radar reflectivities Description: Higher level product used to derive land-surface reflectivity. Data format and content: gbrRfl*.nc L1A along-track calibrated relative power between reflected and direct signals (e.g., bistatic radar reflectivities) measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, soil moisture, ocean wind, and sea ice applications Name: GNSS-R calibrated bistatic radar cross-sections Description: Higher level product used to derive ocean surface roughness products. Data format and content: gbrRCS*.nc L1B along-track calibrated and normalized bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. Application: GNSS-R studies, ocean wind and sea ice applications Name: Combined Surface Soil Moisture Description: Combined CYGNSS and SMAP soil moisture data are provided as a combined surface soil moisture (COMB-SSM) product in two data level formats: L2U1 and L3U1. 6 x 6 km grid cell. L-band measurements of surface soil moisture benefit from better vegetation penetration in comparison to traditional C-band measurements. Data format and content: COMB-SSM.nc This file contains the combined data product containing measurements from both CYGNSS and SMAP reported on a 6 km global Equi7Grid grid. Application: Agriculture, crop insurance, farming solutions, climatology, terrain awareness, peatlands and wetlands monitoring etc. Name: Ionosphere total electron content Description: Spire routinely collects and processes a large volume of total electron content (TEC) data, representing the line-of-sight integration of electron density between a Spire satellite and a GNSS satellite. Each file contains line-of-sight ionospheric total electron content (TEC) estimates derived for a \\u2018single viewing arc\\u2019 contained in the POD observation file. Viewing arcs are at least 10 minutes in duration. Data format and content: podTec*.nc This file contains the line-of-sight total electron content with associated orbital information. Application: Space weather research, tsunamigenic earthquakes, weather applications, space situational awareness (SSA), autonomous vehicles etc Name: Ionosphere scintillation Description: The scintillation index for each GNSS frequency is computed onboard the spacecraft. This index provides a measure of the fluctuations of the GNSS signal over the course of 10 seconds caused by propagation of the radio signals through electron density irregularities in the ionosphere. After the raw indices are downlinked to the ground, they are packaged along with associated metadata such as orbit position to create the final scintillation data product. Data format and content: scnLv1*.nc This file contains on-board computed scintillation data (S4 only) with associated orbital information Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications etc. Name: Electron density profile Description: Electron density profiles are retrieved as a function of altitude. Electron density profiles are processed from podTec netcdf files, which span a sufficient elevation angle range. A standard Abel inversion algorithm is applied to retrieve the profiles. Data format and content: ionPrf*.nc This file contains electron density profile retrieved from podTec files spanning appropriate elevation angle range Application: Space weather research, solar events, TIDs, weather applications positioning and navigation, communications. The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639).\",\n \ \"type\": \"Collection\",\n \"title\": \"Spire live and historical data\",\n \"license\": \"various\",\n \"assets\": {\n \"metadata_ogc_17_069r3\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data\",\n \ \"type\": \"application/geo+json;profile=\\\"http://www.opengis.net/spec/ogcapi-features-1/1.0\\\"\",\n \ \"title\": \"OGC 17-069r3 metadata\"\n },\n \"metadata_iso_19139\": {\n \"roles\": [\"metadata\"],\n \"href\": \"https://emc.spacebel.be/collections/Spire.live.and.historical.data?httpAccept=application/vnd.iso.19139%2Bxml\",\n \ \"title\": \"ISO 19139 metadata\",\n \"type\": \"application/vnd.iso.19139+xml\"\n \ },\n \"metadata_iso_19139_2\": {\n 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collections for ESA/ESRIN\"\n },\n {\n \ \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/eo:platform/Spire\",\n \ \"title\": \"More collections for Spire platform\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/1c3bd49f-bdc9-5c33-a273-2f0f7a5b3f88\",\n \ \"title\": \"More collections for STRATOS instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/e5735a20-bdb8-5cff-b385-c16c84c855f4\",\n \ \"title\": \"More collections for SENSE instrument\"\n },\n \ {\n \"rel\": \"related\",\n \"href\": \"https://emc.spacebel.be/series/concepts/instruments/11f58510-1770-53a7-9b13-5a5564b4e9c2\",\n \ \"title\": \"More collections for AIRSAFE instrument\"\n }\n \ ],\n \"id\": \"Spire.live.and.historical.data\",\n \"updated\": \"2023-06-13T13:23:42Z\",\n \"providers\": [\n {\n \"roles\": [\"producer\"],\n \"name\": \"ESA/ESRIN\",\n \"url\": \"http://www.esa.int\"\n \ 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SCIENCE\",\n \"EARTH SCIENCE > LAND SURFACE > LANDSCAPE\",\n \"EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY\",\n \"EARTH SCIENCE > ATMOSPHERE > WEATHER EVENTS\",\n \"VIS (0.40 - 0.75 \xB5m)\",\n \"NIR (0.75 - 1.30 \xB5m)\",\n \"SWIR (1.3 - 3.0 \xB5m)\",\n \"Sun-synchronous\",\n \ \"Very High Resolution - VHR (0 - 5m)\",\n \"High Resolution - HR (5 - 20 m)\",\n \"832 km for SPOT-1 to 5, 694 km for SPOT-6 and 7\",\n \"60 km\",\n \"Cameras\",\n \"SPOT 6\",\n \"SPOT 7\",\n \"NAOMI\"\n ],\n \"created\": \"2019-07-16T00:00:00.00Z\",\n \ \"description\": \"The SPOT 6 and 7 satellites ensure data continuity with the no longer operational SPOT 5 satellite and provide an archive of very high resolution optical acquisition as well as the possibility to task the satellites for new acquisitions. 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[Landsat 8](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-8) is the most recent satellite in the Landsat series. Launched in 2013, Landsat 8 captures data in eleven spectral bands: ten optical/IR bands from the [Operational Land Imager](https://landsat.gsfc.nasa.gov/landsat-8/operational-land-imager) (OLI) instrument, and two thermal bands from the [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/landsat-8/thermal-infrared-sensor-tirs) (TIRS) instrument.\n\nThis dataset represents the global archive of Level-2 Landsat 8 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2). Because there is some latency before Level-2 data is available, a rolling window of recent Level-1 data is available as well. 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This dataset represents the global Sentinel-2 archive, from 2016 to the present, processed to L2A (bottom-of-atmosphere) using [Sen2Cor](https://step.esa.int/main/snap-supported-plugins/sen2cor/) and converted to [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.","stac_version":"1.0.0-beta.2","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-2-l2a","rel":"self","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/","rel":"parent","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-2-l2a/items","rel":"item","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","rel":"root","type":"application/json"},{"href":"https://scihub.copernicus.eu/twiki/pub/SciHubWebPortal/TermsConditions/Sentinel_Data_Terms_and_Conditions.pdf","rel":"license","title":"Copernicus Sentinel data terms"}],"stac_extensions":["item-assets","collection-assets"],"title":"Sentinel-2 Level-2A","license":"proprietary","extent":{"spatial":{"bbox":[[-180.0,-90.0,180.0,90.0]]},"temporal":{"interval":[["2015-06-27T10:25:31.456000Z",null]]}},"keywords":["sentinel","copernicus","esa","satellite","global","reflectance"],"providers":[{"name":"ESA","roles":["producer","licensor"],"url":"https://sentinel.esa.int/web/sentinel/missions/sentinel-2"},{"name":"Esri","roles":["processor"],"url":"https://www.esri.com/"},{"name":"Microsoft","roles":["host","processor"],"url":"https://planetarycomputer.microsoft.com"}],"summaries":{"gsd":[10,20,60],"eo:bands":[{"name":"AOT","description":"aerosol optical thickness"},{"gsd":60,"name":"B01","common_name":"coastal","description":"coastal aerosol","center_wavelength":0.443,"full_width_half_max":0.027},{"gsd":10,"name":"B02","common_name":"blue","description":"visible blue","center_wavelength":0.49,"full_width_half_max":0.098},{"gsd":10,"name":"B03","common_name":"green","description":"visible green","center_wavelength":0.56,"full_width_half_max":0.045},{"gsd":10,"name":"B04","common_name":"red","description":"visible red","center_wavelength":0.665,"full_width_half_max":0.038},{"gsd":20,"name":"B05","common_name":"rededge","description":"vegetation classification red edge","center_wavelength":0.704,"full_width_half_max":0.019},{"gsd":20,"name":"B06","common_name":"rededge","description":"vegetation classification red edge","center_wavelength":0.74,"full_width_half_max":0.018},{"gsd":20,"name":"B07","common_name":"rededge","description":"vegetation classification red edge","center_wavelength":0.783,"full_width_half_max":0.028},{"gsd":10,"name":"B08","common_name":"nir","description":"near infrared","center_wavelength":0.842,"full_width_half_max":0.145},{"gsd":20,"name":"B8A","common_name":"rededge","description":"vegetation classification red edge","center_wavelength":0.865,"full_width_half_max":0.033},{"gsd":60,"name":"B09","description":"water vapor","center_wavelength":0.945,"full_width_half_max":0.026},{"gsd":20,"name":"B11","common_name":"swir16","description":"short-wave infrared, snow/ice/cloud classification","center_wavelength":1.61,"full_width_half_max":0.143},{"gsd":20,"name":"B12","common_name":"swir22","description":"short-wave infrared, snow/ice/cloud classification","center_wavelength":2.19,"full_width_half_max":0.242}],"platform":["Sentinel-2A","Sentinel-2B"],"instruments":["msi"],"constellation":["sentinel-2"],"view:off_nadir":[0]},"item_assets":{"B01":{"title":"Band 1 - Coastal aerosol","gsd":60.0,"type":"image/tiff; 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NAIP is administered by the [Aerial Field Photography Office](https://www.fsa.usda.gov/programs-and-services/aerial-photography/) (AFPO) within the [US Department of Agriculture](https://www.usda.gov/) (USDA). Data are captured at least once every three years for each state. This dataset represents NAIP data from 2010-present, in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n","stac_version":"1.0.0-beta.2","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip","rel":"self","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/","rel":"parent","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip/items","rel":"item","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","rel":"root","type":"application/json"},{"href":"https://www.fsa.usda.gov/help/policies-and-links/","rel":"license","title":"public domain"}],"stac_extensions":["item-assets","collection-assets"],"title":"NAIP: National Agriculture Imagery Program","license":"proprietary","extent":{"spatial":{"bbox":[[-124.784,24.744,-66.951,49.346]]},"temporal":{"interval":[["2010-01-01T00:00:00Z","2019-12-31T00:00:00Z"]]}},"keywords":["naip","aerial","imagery","usda","afpo","agriculture","united states"],"providers":[{"name":"USDA Farm Service Agency","roles":["producer","licensor"],"url":"https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/"},{"name":"Esri","roles":["processor"],"url":"https://www.esri.com/"},{"name":"Microsoft","roles":["host","processor"],"url":"https://planetarycomputer.microsoft.com"}],"summaries":{"gsd":[0.6,1.0],"eo:bands":[{"name":"Red","common_name":"red","description":"visible red"},{"name":"Green","common_name":"green","description":"visible green"},{"name":"Blue","common_name":"blue","description":"visible blue"},{"name":"NIR","common_name":"nir","description":"near-infrared"}]},"msft:container":"naip","msft:storage_account":"naipeuwest","item_assets":{"image":{"title":"RGBIR COG tile","type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data"],"eo:bands":[{"name":"Red","common_name":"red"},{"name":"Green","common_name":"green"},{"name":"Blue","common_name":"blue"},{"name":"NIR","common_name":"nir","description":"near-infrared"}]},"metadata":{"title":"FGDC Metdata","type":"text/plain","roles":["metadata"]},"thumbnail":{"title":"Thumbnail","type":"image/jpeg","roles":["thumbnail"]}},"msft:short_description":"NAIP provides US-wide, high-resolution aerial imagery. 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Images are in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n","stac_version":"1.0.0-beta.2","links":[{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t","rel":"self","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/","rel":"parent","type":"application/json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/aster-l1t/items","rel":"item","type":"application/geo+json"},{"href":"https://planetarycomputer.microsoft.com/api/stac/v1/","rel":"root","type":"application/json"},{"href":"https://www.usgs.gov/core-science-systems/hdds/data-policy","rel":"license","title":"public domain"}],"stac_extensions":["item-assets","collection-assets"],"title":"ASTER L1T","license":"proprietary","extent":{"spatial":{"bbox":[[-180.0,-90.0,180.0,90.0]]},"temporal":{"interval":[["2000-03-04T12:00:00.000000Z","2006-12-31T12:00:00.000000Z"]]}},"keywords":["aster","usgs","nasa","satellite","global"],"providers":[{"name":"NASA","roles":["producer","licensor"],"url":"https://terra.nasa.gov/about/terra-instruments/aster"},{"name":"USGS","roles":["processor","producer","licensor"],"url":"https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/aster-overview/"},{"name":"Microsoft","roles":["host","processor"],"url":"https://planetarycomputer.microsoft.com"}],"summaries":{"gsd":[15,30,90],"eo:bands":[{"gsd":15,"name":"VNIR_Band1","common_name":"yellow/green","description":"visible yellow/green","center_wavelength":0.56,"full_width_half_max":0.08},{"gsd":15,"name":"VNIR_Band2","common_name":"red","description":"visible red","center_wavelength":0.66,"full_width_half_max":0.06},{"gsd":15,"name":"VNIR_Band3N","common_name":"near infrared","description":"near infrared","center_wavelength":0.82,"full_width_half_max":0.08},{"gsd":30,"name":"SWIR_Band4","common_name":"swir","description":"short-wave infrared","center_wavelength":1.65,"full_width_half_max":0.1},{"gsd":30,"name":"SWIR_Band5","common_name":"swir","description":"short-wave infrared","center_wavelength":2.165,"full_width_half_max":0.04},{"gsd":30,"name":"SWIR_Band6","common_name":"swir","description":"short-wave infrared","center_wavelength":2.205,"full_width_half_max":0.04},{"gsd":30,"name":"SWIR_Band7","common_name":"swir","description":"short-wave infrared","center_wavelength":2.26,"full_width_half_max":0.05},{"gsd":30,"name":"SWIR_Band8","common_name":"swir","description":"short-wave infrared","center_wavelength":2.339,"full_width_half_max":0.07},{"gsd":30,"name":"SWIR_Band9","common_name":"swir","description":"short-wave infrared","center_wavelength":2.395,"full_width_half_max":0.07},{"gsd":90,"name":"TIR_Band10","common_name":"lwir","description":"thermal infrared","center_wavelength":8.3,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band11","common_name":"lwir","description":"thermal infrared","center_wavelength":8.65,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band12","common_name":"lwir","description":"thermal infrared","center_wavelength":9.11,"full_width_half_max":0.35},{"gsd":90,"name":"TIR_Band13","common_name":"lwir","description":"thermal infrared","center_wavelength":10.6,"full_width_half_max":0.7},{"gsd":90,"name":"TIR_Band14","common_name":"lwir","description":"thermal infrared","center_wavelength":11.3,"full_width_half_max":0.7}],"platform":["terra"],"instruments":["aster"]},"assets":{"thumbnail":{"title":"ASTER L1T","href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/aster.png","media_type":"image/png"}},"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. 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[Landsat 8](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-8) is the most recent satellite in the Landsat series. Launched in 2013, Landsat 8 captures data in eleven spectral bands: ten optical/IR bands from the [Operational Land Imager](https://landsat.gsfc.nasa.gov/landsat-8/operational-land-imager) (OLI) instrument, and two thermal bands from the [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/landsat-8/thermal-infrared-sensor-tirs) (TIRS) instrument.\n\nThis dataset represents the global archive of Level-2 Landsat 8 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2). Because there is some latency before Level-2 data is available, a rolling window of recent Level-1 data is available as well. 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"http://www.opengis.net/spec/ogcapi-features-3/1.0/conf/features-filter", "https://api.stacspec.org/v1.0.0-rc.2/core", "https://api.stacspec.org/v1.0.0-rc.2/stac-search", "https://api.stacspec.org/v1.0.0-rc.2/stac-response", "https://api.stacspec.org/v1.0.0-rc.2/collection-search", "https://api.stacspec.org/v1.0.0-rc.2/collection-search#filter", "https://api.stacspec.org/v1.0.0-rc.1/collection-search#free-text", "https://api.stacspec.org/v1.0.0-rc.1/collection-search#sort", "https://api.stacspec.org/v1.0.0-rc.2/item-search", "https://api.stacspec.org/v1.0.0-rc.2/item-search#filter", "http://www.opengis.net/spec/cql2/1.0/conf/cql2-text", "http://www.opengis.net/spec/cql2/1.0/conf/basic-cql2", "https://api.stacspec.org/v1.0.0/item-search#sort", "https://api.stacspec.org/v1.0.0/ogcapi-features#sort" ], "title": "FedEO Clearinghouse", "type": "Catalog" } ================================================ FILE: tests/data/invalid-collection.json ================================================ { "id": "planet", "title": "Planet", "extent": {}, "license": "proprietary", "description": "Planet", "stac_version": "1.0.0", "item_type_accessor": "pl:item_type", "links": [ { "rel": "items", "type": "application/geo+json", "href": "https://csdap.earthdata.nasa.gov/stac/collections/planet/items" }, { "rel": "parent", "type": "application/json", "href": "https://csdap.earthdata.nasa.gov/stac/" }, { "rel": "root", "type": "application/json", "href": "https://csdap.earthdata.nasa.gov/stac/" }, { "rel": "self", "type": "application/json", "href": "https://csdap.earthdata.nasa.gov/stac/collections/planet" }, { "rel": "http://www.opengis.net/def/rel/ogc/1.0/queryables", "type": "application/schema+json", "title": "Queryables", "href": "https://csdap.earthdata.nasa.gov/stac/collections/planet/queryables" } ] } ================================================ FILE: tests/data/netherlands_aoi.json ================================================ { "type": "Polygon", "coordinates": [ [ [ 6.42425537109375, 53.174765470134616 ], [ 7.344360351562499, 53.174765470134616 ], [ 7.344360351562499, 53.67393435835391 ], [ 6.42425537109375, 53.67393435835391 ], [ 6.42425537109375, 53.174765470134616 ] ] ] } ================================================ FILE: tests/data/planetary-computer-aster-l1t-collection.json ================================================ { "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 Domain" }, { "rel": "describedby", "href": "https://planetarycomputer.microsoft.com/dataset/aster-l1t", "title": "Human readable dataset overview and reference", "type": "text/html" } ], "title": "ASTER L1T", "assets": { "thumbnail": { "href": "https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/aster.png", "type": "image/png", "roles": [ "thumbnail" ], "title": "ASTER L1T" } }, "extent": { "spatial": { "bbox": [ [ -180, -90, 180, 90 ] ] }, "temporal": { "interval": [ [ "2000-03-04T12:00:00.000000Z", "2006-12-31T12:00:00.000000Z" ] ] } }, "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 yellow/green", "center_wavelength": 0.56, "full_width_half_max": 0.08 }, { "gsd": 15, "name": "VNIR_Band2", "common_name": "red", "description": "visible red", "center_wavelength": 0.66, "full_width_half_max": 0.06 }, { "gsd": 15, "name": "VNIR_Band3N", "common_name": "nir08", "description": "near infrared", "center_wavelength": 0.82, "full_width_half_max": 0.08 }, { "gsd": 30, "name": "SWIR_Band4", "common_name": "swir16", "description": "short-wave infrared", "center_wavelength": 1.65, "full_width_half_max": 0.1 }, { "gsd": 30, "name": "SWIR_Band5", "common_name": "swir22", "description": "short-wave infrared", "center_wavelength": 2.165, "full_width_half_max": 0.04 }, { "gsd": 30, "name": "SWIR_Band6", "common_name": "swir22", "description": "short-wave infrared", "center_wavelength": 2.205, "full_width_half_max": 0.04 }, { "gsd": 30, "name": "SWIR_Band7", "common_name": "swir22", "description": "short-wave infrared", "center_wavelength": 2.26, "full_width_half_max": 0.05 }, { "gsd": 30, "name": "SWIR_Band8", "description": "short-wave infrared", "center_wavelength": 2.339, "full_width_half_max": 0.07 }, { "gsd": 30, "name": "SWIR_Band9", "description": "short-wave infrared", "center_wavelength": 2.395, "full_width_half_max": 0.07 }, { "gsd": 90, "name": "TIR_Band10", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 8.3, "full_width_half_max": 0.35 }, { "gsd": 90, "name": "TIR_Band11", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 8.65, "full_width_half_max": 0.35 }, { "gsd": 90, "name": "TIR_Band12", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 9.11, "full_width_half_max": 0.35 }, { "gsd": 90, "name": "TIR_Band13", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 10.6, "full_width_half_max": 0.7 }, { "gsd": 90, "name": "TIR_Band14", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 11.3, "full_width_half_max": 0.7 } ], "platform": [ "terra" ], "instruments": [ "aster" ] }, "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", "item_assets": { "TIR": { "roles": [ "data" ], "title": "TIR Swath data", "eo:bands": [ { "gsd": 90, "name": "TIR_Band10", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 8.3, "full_width_half_max": 0.35 }, { "gsd": 90, "name": "TIR_Band11", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 8.65, "full_width_half_max": 0.35 }, { "gsd": 90, "name": "TIR_Band12", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 9.11, "full_width_half_max": 0.35 }, { "gsd": 90, "name": "TIR_Band13", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 10.6, "full_width_half_max": 0.7 }, { "gsd": 90, "name": "TIR_Band14", "common_name": "lwir", "description": "thermal infrared", "center_wavelength": 11.3, "full_width_half_max": 0.7 } ] }, "xml": { "type": "application/xml", "roles": [ "metadata" ], "title": "XML metadata" }, "SWIR": { "type": "image/tiff; application=geotiff; profile=cloud-optimized", "roles": [ "data" ], "title": "SWIR Swath data", "eo:bands": [ { "gsd": 30, "name": "SWIR_Band4", "common_name": "swir", "description": "short-wave infrared", "center_wavelength": 1.65, "full_width_half_max": 0.1 }, { "gsd": 30, "name": "SWIR_Band5", "common_name": "swir", "description": "short-wave infrared", "center_wavelength": 2.165, "full_width_half_max": 0.04 }, { "gsd": 30, "name": "SWIR_Band6", "common_name": "swir", "description": "short-wave infrared", "center_wavelength": 2.205, "full_width_half_max": 0.04 }, { "gsd": 30, "name": "SWIR_Band7", "common_name": "swir", "description": "short-wave infrared", "center_wavelength": 2.26, "full_width_half_max": 0.05 }, { "gsd": 30, "name": "SWIR_Band8", "common_name": "swir", "description": "short-wave infrared", "center_wavelength": 2.339, "full_width_half_max": 0.07 }, { "gsd": 30, "name": "SWIR_Band9", "common_name": "swir", "description": "short-wave infrared", "center_wavelength": 2.395, "full_width_half_max": 0.07 } ] }, "VNIR": { "type": "image/tiff; application=geotiff; profile=cloud-optimized", "roles": [ "data" ], "title": "VNIR Swath data", "eo:bands": [ { "gsd": 15, "name": "VNIR_Band1", "common_name": "yellow/green", "description": "visible yellow/green", "center_wavelength": 0.56, "full_width_half_max": 0.08 }, { "gsd": 15, "name": "VNIR_Band2", "common_name": "red", "description": "visible red", "center_wavelength": 0.66, "full_width_half_max": 0.06 }, { "gsd": 15, "name": "VNIR_Band3N", "common_name": "near infrared", "description": "near infrared", "center_wavelength": 0.82, "full_width_half_max": 0.08 } ] }, "qa-txt": { "type": "text/plain", "roles": [ "metadata" ], "title": "QA browse file", "description": "Geometric quality assessment report." }, "qa-browse": { "type": "image/jpeg", "roles": [ "thumbnail" ], "title": "QA browse file", "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." }, "tir-browse": { "type": "image/jpeg", "roles": [ "thumbnail" ], "title": "Standalone reduced resolution TIR" }, "vnir-browse": { "type": "image/jpeg", "roles": [ "thumbnail" ], "title": "VNIR 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" ], "msft:storage_account": "astersa", "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." } ================================================ FILE: tests/data/planetary-computer-collection.json ================================================ { "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/core-science-systems/hdds/data-policy", "title": "public domain" }, { "rel": "describedby", "href": "https://planetarycomputer.microsoft.com/dataset/gap", "title": "Human readable dataset overview and reference", "type": "text/html" } ], "title": "USGS Gap Land Cover", "assets": { "thumbnail": { "href": "https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/gap.png", "type": "image/png", "roles": [ "data" ], "title": "USGS GAP" }, "original-data-ak": { "href": "https://ai4edataeuwest.blob.core.windows.net/usgs-gap/originals/gaplandcov_ak.zip", "type": "application/zip", "roles": [ "data" ], "title": "Alaska 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 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 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 source data", "description": "Original source data for the continental United 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 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 Florida Bayhead Swamp", "South Florida Cypress Dome", "South Florida Dwarf Cypress Savanna", "South Florida Mangrove Swamp", "South Florida Hardwood Hammock", "Southeast Florida Coastal Strand and Maritime Hammock", "Southwest Florida Coastal Strand and Maritime Hammock", "South Florida Pine Rockland", "Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Open Understory", "Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Scrub/Shrub Understory", "Atlantic Coastal Plain Upland Longleaf Pine Woodland", "Atlantic Coastal Plain Xeric River Dune", "East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Open Understory Modifier", "East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Scrub/Shrub Modifier", "Florida Longleaf Pine Sandhill - Scrub/Shrub Understory Modifier", "Florida Longleaf Pine Sandhill- Open Understory Modifier", "West Gulf Coastal Plain Upland Longleaf Pine Forest and Woodland", "Atlantic Coastal Plain Central Maritime Forest", "Atlantic Coastal Plain Southern Maritime Forest", "Central and South Texas Coastal Fringe Forest and Woodland", "East Gulf Coastal Plain Limestone Forest", "East Gulf Coastal Plain Maritime Forest", "East Gulf Coastal Plain Southern Loess Bluff Forest", "East Gulf Coastal Plain Southern Mesic Slope Forest", "Mississippi Delta Maritime Forest", "Southern Coastal Plain Dry Upland Hardwood Forest", "Southern Coastal Plain Oak Dome and Hammock", "West Gulf Coastal Plain Chenier and Upper Texas Coastal Fringe Forest and Woodland", "West Gulf Coastal Plain Mesic Hardwood Forest", "East-Central Texas Plains Pine Forest and Woodland", "West Gulf Coastal Plain Pine-Hardwood Forest", "West Gulf Coastal Plain Sandhill Oak and Shortleaf Pine Forest and Woodland", "Atlantic Coastal Plain Fall-Line Sandhills Longleaf Pine Woodland - Loblolly Modifier", "Deciduous Plantations", "East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Loblolly Modifier", "East Gulf Coastal Plain Interior Upland Longleaf Pine Woodland - Offsite Hardwood Modifier", "East Gulf Coastal Plain Near-Coast Pine Flatwoods - Offsite Hardwood Modifier", "Evergreen Plantation or Managed Pine", "California Central Valley Mixed Oak Savanna", "California Coastal Closed-Cone Conifer Forest and Woodland", "California Coastal Live Oak Woodland and Savanna", "California Lower Montane Blue Oak-Foothill Pine Woodland and Savanna", "Central and Southern California Mixed Evergreen Woodland", "Mediterranean California Lower Montane Black Oak-Conifer Forest and Woodland", "Southern California Oak Woodland and Savanna", "Madrean Encinal", "Madrean Pinyon-Juniper Woodland", "Madrean Pine-Oak Forest and Woodland", "Madrean Upper Montane Conifer-Oak Forest and Woodland", "Edwards Plateau Dry-Mesic Slope Forest and Woodland", "Edwards Plateau Limestone Savanna and Woodland", "Edwards Plateau Mesic Canyon", "Llano Uplift Acidic Forest, Woodland and Glade", "East Cascades Oak-Ponderosa Pine Forest and Woodland", "Mediterranean California Mixed Evergreen Forest", "Mediterranean California Mixed Oak Woodland", "North Pacific Dry Douglas-fir-(Madrone) Forest and Woodland", "North Pacific Oak Woodland", "Edwards Plateau Limestone Shrubland", "Allegheny-Cumberland Dry Oak Forest and Woodland - Hardwood", "Allegheny-Cumberland Dry Oak Forest and Woodland - Pine Modifier", "Central and Southern Appalachian Montane Oak Forest", "Central and Southern Appalachian Northern Hardwood Forest", "Central Appalachian Oak and Pine Forest", "Crosstimbers Oak Forest and Woodland", "East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Woodland Modifier", "East Gulf Coastal Plain Northern Dry Upland Hardwood Forest", "East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Hardwood Modifier", "East Gulf Coastal Plain Northern Loess Plain Oak-Hickory Upland - Juniper Modifier", "East-Central Texas Plains Post Oak Savanna and Woodland", "Lower Mississippi River Dune Woodland and Forest", "Mississippi River Alluvial Plain Dry-Mesic Loess Slope Forest", "North-Central Interior Dry Oak Forest and Woodland", "North-Central Interior Dry-Mesic Oak Forest and Woodland", "Northeastern Interior Dry Oak Forest - Mixed Modifier", "Northeastern Interior Dry Oak Forest - Virginia/Pitch Pine Modifier", "Northeastern Interior Dry Oak Forest-Hardwood Modifier", "Northeastern Interior Dry-Mesic Oak Forest", "Northern Atlantic Coastal Plain Dry Hardwood Forest", "Crowley's Ridge Sand Forest", "Ouachita Montane Oak Forest", "Ozark-Ouachita Dry Oak Woodland", "Ozark-Ouachita Dry-Mesic Oak Forest", "Southern and Central Appalachian Oak Forest", "Southern and Central Appalachian Oak Forest - Xeric", "Southern Interior Low Plateau Dry-Mesic Oak Forest", "Southern Ridge and Valley Dry Calcareous Forest", "Southern Ridge and Valley Dry Calcareous Forest - Pine modifier", "East Gulf Coastal Plain Northern Dry Upland Hardwood Forest - Offsite Pine Modifier", "Managed Tree Plantation", "Ruderal forest", "Southern Piedmont Dry Oak-(Pine) Forest - Loblolly Pine Modifier", "Acadian Low-Elevation Spruce-Fir-Hardwood Forest", "Acadian-Appalachian Montane Spruce-Fir Forest", "Appalachian Hemlock-Hardwood Forest", "Central and Southern Appalachian Spruce-Fir Forest", "0", "Laurentian-Acadian Northern Hardwoods Forest", "Laurentian-Acadian Northern Pine-(Oak) Forest", "Laurentian-Acadian Pine-Hemlock-Hardwood Forest", "Paleozoic Plateau Bluff and Talus", "Southern Appalachian Northern Hardwood Forest", "Atlantic Coastal Plain Dry and Dry-Mesic Oak Forest", "Atlantic Coastal Plain Fall-line Sandhills Longleaf Pine Woodland - Offsite Hardwood", "East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Hardwood Modifier", "East Gulf Coastal Plain Interior Shortleaf Pine-Oak Forest - Mixed Modifier", "Ozark-Ouachita Shortleaf Pine-Bluestem Woodland", "Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland", "Southeastern Interior Longleaf Pine Woodland", "Southern Appalachian Low Mountain Pine Forest", "Southern Piedmont Dry Oak-(Pine) Forest", "Southern Piedmont Dry Oak-(Pine) Forest - Hardwood Modifier", "Southern Piedmont Dry Oak-(Pine) Forest - Mixed Modifier", "Southern Piedmont Dry Oak-Heath Forest - Mixed Modifier", "Eastern Great Plains Tallgrass Aspen Parkland", "Northwestern Great Plains Aspen Forest and Parkland", "Northwestern Great Plains Shrubland", "Western Great Plains Dry Bur Oak Forest and Woodland", "Western Great Plains Wooded Draw and Ravine", "Southern Atlantic Coastal Plain Mesic Hardwood Forest", "East Gulf Coastal Plain Northern Loess Bluff Forest", "East Gulf Coastal Plain Northern Mesic Hardwood Forest", "North-Central Interior Beech-Maple Forest", "North-Central Interior Maple-Basswood Forest", "Ozark-Ouachita Mesic Hardwood Forest", "South-Central Interior Mesophytic Forest", "Southern and Central Appalachian Cove Forest", "Crowley's Ridge Mesic Loess Slope Forest", "Southern Piedmont Mesic Forest", "Appalachian Shale Barrens", "Atlantic Coastal Plain Northern Maritime Forest", "Laurentian Pine-Oak Barrens", "Northeastern Interior Pine Barrens", "Northern Atlantic Coastal Plain Pitch Pine Barrens", "Southern Appalachian Montane Pine Forest and Woodland", "East Cascades Mesic Montane Mixed-Conifer Forest and Woodland", "Middle Rocky Mountain Montane Douglas-fir Forest and Woodland", "Northern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest", "Northern Rocky Mountain Foothill Conifer Wooded Steppe", "Northern Rocky Mountain Mesic Montane Mixed Conifer Forest", "Northern Rocky Mountain Ponderosa Pine Woodland and Savanna", "Northern Rocky Mountain Western Larch Savanna", "Northwestern Great Plains - Black Hills Ponderosa Pine Woodland and Savanna", "Rocky Mountain Foothill Limber Pine-Juniper Woodland", "Inter-Mountain Basins Aspen-Mixed Conifer Forest and Woodland", "Inter-Mountain Basins Subalpine Limber-Bristlecone Pine Woodland", "Northern Rocky Mountain Subalpine Woodland and Parkland", "Rocky Mountain Aspen Forest and Woodland", "Rocky Mountain Lodgepole Pine Forest", "Rocky Mountain Poor-Site Lodgepole Pine Forest", "Rocky Mountain Subalpine Dry-Mesic Spruce-Fir Forest and Woodland", "Rocky Mountain Subalpine Mesic Spruce-Fir Forest and Woodland", "Rocky Mountain Subalpine-Montane Limber-Bristlecone Pine Woodland", "Rocky Mountain Bigtooth Maple Ravine Woodland", "Southern Rocky Mountain Dry-Mesic Montane Mixed Conifer Forest and Woodland", "Southern Rocky Mountain Mesic Montane Mixed Conifer Forest and Woodland", "Southern Rocky Mountain Ponderosa Pine Savanna", "Southern Rocky Mountain Ponderosa Pine Woodland", "California Montane Jeffrey Pine-(Ponderosa Pine) Woodland", "Klamath-Siskiyou Lower Montane Serpentine Mixed Conifer Woodland", "Klamath-Siskiyou Upper Montane Serpentine Mixed Conifer Woodland", "Mediterranean California Dry-Mesic Mixed Conifer Forest and Woodland", "Mediterranean California Mesic Mixed Conifer Forest and Woodland", "Sierran-Intermontane Desert Western White Pine-White Fir Woodland", "California Coastal Redwood Forest", "North Pacific Broadleaf Landslide Forest and Shrubland", "North Pacific Dry-Mesic Silver Fir-Western Hemlock-Douglas-fir Forest", "North Pacific Hypermaritime Sitka Spruce Forest", "North Pacific Hypermaritime Western Red-cedar-Western Hemlock Forest", "North Pacific Lowland Mixed Hardwood-Conifer Forest and Woodland", "North Pacific Maritime Dry-Mesic Douglas-fir-Western Hemlock Forest", "North Pacific Maritime Mesic-Wet Douglas-fir-Western Hemlock Forest", "North Pacific Mesic Western Hemlock-Silver Fir Forest", "North Pacific Wooded Volcanic Flowage", "Mediterranean California Red Fir Forest", "Mediterranean California Subalpine Woodland", "North Pacific Maritime Mesic Subalpine Parkland", "North Pacific Mountain Hemlock Forest", "Northern California Mesic Subalpine Woodland", "Northern Pacific Mesic Subalpine Woodland", "Sierra Nevada Subalpine Lodgepole Pine Forest and Woodland", "Columbia Plateau Western Juniper Woodland and Savanna", "Great Basin Pinyon-Juniper Woodland", "Inter-Mountain Basins Curl-leaf Mountain Mahogany Woodland and Shrubland", "Inter-Mountain Basins Juniper Savanna", "Colorado Plateau Pinyon-Juniper Shrubland", "Colorado Plateau Pinyon-Juniper Woodland", "Southern Rocky Mountain Juniper Woodland and Savanna", "Southern Rocky Mountain Pinyon-Juniper Woodland", "Northwestern Great Plains Floodplain", "Northwestern Great Plains Riparian", "Western Great Plains Floodplain", "Western Great Plains Floodplain Systems", "Western Great Plains Riparian Woodland and Shrubland", "Central Appalachian Floodplain - Forest Modifier", "Central Appalachian Riparian - Forest Modifier", "Central Interior and Appalachian Floodplain Systems", "Central Interior and Appalachian Riparian Systems", "Laurentian-Acadian Floodplain Systems", "Ozark-Ouachita Riparian", "South-Central Interior Large Floodplain", "South-Central Interior Large Floodplain - Forest Modifier", "South-Central Interior Small Stream and Riparian", "North-Central Interior and Appalachian Rich Swamp", "0", "0", "Laurentian-Acadian Swamp Systems", "North-Central Interior Wet Flatwoods", "0", "South-Central Interior / Upper Coastal Plain Wet Flatwoods", "0", "Southern Piedmont/Ridge and Valley Upland Depression Swamp", "Atlantic Coastal Plain Blackwater Stream Floodplain Forest - Forest Modifier", "Atlantic Coastal Plain Brownwater Stream Floodplain Forest", "Atlantic Coastal Plain Northern Tidal Wooded Swamp", "Atlantic Coastal Plain Small Blackwater River Floodplain Forest", "Atlantic Coastal Plain Small Brownwater River Floodplain Forest", "Atlantic Coastal Plain Southern Tidal Wooded Swamp", "East Gulf Coastal Plain Large River Floodplain Forest - Forest Modifier", "East Gulf Coastal Plain Small Stream and River Floodplain Forest", "East Gulf Coastal Plain Tidal Wooded Swamp", "0", "Southeastern Great Plains Riparian Forest", "Southeastern Great Plains Floodplain Forest", "Mississippi River Bottomland Depression", "Mississippi River Floodplain and Riparian Forest", "Mississippi River Low Floodplain (Bottomland) Forest", "Mississippi River Riparian Forest", "Red River Large Floodplain Forest", "Southern Coastal Plain Blackwater River Floodplain Forest", "Southern Piedmont Large Floodplain Forest - Forest Modifier", "Southern Piedmont Small Floodplain and Riparian Forest", "West Gulf Coastal Plain Large River Floodplain Forest", "West Gulf Coastal Plain Near-Coast Large River Swamp", "West Gulf Coastal Plain Small Stream and River Forest", "Atlantic Coastal Plain Streamhead Seepage Swamp - Pocosin - and Baygall", "Gulf and Atlantic Coastal Plain Swamp Systems", "Southern Coastal Plain Hydric Hammock", "Southern Coastal Plain Seepage Swamp and Baygall", "West Gulf Coastal Plain Seepage Swamp and Baygall", "Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Taxodium/Nyssa Modifier", "Atlantic Coastal Plain Nonriverine Swamp and Wet Hardwood Forest - Oak Dominated Modifier", "East Gulf Coastal Plain Southern Loblolly-Hardwood Flatwoods", "Lower Mississippi River Bottomland Depressions - Forest Modifier", "Lower Mississippi River Flatwoods", "Northern Atlantic Coastal Plain Basin Swamp and Wet Hardwood Forest", "Southern Coastal Plain Nonriverine Basin Swamp", "Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Bay/Gum Modifier", "Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Pine Modifier", "Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Taxodium Modifier", "West Gulf Coastal Plain Nonriverine Wet Hardwood Flatwoods", "West Gulf Coastal Plain Pine-Hardwood Flatwoods", "Edwards Plateau Riparian", "Atlantic Coastal Plain Clay-Based Carolina Bay Forested Wetland", "Atlantic Coastal Plain Clay-Based Carolina Bay Herbaceous Wetland", "Atlantic Coastal Plain Southern Wet Pine Savanna and Flatwoods", "Central Atlantic Coastal Plain Wet Longleaf Pine Savanna and Flatwoods", "Central Florida Pine Flatwoods", "East Gulf Coastal Plain Near-Coast Pine Flatwoods", "East Gulf Coastal Plain Near-Coast Pine Flatwoods - Open Understory Modifier", "East Gulf Coastal Plain Near-Coast Pine Flatwoods - Scrub/Shrub Understory Modifier", "South Florida Pine Flatwoods", "Southern Coastal Plain Nonriverine Cypress Dome", "West Gulf Coastal Plain Wet Longleaf Pine Savanna and Flatwoods", "Columbia Basin Foothill Riparian Woodland and Shrubland", "Great Basin Foothill and Lower Montane Riparian Woodland and Shrubland", "0", "Northern Rocky Mountain Conifer Swamp", "Northern Rocky Mountain Lower Montane Riparian Woodland and Shrubland", "Rocky Mountain Lower Montane Riparian Woodland and Shrubland", "Rocky Mountain Montane Riparian Systems", "Rocky Mountain Subalpine-Montane Riparian Woodland", "North Pacific Hardwood-Conifer Swamp", "North Pacific Lowland Riparian Forest and Shrubland", "North Pacific Montane Riparian Woodland and Shrubland", "North Pacific Shrub Swamp", "California Central Valley Riparian Woodland and Shrubland", "Mediterranean California Foothill and Lower Montane Riparian Woodland", "Mediterranean California Serpentine Foothill and Lower Montane Riparian Woodland and Seep", "North American Warm Desert Lower Montane Riparian Woodland and Shrubland", "North American Warm Desert Riparian Systems", "North American Warm Desert Riparian Woodland and Shrubland", "Tamaulipan Floodplain", "Tamaulipan Riparian Systems", "Boreal Aspen-Birch Forest", "Boreal Jack Pine-Black Spruce Forest", "Boreal White Spruce-Fir-Hardwood Forest", "Boreal-Laurentian Conifer Acidic Swamp and Treed Poor Fen", "Eastern Boreal Floodplain", "South Florida Shell Hash Beach", "Southeast Florida Beach", "Southwest Florida Beach", "South Florida Everglades Sawgrass Marsh", "South Florida Freshwater Slough and Gator Hole", "South Florida Wet Marl Prairie", "California Maritime Chaparral", "California Mesic Chaparral", "California Xeric Serpentine Chaparral", "Klamath-Siskiyou Xeromorphic Serpentine Savanna and Chaparral", "Mediterranean California Mesic Serpentine Woodland and Chaparral", "Northern and Central California Dry-Mesic Chaparral", "Southern California Dry-Mesic Chaparral", "Southern California Coastal Scrub", "California Central Valley and Southern Coastal Grassland", "California Mesic Serpentine Grassland", "Columbia Basin Foothill and Canyon Dry Grassland", "Columbia Basin Palouse Prairie", "North Pacific Alpine and Subalpine Dry Grassland", "North Pacific Montane Grassland", "North Pacific Montane Shrubland", "Northern Rocky Mountain Lower Montane, Foothill and Valley Grassland", "Northern Rocky Mountain Montane-Foothill Deciduous Shrubland", "Northern Rocky Mountain Subalpine Deciduous Shrubland", "Northern Rocky Mountain Subalpine-Upper Montane Grassland", "Southern Rocky Mountain Montane-Subalpine Grassland", "Rocky Mountain Gambel Oak-Mixed Montane Shrubland", "Rocky Mountain Lower Montane-Foothill Shrubland", "California Northern Coastal Grassland", "North Pacific Herbaceous Bald and Bluff", "North Pacific Hypermaritime Shrub and Herbaceous Headland", "Willamette Valley Upland Prairie and Savanna", "Mediterranean California Subalpine Meadow", "Rocky Mountain Subalpine-Montane Mesic Meadow", "Central Mixedgrass Prairie", "Northwestern Great Plains Mixedgrass Prairie", "Western Great Plains Foothill and Piedmont Grassland", "Western Great Plains Tallgrass Prairie", "Western Great Plains Sand Prairie", "Western Great Plains Sandhill Steppe", "Western Great Plains Mesquite Woodland and Shrubland", "Western Great Plains Shortgrass Prairie", "Arkansas Valley Prairie and Woodland", "Central Tallgrass Prairie", "North-Central Interior Oak Savanna", "North-Central Interior Sand and Gravel Tallgrass Prairie", "North-Central Oak Barrens", "Northern Tallgrass Prairie", "Southeastern Great Plains Tallgrass Prairie", "Texas Blackland Tallgrass Prairie", "Texas-Louisiana Coastal Prairie", "Central Appalachian Pine-Oak Rocky Woodland", "Southern Appalachian Grass and Shrub Bald", "Southern Appalachian Grass and Shrub Bald - Herbaceous Modifier", "Southern Appalachian Grass and Shrub Bald - Shrub Modifier", "Central Appalachian Alkaline Glade and Woodland", "Central Interior Highlands Calcareous Glade and Barrens", "Central Interior Highlands Dry Acidic Glade and Barrens", "Cumberland Sandstone Glade and Barrens", "Great Lakes Alvar", "Nashville Basin Limestone Glade", "Southern Ridge and Valley / Cumberland Dry Calcareous Forest", "Southern Piedmont Glade and Barrens", "East Gulf Coastal Plain Black Belt Calcareous Prairie and Woodland - Herbaceous Modifier", "East Gulf Coastal Plain Jackson Prairie and Woodland", "Eastern Highland Rim Prairie and Barrens - Dry Modifier", "Coahuilan Chaparral", "Madrean Oriental Chaparral", "Mogollon Chaparral", "Sonora-Mojave Semi-Desert Chaparral", "California Montane Woodland and Chaparral", "Great Basin Semi-Desert Chaparral", "Florida Dry Prairie", "Florida Peninsula Inland Scrub", "West Gulf Coastal Plain Catahoula Barrens", "West Gulf Coastal Plain Nepheline Syenite Glade", "East Gulf Coastal Plain Jackson Plain Dry Flatwoods - Open Understory Modifier", "West Gulf Coastal Plain Northern Calcareous Prairie", "West Gulf Coastal Plain Southern Calcareous Prairie", "Acadian-Appalachian Subalpine Woodland and Heath-Krummholz", "Atlantic and Gulf Coastal Plain Interdunal Wetland", "Atlantic Coastal Plain Southern Dune and Maritime Grassland", "Central and Upper Texas Coast Dune and Coastal Grassland", "East Gulf Coastal Plain Dune and Coastal Grassland", "Great Lakes Dune", "Northern Atlantic Coastal Plain Dune and Swale", "Northern Atlantic Coastal Plain Heathland and Grassland", "South Texas Dune and Coastal Grassland", "South Texas Sand Sheet Grassland", "Southwest Florida Dune and Coastal Grassland", "North Pacific Coastal Cliff and Bluff", "North Pacific Maritime Coastal Sand Dune and Strand", "Northern California Coastal Scrub", "Mediterranean California Coastal Bluff", "Mediterranean California Northern Coastal Dune", "Mediterranean California Southern Coastal Dune", "Atlantic Coastal Plain Northern Sandy Beach", "Atlantic Coastal Plain Sea Island Beach", "Atlantic Coastal Plain Southern Beach", "Florida Panhandle Beach Vegetation", "Louisiana Beach", "Northern Atlantic Coastal Plain Sandy Beach", "Texas Coastal Bend Beach", "Upper Texas Coast Beach", "0", "Mediterranean California Serpentine Fen", "Mediterranean California Subalpine-Montane Fen", "North Pacific Bog and Fen", "Rocky Mountain Subalpine-Montane Fen", "Atlantic Coastal Plain Peatland Pocosin", "Southern and Central Appalachian Bog and Fen", "Atlantic Coastal Plain Central Fresh-Oligohaline Tidal Marsh", "Atlantic Coastal Plain Embayed Region Tidal Freshwater Marsh", "Atlantic Coastal Plain Northern Fresh and Oligohaline Tidal Marsh", "Florida Big Bend Fresh-Oligohaline Tidal Marsh", "Atlantic Coastal Plain Depression Pondshore", "Atlantic Coastal Plain Large Natural Lakeshore", "Central Florida Herbaceous Pondshore", "Central Florida Herbaceous Seep", "East Gulf Coastal Plain Savanna and Wet Prairie", "East Gulf Coastal Plain Depression Pondshore", "Floridian Highlands Freshwater Marsh", "Southern Coastal Plain Herbaceous Seepage Bog", "Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Clethra Modifier", "Southern Coastal Plain Nonriverine Basin Swamp - Okefenokee Nupea Modifier", "Texas-Louisiana Coastal Prairie Slough", "Central Interior and Appalachian Shrub-Herbaceous Wetland Systems", "Great Lakes Coastal Marsh Systems", "0", "0", "Laurentian-Acadian Shrub-Herbaceous Wetland Systems", "0", "Eastern Great Plains Wet Meadow, Prairie and Marsh", "Great Lakes Wet-Mesic Lakeplain Prairie", "Great Plains Prairie Pothole", "Western Great Plains Closed Depression Wetland", "Western Great Plains Depressional Wetland Systems", "Western Great Plains Open Freshwater Depression Wetland", "Cumberland Riverscour", "Inter-Mountain Basins Interdunal Swale Wetland", "North Pacific Avalanche Chute Shrubland", "North Pacific Intertidal Freshwater Wetland", "Temperate Pacific Freshwater Emergent Marsh", "Temperate Pacific Freshwater Mudflat", "Columbia Plateau Vernal Pool", "Northern California Claypan Vernal Pool", "Northern Rocky Mountain Wooded Vernal Pool", "Columbia Plateau Silver Sagebrush Seasonally Flooded Shrub-Steppe", "Rocky Mountain Alpine-Montane Wet Meadow", "Rocky Mountain Subalpine-Montane Riparian Shrubland", "Temperate Pacific Montane Wet Meadow", "Willamette Valley Wet Prairie", "Chihuahuan-Sonoran Desert Bottomland and Swale Grassland", "North American Arid West Emergent Marsh", "North American Warm Desert Riparian Mesquite Bosque", "Western Great Plains Saline Depression Wetland", "Acadian Salt Marsh and Estuary Systems", "Atlantic Coastal Plain Central Salt and Brackish Tidal Marsh", "Atlantic Coastal Plain Embayed Region Tidal Salt and Brackish Marsh", "Atlantic Coastal Plain Indian River Lagoon Tidal Marsh", "Atlantic Coastal Plain Northern Tidal Salt Marsh", "Florida Big Bend Salt-Brackish Tidal Marsh", "Gulf and Atlantic Coastal Plain Tidal Marsh Systems", "Mississippi Sound Salt and Brackish Tidal Marsh", "Texas Saline Coastal Prairie", "Temperate Pacific Tidal Salt and Brackish Marsh", "Inter-Mountain Basins Alkaline Closed Depression", "Inter-Mountain Basins Greasewood Flat", "Inter-Mountain Basins Playa", "North American Warm Desert Playa", "Apacherian-Chihuahuan Mesquite Upland Scrub", "Apacherian-Chihuahuan Semi-Desert Grassland and Steppe", "Chihuahuan Creosotebush, Mixed Desert and Thorn Scrub", "Chihuahuan Gypsophilous Grassland and Steppe", "Chihuahuan Loamy Plains Desert Grassland", "Chihuahuan Mixed Desert and Thorn Scrub", "Chihuahuan Sandy Plains Semi-Desert Grassland", "Chihuahuan Stabilized Coppice Dune and Sand Flat Scrub", "Chihuahuan Succulent Desert Scrub", "Madrean Juniper Savanna", "Mojave Mid-Elevation Mixed Desert Scrub", "North American Warm Desert Active and Stabilized Dune", "Sonora-Mojave Creosotebush-White Bursage Desert Scrub", "Sonoran Mid-Elevation Desert Scrub", "Sonoran Paloverde-Mixed Cacti Desert Scrub", "Chihuahuan Mixed Salt Desert Scrub", "Sonora-Mojave Mixed Salt Desert Scrub", "North American Warm Desert Wash", "South Texas Lomas", "Tamaulipan Calcareous Thornscrub", "Tamaulipan Clay Grassland", "Tamaulipan Mesquite Upland Scrub", "Tamaulipan Mixed Deciduous Thornscrub", "Tamaulipan Savanna Grassland", "Inter-Mountain Basins Mat Saltbush Shrubland", "Inter-Mountain Basins Mixed Salt Desert Scrub", "Inter-Mountain Basins Wash", "Columbia Plateau Steppe and Grassland", "Great Basin Xeric Mixed Sagebrush Shrubland", "Inter-Mountain Basins Big Sagebrush Shrubland", "Inter-Mountain Basins Big Sagebrush Steppe", "Inter-Mountain Basins Montane Sagebrush Steppe", "Colorado Plateau Mixed Low Sagebrush Shrubland", "Columbia Plateau Low Sagebrush Steppe", "Columbia Plateau Scabland Shrubland", "Wyoming Basins Dwarf Sagebrush Shrubland and Steppe", "Colorado Plateau Blackbrush-Mormon-tea Shrubland", "Inter-Mountain Basins Semi-Desert Grassland", "Inter-Mountain Basins Semi-Desert Shrub Steppe", "Southern Colorado Plateau Sand Shrubland", "Acadian-Appalachian Alpine Tundra", "Rocky Mountain Alpine Dwarf-Shrubland", "Rocky Mountain Alpine Fell-Field", "Rocky Mountain Alpine Turf", "Mediterranean California Alpine Dry Tundra", "Mediterranean California Alpine Fell-Field", "North Pacific Dry and Mesic Alpine Dwarf-Shrubland, Fell-field and Meadow", "Rocky Mountain Alpine Tundra/Fell-field/Dwarf-shrub Map Unit", "Temperate Pacific Intertidal Mudflat", "Mediterranean California Eelgrass Bed", "North Pacific Maritime Eelgrass Bed", "South-Central Interior Large Floodplain - Herbaceous Modifier", "East Gulf Coastal Plain Large River Floodplain Forest - Herbaceous Modifier", "Temperate Pacific Freshwater Aquatic Bed", "Central California Coast Ranges Cliff and Canyon", "Mediterranean California Serpentine Barrens", "Southern California Coast Ranges Cliff and Canyon", "Central Interior Acidic Cliff and Talus", "Central Interior Calcareous Cliff and Talus", "East Gulf Coastal Plain Dry Chalk Bluff", "North-Central Appalachian Acidic Cliff and Talus", "North-Central Appalachian Circumneutral Cliff and Talus", "Southern Appalachian Montane Cliff", "Southern Interior Acid Cliff", "Southern Interior Calcareous Cliff", "Southern Piedmont Cliff", "Southern Appalachian Granitic Dome", "Southern Appalachian Rocky Summit", "Southern Piedmont Granite Flatrock", "Rocky Mountain Cliff, Canyon and Massive Bedrock", "Klamath-Siskiyou Cliff and Outcrop", "North Pacific Montane Massive Bedrock, Cliff and Talus", "North Pacific Serpentine Barren", "North Pacific Active Volcanic Rock and Cinder Land", "Sierra Nevada Cliff and Canyon", "Western Great Plains Badland", "Southwestern Great Plains Canyon", "Western Great Plains Cliff and Outcrop", "North American Warm Desert Badland", "North American Warm Desert Bedrock Cliff and Outcrop", "North American Warm Desert Pavement", "North American Warm Desert Volcanic Rockland", "Colorado Plateau Mixed Bedrock Canyon and Tableland", "Columbia Plateau Ash and Tuff Badland", "Geysers and Hot Springs", "Inter-Mountain Basins Active and Stabilized Dune", "Inter-Mountain Basins Cliff and Canyon", "Inter-Mountain Basins Shale Badland", "Inter-Mountain Basins Volcanic Rock and Cinder Land", "Rocky Mountain Alpine Bedrock and Scree", "Mediterranean California Alpine Bedrock and Scree", "North Pacific Alpine and Subalpine Bedrock and Scree", "Unconsolidated Shore", "Undifferentiated Barren Land", "North American Alpine Ice Field", "Orchards Vineyards and Other High Structure Agriculture", "Cultivated Cropland", "Pasture/Hay", "Introduced Upland Vegetation - Annual Grassland", "Introduced Upland Vegetation - Perennial Grassland and Forbland", "Modified/Managed Southern Tall Grassland", "Introduced Upland Vegetation - Shrub", "Introduced Riparian and Wetland Vegetation", "Introduced Upland Vegetation - Treed", "0", "Disturbed, Non-specific", "Recently Logged Areas", "Harvested Forest - Grass/Forb Regeneration", "Harvested Forest-Shrub Regeneration", "Harvested Forest - Northwestern Conifer Regeneration", "Recently Burned", "Recently burned grassland", "Recently burned shrubland", "Recently burned forest", "Disturbed/Successional - Grass/Forb Regeneration", "Disturbed/Successional - Shrub Regeneration", "Disturbed/Successional - Recently Chained Pinyon-Juniper", "Open Water (Aquaculture)", "Open Water (Brackish/Salt)", "Open Water (Fresh)", "Quarries, Mines, Gravel Pits and Oil Wells", "Developed, Open Space", "Developed, Low Intensity", "Developed, Medium Intensity", "Developed, High Intensity" ] } ] }, "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. 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"rel": "collection", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/HLSL30.v1.5" }, { "rel": "root", "href": "https://cmr.earthdata.nasa.gov/stac/" }, { "rel": "provider", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD" }, { "rel": "via", "href": "https://cmr.earthdata.nasa.gov/search/concepts/G1994877008-LPCLOUD.json" }, { "rel": "via", "href": "https://cmr.earthdata.nasa.gov/search/concepts/G1994877008-LPCLOUD.umm_json" } ], "properties": { "datetime": "2021-01-14T22:18:46.319Z", "start_datetime": "2021-01-14T22:18:46.319Z", "end_datetime": "2021-01-14T22:19:10.219Z", "eo:cloud_cover": 41 }, "assets": { "B07": { "title": "Download HLS.L30.T01RCM.2021014T221846.v1.5.B07.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T01RCM.2021014T221846.v1.5.B07.tif" }, "B04": { "title": "Download HLS.L30.T01RCM.2021014T221846.v1.5.B04.tif", "href": 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"https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B07.tif" }, "B09": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.B09.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B09.tif" }, "B06": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.B06.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B06.tif" }, "Fmask": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.Fmask.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.Fmask.tif" }, "VZA": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.VZA.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.VZA.tif" }, "VAA": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.VAA.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.VAA.tif" }, "B10": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.B10.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B10.tif" }, "B02": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.B02.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B02.tif" }, "SZA": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.SZA.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.SZA.tif" }, "B04": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.B04.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B04.tif" }, "B01": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.B01.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B01.tif" }, "B05": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.B05.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B05.tif" }, "SAA": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.SAA.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.SAA.tif" }, "B11": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.B11.tif", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-protected/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.B11.tif" }, "browse": { "title": "Download HLS.L30.T60MUD.2021014T222708.v1.5.jpg", "href": "https://lpdaac.earthdata.nasa.gov/lp-prod-public/HLSL30.015/HLS.L30.T60MUD.2021014T222708.v1.5.jpg", "type": "image/jpeg" }, "metadata": { "href": "https://cmr.earthdata.nasa.gov/search/concepts/G1994873826-LPCLOUD.xml", "type": "application/xml" } } } ], "links": [ { "rel": "self", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/HLSL30.v1.5/items" }, { "rel": "root", "href": "https://cmr.earthdata.nasa.gov/stac/" }, { "rel": "next", "method": "GET", "href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/HLSL30.v1.5/items?page=2" } ], "context": { "returned": 10, "limit": 1000000, "matched": 324132 } } ================================================ FILE: tests/data/sample-item.json ================================================ { "type": "Feature", "stac_version": "1.0.0", "id": "CS3-20160503_132131_05", "properties": { "datetime": "2016-05-03T13:22:30.040000Z", "title": "A CS3 item", "license": "PDDL-1.0", "providers": [ { "name": "CoolSat", "roles": [ "producer", "licensor" ], "url": "https://cool-sat.com/" } ] }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -122.308150179, 37.488035566 ], [ -122.597502109, 37.538869539 ], [ -122.576687533, 37.613537207 ], [ -122.2880486, 37.562818007 ], [ -122.308150179, 37.488035566 ] ] ] }, "links": [ { "rel": "collection", "href": "https://raw.githubusercontent.com/radiantearth/stac-spec/v0.8.1/collection-spec/examples/sentinel2.json" } ], "assets": { "analytic": { "href": "http://cool-sat.com/catalog/CS3-20160503_132130_04/analytic.tif", "title": "4-Band Analytic", "product": "http://cool-sat.com/catalog/products/analytic.json", "type": "image/tiff; application=geotiff; profile=cloud-optimized", "roles": [ "data", "analytic" ] }, "thumbnail": { "href": "http://cool-sat.com/catalog/CS3-20160503_132130_04/thumbnail.png", "title": "Thumbnail", "type": "image/png", "roles": [ "thumbnail" ] } }, "bbox": [ -122.59750209, 37.48803556, -122.2880486, 37.613537207 ], "stac_extensions": [], "collection": "CS3" } ================================================ FILE: tests/data/test-case-1/catalog.json ================================================ { "type": "Catalog", "id": "test", "stac_version": "1.1.0", "description": "test catalog", "links": [ { "rel": "child", "href": "./country-1/catalog.json", "type": "application/json" }, { "rel": "child", "href": "./country-2/catalog.json", "type": "application/json" }, { "rel": "root", "href": "./catalog.json", "type": "application/json" } ], "stac_extensions": [] } ================================================ FILE: tests/data/test-case-1/country-1/area-1-1/area-1-1-imagery/area-1-1-imagery.json ================================================ { "type": "Feature", "stac_version": "1.1.0", "id": "area-1-1-imagery", "properties": { "datetime": "2019-10-04T18:55:37Z" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.5048828125, 3.8916575492899987 ], [ -1.9610595703125, 3.8916575492899987 ], [ -1.9610595703125, 4.275202171119132 ], [ -2.5048828125, 4.275202171119132 ], [ -2.5048828125, 3.8916575492899987 ] ] ] }, "links": [ { "rel": "collection", "href": "../collection.json", "type": "application/json" }, { "rel": "root", "href": "../../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../collection.json", "type": "application/json" } ], "assets": { "ortho": { "href": "http://example.com/area-1-1_ortho.tif", "type": "image/vnd.stac.geotiff" }, "dsm": { "href": "http://example.com/area-1-1_dsm.tif", "type": "image/vnd.stac.geotiff" } }, "bbox": [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 3.8916575492899987 ], "stac_extensions": [], "collection": "area-1-1" } ================================================ FILE: tests/data/test-case-1/country-1/area-1-1/area-1-1-labels/area-1-1-labels.json ================================================ { "type": "Feature", "stac_version": "1.1.0", "id": "area-1-1-labels", "properties": { "datetime": "2019-10-04T18:55:37Z", "label:description": "labels for area-1-1", "label:type": "vector", "label:properties": [ "label" ], "label:classes": [ { "name": "label", "classes": [ "one", "two" ] } ], "label:tasks": [ "classification" ], "label:methods": [ "manual" ] }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.5048828125, 3.8916575492899987 ], [ -1.9610595703125, 3.8916575492899987 ], [ -1.9610595703125, 4.275202171119132 ], [ -2.5048828125, 4.275202171119132 ], [ -2.5048828125, 3.8916575492899987 ] ] ] }, "links": [ { "rel": "source", "href": "../area-1-1-imagery/area-1-1-imagery.json", "type": "application/json" }, { "rel": "collection", "href": "../collection.json", "type": "application/json" }, { "rel": "root", "href": "../../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../collection.json", "type": "application/json" } ], "assets": { "labels": { "href": "http://example.com/area-1-1-labels.geojson", "type": "application/geo+json" } }, "bbox": [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 3.8916575492899987 ], "stac_extensions": [ "https://stac-extensions.github.io/label/v1.0.1/schema.json" ], "collection": "area-1-1" } ================================================ FILE: tests/data/test-case-1/country-1/area-1-1/collection.json ================================================ { "type": "Collection", "id": "area-1-1", "stac_version": "1.1.0", "description": "test collection country-1", "links": [ { "rel": "item", "href": "./area-1-1-imagery/area-1-1-imagery.json", "type": "application/json" }, { "rel": "item", "href": "./area-1-1-labels/area-1-1-labels.json", "type": "application/json" }, { "rel": "root", "href": "../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../catalog.json", "type": "application/json" } ], "stac_extensions": [], "extent": { "spatial": { "bbox": [ [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 4.275202171119132 ] ] }, "temporal": { "interval": [ [ "2019-10-04T18:55:37Z", null ] ] } }, "license": "other" } ================================================ FILE: tests/data/test-case-1/country-1/area-1-2/area-1-2-imagery/area-1-2-imagery.json ================================================ { "type": "Feature", "stac_version": "1.1.0", "id": "area-1-2-imagery", "properties": { "datetime": "2019-10-04T18:55:37Z" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.5048828125, 3.8916575492899987 ], [ -1.9610595703125, 3.8916575492899987 ], [ -1.9610595703125, 4.275202171119132 ], [ -2.5048828125, 4.275202171119132 ], [ -2.5048828125, 3.8916575492899987 ] ] ] }, "links": [ { "rel": "collection", "href": "../collection.json", "type": "application/json" }, { "rel": "root", "href": "../../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../collection.json", "type": "application/json" } ], "assets": { "ortho": { "href": "http://example.com/area-1-2_ortho.tif", "type": "image/vnd.stac.geotiff" }, "dsm": { "href": "http://example.com/area-1-2_dsm.tif", "type": "image/vnd.stac.geotiff" } }, "bbox": [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 3.8916575492899987 ], "stac_extensions": [], "collection": "area-1-2" } ================================================ FILE: tests/data/test-case-1/country-1/area-1-2/area-1-2-labels/area-1-2-labels.json ================================================ { "type": "Feature", "stac_version": "1.1.0", "id": "area-1-2-labels", "properties": { "datetime": "2019-10-04T18:55:37Z", "label:description": "labels for area-1-2", "label:type": "vector", "label:properties": [ "label" ], "label:classes": [ { "name": "label", "classes": [ "one", "two" ] } ], "label:tasks": [ "classification" ], "label:methods": [ "manual" ] }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.5048828125, 3.8916575492899987 ], [ -1.9610595703125, 3.8916575492899987 ], [ -1.9610595703125, 4.275202171119132 ], [ -2.5048828125, 4.275202171119132 ], [ -2.5048828125, 3.8916575492899987 ] ] ] }, "links": [ { "rel": "source", "href": "../area-1-2-imagery/area-1-2-imagery.json", "type": "application/json" }, { "rel": "collection", "href": "../collection.json", "type": "application/json" }, { "rel": "root", "href": "../../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../collection.json", "type": "application/json" } ], "assets": { "labels": { "href": "http://example.com/area-1-2-labels.geojson", "type": "application/geo+json" } }, "bbox": [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 3.8916575492899987 ], "stac_extensions": [ "https://stac-extensions.github.io/label/v1.0.1/schema.json" ], "collection": "area-1-2" } ================================================ FILE: tests/data/test-case-1/country-1/area-1-2/collection.json ================================================ { "type": "Collection", "id": "area-1-2", "stac_version": "1.1.0", "description": "test collection country-1", "links": [ { "rel": "item", "href": "./area-1-2-imagery/area-1-2-imagery.json", "type": "application/json" }, { "rel": "item", "href": "./area-1-2-labels/area-1-2-labels.json", "type": "application/json" }, { "rel": "root", "href": "../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../catalog.json", "type": "application/json" } ], "stac_extensions": [], "extent": { "spatial": { "bbox": [ [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 4.275202171119132 ] ] }, "temporal": { "interval": [ [ "2019-10-04T18:55:37Z", null ] ] } }, "license": "other" } ================================================ FILE: tests/data/test-case-1/country-1/catalog.json ================================================ { "type": "Catalog", "id": "country-1", "stac_version": "1.1.0", "description": "test catalog country-1", "links": [ { "rel": "child", "href": "./area-1-1/collection.json", "type": "application/json" }, { "rel": "child", "href": "./area-1-2/collection.json", "type": "application/json" }, { "rel": "root", "href": "../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../catalog.json", "type": "application/json" } ], "stac_extensions": [] } ================================================ FILE: tests/data/test-case-1/country-2/area-2-1/area-2-1-imagery/area-2-1-imagery.json ================================================ { "type": "Feature", "stac_version": "1.1.0", "id": "area-2-1-imagery", "properties": { "datetime": "2019-10-04T18:55:37Z" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.5048828125, 3.8916575492899987 ], [ -1.9610595703125, 3.8916575492899987 ], [ -1.9610595703125, 4.275202171119132 ], [ -2.5048828125, 4.275202171119132 ], [ -2.5048828125, 3.8916575492899987 ] ] ] }, "links": [ { "rel": "collection", "href": "../collection.json", "type": "application/json" }, { "rel": "root", "href": "../../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../collection.json", "type": "application/json" } ], "assets": { "ortho": { "href": "http://example.com/area-2-1_ortho.tif", "type": "image/vnd.stac.geotiff" }, "dsm": { "href": "http://example.com/area-2-1_dsm.tif", "type": "image/vnd.stac.geotiff" } }, "bbox": [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 3.8916575492899987 ], "stac_extensions": [], "collection": "area-2-1" } ================================================ FILE: tests/data/test-case-1/country-2/area-2-1/area-2-1-labels/area-2-1-labels.json ================================================ { "type": "Feature", "stac_version": "1.1.0", "id": "area-2-1-labels", "properties": { "datetime": "2019-10-04T18:55:37Z", "label:description": "labels for area-2-1", "label:type": "vector", "label:properties": [ "label" ], "label:classes": [ { "name": "label", "classes": [ "one", "two" ] } ], "label:tasks": [ "classification" ], "label:methods": [ "manual" ] }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.5048828125, 3.8916575492899987 ], [ -1.9610595703125, 3.8916575492899987 ], [ -1.9610595703125, 4.275202171119132 ], [ -2.5048828125, 4.275202171119132 ], [ -2.5048828125, 3.8916575492899987 ] ] ] }, "links": [ { "rel": "source", "href": "../area-2-1-imagery/area-2-1-imagery.json", "type": "application/json" }, { "rel": "collection", "href": "../collection.json", "type": "application/json" }, { "rel": "root", "href": "../../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../collection.json", "type": "application/json" } ], "assets": { "labels": { "href": "http://example.com/area-2-1-labels.geojson", "type": "application/geo+json" } }, "bbox": [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 3.8916575492899987 ], "stac_extensions": [ "https://stac-extensions.github.io/label/v1.0.1/schema.json" ], "collection": "area-2-1" } ================================================ FILE: tests/data/test-case-1/country-2/area-2-1/collection.json ================================================ { "type": "Collection", "id": "area-2-1", "stac_version": "1.1.0", "description": "test collection country-2", "links": [ { "rel": "item", "href": "./area-2-1-imagery/area-2-1-imagery.json", "type": "application/json" }, { "rel": "item", "href": "./area-2-1-labels/area-2-1-labels.json", "type": "application/json" }, { "rel": "root", "href": "../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../catalog.json", "type": "application/json" } ], "stac_extensions": [], "extent": { "spatial": { "bbox": [ [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 4.275202171119132 ] ] }, "temporal": { "interval": [ [ "2019-10-04T18:55:37Z", null ] ] } }, "license": "other" } ================================================ FILE: tests/data/test-case-1/country-2/area-2-2/area-2-2-imagery/area-2-2-imagery.json ================================================ { "type": "Feature", "stac_version": "1.1.0", "id": "area-2-2-imagery", "properties": { "datetime": "2019-10-04T18:55:37Z" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.5048828125, 3.8916575492899987 ], [ -1.9610595703125, 3.8916575492899987 ], [ -1.9610595703125, 4.275202171119132 ], [ -2.5048828125, 4.275202171119132 ], [ -2.5048828125, 3.8916575492899987 ] ] ] }, "links": [ { "rel": "collection", "href": "../collection.json", "type": "application/json" }, { "rel": "root", "href": "../../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../collection.json", "type": "application/json" } ], "assets": { "ortho": { "href": "http://example.com/area-2-2_ortho.tif", "type": "image/vnd.stac.geotiff" }, "dsm": { "href": "http://example.com/area-2-2_dsm.tif", "type": "image/vnd.stac.geotiff" } }, "bbox": [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 3.8916575492899987 ], "stac_extensions": [], "collection": "area-2-2" } ================================================ FILE: tests/data/test-case-1/country-2/area-2-2/area-2-2-labels/area-2-2-labels.json ================================================ { "type": "Feature", "stac_version": "1.1.0", "id": "area-2-2-labels", "properties": { "datetime": "2019-10-04T18:55:37Z", "label:description": "labels for area-2-2", "label:type": "vector", "label:properties": [ "label" ], "label:classes": [ { "name": "label", "classes": [ "one", "two" ] } ], "label:tasks": [ "classification" ], "label:methods": [ "manual" ] }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.5048828125, 3.8916575492899987 ], [ -1.9610595703125, 3.8916575492899987 ], [ -1.9610595703125, 4.275202171119132 ], [ -2.5048828125, 4.275202171119132 ], [ -2.5048828125, 3.8916575492899987 ] ] ] }, "links": [ { "rel": "source", "href": "../area-2-2-imagery/area-2-2-imagery.json", "type": "application/json" }, { "rel": "collection", "href": "../collection.json", "type": "application/json" }, { "rel": "root", "href": "../../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../collection.json", "type": "application/json" } ], "assets": { "labels": { "href": "http://example.com/area-2-2-labels.geojson", "type": "application/geo+json" } }, "bbox": [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 3.8916575492899987 ], "stac_extensions": [ "https://stac-extensions.github.io/label/v1.0.1/schema.json" ], "collection": "area-2-2" } ================================================ FILE: tests/data/test-case-1/country-2/area-2-2/collection.json ================================================ { "type": "Collection", "id": "area-2-2", "stac_version": "1.1.0", "description": "test collection country-2", "links": [ { "rel": "item", "href": "./area-2-2-imagery/area-2-2-imagery.json", "type": "application/json" }, { "rel": "item", "href": "./area-2-2-labels/area-2-2-labels.json", "type": "application/json" }, { "rel": "root", "href": "../../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../catalog.json", "type": "application/json" } ], "stac_extensions": [], "extent": { "spatial": { "bbox": [ [ -2.5048828125, 3.8916575492899987, -1.9610595703125, 4.275202171119132 ] ] }, "temporal": { "interval": [ [ "2019-10-04T18:55:37Z", null ] ] } }, "license": "other" } ================================================ FILE: tests/data/test-case-1/country-2/catalog.json ================================================ { "type": "Catalog", "id": "country-2", "stac_version": "1.1.0", "description": "test catalog country-2", "links": [ { "rel": "child", "href": "./area-2-1/collection.json", "type": "application/json" }, { "rel": "child", "href": "./area-2-2/collection.json", "type": "application/json" }, { "rel": "root", "href": "../catalog.json", "type": "application/json" }, { "rel": "parent", "href": "../catalog.json", "type": "application/json" } ], "stac_extensions": [] } ================================================ FILE: tests/helpers.py ================================================ import json from pathlib import Path from typing import Any TEST_DATA = Path(__file__).parent / "data" STAC_URLS = { "PLANETARY-COMPUTER": "https://planetarycomputer.microsoft.com/api/stac/v1", "EARTH-SEARCH": "https://earth-search.aws.element84.com/v1", "MLHUB": "https://api.radiant.earth/mlhub/v1", "SPACEBEL": "https://emc.spacebel.be", } def read_data_file(file_name: str, mode: str = "r", parse_json: bool = False) -> Any: file_path = TEST_DATA / file_name with file_path.open(mode=mode) as src: if parse_json: return json.load(src) else: return src.read() ================================================ FILE: tests/test_base_search.py ================================================ import json from collections.abc import Iterator from datetime import datetime from typing import Any import pytest from dateutil.tz import gettz, tzutc from pystac_client import Client from pystac_client.item_search import BaseSearch from .helpers import STAC_URLS, read_data_file SEARCH_URL = f"{STAC_URLS['PLANETARY-COMPUTER']}/search" INTERSECTS_EXAMPLE = { "type": "Polygon", "coordinates": [ [ [-73.21, 43.99], [-73.21, 44.05], [-73.12, 44.05], [-73.12, 43.99], [-73.21, 43.99], ] ], } ITEM_EXAMPLE: dict[str, Any] = {"collections": "io-lulc", "ids": "60U-2020"} class TestBaseSearchParams: @pytest.fixture(scope="function") def sample_client(self) -> Client: api_content = read_data_file("planetary-computer-root.json", parse_json=True) return Client.from_dict(api_content) def test_tuple_bbox(self) -> None: # Tuple input search = BaseSearch(url=SEARCH_URL, bbox=(-104.5, 44.0, -104.0, 45.0)) assert search.get_parameters()["bbox"] == (-104.5, 44.0, -104.0, 45.0) def test_list_bbox(self) -> None: # List input search = BaseSearch(url=SEARCH_URL, bbox=[-104.5, 44.0, -104.0, 45.0]) assert search.get_parameters()["bbox"] == (-104.5, 44.0, -104.0, 45.0) def test_string_bbox(self) -> None: # String Input search = BaseSearch(url=SEARCH_URL, bbox="-104.5,44.0,-104.0,45.0") assert search.get_parameters()["bbox"] == (-104.5, 44.0, -104.0, 45.0) def test_generator_bbox(self) -> None: # Generator Input def bboxer() -> Iterator[float]: yield from [-104.5, 44.0, -104.0, 45.0] search = BaseSearch(url=SEARCH_URL, bbox=bboxer()) assert search.get_parameters()["bbox"] == (-104.5, 44.0, -104.0, 45.0) def test_url_with_parameters(self) -> None: # Single timestamp input search = BaseSearch( url=SEARCH_URL, datetime="2020-02-01T00:00:00Z", bbox=[-104.5, 44.0, -104.0, 45.0], ) assert "bbox=-104.5%2C44.0%2C-104.0%2C45.0" in search.url_with_parameters() # Motivating example: https://github.com/stac-utils/pystac-client/issues/299 search = BaseSearch( url="https://planetarycomputer.microsoft.com/api/stac/v1/search", collections=["cop-dem-glo-30"], bbox=[88.214, 27.927, 88.302, 28.034], ) assert ( search.url_with_parameters() == "https://planetarycomputer.microsoft.com/api/stac/v1/search?" "bbox=88.214%2C27.927%2C88.302%2C28.034&collections=cop-dem-glo-30" ) def test_single_string_datetime(self) -> None: # Single timestamp input search = BaseSearch(url=SEARCH_URL, datetime="2020-02-01T00:00:00Z") assert search.get_parameters()["datetime"] == "2020-02-01T00:00:00Z" def test_range_string_datetime(self) -> None: # Timestamp range input search = BaseSearch( url=SEARCH_URL, datetime="2020-02-01T00:00:00Z/2020-02-02T00:00:00Z" ) assert ( search.get_parameters()["datetime"] == "2020-02-01T00:00:00Z/2020-02-02T00:00:00Z" ) def test_list_of_strings_datetime(self) -> None: # Timestamp list input search = BaseSearch( url=SEARCH_URL, datetime=["2020-02-01T00:00:00Z", "2020-02-02T00:00:00Z"] ) assert ( search.get_parameters()["datetime"] == "2020-02-01T00:00:00Z/2020-02-02T00:00:00Z" ) def test_open_range_string_datetime(self) -> None: # Open timestamp range input search = BaseSearch(url=SEARCH_URL, datetime="2020-02-01T00:00:00Z/..") assert search.get_parameters()["datetime"] == "2020-02-01T00:00:00Z/.." def test_single_datetime_object(self) -> None: start = datetime(2020, 2, 1, 0, 0, 0, tzinfo=tzutc()) # Single datetime input search = BaseSearch(url=SEARCH_URL, datetime=start) assert search.get_parameters()["datetime"] == "2020-02-01T00:00:00Z" def test_list_of_datetimes(self) -> None: start = datetime(2020, 2, 1, 0, 0, 0, tzinfo=tzutc()) end = datetime(2020, 2, 2, 0, 0, 0, tzinfo=tzutc()) # Datetime range input search = BaseSearch(url=SEARCH_URL, datetime=[start, end]) assert ( search.get_parameters()["datetime"] == "2020-02-01T00:00:00Z/2020-02-02T00:00:00Z" ) def test_open_list_of_datetimes(self) -> None: start = datetime(2020, 2, 1, 0, 0, 0, tzinfo=tzutc()) # Open datetime range input search = BaseSearch(url=SEARCH_URL, datetime=(start, None)) assert search.get_parameters()["datetime"] == "2020-02-01T00:00:00Z/.." def test_localized_datetime_converted_to_utc(self) -> None: # Localized datetime input (should be converted to UTC) start_localized = datetime(2020, 2, 1, 0, 0, 0, tzinfo=gettz("US/Eastern")) search = BaseSearch(url=SEARCH_URL, datetime=start_localized) assert search.get_parameters()["datetime"] == "2020-02-01T05:00:00Z" def test_single_year(self) -> None: search = BaseSearch(url=SEARCH_URL, datetime="2020") assert ( search.get_parameters()["datetime"] == "2020-01-01T00:00:00Z/2020-12-31T23:59:59Z" ) def test_range_of_years(self) -> None: search = BaseSearch(url=SEARCH_URL, datetime="2019/2020") assert ( search.get_parameters()["datetime"] == "2019-01-01T00:00:00Z/2020-12-31T23:59:59Z" ) def test_single_month(self) -> None: search = BaseSearch(url=SEARCH_URL, datetime="2020-06") assert ( search.get_parameters()["datetime"] == "2020-06-01T00:00:00Z/2020-06-30T23:59:59Z" ) def test_range_of_months(self) -> None: search = BaseSearch(url=SEARCH_URL, datetime="2020-04/2020-06") assert ( search.get_parameters()["datetime"] == "2020-04-01T00:00:00Z/2020-06-30T23:59:59Z" ) def test_single_date(self) -> None: search = BaseSearch(url=SEARCH_URL, datetime="2020-06-10") assert ( search.get_parameters()["datetime"] == "2020-06-10T00:00:00Z/2020-06-10T23:59:59Z" ) def test_range_of_dates(self) -> None: search = BaseSearch(url=SEARCH_URL, datetime="2020-06-10/2020-06-20") assert ( search.get_parameters()["datetime"] == "2020-06-10T00:00:00Z/2020-06-20T23:59:59Z" ) def test_mixed_simple_date_strings(self) -> None: search = BaseSearch(url=SEARCH_URL, datetime="2019/2020-06-10") assert ( search.get_parameters()["datetime"] == "2019-01-01T00:00:00Z/2020-06-10T23:59:59Z" ) def test_time(self) -> None: search = BaseSearch( url=SEARCH_URL, datetime="2019-01-01T00:00:00Z/2019-01-01T00:12:00" ) assert ( search.get_parameters()["datetime"] == "2019-01-01T00:00:00Z/2019-01-01T00:12:00Z" ) def test_many_datetimes(self) -> None: datetimes = [ "1985-04-12T23:20:50.52Z", "1996-12-19T16:39:57-08:00", "1990-12-31T23:59:60Z", "1990-12-31T15:59:60-08:00", "1937-01-01T12:00:27.87+01:00", "1985-04-12T23:20:50.52Z", "1937-01-01T12:00:27.8710+01:00", "1937-01-01T12:00:27.8+01:00", "1937-01-01T12:00:27.8Z", "1985-04-12t23:20:50.5202020z", "2020-07-23T00:00:00Z", "2020-07-23T00:00:00.0Z", "2020-07-23T00:00:00.01Z", "2020-07-23T00:00:00.012Z", "2020-07-23T00:00:00.0123Z", "2020-07-23T00:00:00.01234Z", "2020-07-23T00:00:00.012345Z", "2020-07-23T00:00:00.000Z", "2020-07-23T00:00:00.000+03:00", "2020-07-23T00:00:00+03:00", "2020-07-23T00:00:00.000+03:00", "2020-07-23T00:00:00.000z", "/2023-01-01T00:00:00Z", "2023-01-01T00:00:00Z/", ] for date_time in datetimes: BaseSearch(url=SEARCH_URL, datetime=date_time) def test_three_datetimes(self) -> None: start = datetime(2020, 2, 1, 0, 0, 0, tzinfo=tzutc()) middle = datetime(2020, 2, 2, 0, 0, 0, tzinfo=tzutc()) end = datetime(2020, 2, 3, 0, 0, 0, tzinfo=tzutc()) with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, datetime=[start, middle, end]) def test_double_open_ended_interval(self) -> None: with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, datetime=[None, None]) def test_datetime_list_of_one_none(self) -> None: with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, datetime=[None]) def test_poorly_formed_datetimes(self) -> None: with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, datetime="2020-7/2020-8") def test_single_collection_string(self) -> None: # Single ID string search = BaseSearch(url=SEARCH_URL, collections="naip") assert search.get_parameters()["collections"] == ("naip",) def test_multiple_collection_string(self) -> None: # Comma-separated ID string search = BaseSearch(url=SEARCH_URL, collections="naip,landsat8_l1tp") assert search.get_parameters()["collections"] == ("naip", "landsat8_l1tp") def test_list_of_collection_strings(self) -> None: # List of ID strings search = BaseSearch(url=SEARCH_URL, collections=["naip", "landsat8_l1tp"]) assert search.get_parameters()["collections"] == ("naip", "landsat8_l1tp") def test_generator_of_collection_strings(self) -> None: # Generator of ID strings def collectioner() -> Iterator[str]: yield from ["naip", "landsat8_l1tp"] search = BaseSearch(url=SEARCH_URL, collections=collectioner()) assert search.get_parameters()["collections"] == ("naip", "landsat8_l1tp") def test_single_id_string(self) -> None: # Single ID search = BaseSearch(url=SEARCH_URL, ids="m_3510836_se_12_060_20180508_20190331") assert search.get_parameters()["ids"] == ( "m_3510836_se_12_060_20180508_20190331", ) def test_multiple_id_string(self) -> None: # Comma-separated ID string search = BaseSearch( url=SEARCH_URL, ids="m_3510836_se_12_060_20180508_20190331,m_3510840_se_12_060_20180504_20190331", ) assert search.get_parameters()["ids"] == ( "m_3510836_se_12_060_20180508_20190331", "m_3510840_se_12_060_20180504_20190331", ) def test_list_of_id_strings(self) -> None: # List of IDs search = BaseSearch( url=SEARCH_URL, ids=[ "m_3510836_se_12_060_20180508_20190331", "m_3510840_se_12_060_20180504_20190331", ], ) assert search.get_parameters()["ids"] == ( "m_3510836_se_12_060_20180508_20190331", "m_3510840_se_12_060_20180504_20190331", ) def test_generator_of_id_string(self) -> None: # Generator of IDs def ids() -> Iterator[str]: yield from [ "m_3510836_se_12_060_20180508_20190331", "m_3510840_se_12_060_20180504_20190331", ] search = BaseSearch(url=SEARCH_URL, ids=ids()) assert search.get_parameters()["ids"] == ( "m_3510836_se_12_060_20180508_20190331", "m_3510840_se_12_060_20180504_20190331", ) def test_intersects_dict(self) -> None: # Dict input search = BaseSearch(url=SEARCH_URL, intersects=INTERSECTS_EXAMPLE) assert search.get_parameters()["intersects"] == INTERSECTS_EXAMPLE def test_intersects_json_string(self) -> None: # JSON string input search = BaseSearch(url=SEARCH_URL, intersects=json.dumps(INTERSECTS_EXAMPLE)) assert search.get_parameters()["intersects"] == INTERSECTS_EXAMPLE def test_intersects_non_geo_interface_object(self) -> None: with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, intersects=object()) # type: ignore def test_filter_lang_default_for_method_despite_filter_as_dict(self) -> None: search = BaseSearch(url=SEARCH_URL, method="GET", filter={}) assert search.get_parameters()["filter-lang"] == "cql2-text" def test_filter_lang_default_for_method_despite_filter_as_str(self) -> None: search = BaseSearch(url=SEARCH_URL, method="POST", filter="") assert search.get_parameters()["filter-lang"] == "cql2-json" def test_filter_lang_cql2_text(self) -> None: # Use specified filter_lang search = BaseSearch(url=SEARCH_URL, filter_lang="cql2-text", filter={}) assert search.get_parameters()["filter-lang"] == "cql2-text" def test_filter_lang_cql2_json(self) -> None: # Use specified filter_lang search = BaseSearch(url=SEARCH_URL, filter_lang="cql2-json", filter="") assert search.get_parameters()["filter-lang"] == "cql2-json" def test_filter_lang_without_filter(self) -> None: # No filter provided search = BaseSearch(url=SEARCH_URL) assert "filter-lang" not in search.get_parameters() def test_filter_conversion_to_cql2_json(self) -> None: search = BaseSearch(url=SEARCH_URL, method="POST", filter="eo:cloud_cover<=10") assert search.get_parameters()["filter-lang"] == "cql2-json" assert search.get_parameters()["filter"] == { "args": [{"property": "eo:cloud_cover"}, 10], "op": "<=", } def test_filter_conversion_to_cql2_text(self) -> None: search = BaseSearch( url=SEARCH_URL, method="GET", filter={"op": "<=", "args": [{"property": "eo:cloud_cover"}, 10]}, ) assert search.get_parameters()["filter-lang"] == "cql2-text" assert search.get_parameters()["filter"] == '("eo:cloud_cover" <= 10)' def test_filter_conversion_does_not_happen_if_filter_lang_specified_json( self, ) -> None: search = BaseSearch( url=SEARCH_URL, method="GET", filter={"op": "<=", "args": [{"property": "eo:cloud_cover"}, 10]}, filter_lang="cql2-json", ) # assert search.get_parameters()["filter-lang"] == "cql2-json" assert ( search.get_parameters()["filter"] == '{"op": "<=", "args": [{"property": "eo:cloud_cover"}, 10]}' ) def test_filter_conversion_does_not_happen_if_filter_lang_specified_text( self, ) -> None: search = BaseSearch( url=SEARCH_URL, method="POST", filter="eo:cloud_cover<=10", filter_lang="cql2-text", ) # note that this is likely to fail when it hits the server assert search.get_parameters()["filter-lang"] == "cql2-text" assert search.get_parameters()["filter"] == "eo:cloud_cover<=10" def test_sortby(self) -> None: search = BaseSearch(url=SEARCH_URL, sortby="properties.datetime") assert search.get_parameters()["sortby"] == [ {"direction": "asc", "field": "properties.datetime"} ] search = BaseSearch(url=SEARCH_URL, sortby="+properties.datetime") assert search.get_parameters()["sortby"] == [ {"direction": "asc", "field": "properties.datetime"} ] search = BaseSearch(url=SEARCH_URL, sortby="-properties.datetime") assert search.get_parameters()["sortby"] == [ {"direction": "desc", "field": "properties.datetime"} ] search = BaseSearch( url=SEARCH_URL, sortby="-properties.datetime,+id,collection" ) assert search.get_parameters()["sortby"] == [ {"direction": "desc", "field": "properties.datetime"}, {"direction": "asc", "field": "id"}, {"direction": "asc", "field": "collection"}, ] search = BaseSearch( url=SEARCH_URL, sortby=[ {"direction": "desc", "field": "properties.datetime"}, {"direction": "asc", "field": "id"}, {"direction": "asc", "field": "collection"}, ], ) assert search.get_parameters()["sortby"] == [ {"direction": "desc", "field": "properties.datetime"}, {"direction": "asc", "field": "id"}, {"direction": "asc", "field": "collection"}, ] search = BaseSearch( url=SEARCH_URL, sortby=["-properties.datetime", "id", "collection"] ) assert search.get_parameters()["sortby"] == [ {"direction": "desc", "field": "properties.datetime"}, {"direction": "asc", "field": "id"}, {"direction": "asc", "field": "collection"}, ] search = BaseSearch( url=SEARCH_URL, method="GET", sortby=["-properties.datetime", "id", "collection"], ) assert ( search.get_parameters()["sortby"] == "-properties.datetime,+id,+collection" ) search = BaseSearch( url=SEARCH_URL, method="GET", sortby="-properties.datetime,id,collection" ) assert ( search.get_parameters()["sortby"] == "-properties.datetime,+id,+collection" ) with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, sortby=1) # type: ignore with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, sortby=[1]) # type: ignore def test_fields(self) -> None: with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, fields=1) # type: ignore with pytest.raises(Exception): BaseSearch(url=SEARCH_URL, fields=[1]) # type: ignore search = BaseSearch(url=SEARCH_URL, fields="id,collection,+foo,-bar") assert search.get_parameters()["fields"] == { "exclude": ["bar"], "include": ["id", "collection", "foo"], } search = BaseSearch(url=SEARCH_URL, fields=["id", "collection", "+foo", "-bar"]) assert search.get_parameters()["fields"] == { "exclude": ["bar"], "include": ["id", "collection", "foo"], } search = BaseSearch( url=SEARCH_URL, fields={"exclude": ["bar"], "include": ["id", "collection"]}, ) assert search.get_parameters()["fields"] == { "exclude": ["bar"], "include": ["id", "collection"], } search = BaseSearch( url=SEARCH_URL, method="GET", fields="id,collection,+foo,-bar" ) assert search.get_parameters()["fields"] == "+id,+collection,+foo,-bar" search = BaseSearch( url=SEARCH_URL, method="GET", fields=["id", "collection", "+foo", "-bar"] ) assert search.get_parameters()["fields"] == "+id,+collection,+foo,-bar" search = BaseSearch( url=SEARCH_URL, method="GET", fields={"exclude": ["bar"], "include": ["id", "collection"]}, ) assert search.get_parameters()["fields"] == "+id,+collection,-bar" ================================================ FILE: tests/test_cli.py ================================================ import json import tempfile import pytest from pytest_console_scripts import ScriptRunner import pystac_client.cli from tests.helpers import STAC_URLS, TEST_DATA FILTER_JSON = {"op": "lte", "args": [{"property": "eo:cloud_cover"}, 40]} # We want to ensure that the CLI is handling these warnings properly @pytest.mark.filterwarnings("ignore::pystac_client.warnings.PystacClientWarning") class TestCLISearch: @pytest.mark.vcr def test_item_search(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "search", STAC_URLS["PLANETARY-COMPUTER"], "-c", "naip", "--max-items", "20", ] result = script_runner.run(args, print_result=False) assert result.success @pytest.mark.vcr def test_filter(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "search", STAC_URLS["EARTH-SEARCH"], "--filter", json.dumps(FILTER_JSON), "--max-items", "20", ] result = script_runner.run(args, print_result=False) assert result.success, result.stderr assert result.stdout @pytest.mark.vcr @pytest.mark.parametrize("filename", ["netherlands_aoi.json", "sample-item.json"]) def test_intersects(self, script_runner: ScriptRunner, filename: str) -> None: args = [ "stac-client", "search", STAC_URLS["PLANETARY-COMPUTER"], "--collections", "landsat-8-c2-l2", "--intersects", str(TEST_DATA / filename), "--max-items", "10", ] result = script_runner.run(args, print_result=False) assert result.success, result.stderr assert result.stdout @pytest.mark.vcr def test_intersects_despite_warning(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "search", STAC_URLS["PLANETARY-COMPUTER"], "--collections", "landsat-8-c2-l2", "--intersects", str(TEST_DATA / "sample-item-collection.json"), "--max-items", "10", ] result = script_runner.run(args, print_result=False) assert result.success assert result.stdout assert "input to intersects is a FeatureCollection" in result.stderr @pytest.mark.parametrize( "headers,good_header_count", [ (["kick=flip", "home=run"], 2), (["mad=pow"], 1), (["=no-var"], 0), (["no-val="], 0), (["good=header", "bad-header"], 1), (["header=value-with-three-=-signs-=", "plain=jane"], 2), ], ) def test_headers(self, headers: list[str], good_header_count: int) -> None: args = [ "search", STAC_URLS["PLANETARY-COMPUTER"], "-c", "naip", "--max-items", "20", "--headers", ] + headers pargs = pystac_client.cli.parse_args(args) assert len(pargs["headers"]) == good_header_count def test_no_arguments(self, script_runner: ScriptRunner) -> None: args = ["stac-client"] result = script_runner.run(*args, print_result=False) assert not result.success assert result.returncode == 1 @pytest.mark.vcr def test_non_conformant_raises_by_default( self, script_runner: ScriptRunner ) -> None: args = [ "stac-client", "search", "https://earth-search.aws.element84.com/v0", "-c", "sentinel-s2-l2a-cogs", "--matched", ] result = script_runner.run(args, print_result=False) assert result.success is False assert "Server does not conform to ITEM_SEARCH" in result.stderr assert result.returncode == 1 @pytest.mark.vcr @pytest.mark.parametrize("warning_flag", ["--error", "--error=no-conforms-to"]) def test_non_conformant_raises_if_warning_set_to_error( self, script_runner: ScriptRunner, warning_flag: str ) -> None: args = [ "stac-client", "search", "https://earth-search.aws.element84.com/v0", "-c", "sentinel-s2-l2a-cogs", warning_flag, "--matched", ] result = script_runner.run(args, print_result=False) assert result.success is False assert "Server does not advertise any conformance classes" in result.stderr assert result.returncode == 1 @pytest.mark.vcr def test_non_conformant_can_be_fixed(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "search", "https://earth-search.aws.element84.com/v0", "-c", "sentinel-s2-l2a-cogs", "--add-conforms-to=ITEM_SEARCH", "--matched", ] result = script_runner.run(args, print_result=False) assert result.success @pytest.mark.vcr @pytest.mark.parametrize("warning_flag", ["--ignore", "--ignore=no-conforms-to"]) def test_non_conformant_can_be_ignored( self, script_runner: ScriptRunner, warning_flag: str ) -> None: args = [ "stac-client", "search", "https://earth-search.aws.element84.com/v0", "-c", "sentinel-s2-l2a-cogs", warning_flag, "--add-conforms-to=ITEM_SEARCH", "--matched", ] result = script_runner.run(args, print_result=False) assert result.success @pytest.mark.vcr @pytest.mark.parametrize( "conforms_to_flag", ["--clear-conforms-to", "--remove-conforms-to=ITEM_SEARCH"] ) def test_altering_conforms_to( self, script_runner: ScriptRunner, conforms_to_flag: str ) -> None: args = [ "stac-client", "search", STAC_URLS["EARTH-SEARCH"], conforms_to_flag, ] result = script_runner.run(args, print_result=False) assert result.success is False assert "Server does not conform to ITEM_SEARCH" in result.stderr assert result.returncode == 1 @pytest.mark.vcr @pytest.mark.filterwarnings("ignore::Warning") def test_matched_not_available(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "search", STAC_URLS["PLANETARY-COMPUTER"], "-c", "naip", "--matched", ] result = script_runner.run(args, print_result=False) assert result.success is False assert "'matched' is not supported for this catalog" in result.stderr assert result.returncode == 1 @pytest.mark.vcr def test_matched(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "search", STAC_URLS["EARTH-SEARCH"], "-c", "cop-dem-glo-30", "--max-items", "1", "--matched", ] result = script_runner.run(args, print_result=False) assert result.success assert result.stdout[0].isdigit(), "Output does not start with a number" @pytest.mark.vcr def test_fields(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "search", STAC_URLS["EARTH-SEARCH"], "-c", "cop-dem-glo-30", "--max-items", "1", "--fields=-geometry,-assets", ] result = script_runner.run(args, print_result=True) assert result.success assert "assets" not in result.stdout assert "geometry" not in result.stdout @pytest.mark.vcr def test_save(self, script_runner: ScriptRunner) -> None: with tempfile.NamedTemporaryFile() as fp: path = fp.name args = [ "stac-client", "search", STAC_URLS["EARTH-SEARCH"], "-c", "cop-dem-glo-30", "--max-items", "1", "--save", path, ] result = script_runner.run(args, print_result=False) assert result.success with open(path) as f: output = json.loads(f.read()) assert "features" in output assert len(output["features"]) == 1 @pytest.mark.filterwarnings("ignore::pystac_client.warnings.PystacClientWarning") class TestCLICollections: @pytest.mark.vcr def test_collections(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "collections", STAC_URLS["EARTH-SEARCH"], ] result = script_runner.run(args, print_result=False) assert result.success assert result.stdout.startswith('[{"type": "Collection"') @pytest.mark.vcr def test_collection_search(self, script_runner: ScriptRunner) -> None: args = [ "stac-client", "collections", STAC_URLS["EARTH-SEARCH"], "--q", "sentinel", ] with pytest.warns(UserWarning, match="COLLECTION_SEARCH"): result = script_runner.run(args, print_result=False) assert result.success collections = json.loads(result.stdout) assert len(collections) == 5 @pytest.mark.vcr def test_save(self, script_runner: ScriptRunner) -> None: with tempfile.NamedTemporaryFile() as fp: path = fp.name args = [ "stac-client", "collections", STAC_URLS["EARTH-SEARCH"], "--save", path, ] result = script_runner.run(args, print_result=False) assert result.success with open(path) as f: output = json.loads(f.read()) assert isinstance(output, list) assert len(output) == 9, "earth-search does not have 9 collections" assert all(c["type"] == "Collection" for c in output) ================================================ FILE: tests/test_client.py ================================================ import json import os.path import warnings from datetime import datetime from tempfile import TemporaryDirectory from typing import Any from urllib.parse import parse_qs, urlsplit import pystac import pytest from dateutil.tz import tzutc from pystac import MediaType from requests_mock import Mocker from pystac_client import Client, CollectionClient from pystac_client._utils import Modifiable from pystac_client.conformance import ConformanceClasses from pystac_client.errors import ClientTypeError, IgnoredResultWarning from pystac_client.exceptions import APIError from pystac_client.stac_api_io import StacApiIO from pystac_client.warnings import ( DoesNotConformTo, FallbackToPystac, MissingLink, NoConformsTo, strict, ) from .helpers import STAC_URLS, TEST_DATA, read_data_file class TestAPI: @pytest.mark.vcr def test_instance(self) -> None: api = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) # An API instance is also a Catalog instance assert isinstance(api, pystac.Catalog) assert str(api) == "" @pytest.mark.vcr def test_links(self) -> None: api = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) # Should be able to get collections via links as with a typical PySTAC Catalog collection_links = api.get_links("child") assert len(collection_links) > 0 collections = list(api.get_collections()) assert len(collection_links) == len(collections) first_child_link = api.get_single_link("child") assert first_child_link is not None first_collection = first_child_link.resolve_stac_object(root=api).target assert isinstance(first_collection, pystac.Collection) @pytest.mark.vcr def test_from_file(self) -> None: api = Client.from_file(STAC_URLS["PLANETARY-COMPUTER"]) assert api.title == "Microsoft Planetary Computer STAC API" def test_invalid_url(self) -> None: with pytest.raises(TypeError): Client.open() # type: ignore[call-arg] def test_get_collections_with_conformance(self, requests_mock: Mocker) -> None: """Checks that the "data" endpoint is used if the API published the STAC API Collections conformance class.""" pc_root_text = read_data_file("planetary-computer-root.json") pc_collection_dict = read_data_file( "planetary-computer-aster-l1t-collection.json", parse_json=True ) # Mock the root catalog requests_mock.get( STAC_URLS["PLANETARY-COMPUTER"], status_code=200, text=pc_root_text ) api = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) assert api.conforms_to(ConformanceClasses.COLLECTIONS) # Get & mock the collections (rel type "data") link collections_link = api.get_single_link("data") assert collections_link is not None requests_mock.get( collections_link.href, status_code=200, json={"collections": [pc_collection_dict], "links": []}, ) _ = next(api.get_collections()) history = requests_mock.request_history assert len(history) == 2 assert history[1].url == collections_link.href def test_get_collections_single_slash(self, requests_mock: Mocker) -> None: pc_root_text = read_data_file("planetary-computer-root.json") root_url = "http://pystac-client.test/" requests_mock.get(root_url, status_code=200, text=pc_root_text) api = Client.open(root_url) pc_collection_dict = read_data_file( "planetary-computer-aster-l1t-collection.json", parse_json=True ) requests_mock.get( f"{root_url}collections", # note the lack of the slash status_code=200, json={"collections": [pc_collection_dict], "links": []}, ) api.remove_links("data") with pytest.warns(MissingLink, match="No link with rel='data'"): _ = next(api.get_collections()) history = requests_mock.request_history assert len(history) == 2 assert history[1].url == f"{root_url}collections" def test_keep_trailing_slash_on_root(self, requests_mock: Mocker) -> None: pc_root_text = read_data_file("planetary-computer-root.json") root_url = "http://pystac-client.test/" requests_mock.get(root_url, status_code=200, text=pc_root_text) client = Client.open(root_url) self_href = client.get_self_href() assert self_href assert self_href.endswith("/") def test_fall_back_to_data_link_for_collections( self, requests_mock: Mocker ) -> None: pc_root_text = read_data_file("planetary-computer-root.json") root_url = "http://pystac-client.test/" requests_mock.get(root_url, status_code=200, text=pc_root_text) api = Client.open(root_url) api.set_self_href(None) pc_collection_dict = read_data_file( "planetary-computer-aster-l1t-collection.json", parse_json=True ) requests_mock.get( # the href of the data link "https://planetarycomputer.microsoft.com/api/stac/v1/collections", status_code=200, json={"collections": [pc_collection_dict], "links": []}, ) _ = next(api.get_collections()) history = requests_mock.request_history assert len(history) == 2 assert ( history[1].url == "https://planetarycomputer.microsoft.com/api/stac/v1/collections" ) def test_build_absolute_href_from_data_link(self, requests_mock: Mocker) -> None: pc_root = read_data_file("planetary-computer-root.json", parse_json=True) assert isinstance(pc_root, dict) for link in pc_root["links"]: if link["rel"] == "data": link["href"] = "./collections" root_url = "http://pystac-client.test/" requests_mock.get(root_url, status_code=200, text=json.dumps(pc_root)) api = Client.open(root_url) api.set_self_href(None) api.add_link( pystac.Link( rel="self", target="https://planetarycomputer.microsoft.com/api/stac/v1/", ) ) pc_collection_dict = read_data_file( "planetary-computer-aster-l1t-collection.json", parse_json=True ) requests_mock.get( # the href of the data link "https://planetarycomputer.microsoft.com/api/stac/v1/collections", status_code=200, json={"collections": [pc_collection_dict], "links": []}, ) _ = next(api.get_collections()) history = requests_mock.request_history assert len(history) == 2 assert ( history[1].url == "https://planetarycomputer.microsoft.com/api/stac/v1/collections" ) def test_error_if_no_self_href_or_data_link(self, requests_mock: Mocker) -> None: pc_root = read_data_file("planetary-computer-root.json", parse_json=True) assert isinstance(pc_root, dict) pc_root["links"] = [link for link in pc_root["links"] if link["rel"] != "data"] root_url = "http://pystac-client.test/" requests_mock.get(root_url, status_code=200, text=json.dumps(pc_root)) api = Client.open(root_url) api.set_self_href(None) with pytest.warns(MissingLink, match="No link with rel='data'"): with pytest.raises(ValueError, match="does not have a self_href set"): _ = api.get_collection("an-id") def test_custom_request_parameters(self, requests_mock: Mocker) -> None: pc_root_text = read_data_file("planetary-computer-root.json") pc_collection_dict = read_data_file( "planetary-computer-collection.json", parse_json=True ) requests_mock.get( STAC_URLS["PLANETARY-COMPUTER"], status_code=200, text=pc_root_text ) init_qp_name = "my-param" init_qp_value = "some-value" api = Client.open( STAC_URLS["PLANETARY-COMPUTER"], parameters={init_qp_name: init_qp_value} ) # Ensure that the Client will use the /collections endpoint and not fall back # to traversing child links. assert api.conforms_to(ConformanceClasses.COLLECTIONS) # Get the /collections endpoint collections_link = api.get_single_link("data") assert collections_link is not None # Mock the request requests_mock.get( collections_link.href, status_code=200, json={"collections": [pc_collection_dict], "links": []}, ) # Make the collections request _ = next(api.get_collections()) history = requests_mock.request_history assert len(history) == 2 actual_qs = urlsplit(history[1].url).query actual_qp = parse_qs(actual_qs) # Check that the param from the init method is present assert init_qp_name in actual_qp assert len(actual_qp[init_qp_name]) == 1 assert actual_qp[init_qp_name][0] == init_qp_value def test_custom_query_params_get_collections_propagation( self, requests_mock: Mocker ) -> None: """Checks that query params passed to the init method are added to requests for CollectionClients fetched from the /collections endpoint.""" pc_root_text = read_data_file("planetary-computer-root.json") pc_collection_dict = read_data_file( "planetary-computer-collection.json", parse_json=True ) requests_mock.get( STAC_URLS["PLANETARY-COMPUTER"], status_code=200, text=pc_root_text ) init_qp_name = "my-param" init_qp_value = "some-value" client = Client.open( STAC_URLS["PLANETARY-COMPUTER"], parameters={init_qp_name: init_qp_value} ) # Get the /collections endpoint collections_link = client.get_single_link("data") assert collections_link is not None # Mock the request requests_mock.get( collections_link.href, status_code=200, json={"collections": [pc_collection_dict], "links": []}, ) # Make the collections request collection = next(client.get_collections()) # Mock the items endpoint items_link = collection.get_single_link("items") assert items_link is not None requests_mock.get( items_link.href, status_code=200, json={ "type": "FeatureCollection", "stac_version": "1.0.0", "features": [], "links": [], }, ) # Make the items request _ = list(collection.get_items()) history = requests_mock.request_history assert len(history) == 3 actual_qs = urlsplit(history[2].url).query actual_qp = parse_qs(actual_qs) # Check that the query param from the root Client is present assert init_qp_name in actual_qp assert len(actual_qp[init_qp_name]) == 1 assert actual_qp[init_qp_name][0] == init_qp_value def test_custom_query_params_get_collection_propagation( self, requests_mock: Mocker ) -> None: """Checks that query params passed to the init method are added to requests for CollectionClients fetched from the /collections endpoint.""" pc_root_text = read_data_file("planetary-computer-root.json") pc_collection_dict = read_data_file( "planetary-computer-collection.json", parse_json=True ) assert isinstance(pc_collection_dict, dict) pc_collection_id = pc_collection_dict["id"] requests_mock.get( STAC_URLS["PLANETARY-COMPUTER"], status_code=200, text=pc_root_text ) init_qp_name = "my-param" init_qp_value = "some-value" client = Client.open( STAC_URLS["PLANETARY-COMPUTER"], parameters={init_qp_name: init_qp_value} ) # Get the /collections endpoint collections_link = client.get_single_link("data") assert collections_link is not None collection_href = collections_link.href + "/" + pc_collection_id # Mock the request requests_mock.get(collection_href, status_code=200, json=pc_collection_dict) # Make the collections request collection = client.get_collection(pc_collection_id) assert collection is not None # Mock the items endpoint items_link = collection.get_single_link("items") assert items_link is not None requests_mock.get( items_link.href, status_code=200, json={ "type": "FeatureCollection", "stac_version": "1.0.0", "features": [], "links": [], }, ) # Make the items request _ = list(collection.get_items()) history = requests_mock.request_history assert len(history) == 3 actual_qs = urlsplit(history[2].url).query actual_qp = parse_qs(actual_qs) # Check that the query param from the root Client is present assert init_qp_name in actual_qp assert len(actual_qp[init_qp_name]) == 1 assert actual_qp[init_qp_name][0] == init_qp_value def test_get_collections_without_conformance_fallsback_to_pystac( self, requests_mock: Mocker ) -> None: pc_root_dict = read_data_file("planetary-computer-root.json", parse_json=True) pc_collection_dict = read_data_file( "planetary-computer-aster-l1t-collection.json", parse_json=True ) # Remove the collections conformance class pc_root_dict["conformsTo"].remove( "https://api.stacspec.org/v1.0.0-beta.1/collections" ) # Remove all child links except for the collection that we are mocking pc_collection_href = next( link["href"] for link in pc_collection_dict["links"] if link["rel"] == "self" ) pc_root_dict["links"] = [ link for link in pc_root_dict["links"] if link["rel"] != "child" or link["href"] == pc_collection_href ] # Mock the root catalog requests_mock.get( STAC_URLS["PLANETARY-COMPUTER"], status_code=200, json=pc_root_dict ) api = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) assert api.has_conforms_to() assert not api.conforms_to(ConformanceClasses.COLLECTIONS) assert not api.conforms_to(ConformanceClasses.FEATURES) # Mock the collection requests_mock.get(pc_collection_href, status_code=200, json=pc_collection_dict) with pytest.warns(FallbackToPystac): with pytest.warns(DoesNotConformTo, match="COLLECTIONS, FEATURES"): _ = next(api.get_collections()) history = requests_mock.request_history assert len(history) == 2 def test_opening_a_collection(self) -> None: path = str(TEST_DATA / "planetary-computer-aster-l1t-collection.json") with pytest.raises(ClientTypeError): Client.open(path) def test_headers_with_custom_stac_io(self, requests_mock: Mocker) -> None: pc_root_dict = read_data_file("planetary-computer-root.json", parse_json=True) requests_mock.get( STAC_URLS["PLANETARY-COMPUTER"], status_code=200, json=pc_root_dict, request_headers={"ski": "pow", "shred": "gnar"}, ) stac_io = StacApiIO(headers={"ski": "pow"}) _ = Client.open( STAC_URLS["PLANETARY-COMPUTER"], headers={"shred": "gnar"}, stac_io=stac_io ) class TestAPISearch: @pytest.fixture(scope="function") def api(self) -> Client: return Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) def test_search_conformance_error(self, api: Client) -> None: # Remove item search conformance api.remove_conforms_to("ITEM_SEARCH") with strict(): with pytest.raises(DoesNotConformTo, match="ITEM_SEARCH"): api.search(limit=10, max_items=10, collections="mr-peebles") def test_no_search_link(self, api: Client) -> None: # Remove the search link api.remove_links("search") with strict(): with pytest.raises( MissingLink, match="No link with rel='search' could be found on this Client", ): api.search(limit=10, max_items=10, collections="naip") def test_no_conforms_to(self) -> None: with open(str(TEST_DATA / "planetary-computer-root.json")) as f: data = json.load(f) del data["conformsTo"] with TemporaryDirectory() as temporary_directory: path = os.path.join(temporary_directory, "catalog.json") with open(path, "w") as f: json.dump(data, f) api = Client.from_file(path) with strict(): with pytest.raises(DoesNotConformTo, match="ITEM_SEARCH"): api.search(limit=10, max_items=10, collections="naip") def test_search(self, api: Client) -> None: results = api.search( bbox=[-73.21, 43.99, -73.12, 44.05], collections="naip", limit=10, max_items=20, datetime=[datetime(2020, 1, 1, 0, 0, 0, tzinfo=tzutc()), None], ) assert results._parameters == { "bbox": (-73.21, 43.99, -73.12, 44.05), "collections": ("naip",), "limit": 10, "datetime": "2020-01-01T00:00:00Z/..", } def test_json_search_link(self, api: Client) -> None: search_link = api.get_single_link(rel="search") assert search_link api.remove_links(rel="search") search_link.media_type = MediaType.JSON api.add_link(search_link) api.search(limit=1, max_items=1, collections="naip") @pytest.mark.vcr def test_search_max_items_unlimited_default(self, api: Client) -> None: search = api.search( bbox=[-73.21, 43.99, -73.12, 45.05], collections="naip", datetime="2014-01-01/2020-12-31", ) items = list(search.items()) assert len(items) > 100 class TestAPICollectionSearch: @pytest.fixture(scope="function") def api(self) -> Client: return Client.from_file(str(TEST_DATA / "fedeo_clearinghouse.json")) def test_search_conformance_error(self, api: Client) -> None: # Remove collection search conformance api.remove_conforms_to("COLLECTION_SEARCH") api.remove_conforms_to("COLLECTIONS") with strict(): with pytest.raises(DoesNotConformTo, match="COLLECTION_SEARCH"): api.collection_search(limit=10, max_collections=10, q="test") def test_search_conformance_warning(self) -> None: api = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) # Remove collection search conformance just in case... api.remove_conforms_to("COLLECTION_SEARCH") with strict(): with pytest.warns(UserWarning, match="COLLECTION_SEARCH"): api.collection_search(limit=10, max_collections=10, q="test") @pytest.mark.vcr def test_search(self, api: Client) -> None: results = api.collection_search( bbox=[-73.21, 43.99, -73.12, 44.05], limit=10, datetime=[datetime(2020, 1, 1, 0, 0, 0, tzinfo=tzutc()), None], ) assert results._parameters == { "bbox": (-73.21, 43.99, -73.12, 44.05), "limit": 10, "datetime": "2020-01-01T00:00:00Z/..", } class MySign: def __init__(self) -> None: self.call_count = 0 def __call__(self, x: Modifiable) -> None: self.call_count += 1 class TestSigning: @pytest.mark.vcr def test_signing(self) -> None: sign = MySign() # sign is callable, but mypy keeps trying to interpret it as a "MySign" object. client = Client.open(STAC_URLS["PLANETARY-COMPUTER"], modifier=sign) assert client.modifier is sign collection = client.get_collection("cil-gdpcir-cc0") assert collection assert isinstance(collection, CollectionClient) assert collection.modifier is sign assert sign.call_count == 1 collection.get_item("cil-gdpcir-INM-INM-CM5-0-ssp585-r1i1p1f1-day") assert sign.call_count == 2 next(client.get_collections()) assert sign.call_count == 3 search = client.search(collections=["sentinel-2-l2a"], max_items=10) next(search.items_as_dicts()) assert sign.call_count == 4 next(search.items()) assert sign.call_count == 5 next(search.pages_as_dicts()) assert sign.call_count == 6 next(search.pages()) assert sign.call_count == 7 search.item_collection_as_dict() assert sign.call_count == 8 search.item_collection() assert sign.call_count == 9 @pytest.mark.vcr def test_sign_with_return_warns(self) -> None: def modifier_ok(x: Any) -> Any: return x def modifier_bad(x: Any) -> Any: return 0 client = Client.open(STAC_URLS["PLANETARY-COMPUTER"], modifier=modifier_ok) with warnings.catch_warnings(): warnings.simplefilter("error") client.get_collection("sentinel-2-l2a") client = Client.open(STAC_URLS["PLANETARY-COMPUTER"], modifier=modifier_bad) with pytest.warns(IgnoredResultWarning): client.get_collection("sentinel-2-l2a") class TestQueryables: @pytest.mark.vcr def test_get_queryables_collections(self) -> None: api = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) with pytest.warns(MissingLink, match="queryables"): col = api.get_collection("3dep-seamless") assert isinstance(col, CollectionClient) tdep_seamless_props = col.get_queryables()["properties"] col = api.get_collection("fia") assert isinstance(col, CollectionClient) fia_props = col.get_queryables()["properties"] result = api.get_merged_queryables(["fia", "3dep-seamless"]) assert "properties" in result assert "id" in result["properties"] assert set(fia_props.keys()).issubset(result["properties"]) assert set(tdep_seamless_props.keys()).issubset(result["properties"]) def test_get_queryables_errors(self, requests_mock: Mocker) -> None: pc_root_text = read_data_file("planetary-computer-root.json") root_url = "http://pystac-client.test/" requests_mock.get(root_url, status_code=200, text=pc_root_text) api = Client.open(root_url) with pytest.raises(DoesNotConformTo, match="FILTER"): api.get_queryables() assert api._stac_io is not None api.add_conforms_to("FILTER") self_href = api.get_self_href() api.set_self_href(None) with pytest.warns(MissingLink, match="queryables"): with pytest.raises(ValueError, match="does not have a self_href set"): api.get_queryables() api.set_self_href(self_href) api._stac_io = None with pytest.warns(MissingLink, match="queryables"): with pytest.raises(APIError, match="API access is not properly configured"): api.get_queryables() class TestConformsTo: def test_ignore_conformance_is_deprecated_and_noop(self) -> None: with pytest.warns( FutureWarning, match="`ignore_conformance` option is deprecated" ): client = Client.open( str(TEST_DATA / "planetary-computer-root.json"), ignore_conformance=True, ) assert client.has_conforms_to() assert client.conforms_to(ConformanceClasses.CORE) def test_set_conforms_to_using_list_of_uris(self) -> None: client = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) client.set_conforms_to(["https://api.stacspec.org/v1.0.0-rc.2/core"]) assert client.conforms_to(ConformanceClasses.CORE) def test_add_and_remove_conforms_to_by_string(self) -> None: client = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) client.remove_conforms_to("core") assert not client.conforms_to(ConformanceClasses.CORE) client.add_conforms_to("core") assert client.conforms_to("CORE") def test_clear_all_conforms_to(self) -> None: client = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) client.clear_conforms_to() assert not client.has_conforms_to() def test_empty_conforms_to(self) -> None: client = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) client.set_conforms_to([]) assert client.has_conforms_to(), "The conformsTo field should still exist" assert not client.conforms_to(ConformanceClasses.CORE) assert not client.conforms_to(ConformanceClasses.ITEM_SEARCH) def test_no_conforms_to_falls_back_to_pystac(self) -> None: client = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) client.clear_conforms_to() with strict(): with pytest.raises(FallbackToPystac): next(client.get_collections()) @pytest.mark.vcr def test_changing_conforms_to_changes_behavior(self) -> None: with pytest.warns(NoConformsTo): client = Client.open("https://earth-search.aws.element84.com/v0") with pytest.warns(FallbackToPystac): next(client.get_collections()) client.add_conforms_to("COLLECTIONS") with pytest.warns(MissingLink, match="rel='data'"): next(client.get_collections()) @pytest.mark.vcr def test_collections_are_clients() -> None: # https://github.com/stac-utils/pystac-client/issues/548 catalog = Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1/", ) collection = catalog.get_collection("cil-gdpcir-cc-by") assert collection is not None item = collection.get_item("cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day") assert item is not None item.get_collection() @pytest.mark.vcr def test_get_items_without_ids() -> None: client = Client.open( "https://stac.eoapi.dev", ) next(client.get_items()) @pytest.mark.vcr def test_non_recursion_on_fallback() -> None: path = "https://raw.githubusercontent.com/stac-utils/pystac/v1.9.0/docs/example-catalog/catalog.json" catalog = Client.from_file(path) with pytest.warns(FallbackToPystac): [i for i in catalog.get_items()] @pytest.mark.vcr def test_fallback_strategy() -> None: """Make sure links get recreated correctly using APILayoutStrategy.""" client = Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1/", ) col = client.get_collection("landsat-c2-l2") assert col is not None item = next(col.get_items()) item_href = item.self_href col_href = col.self_href root_href = client.self_href col.links = [] item.links = [] client.add_child(col) col.add_item(item) assert col.self_href == col_href assert (col_parent := col.get_single_link("parent")) assert col_parent.href == root_href assert (col_root := col.get_single_link("root")) assert col_root.href == root_href assert item.self_href == item_href assert (item_col := item.get_single_link("collection")) assert item_col.href == col_href assert (item_parent := item.get_single_link("parent")) assert item_parent.href == col_href assert (item_root := item.get_single_link("root")) assert item_root.href == root_href def test_get_collection_fallback_strategy_for_static_catalogs() -> None: client = Client.from_file( str(TEST_DATA / "test-case-1" / "country-1" / "catalog.json") ) with pytest.warns(FallbackToPystac): area_1_1 = client.get_collection("area-1-1") assert area_1_1 is not None with pytest.warns(FallbackToPystac): area_1_2 = client.get_collection("area-1-2") assert area_1_2 is not None assert area_1_1.id == "area-1-1" assert area_1_2.id == "area-1-2" def test_get_collection_for_static_catalogs_returns_none_if_not_found() -> None: client = Client.from_file(str(TEST_DATA / "test-case-1" / "catalog.json")) with pytest.warns(FallbackToPystac): collection = client.get_collection("country-1") assert collection is None def test_get_collection_raises_if_collection_id_is_empty() -> None: client = Client.from_file(str(TEST_DATA / "test-case-1" / "catalog.json")) with pytest.raises(ValueError, match="A `collection_id` must be provided"): client.get_collection("") with pytest.raises(ValueError, match="A `collection_id` must be provided"): client.get_collection(None) @pytest.mark.vcr def test_get_collection_returns_none_if_not_found() -> None: client = Client.open(STAC_URLS["EARTH-SEARCH"]) collection = client.get_collection("foo") assert collection is None @pytest.mark.vcr def test_query_string_in_collections_url() -> None: # https://github.com/stac-utils/pystac-client/issues/745 client = Client.open("https://paituli.csc.fi/geoserver/ogc/stac/v1") client.get_collection("sentinel2-l2a") ================================================ FILE: tests/test_collection_client.py ================================================ import pytest from pystac_client import CollectionClient from pystac_client.client import Client from pystac_client.warnings import FallbackToPystac, MissingLink from .helpers import STAC_URLS class TestCollectionClient: @pytest.mark.vcr def test_instance(self) -> None: client = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) collection = client.get_collection("aster-l1t") assert isinstance(collection, CollectionClient) assert str(collection) == "" @pytest.mark.vcr def test_get_items(self) -> None: client = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) collection = client.get_collection("aster-l1t") assert collection is not None for item in collection.get_items(): assert item.collection_id == collection.id return @pytest.mark.vcr def test_get_items_with_ids(self) -> None: client = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) collection = client.get_collection("aster-l1t") ids = [ "AST_L1T_00312272006020322_20150518201805", "AST_L1T_00312272006020313_20150518201753", "AST_L1T_00312272006020304_20150518201753", ] assert collection is not None for item in collection.get_items(*ids): assert item.collection_id == collection.id assert item.id in ids @pytest.mark.vcr def test_get_item(self) -> None: client = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) collection = client.get_collection("aster-l1t") assert collection is not None item = collection.get_item("AST_L1T_00312272006020322_20150518201805") assert item assert item.id == "AST_L1T_00312272006020322_20150518201805" item = collection.get_item("for-sure-not-a-real-id") assert item is None @pytest.mark.vcr def test_get_item_with_item_search(self) -> None: client = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) collection = client.get_collection("aster-l1t") assert collection is not None client.set_conforms_to( [ "https://api.stacspec.org/v1.0.0-rc.2/core", "https://api.stacspec.org/v1.0.0-rc.2/item-search", ] ) item = collection.get_item("AST_L1T_00312272006020322_20150518201805") assert item assert item.id == "AST_L1T_00312272006020322_20150518201805" item = collection.get_item("for-sure-not-a-real-id") assert item is None with pytest.warns(FallbackToPystac): item = collection.get_item( "AST_L1T_00312272006020322_20150518201805", recursive=True ) assert item assert item.id == "AST_L1T_00312272006020322_20150518201805" @pytest.mark.vcr def test_get_queryables(self) -> None: api = Client.open(STAC_URLS["PLANETARY-COMPUTER"]) collection_client = api.get_collection("landsat-c2-l2") assert collection_client is not None assert isinstance(collection_client, CollectionClient) with pytest.warns(MissingLink, match="/queryables"): result = collection_client.get_queryables() assert "instrument" in result["properties"] assert "landsat:scene_id" in result["properties"] ================================================ FILE: tests/test_collection_search.py ================================================ import json from datetime import datetime from math import ceil from typing import Any import pystac import pytest import requests from dateutil.tz import tzutc from pytest_benchmark.fixture import BenchmarkFixture from pystac_client.client import Client from pystac_client.collection_search import ( CollectionSearch, bboxes_overlap, collection_matches, temporal_intervals_overlap, ) from pystac_client.warnings import PystacClientWarning from .helpers import STAC_URLS, read_data_file COLLECTION_SEARCH_URL = f"{STAC_URLS['SPACEBEL']}/collections" COLLECTION_EXAMPLE: dict[str, Any] = { "id": "F-TCC", "q": '"cloudless mosaics"', "datetime": "2021-09-15T00:00:00Z", } class TestCollectionPerformance: @pytest.fixture(scope="function") def single_href(self) -> str: collection_href = COLLECTION_SEARCH_URL + f"/{COLLECTION_EXAMPLE['id']}" return collection_href def test_requests(self, benchmark: BenchmarkFixture, single_href: str) -> None: response = benchmark(requests.get, single_href) assert response.status_code == 200 assert response.json()["id"] == COLLECTION_EXAMPLE["id"] def test_single_collection( self, benchmark: BenchmarkFixture, single_href: str ) -> None: collection = benchmark(pystac.Collection.from_file, single_href) assert collection.id == COLLECTION_EXAMPLE["id"] def test_single_collection_search( self, benchmark: BenchmarkFixture, single_href: str ) -> None: search = CollectionSearch(url=COLLECTION_SEARCH_URL, **COLLECTION_EXAMPLE) collection_list = benchmark(search.collection_list()) assert len(collection_list) == 1 assert collection_list[0].id == COLLECTION_EXAMPLE["id"] class TestCollectionSearch: @pytest.fixture(scope="function") def fedeo_api(self) -> Client: api_content = read_data_file("fedeo_clearinghouse.json", parse_json=True) return Client.from_dict(api_content) def test_method(self) -> None: # Default method should be POST... search = CollectionSearch(url=COLLECTION_SEARCH_URL) assert search.method == "GET" def test_method_params(self) -> None: params_in: dict[str, Any] = { "bbox": (-72, 41, -71, 42), "datetime": "2021-01-01T00:00:00Z/2021-12-31T23:59:59Z", "q": "vegetation AND biomass", } # For POST this is pass through search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=True, **params_in, ) params = search.get_parameters() assert params == search.get_parameters() # For GET requests, parameters are in query string and must be serialized search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=True, **params_in, ) params = search.get_parameters() assert search.method == "GET" assert all(key in params for key in params_in) assert all(isinstance(params[key], str) for key in params_in) @pytest.mark.vcr def test_q_results(self) -> None: search_string = "monthly cloudless mosaics" search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=True, q=f'"{search_string}"', max_collections=20, limit=10, ) results = search.collection_list() assert all(isinstance(collection, pystac.Collection) for collection in results) assert all(search_string in collection.description for collection in results) assert search.matched() == 1 @pytest.mark.vcr def test_datetime_results(self) -> None: min_datetime = datetime(2024, 1, 1, 0, 0, 1, tzinfo=tzutc()) max_datetime = datetime(2024, 1, 1, 0, 0, 10, tzinfo=tzutc()) datetime_ = "/".join( ts.isoformat().replace("+00:00", "Z") for ts in [min_datetime, max_datetime] ) search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=True, datetime=datetime_, max_collections=20, ) for collection in search.collections(): for temporal_intervals in collection.extent.temporal.intervals: assert temporal_intervals_overlap( (temporal_intervals[0], temporal_intervals[1]), (min_datetime, max_datetime), ) search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=True, datetime=(min_datetime, max_datetime), max_collections=20, ) new_results = search.collections() for collection in new_results: for temporal_intervals in collection.extent.temporal.intervals: assert temporal_intervals_overlap( (temporal_intervals[0], temporal_intervals[1]), (min_datetime, max_datetime), ) @pytest.mark.vcr def test_bbox_results(self) -> None: bbox = (-159.893, 21.843, -159.176, 22.266) search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=True, bbox=bbox, limit=10, max_collections=10, ) for collection in search.collections(): for _bbox in collection.extent.spatial.bboxes: assert bboxes_overlap(bbox, tuple(float(coord) for coord in _bbox)) @pytest.mark.vcr def test_result_paging(self) -> None: search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=True, q="sentinel", limit=10, max_collections=20, ) # Check that the current page changes on the ItemSearch instance when a new page # is requested pages = list(search.pages()) assert pages[0] != pages[1] @pytest.mark.vcr def test_matched(self) -> None: q = "sentinel" search = CollectionSearch( url=f"{STAC_URLS['EARTH-SEARCH']}/collections", collection_search_extension_enabled=False, collection_search_free_text_enabled=False, q=q, limit=4, ) assert search.matched() == 5 @pytest.mark.vcr def test_enabled_but_client_side_q(self) -> None: q = "sentinel" limit = 5 search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=False, limit=limit, max_collections=limit, q=q, ) collection_list = search.collection_list() assert len(collection_list) <= limit collection_list_dict = search.collection_list_as_dict() assert len(collection_list_dict["collections"]) == len(collection_list) for collection in search.collections(): text_fields = [] if collection.description: text_fields.append(collection.description) if collection.title: text_fields.append(collection.title) if collection.keywords: text_fields.extend(collection.keywords) assert any(q in text_field.lower() for text_field in text_fields), ( f"{collection.id} failed check" ) @pytest.mark.vcr def test_client_side_q(self) -> None: q = "sentinel" limit = 10 search = CollectionSearch( url=f"{STAC_URLS['EARTH-SEARCH']}/collections", collection_search_extension_enabled=False, collection_search_free_text_enabled=False, limit=limit, max_collections=limit, q=q, ) collection_list = search.collection_list() assert len(collection_list) <= limit for collection in search.collections(): text_fields = [] if collection.description: text_fields.append(collection.description) if collection.title: text_fields.append(collection.title) if collection.keywords: text_fields.extend(collection.keywords) assert any(q in text_field.lower() for text_field in text_fields), ( f"{collection.id} failed check" ) @pytest.mark.vcr def test_client_side_bbox(self) -> None: bbox = (60, 0, 70, 10) limit = 10 search = CollectionSearch( url=f"{STAC_URLS['EARTH-SEARCH']}/collections", collection_search_extension_enabled=False, collection_search_free_text_enabled=False, limit=limit, max_collections=limit, bbox=bbox, ) collection_list = search.collection_list() assert len(collection_list) <= limit for collection in search.collections(): assert any( bboxes_overlap(bbox, tuple(float(coord) for coord in collection_bbox)) for collection_bbox in collection.extent.spatial.bboxes ), f"{collection.id} failed check" @pytest.mark.vcr def test_client_side_datetime(self) -> None: _datetime_interval = ("2024-09-15T00:00:00+00:00", "2024-09-16T00:00:00+00:00") limit = 10 search = CollectionSearch( url=f"{STAC_URLS['EARTH-SEARCH']}/collections", collection_search_extension_enabled=False, collection_search_free_text_enabled=False, limit=limit, max_collections=limit, datetime="/".join(_datetime_interval), ) collection_list = search.collection_list() assert len(collection_list) <= limit temporal_interval = ( datetime.fromisoformat(_datetime_interval[0]), datetime.fromisoformat(_datetime_interval[1]), ) for collection in search.collections(): assert any( temporal_intervals_overlap( temporal_interval, (collection_temporal_interval[0], collection_temporal_interval[1]), ) for collection_temporal_interval in collection.extent.temporal.intervals ), f"{collection.id} failed check" def test_client_side_extra_args(self) -> None: with pytest.raises(ValueError): CollectionSearch( url=f"{STAC_URLS['EARTH-SEARCH']}/collections", collection_search_extension_enabled=False, collection_search_free_text_enabled=False, filter="title LIKE '%nope%'", ) @pytest.mark.vcr def test_result_paging_max_collections(self) -> None: max_collections = 15 limit = 10 search = CollectionSearch( url=COLLECTION_SEARCH_URL, collection_search_extension_enabled=True, collection_search_free_text_enabled=True, bbox=(-92.379, 46.662, -91.788, 46.919), limit=limit, max_collections=max_collections, ) num_pages = 0 collections = list() for page in search.pages_as_dicts(): num_pages += 1 collections.extend(page["collections"]) assert num_pages == ceil(max_collections / limit) assert len(collections) == max_collections def test_bboxes_overlap() -> None: assert bboxes_overlap( (0, 0, 2, 2), (1, 1, 3, 3), ) assert not bboxes_overlap( (0, 0, 2, 2), (3, 3, 4, 4), ) def test_temporal_intervals_overlap() -> None: assert temporal_intervals_overlap( (datetime(2024, 9, 1, tzinfo=tzutc()), datetime(2024, 9, 2, tzinfo=tzutc())), ( datetime(2024, 9, 1, 12, tzinfo=tzutc()), datetime(2024, 9, 2, 12, tzinfo=tzutc()), ), ) assert temporal_intervals_overlap( (datetime(2024, 9, 1, tzinfo=tzutc()), None), ( datetime(2024, 9, 1, 12, tzinfo=tzutc()), datetime(2024, 9, 2, 12, tzinfo=tzutc()), ), ) assert temporal_intervals_overlap( (None, None), ( datetime(2024, 9, 1, 12, tzinfo=tzutc()), datetime(2024, 9, 2, 12, tzinfo=tzutc()), ), ) assert not temporal_intervals_overlap( (datetime(2023, 9, 1, tzinfo=tzutc()), datetime(2023, 9, 2, tzinfo=tzutc())), ( datetime(2024, 9, 1, 12, tzinfo=tzutc()), datetime(2024, 9, 2, 12, tzinfo=tzutc()), ), ) assert not temporal_intervals_overlap( (datetime(2023, 9, 1, tzinfo=tzutc()), datetime(2023, 9, 2, tzinfo=tzutc())), ( datetime(2024, 9, 1, 12, tzinfo=tzutc()), None, ), ) def test_invalid_collection() -> None: # https://github.com/stac-utils/pystac-client/issues/786 data = json.loads(read_data_file("invalid-collection.json")) with pytest.warns(PystacClientWarning): collection_matches(data) ================================================ FILE: tests/test_conformance.py ================================================ import pytest from pystac_client.conformance import ConformanceClasses class TestConformanceClasses: def test_get_by_name_raises_for_invalid_names(self) -> None: """Test get_by_name raises ValueError for invalid conformance class names.""" with pytest.raises(ValueError, match="Invalid conformance class 'invalid'"): ConformanceClasses.get_by_name("invalid") with pytest.raises(ValueError, match="Invalid conformance class 'nonexistent'"): ConformanceClasses.get_by_name("nonexistent") with pytest.raises(ValueError, match="Invalid conformance class ''"): ConformanceClasses.get_by_name("") def test_get_by_name_valid(self) -> None: """Test get_by_name with valid conformance class names.""" assert ConformanceClasses.get_by_name("core") == ConformanceClasses.CORE assert ConformanceClasses.get_by_name("CORE") == ConformanceClasses.CORE def test_valid_uri_property(self) -> None: """Test valid_uri property returns correct URI pattern.""" assert ( ConformanceClasses.CORE.valid_uri == "https://api.stacspec.org/v1.0.*/core" ) def test_pattern_property(self) -> None: """Test pattern property returns compiled regex.""" core_pattern = ConformanceClasses.CORE.pattern assert core_pattern.match("https://api.stacspec.org/v1.0.0/core") ================================================ FILE: tests/test_free_text.py ================================================ from pystac_client.free_text import ( sqlite_text_search, ) def test_sqlite_single_term() -> None: query = "sentinel" assert sqlite_text_search(query, {"description": "The sentinel node was true"}) assert not sqlite_text_search(query, {"description": "No match here"}) def test_sqlite_special_characters() -> None: query = "sentinel-2" assert sqlite_text_search(query, {"description": "The sentinel-2 node was true"}) assert not sqlite_text_search(query, {"description": "No, just sentinel-1 here"}) query = "sentinel+2" assert sqlite_text_search(query, {"description": "The sentinel+2 node was true"}) assert not sqlite_text_search(query, {"description": "No, just sentinel+1 here"}) query = "sentinel@2" assert sqlite_text_search(query, {"description": "The sentinel@2 node was true"}) assert not sqlite_text_search(query, {"description": "No, just sentinel@1 here"}) def test_sqlite_exact_phrase() -> None: query = '"climate model"' assert sqlite_text_search(query, {"description": "The climate model is impressive"}) assert not sqlite_text_search( query, {"description": "This model is for climate modeling"} ) # an exact phrase with a comma inside query = '"models, etc"' assert sqlite_text_search( query, {"description": "Produced with equations, and models, etc."} ) assert not sqlite_text_search(query, {"description": "Produced with models"}) def test_sqlite_and_terms_default() -> None: query = "climate model" assert sqlite_text_search( query, { "description": "Climate change is a significant challenge", "keywords": "model, prediction", }, ) assert sqlite_text_search( query, {"description": "The model was developed using climate observation data"} ) assert not sqlite_text_search(query, {"description": "This is an advanced model"}) assert not sqlite_text_search(query, {"description": "No relevant terms here"}) def test_sqlite_or_terms_explicit() -> None: query = "climate OR model" assert sqlite_text_search(query, {"description": "Climate discussion"}) assert sqlite_text_search(query, {"description": "FPGA model creation"}) assert not sqlite_text_search(query, {"description": "No matching term here"}) def test_sqlite_or_terms_commas() -> None: query = "climate,model" assert sqlite_text_search(query, {"description": "Climate change is here"}) assert sqlite_text_search(query, {"description": "They built a model train"}) assert sqlite_text_search(query, {"description": "It's a climate model!"}) assert not sqlite_text_search( query, {"description": "It's a mathematical equation"} ) def test_sqlite_and_terms() -> None: query = "climate AND model" assert sqlite_text_search(query, {"description": "The climate model is impressive"}) assert not sqlite_text_search( query, {"description": "This climate change discussion"} ) assert not sqlite_text_search(query, {"description": "Advanced model system"}) def test_sqlite_parentheses_grouping() -> None: query = "(quick OR brown) AND fox" assert sqlite_text_search(query, {"description": "The quick brown fox"}) assert sqlite_text_search(query, {"description": "A quick fox jumps"}) assert sqlite_text_search(query, {"description": "brown bear and a fox"}) assert not sqlite_text_search(query, {"description": "The fox is clever"}) query = "(quick AND brown) OR (fast AND red)" assert sqlite_text_search(query, {"description": "quick brown fox"}) assert sqlite_text_search(query, {"description": "fast red car"}) assert not sqlite_text_search(query, {"description": "quick red car"}) def test_sqlite_inclusions_exclusions() -> None: query = "quick +brown -fox" assert sqlite_text_search(query, {"description": "The quick brown bear"}) assert not sqlite_text_search(query, {"description": "The quick fox"}) assert not sqlite_text_search(query, {"description": "The quickest"}) assert sqlite_text_search(query, {"description": "A quick light brown jumper"}) def test_sqlite_partial_match() -> None: query = "climat" assert not sqlite_text_search(query, {"description": "climatology"}) assert not sqlite_text_search(query, {"description": "climate"}) assert not sqlite_text_search(query, {"description": "climbing"}) ================================================ FILE: tests/test_item_search.py ================================================ import operator import urllib.parse from datetime import datetime, timedelta from typing import Any import pystac import pytest import requests from dateutil.tz import tzutc from pytest_benchmark.fixture import BenchmarkFixture from requests_mock import Mocker from pystac_client import Client from pystac_client.item_search import ItemSearch from .helpers import STAC_URLS, read_data_file SEARCH_URL = f"{STAC_URLS['PLANETARY-COMPUTER']}/search" INTERSECTS_EXAMPLE = { "type": "Polygon", "coordinates": [ [ [-73.21, 43.99], [-73.21, 44.05], [-73.12, 44.05], [-73.12, 43.99], [-73.21, 43.99], ] ], } ITEM_EXAMPLE: dict[str, Any] = {"collections": "io-lulc", "ids": "60U-2020"} class TestItemPerformance: @pytest.fixture(scope="function") def single_href(self) -> str: item_href = ( "https://planetarycomputer.microsoft.com/api/stac/v1/collections/" f"{ITEM_EXAMPLE['collections']}/items/{ITEM_EXAMPLE['ids']}" ) return item_href def test_requests(self, benchmark: BenchmarkFixture, single_href: str) -> None: response = benchmark(requests.get, single_href) assert response.status_code == 200 assert response.json()["id"] == ITEM_EXAMPLE["ids"] def test_single_item(self, benchmark: BenchmarkFixture, single_href: str) -> None: item = benchmark(pystac.Item.from_file, single_href) assert item.id == ITEM_EXAMPLE["ids"] def test_single_item_search( self, benchmark: BenchmarkFixture, single_href: str ) -> None: search = ItemSearch(url=SEARCH_URL, **ITEM_EXAMPLE) item_collection = benchmark(search.item_collection()) assert len(item_collection.items) == 1 assert item_collection.items[0].id == ITEM_EXAMPLE["ids"] class TestItemSearch: @pytest.fixture(scope="function") def astraea_api(self) -> Client: api_content = read_data_file("astraea_api.json", parse_json=True) return Client.from_dict(api_content) def test_method(self) -> None: # Default method should be POST... search = ItemSearch(url=SEARCH_URL) assert search.method == "POST" # "method" argument should take precedence over presence of "intersects" search = ItemSearch(url=SEARCH_URL, method="GET", intersects=INTERSECTS_EXAMPLE) assert search.method == "GET" def test_method_params(self) -> None: params_in: dict[str, Any] = { "bbox": (-72, 41, -71, 42), "ids": ( "idone", "idtwo", ), "collections": ("collectionone",), "intersects": INTERSECTS_EXAMPLE, } # For POST this is pass through search = ItemSearch(url=SEARCH_URL, **params_in) params = search.get_parameters() assert params == search.get_parameters() # For GET requests, parameters are in query string and must be serialized search = ItemSearch(url=SEARCH_URL, method="GET", **params_in) params = search.get_parameters() assert all(key in params for key in params_in) assert all(isinstance(params[key], str) for key in params_in) @pytest.mark.vcr def test_results(self) -> None: search = ItemSearch( url=SEARCH_URL, collections="naip", max_items=20, limit=10, ) results = search.items() assert all(isinstance(item, pystac.Item) for item in results) @pytest.mark.vcr def test_ids_results(self) -> None: ids = [ "S2B_MSIL2A_20210610T115639_N0212_R066_T33XXG_20210613T185024.SAFE", "fl_m_2608004_nw_17_060_20191215_20200113", ] search = ItemSearch( url=SEARCH_URL, collections=["naip"], ids=ids, ) results = list(search.items()) assert len(results) == 1 assert all(item.id in ids for item in results) @pytest.mark.vcr def test_datetime_results(self) -> None: min_datetime = datetime(2019, 1, 1, 0, 0, 1, tzinfo=tzutc()) max_datetime = datetime(2019, 1, 1, 0, 0, 10, tzinfo=tzutc()) datetime_ = "2019-01-01T00:00:01Z/2019-01-01T00:00:10Z" search = ItemSearch( url=SEARCH_URL, datetime=datetime_, collections=["sentinel-2-l2a"], max_items=20, ) for item in search.items(): assert item.datetime is not None assert ( min_datetime <= item.datetime <= (max_datetime + timedelta(seconds=1)) ) search = ItemSearch( url=SEARCH_URL, datetime=(min_datetime, max_datetime), collections=["sentinel-2-l2a"], max_items=20, ) new_results = search.items() for item in new_results: assert item.datetime is not None assert ( min_datetime <= item.datetime <= (max_datetime + timedelta(seconds=1)) ) @pytest.mark.vcr def test_intersects_results(self) -> None: # GeoJSON-like dict intersects_dict = { "type": "Polygon", "coordinates": [ [ [-73.21, 43.99], [-73.21, 44.05], [-73.12, 44.05], [-73.12, 43.99], [-73.21, 43.99], ] ], } search = ItemSearch( url=SEARCH_URL, intersects=intersects_dict, collections="naip" ) results = list(search.items()) assert len(results) == 42 # Geo-interface object class MockGeoObject: @property def __geo_interface__(self) -> dict[str, Any]: return intersects_dict intersects_obj = MockGeoObject() search = ItemSearch( url=SEARCH_URL, intersects=intersects_obj, collections="naip" ) new_results = search.items() assert all(isinstance(item, pystac.Item) for item in new_results) def test_get_with_query(self, requests_mock: Mocker) -> None: requests_mock.get( ( f"{SEARCH_URL}?query=%7B%22eo%3Acloud_cover%22%3A%7B%22gte%22%3A0%2C%22lte%22%3A10%7D%7D" ), status_code=200, json={"features": [{"foo": "bar"}], "links": []}, ) items = list( ItemSearch( url=SEARCH_URL, method="GET", query={"eo:cloud_cover": {"gte": 0, "lte": 10}}, ).items_as_dicts() ) assert len(items) == 1 @pytest.mark.vcr def test_result_paging(self) -> None: search = ItemSearch( url=SEARCH_URL, bbox=(-73.21, 43.99, -73.12, 44.05), collections="naip", limit=10, max_items=20, ) # Check that the current page changes on the ItemSearch instance when a new page # is requested pages = list(search.pages()) assert pages[0] != pages[1] assert pages[0].items != pages[1].items @pytest.mark.vcr def test_result_paging_max_items(self) -> None: search = ItemSearch( url=SEARCH_URL, collections="naip", limit=10, max_items=25, ) num_pages = 0 items = list() for page in search.pages_as_dicts(): num_pages += 1 items.extend(page["features"]) assert num_pages == 3 assert len(items) == 25 @pytest.mark.vcr def test_item_collection(self) -> None: search = ItemSearch( url=SEARCH_URL, bbox=(-73.21, 43.99, -73.12, 44.05), collections="naip", limit=10, max_items=20, ) item_collection = search.item_collection() assert isinstance(item_collection, pystac.ItemCollection) assert len(item_collection) == 20 @pytest.mark.vcr @pytest.mark.parametrize( "method, alternative, is_sequence, is_pystac", [ ("get_item_collections", "pages", True, True), ("item_collections", "pages", True, True), ("get_items", "items", True, True), ("get_all_items", "item_collection", False, True), ("get_all_items_as_dict", "item_collection_as_dict", False, False), ], ) def test_deprecations( self, method: str, alternative: str, is_sequence: bool, is_pystac: bool ) -> None: search = ItemSearch( url=SEARCH_URL, bbox=(-73.21, 43.99, -73.12, 44.05), collections="naip", limit=10, max_items=20, ) with pytest.warns(FutureWarning, match=method): result = operator.methodcaller(method)(search) expected = operator.methodcaller(alternative)(search) if is_sequence: result = list(result) expected = list(expected) if is_pystac: result = [x.to_dict() for x in result] expected = [x.to_dict() for x in expected] else: if is_pystac: result = result.to_dict() expected = expected.to_dict() assert result == expected @pytest.mark.vcr def test_items_as_dicts(self) -> None: search = ItemSearch( url=SEARCH_URL, bbox=(-73.21, 43.99, -73.12, 44.05), collections="naip", limit=10, max_items=20, ) assert len(list(search.items_as_dicts())) == 20 class TestItemSearchQuery: @pytest.mark.vcr def test_query_shortcut_syntax(self) -> None: search = ItemSearch( url=SEARCH_URL, collections="naip", bbox=(-73.21, 43.99, -73.12, 44.05), query=["gsd=0.6"], max_items=1, ) items1 = list(search.items()) search = ItemSearch( url=SEARCH_URL, collections="naip", bbox=(-73.21, 43.99, -73.12, 44.05), query={"gsd": {"eq": 0.6}}, max_items=1, ) items2 = list(search.items()) assert len(items1) == 1 assert len(items2) == 1 assert items1[0].id == items2[0].id @pytest.mark.vcr def test_query_json_syntax(self) -> None: # with a list of json strs (format of CLI argument to ItemSearch) search = ItemSearch( url=SEARCH_URL, collections="naip", bbox=(-73.21, 43.99, -73.12, 44.05), query=['{"gsd": { "gte": 0, "lte": 1 }}'], max_items=1, ) item1 = list(search.items())[0] assert item1.properties["gsd"] <= 1 # with a single dict search = ItemSearch( url=SEARCH_URL, collections="naip", bbox=(-73.21, 43.99, -73.12, 44.05), query={"gsd": {"gte": 0, "lte": 1}}, max_items=1, ) item2 = list(search.items())[0] assert item2.properties["gsd"] <= 1 assert item1.id == item2.id def test_query_json_syntax() -> None: item_search = ItemSearch("") assert item_search._format_query( ['{"eo:cloud_cover": { "gte": 0, "lte": 1 }}'] ) == {"eo:cloud_cover": {"gte": 0, "lte": 1}} assert item_search._format_query({"eo:cloud_cover": {"gte": 0, "lte": 1}}) == { "eo:cloud_cover": {"gte": 0, "lte": 1} } assert item_search._format_query(["eo:cloud_cover<=1"]) == { "eo:cloud_cover": {"lte": "1"} } assert item_search._format_query(["eo:cloud_cover<=1", "eo:cloud_cover>0"]) == { "eo:cloud_cover": {"lte": "1", "gt": "0"} } def test_url_with_query_parameter() -> None: # https://github.com/stac-utils/pystac-client/issues/522 search = ItemSearch( url="http://pystac-client.test", query={"eo:cloud_cover": {"lt": 42}} ) url = urllib.parse.urlparse(search.url_with_parameters()) query = urllib.parse.parse_qs(url.query) assert query["query"] == [r'{"eo:cloud_cover":{"lt":42}}'] @pytest.mark.vcr def test_multiple_collections() -> None: search = ItemSearch( url="https://earth-search.aws.element84.com/v1/search", collections=["sentinel-2-l2a", "landsat-c2-l2"], intersects={"type": "Point", "coordinates": [-105.1019, 40.1672]}, datetime="2023-10-08", ) collections = {item.collection_id for item in search.items()} assert collections == {"sentinel-2-l2a", "landsat-c2-l2"} def test_naive_datetime() -> None: search = ItemSearch( url="https://earth-search.aws.element84.com/v1/search", datetime=datetime(2024, 5, 14, 4, 25, 42, tzinfo=None), method="POST", ) assert search.get_parameters()["datetime"] == "2024-05-14T04:25:42Z" @pytest.mark.vcr def test_fields() -> None: search = ItemSearch( url="https://earth-search.aws.element84.com/v1/search", collections=["sentinel-2-c1-l2a"], intersects={"type": "Point", "coordinates": [-105.1019, 40.1672]}, max_items=1, fields=["-geometry", "-assets", "-links"], ) item = next(search.items_as_dicts()) assert "geometry" not in item assert "assets" not in item assert "links" not in item def test_feature() -> None: search = ItemSearch( url="https://earth-search.aws.element84.com/v1/search", intersects={ "type": "Feature", "geometry": {"type": "Point", "coordinates": [-105.1019, 40.1672]}, }, ) assert search.get_parameters()["intersects"] == { "type": "Point", "coordinates": [-105.1019, 40.1672], } ================================================ FILE: tests/test_stac_api_io.py ================================================ import typing from pathlib import Path from urllib.parse import parse_qs, urlsplit import pystac import pytest from pytest import MonkeyPatch from requests_mock.mocker import Mocker from pystac_client.exceptions import APIError from pystac_client.stac_api_io import StacApiIO from .helpers import STAC_URLS class TestSTAC_IOOverride: @pytest.mark.vcr def test_request_input(self) -> None: stac_api_io = StacApiIO() response = stac_api_io.read_text(STAC_URLS["PLANETARY-COMPUTER"]) assert isinstance(response, str) @pytest.mark.vcr def test_str_input(self) -> None: stac_api_io = StacApiIO() response = stac_api_io.read_text(STAC_URLS["PLANETARY-COMPUTER"]) assert isinstance(response, str) @pytest.mark.vcr def test_http_error(self) -> None: stac_api_io = StacApiIO() # Attempt to access an authenticated endpoint with pytest.raises(APIError) as excinfo: stac_api_io.read_text(f"{STAC_URLS['MLHUB']}/search") assert isinstance(excinfo.value, APIError) def test_local_file(self, tmp_path: Path) -> None: stac_api_io = StacApiIO() test_file = tmp_path / "test.txt" with test_file.open("w") as dst: dst.write("Hi there!") response = stac_api_io.read_text(str(test_file)) assert response == "Hi there!" def test_conformance_deprecated(self) -> None: with pytest.warns(FutureWarning, match="`conformance` option is deprecated"): stac_api_io = StacApiIO(conformance=[]) assert not hasattr(stac_api_io, "conformance") def test_custom_headers(self, requests_mock: Mocker) -> None: """Checks that headers passed to the init method are added to requests.""" header_name = "x-my-header" header_value = "Some Value" url = "https://some-url.com/some-file.json" stac_api_io = StacApiIO(headers={header_name: header_value}) requests_mock.get(url, status_code=200, json={}) stac_api_io.read_json(url) history = requests_mock.request_history assert len(history) == 1 assert header_name in history[0].headers assert history[0].headers[header_name] == header_value def test_modifier(self, requests_mock: Mocker) -> None: """Verify the modifier is correctly called with a returned object.""" header_name = "x-my-header" header_value = "Some Value" url = "https://some-url.com/some-file.json" def custom_modifier(request: typing.Any) -> typing.Any | None: request.headers["x-pirate-name"] = "yellowbeard" return request stac_api_io = StacApiIO( headers={header_name: header_value}, request_modifier=custom_modifier ) requests_mock.get(url, status_code=200, json={}) stac_api_io.read_json(url) history = requests_mock.request_history assert len(history) == 1 assert header_name in history[0].headers assert history[0].headers["x-pirate-name"] == "yellowbeard" def test_modifier_noreturn(self, requests_mock: Mocker) -> None: """Verify the modifier is correctly called when None is returned.""" header_name = "x-my-header" header_value = "Some Value" url = "https://some-url.com/some-file.json" def custom_modifier(request: typing.Any) -> typing.Any | None: request.headers["x-pirate-name"] = "yellowbeard" return None stac_api_io = StacApiIO( headers={header_name: header_value}, request_modifier=custom_modifier ) requests_mock.get(url, status_code=200, json={}) stac_api_io.read_json(url) history = requests_mock.request_history assert len(history) == 1 assert header_name in history[0].headers assert history[0].headers["x-pirate-name"] == "yellowbeard" def test_custom_query_params(self, requests_mock: Mocker) -> None: """Checks that query params passed to the init method are added to requests.""" init_qp_name = "my-param" init_qp_value = "something" url = "https://some-url.com/some-file.json" stac_api_io = StacApiIO(parameters={init_qp_name: init_qp_value}) request_qp_name = "another-param" request_qp_value = "another_value" requests_mock.get(url, status_code=200, json={}) stac_api_io.read_json(url, parameters={request_qp_name: request_qp_value}) history = requests_mock.request_history assert len(history) == 1 actual_qs = urlsplit(history[0].url).query actual_qp = parse_qs(actual_qs) # Check that the param from the init method is present assert init_qp_name in actual_qp assert len(actual_qp[init_qp_name]) == 1 assert actual_qp[init_qp_name][0] == init_qp_value # Check that the param from the request is present assert request_qp_name in actual_qp assert len(actual_qp[request_qp_name]) == 1 assert actual_qp[request_qp_name][0] == request_qp_value def test_write(self, tmp_path: Path) -> None: stac_api_io = StacApiIO() test_file = tmp_path / "test.txt" stac_api_io.write_text_to_href(str(test_file), "Hi there!") with open(test_file) as file: data = file.read() assert data == "Hi there!" @pytest.mark.parametrize( ("attribute", "endpoint"), (("features", "search"), ("collections", "collections")), ) def test_stop_on_empty_page( self, requests_mock: Mocker, attribute: str, endpoint: str ) -> None: url = f"https://pystac-client.test/{endpoint}" requests_mock.get( url, status_code=200, json={ attribute: [{"foo": "bar"}], "links": [ { "rel": "next", "href": url + "?token=baz", } ], }, ) requests_mock.get( url + "?token=baz", status_code=200, json={ attribute: [], "links": [ { "rel": "next", "href": url + "?token=bam", } ], }, ) requests_mock.get( url + "?token=bam", status_code=500, ) stac_api_io = StacApiIO() pages = list(stac_api_io.get_pages(url)) assert len(pages) == 1 assert pages[0][attribute][0]["foo"] == "bar" @pytest.mark.parametrize( ("attribute", "endpoint"), (("features", "search"), ("collections", "collections")), ) def test_stop_on_attributeless_page( self, requests_mock: Mocker, attribute: str, endpoint: str ) -> None: url = f"https://pystac-client.test/{endpoint}" requests_mock.get( url, status_code=200, json={ attribute: [{"foo": "bar"}], "links": [ { "rel": "next", "href": url + "?token=baz", } ], }, ) requests_mock.get( url + "?token=baz", status_code=200, json={ "links": [ { "rel": "next", "href": url + "?token=bam", } ], }, ) requests_mock.get( url + "?token=bam", status_code=500, ) stac_api_io = StacApiIO() pages = list(stac_api_io.get_pages(url)) assert len(pages) == 1 assert pages[0][attribute][0]["foo"] == "bar" @pytest.mark.parametrize( ("attribute", "endpoint"), (("features", "search"), ("collections", "collections")), ) def test_stop_on_first_empty_page( self, requests_mock: Mocker, attribute: str, endpoint: str ) -> None: url = f"https://pystac-client.test/{endpoint}" requests_mock.get( url, status_code=200, json={ attribute: [], "links": [ { "rel": "next", "href": url + "?token=bam", } ], }, ) requests_mock.get(url + "?token=bam", status_code=500) stac_api_io = StacApiIO() pages = list(stac_api_io.get_pages(url)) assert len(pages) == 0 @pytest.mark.vcr def test_timeout_smoke_test(self) -> None: # Testing timeout behavior is hard, so we just have a simple smoke test to make # sure that providing a timeout doesn't break anything. stac_api_io = StacApiIO(timeout=42) response = stac_api_io.read_text(STAC_URLS["PLANETARY-COMPUTER"]) assert isinstance(response, str) @pytest.mark.parametrize("name", ("REQUESTS_CA_BUNDLE", "CURL_CA_BUNDLE")) def test_respect_env_for_certs(self, monkeypatch: MonkeyPatch, name: str) -> None: monkeypatch.setenv(name, "/not/a/real/file") stac_api_io = StacApiIO() with pytest.raises(APIError): stac_api_io.request("https://earth-search.aws.element84.com/v1/") @pytest.mark.vcr def test_stac_io_in_pystac() -> None: # https://github.com/stac-utils/pystac-client/issues/706 collection = pystac.read_file( href="tests/data/planetary-computer-collection.json", stac_io=StacApiIO(timeout=42), ) root = collection.get_root() assert root stac_io = root._stac_io assert isinstance(stac_io, StacApiIO) assert stac_io.timeout == 42 ================================================ FILE: tests/test_warnings.py ================================================ import pytest from pystac_client import Client from pystac_client.warnings import DoesNotConformTo, ignore, strict from .helpers import TEST_DATA class TestWarningContextManagers: def test_ignore_context_manager(self) -> None: """Test that ignore() context manager suppresses warnings.""" api = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) api.remove_conforms_to("COLLECTION_SEARCH") with ignore(): api.collection_search(limit=10, max_collections=10, q="test") def test_strict_context_manager(self) -> None: """Test that strict() context manager converts warnings to exceptions.""" api = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) api.remove_conforms_to("COLLECTION_SEARCH") with strict(): with pytest.raises(DoesNotConformTo, match="COLLECTION_SEARCH"): api.collection_search(limit=10, max_collections=10, q="test") def test_ignore_context_manager_cleanup(self) -> None: """Test that ignore() properly restores warning filters after exit.""" api = Client.from_file(str(TEST_DATA / "planetary-computer-root.json")) api.remove_conforms_to("COLLECTION_SEARCH") with ignore(): api.collection_search(limit=10, max_collections=10, q="test") with strict(): with pytest.raises(DoesNotConformTo, match="COLLECTION_SEARCH"): api.collection_search(limit=10, max_collections=10, q="test")