[ { "path": ".gitignore", "content": "# python internals\n*.ipynb_checkpoints\n*__pycache__*\n\n# local development\n*.img*\n*.envi*\n*.hdr*\n*.nc*\n*.tif*\n*.pptx*\n/data/example_out.csv\n/data/isla_gaviota_3.geojson\n/data/outputs\n/data/envi\n/python/daac_data_download_python\n/python/emit_utils\n/ignore\n/data/emit_asset_urls.txt\n/.vscode/\n" }, { "path": "CHANGE_LOG.md", "content": "# Change Log\n\nAll notable changes to this project will be documented in this file.\nThe format is based on [Keep a Changelog](http://keepachangelog.com/)\nand this project adheres to [Semantic Versioning](http://semver.org/).\n_________________________________________________________________________\n\n## 2024-06-17\n\n> ### Added\n>\n> - Add CITATION.cff file to provide a citation for the repository\n\n> ### Changed\n>\n> - Removed use of `nan` for fill_values to be more consistent with EMIT datasets. This means that users will have to manually assign them before plotting.\n> - Updated notebooks to include the new fill_value behavior, and assign them to `np.nan` for visualizations\n> - Replaced `raw_spatial_crop` with `spatial_subset` function to subset EMIT data spatially before applying orthorectification\n> - Fixed typos in Earthdata Search Guide\n\n## 2024-03-22\n\n> ### Added\n>\n> - `geojson` with plume bounding box for `Visualizing Methane Plume Timeseries` notebook\n\n> ### Changed\n>\n> - Updates to instructions in `Visualizing Methane Plume Timeseries` notebook\n> - Remove horizontal lines from notebooks for web-book creation\n\n## 2024-03-13\n\n> ### Added\n>\n> - Added new `Visualizing Methane Plume Timeseries` notebook \n> - Added new `Generating Methane Spectral Fingerprint` notebook\n> - Added new `tutorial_utils.py` module which has functions specific to the new CH4 notebooks \n\n> ### Changed\n>\n> - Updated `setup_instructions.md` to provide more up to date Python setup instructions \n> - Updated `README.md` to include new notebooks \n> - Added some functions to `emit_tools.py` to support the new methane tutorial notebooks\n> - Minor updates to `How to Extract Area` and `How to Extract Points` notebooks to improve visualizations\n\n## 2023-12-01\n\n> ### Changed\n>\n> - Applied automatic formatting to `emit_tools.py` \n> - Fixed GLT index adjustment in `raw_spatial_crop` function\n> - Reimplemented `PointerXY` stream so interactive plot from `Exploring EMIT L2A Reflectance` shows spectra at current cursor location\n\n## 2023-11-27\n\n> ### Changed\n>\n> - Updated `emit_tools.py` to fix an issue with `raw_spatial_crop`\n> - Updated some plotting visuals within notebooks\n> - Improved interactive plots in `Exploring_EMIT_L2A_Reflectance`\n> - Renamed `CONTRIBUTE.md` to `CONTRIBUTING.md`\n\n## 2023-10-23\n\n> ### Changed\n>\n> - Updated `emit_tools.py` to fix an issue with application of the GLT to elevation\n\n## 2023-10-12\n\n> ### Changed\n>\n> - Updated data download method in how-to and tutorial notebooks\n\n## 2023-08-04\n\n> ### Changed\n>\n> - Updated `How to Direct S3 Access` notebook to include `earthaccess`\n\n## 2023-07-06\n\n> ### Added\n>\n> - `How_to_find_and_access_EMIT_data` Notebook - A guide for how to use `earthaccess` Python library to find and open/download EMIT data.\n\n> ### Changed\n>\n> - **Default behavior of `emit_xarray` function from `emit_tools.py` no longer orthorectifies, must add parameter `ortho=True`**\n> - Removed 'bands' dimension in favor of 'wavelengths' for 'radiance' and 'reflectance' when using `emit_xarray` function from `emit_tools.py`\n> - Updated `write_envi` function from `emit_tools.py`, it should work for L1A,L1B, and L2A products.\n> - various other minor changes to `emit_tools.py`\n> - Updated notebooks to support changes made to `emit_tools.py`\n> - Improved RGB Image creation and brightness adjustment in `Exploring_EMIT_L2A_Reflectance` Tutorial\n> - Each notebook now uses the `earthaccess` python library to handle NASA Earthdata Login and downloads of needed files\n> - Updated `setup_instructions.md` and recommended python environment `emit_tutorials_windows.yml`\n> - Updated `setup_instructions.md` to recommend mamba\n\n## 2023-05-30\n\n> ### Changed\n>\n> - Restructured repository layout to match LP DAAC standards\n\n## 2023-05-16\n>\n> ### Changed\n>\n> - Updated `emit_tools.py` to work for HTTPFileSystem URIs\n\n## 2023-04-11\n>\n> ### Changed\n>\n> - The orthorectification process within `emit_tools.py` was creating a lat/lon grid based upon the upper left pixel coordinates. Corrected this to be pixel centers.\n\n## 2023-03-29\n \n> ### Added\n>\n> - CHANGE_LOG.md\n> - CODE_OF_CONDUCT.md\n> - CONTRIBUTING.md\n> - LICENSE.md\n>\n> ### Changed\n>\n> - content in CHANGE_LOG.md\n>\n> ### Fixed\n>\n> -\n" }, { "path": "CITATION.cff", "content": "cff-version: 1.2.0\ntitle: EMIT Data Resources\nmessage: >-\n If you use this collection of resources in your research\n we would appreciate an appropriate citation.\ntype: software\nauthors:\n - given-names: Erik A\n family-names: Bolch\n affiliation: >-\n KBR, Inc., Contractor to the U.S. Geological Survey,\n Earth Resources Observation and Science Center,\n National Aeronautics and Space Administration Land\n Processes Distributed Active Archive Center\n orcid: 'https://orcid.org/0000-0003-2470-4048'\n - given-names: Aaron M\n family-names: Friesz\n affiliation: >-\n KBR, Inc., Contractor to the U.S. Geological Survey,\n Earth Resources Observation and Science Center,\n National Aeronautics and Space Administration Land\n Processes Distributed Active Archive Center\n orcid: 'https://orcid.org/0000-0003-4096-3824'\n - given-names: Mahsa\n family-names: Jami\n affiliation: >-\n KBR, Inc., Contractor to the U.S. Geological Survey,\n Earth Resources Observation and Science Center,\n National Aeronautics and Space Administration Land\n Processes Distributed Active Archive Center\n orcid: 'https://orcid.org/0000-0002-3594-3004'\n - given-names: Brianna M\n family-names: Lind\n affiliation: >-\n KBR, Inc., Contractor to the U.S. Geological Survey,\n Earth Resources Observation and Science Center,\n National Aeronautics and Space Administration Land\n Processes Distributed Active Archive Center\n orcid: 'https://orcid.org/0000-0002-5306-9963'\n - given-names: Philip G\n family-names: Brodrick\n affiliation: >-\n Jet Propulsion Laboratory, California Institute of\n Technology\n orcid: 'https://orcid.org/0000-0001-9497-7661'\n - given-names: K Dana\n family-names: Chadwick\n affiliation: >-\n Jet Propulsion Laboratory, California Institute of\n Technology\n orcid: 'https://orcid.org/0000-0002-5633-4865'\n - given-names: Cole K\n family-names: Krehbiel\n affiliation: >-\n National Aeronautics and Space Administration Land\n Processes Distributed Active Archive Center,\n U.S. Geological Survey, Earth Resources \n Observation and Science Center\n orcid: 'https://orcid.org/0000-0003-2645-3449'\nrepository-code: 'https://github.com/nasa/EMIT-Data-Resources'\nabstract: >-\n This repository contains guides and Jupyter Notebooks to\n help users access and work with data from the Earth\n Surface Mineral Dust Source Investigation (EMIT) mission.\nlicense: Apache-2.0\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Code of Conduct\n\n## 1. Our Commitment\n\nWe are dedicated to fostering a respectful environment for everyone contributing to this project. We expect all participants to treat each other with respect, professionalism, and kindness.\n\n## 2. Expected Behavior\n\n- Be respectful and considerate of others.\n- Engage in constructive discussions and offer helpful feedback.\n- Gracefully accept constructive criticism.\n\n## 3. Unacceptable Behavior\n\nThe following behaviors will not be tolerated:\n\n- Harassment, discrimination, or intimidation of any kind.\n- Offensive, abusive, or derogatory language and actions.\n- Personal attacks or insults.\n- Trolling or disruptive conduct.\n- Sharing inappropriate content.\n\n## 4. Reporting Violations\nIf you experience or witness any behavior that violates this Code of Conduct, please report it by contacting the project maintainers. All reports will be reviewed confidentially.\n\n## 5. Enforcement\nViolations of this Code of Conduct may result in actions such as warnings, temporary bans, or permanent exclusion from participation at the discretion of the maintainers.\n\n## Contact Info \nEmail: \nVoice: +1-866-573-3222 \nOrganization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \nWebsite: \nDate last modified: 01-22-2025 \n\n¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I." }, { "path": "CONTRIBUTING.md", "content": "# Contributing to Our GitHub\n\nThis page is your one-stop shop for uncovering how to contribute to our Github!\n\n## We Want Your Help!\n\nNo, really, we do! Please come and participate in our community and lets do science together! Depending on your level of interaction with the Land Processes Data Active Archive Center (LP DAAC) and the LP DAAC GitHub, visitors to the site can be described as: \n- A **community member**: anyone in the open science community who visits a LP DAAC site, utilizes LP DAAC online tools, or attends a LP DAAC event.\n- A **participant**: anyone who posts a comment or poses a question in the *GitHub Discussion Space*, reports a site bug or requests a new resource in *GitHub Issues*, or attends a LP DAAC event and utilizes any virtual chat features during that event. \n- A **contributor**: anyone who forks this GitHub repository and submits pull requests to make additions or changes to the posted content.\n\nEveryone reading this page is a community member, and we hope everyone will post comments and join discussions as a participant. Contributors are welcome, particularly to help find and point to other open science resources. \n\n## Ways to Contribute to the GitHub\nThere are two main ways to contribute to the LP DAAC GitHub. \n- **Suggest a change, addition, or deletion to what is already on the GitHub using [Issues](https://github.com/nasa/Transform-to-Open-Science/issues).** Issues can be about any LP DAAC plans, timelines, and content. \n - Before submitting a new [issue](https://github.com/nasa/LPDAAC-Data-Resources/issues), check to make sure [a similar issue isn't already open](https://github.com/nasa/LPDAAC-Data-Resources/issues). If one is, contribute to that issue thread with your feedback.\n - When submitting a bug report, please try to provide as much detail as possible, i.e. a screenshot or [gist](https://gist.github.com/) that demonstrates the problem, the technology you are using, and any relevant links. \n - Issues labeled :sparkles:[`help wanted`](https://github.com/nasa/LPDAAC-Data-Resources/labels/help%20wanted):sparkles: make it easy for you to find ways you can contribute today. \n- **Become a contributor!** [Fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo) the repository and [make commits](https://docs.github.com/en/get-started/quickstart/contributing-to-projects#making-and-pushing-changes) to add resources and additional materials. Here are some ways you can contribute:\n - by reporting bugs\n - by suggesting new features\n - by translating content to a new language\n - by writing or editing documentation\n - by writing specifications\n - by writing code and documentation (**no pull request is too small**: fix typos, add code comments, clean up inconsistent whitespace)\n - by closing [issues](https://github.com/nasa/LPDAAC-Data-Resources/issues)\n\nIn the spirit of open source software, everyone is encouraged to help improve this project!\n \n## New to GitHub? Start here!\nYou can [sign up for GitHub here](https://github.com/)! The NASA Transform to Open Science Team has made a short video demonstrating how to make an easy pull request [here](https://youtu.be/PHoScPeMWHI).\n\nFor a more in-depth start, we suggest *[Getting Started with Git and GitHub: The Complete Beginner’s Guide](https://towardsdatascience.com/getting-started-with-git-and-github-6fcd0f2d4ac6)* and *[The Beginners Guide to Git and GitHub](https://www.freecodecamp.org/news/the-beginners-guide-to-git-github/)*. We've summarized some of the most important points below.\n\n### Making a Change\n*This section is attributed to [NumFOCUS](https://github.com/numfocus/getting-started-with-open-source/blob/master/CONTRIBUTING.md) and [Adrienne Friend](https://github.com/adriennefriend/imposter-syndrome-disclaimer).*\n\nOnce you've identified something you'd like to help with you're ready to make a change to the project repository!\n\n1. First, describe what you're planning to do as a comment to the issue, (and this might mean making a new issue).\n\n [This blog](https://www.igvita.com/2011/12/19/dont-push-your-pull-requests/) is a nice explanation of why putting this work in up front is so useful to everyone involved.\n\n2. Fork this repository to your profile.\n\n You can now do whatever you want with this copy of the project. You won't mess up anyone else's work so you're super safe.\n\n Make sure to [keep your fork up to date]( https://github.com/KirstieJane/STEMMRoleModels/wiki/Syncing-your-fork-to-the-original-repository-via-the-browser) with the master repository.\n\n3. Make the changes you've discussed.\n\n Try to keep the changes focused rather than changing lots of things at once. If you feel tempted to branch out then please *literally* branch out: create separate branches for different updates to make the next step much easier!\n\n4. Submit a pull request.\n\n A member of the executive team will review your changes, have a bit of discussion and hopefully merge them in! \n \n N.B. you don't have to be ready to merge to make a pull request! We encourage you to submit a pull request as early as you want to. They help us to keep track of progress and help you to get earlier feedback.\n\n## Development Model\n\nFor accepting new contributions, TOPS uses the [forking workflow](https://guides.github.com/activities/forking/). As the first step of your contribution, you'll want to fork this repository, make a local clone of it, add your contribution, and then create a pull request back to the LP DAAC repository. \n\nAll documentation should be written using Markdown and Github Markdown-supported HTML. \n\n## Attribution\nThese contributing guidelines are adapted from the NASA Transform to Open Science github, available at https://github.com/nasa/Transform-to-Open-Science/blob/main/CONTRIBUTING.md.\n" }, { "path": "LICENSE", "content": " Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. 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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "README.md", "content": "# EMIT-Data-Resources \n\nWelcome to the EMIT-Data-Resources repository. This repository provides guides, short how-tos, and tutorials to help users access and work with data from the [Earth Surface Mineral Dust Source Investigation (EMIT) mission](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/emit-overview/). In the interest of open science this repository has been made public but is still under active development. All notebooks and scripts should be functional, however, changes or additions may be made. Make sure to consult the [CHANGE_LOG.md](CHANGE_LOG.md) for the most recent changes to the repository. Contributions from all parties are welcome. \n\n---\n\n## EMIT Background \n\nThe [EMIT](https://earth.jpl.nasa.gov/emit/) Project delivers space-based measurements of surface mineralogy of the Earth’s arid dust source regions. These measurements are used to initialize the compositional makeup of dust sources in Earth System Models (ESMs). The dust cycle, which describe the generation, lofting, transport, and deposition of mineral dust, plays an important role in ESMs. Dust composition is presently the largest uncertainty factor in quantifying the magnitude of aerosol direct radiative forcing. By understanding the composition of mineral dust sources, EMIT aims to constrain the sign and magnitude of dust-related radiative forcing at regional and global scales. During its one-year mission on the International Space Station (ISS), EMIT will make measurements over the sunlit Earth’s dust source regions that fall within ±52° latitude. EMIT will schedule up to five visits (three on average) of each arid target region and only acquisitions not dominated by cloud cover will be downlinked. EMIT-based maps of the relative abundance of source minerals will advance the understanding of the current and future impacts of mineral dust in the Earth system. \n\nEMIT Data Products are distributed by the [LP DAAC](https://lpdaac.usgs.gov/). Learn more about EMIT data products from [EMIT Product Pages](https://lpdaac.usgs.gov/product_search/?query=emit&status=Operational&view=cards&sort=title) and search for and download EMIT data products using [NASA EarthData Search](https://search.earthdata.nasa.gov/search?q=%22EMIT%22) \n\n---\n\n## Prerequisites/Setup Instructions \n\nThis repository requires that users set up a compatible Python environment and download the EMIT granules used. See the `setup_instuctions.md` file in the `./setup/` folder. \n\n## Repository Contents \n\nBelow are the resources available for EMIT Data. \n\n|Name|Type|Summary|\n|:---|:---|:---|\n|[Getting EMIT Data using EarthData Search](guides/Getting_EMIT_Data_using_EarthData_Search.md)|Markdown Guide|A thorough walkthrough for using [EarthData Search](https://search.earthdata.nasa.gov/search) to find and download EMIT data|\n|[Streaming NASA Earthdata Cloud-Optimized GeoTIFFs using QGIS](guides/Streaming_cloud_optimized_geotiffs_using_QGIS.md)|Markdown Guide|A walkthrough to set up QGIS to stream cloud-optimized geotiff files from NASA Earthdata|\n|[Exploring EMIT L2A Reflectance](python/tutorials/Exploring_EMIT_L2A_Reflectance.ipynb)|Jupyter Notebook|Explore EMIT L2A Reflectance data using interactive plots|\n|[Visualizing Methane Plume Timeseries](python/tutorials/Visualizing_Methane_Plume_Timeseries.ipynb)|Jupyter Notebook|Find EMIT L2B CH4 Plume Data and build a timeseries of CH4 plume complexes|\n|[Generating_Methane_Spectral_Fingerprint](python/tutorials/Generating_Methane_Spectral_Fingerprint.ipynb)|Jupyter Notebook|Extract Radiance Spectra and build an in-plume/out-of-plume ratio to compare with CH4 absorption coefficient|\n|[Finding_EMIT_L2B_Data](python/tutorials/Finding_EMIT_L2B_Data.ipynb)|Jupyter Notebook|Use the `earthaccess` Python library to find EMIT L2B Mineral Identification Band Depth and Uncertainty data|\n|[Working with EMIT L2B Mineralogy](python/tutorials/Working_with_EMIT_L2B_Mineralogy.ipynb)|Jupyter Notebook|Work with the EMIT L2B Mineral Identification Band Depth and Uncertainty Data and aggregate individual spectral library constituents into the EMIT-10 minerals and estimate abundance| \n|[How to find and access EMIT data](python/how-tos/How_to_find_and_access_EMIT_data.ipynb)|Jupyter Notebook|Use the `earthaccess` Python library to find and download or stream EMIT data|\n|[How to Convert to ENVI Format](python/how-tos/How_to_Convert_to_ENVI.ipynb)|Jupyter Notebook|Convert from downloaded netCDF4 (.nc) format to .envi format|\n|[How to Orthorectify](python/how-tos/How_to_Orthorectify.ipynb)|Jupyter Notebook|Use the geometry lookup table (GLT) included with the EMIT netCDF4 file to project on a geospatial grid (EPSG:4326)|\n|[How to Extract Point Data](python/how-tos/How_to_Extract_Points.ipynb)|Jupyter Notebook|Extract spectra using lat/lon coordinates from a .csv and build a dataframe/.csv output|\n|[How to Extract Area Data](python/how-tos/How_to_Extract_Area.ipynb)|Jupyter Notebook|Extract an area defined by a .geojson or shapefile|\n|[How to use EMIT Quality Data](python/how-tos/How_to_use_EMIT_Quality_data.ipynb)|Jupyter Notebook|Build a mask using an EMIT L2A Mask file and apply it to an L2A Reflectance file|\n|[How to use Direct S3 Access with EMIT](python/how-tos/How_to_Direct_S3_Access.ipynb)|Jupyter Notebook|Use S3 from inside AWS us-west2 to access EMIT Data|\n|[How to find EMIT Data using NASA's CMR API](python/how-tos/How_to_find_EMIT_data_using_CMR_API.ipynb)|Jupyter Notebook|Use NASA's CMR API to programmatically find EMIT Data|\n\n---\n\n## Helpful Links \n\n+ [JPL EMIT Website](https://earth.jpl.nasa.gov/emit/) \n+ [EMIT Tutorial Webinar Series Recordings](https://www.youtube.com/playlist?list=PLO2yB4LGNlWrC5NdxeHMxyAxdwQhSypXe)\n+ [LP DAAC EMIT Product Pages](https://lpdaac.usgs.gov/product_search/?query=emit&status=Operational&view=cards&sort=title) - Learn more about available EMIT products \n+ [VISIONS Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/) - Learn about current and forecasted EMIT coverage \n\n+ [EMIT on Earth Data Search](https://search.earthdata.nasa.gov/search?q=%22EMIT%22) - Download EMIT Data from NASA\n\n+ [EMIT Github Repository](https://github.com/emit-sds) - Main EMIT Repository \n\n+ [EMIT Utilities Github Repository](https://github.com/emit-sds/emit-utils) - General convenience utilities for working with EMIT data\n\n+ [L2A Reflectance User Guide](https://lpdaac.usgs.gov/documents/1569/EMITL2ARFL_User_Guide_v1.pdf) \n\n+ [L2A Algorithm Theoretical Basis Document](https://lpdaac.usgs.gov/documents/1571/EMITL2A_ATBD_v1.pdf) \n\n+ [EMIT on Slack]( https://forms.gle/XefLVG6e6A7ezwpY9) - Join the EMIT slack community!\n\n---\n\n## Contact Info \n\nEmail: \nVoice: +1-866-573-3222 \nOrganization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \nWebsite: \nDate last modified: 08-01-2024 \n\n¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \n" }, { "path": "data/cuprite_bbox.geojson", "content": 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W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_Tarp_GDS339_Green_ASDFRa_AREF.html,1,splib06,,,,,,,,,,,,,,,,-1,,,\n2,744,group.1um/fe3+_hematite.fine.gr.gds76,Hematite.02+Quartz.98 GDS76 W1R1Ha,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite.02+Quartz.98_GDS76_BECKa_AREF.html,1,sprlb06,,,,,,0.02,,,,,,,,,,,,,\n3,882,group.1um/fe3+_goethite.medgr.ws222,Goethite WS222 <250um MedGrn W1R1H_ AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Goethite_WS222_Medium_Gr._BECKa_AREF.html,1,sprlb06,,,,1,,,,,,,,,,,,,,,\n4,5736,group.1um/fe3+_goethite+qtz.medgr.gds240,Goethite0.02+Quartz GDS240 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Goethite0.02+Quartz_GDS240_BECKa_AREF.html,1,splib06,,,,0.02,,,,,,,,,,,,,,,\n5,1878,group.1um/fe3+_goethite.fingr,Goethite MPCMA2-B FineGr adj W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Goethite_MPCMA2-B_FineGr_adj_BECKb_AREF.html,1,splib06,,,,0.2,,,,,,,,,,,,,,,\n6,852,group.1um/fe3+_goethite.medcoarsegr.mpc.trjar,Goethite MPCMA2-C M-Crsgrad2 W1R1Hb AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Goethite_MPCMA2-C_M-Crsgrad2_BECKb_AREF.html,1,sprlb06,,,,0.37,,,,,,,,,,,,,,,\n7,894,group.1um/fe3+_goethite.coarsegr,Goethite WS222 coarse grain W1R1H_,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Goethite_WS222_Coarse_Gr._BECKa_AREF.html,1,sprlb06,,,,1,,,,,,,,,,,,,,,\n8,6168,group.1um/fe3+_goethite.thincoat,Goethite_Thin_Film WS222 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Goethite_Thin_Film_WS222_BECKa_AREF.html,1,splib06,,,,0.1,,,,,,,,,,,,,,,\n9,5730,group.1um/fe3+_goeth+jarosite,Goeth+qtz.5+Jarosite.5 AMX11 W1R1BbS,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Goeth+qtz.5+Jarosite.5_AMX11_BECKb_AREF.html,1,splib06,,,,0.02,,,,,,,,,,,,,,,\n10,6042,group.1um/sulfide_pyrite,Pyrite LV95-6A Weath on Tail W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pyrite_LV95-6A_Weath_on_Tail_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,-1,,,,\n11,4476,group.1um/fe3+_sulfate_schwertmannite,Schwertmannite BZ93-1 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Schwertmannite_BZ93-1_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,-1,,,,,\n12,6144,group.1um/Mn-Coating,Blck_Mn_Coat_Tailngs LV95-3 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Blck_Mn_Coat_Tailngs_LV95-3_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,-1,\n13,5316,group.1um/fe3+mix_AMD.assemb1,Acid_Mine_Dr Assemb1-Fe3+ W1R1Fb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Acid_Mine_Dr_Assemb1-Fe3+_ASDFRb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n14,5322,group.1um/fe3+mix_AMD.assemb2,Acid_Mine_Dr Assemb2-Fe3+ W1R1Fb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Acid_Mine_Dr_Assemb2-Fe3+_ASDFRb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n15,2568,group.1um/fe3+_sulfate_kjarosite200,Jarosite GDS99 K 200C Syn W1R1BaS,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Jarosite_GDS99_K_200C_Syn_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,-1,,,,,\n16,732,group.1um/fe3+_goethite.lepidocrosite,Lepidocrosite GDS80 (Syn) W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Lepidocrocite_GDS80_(Syn)_BECKb_AREF.html,1,sprlb06,,,,1,,,,,,,,,,,,,,,\n17,5634,group.1um/fe2+_chlor+muscphy,Chlorite+Muscovite CU93-65A W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chlorite+Muscovite_CU93-65A_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n18,900,group.1um/fe2+_goeth+musc,Goethite CU91-252 coatedchip W1R1H_,No available USGS Spectral Library 7 Description Reference,1,sprlb06,,,,0.04,,,,,,,,,,,,,,,\n19,5604,group.1um/fe2+fe3+_chlor+goeth.propylzone,Chlor+Goethite CU93-4B Phyl W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chlor+Goethite_CU93-4B_Phyl_BECKa_AREF.html,1,splib06,,,,0.02,,,,,,,,,,,,,,,\n20,1446,group.1um/fe2+generic_nrw.cummingtonite,Cummingtonite HS294.3B W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cummingtonite_HS294.3B_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n21,72,group.1um/fe2+generic_nrw.actinolite,Actinolite NMNHR16485 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Actinolite_NMNHR16485_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n22,42,group.1um/fe2+generic_nrw.hs-actinolite,Actinolite HS22.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Actinolite_HS22.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n23,2334,group.1um/fe2+_pyroxene.hypersthene,Hypersthene NMNHC2368 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hypersthene_NMNHC2368_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n24,1548,group.1um/fe2+_pyroxene.diopside,Diopside NMNHR18685 ~160 Pyx W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Diopside_NMNHR18685_~160_Pyx_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n25,2502,group.1um/fe2+generic_med.jadeite,Jadeite HS343.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Jadeite_HS343.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n26,3966,group.1um/fe2+_pyroxene_clino_pigeonite,Pigeonite HS199.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pigeonite_HS199.3B_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n27,1668,group.1um/epidote,Epidote GDS26.a 75-200um W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Epidote_GDS26.a_75-200um_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n28,5724,group.1um/fe3+bearing1,Fe-Hydroxide SU93-106 amorph W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Fe-Hydroxide_SU93-106_amorph_BECKb_AREF.html,1,splib06,,,,0.2,,,,,,,,,,,,,,,\n29,1740,group.1um/fe3+_ferrihydrite,Ferrihydrite GDS75 Syn F6 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Ferrihydrite_GDS75_Syn_F6_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n30,738,group.1um/fe3+_hematite.med.gr.gds27,Hematite GDS27 W1R1Ha,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite_GDS27_BECKa_AREF.html,1,sprlb06,,,,,,1,,,,,,,,,,,,,\n31,6198,group.1um/fe3+_hematite.thincoat,Hematite_Thin_Film GDS27 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite_Thin_Film_GDS27_BECKa_AREF.html,1,splib06,,,,,,0.05,,,,,,,,,,,,,\n32,2100,group.1um/fe3+_hematite.fine.gr.fe2602,Hematite FE2602 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite_FE2602_BECKb_AREF.html,1,splib06,,,,,,1,,,,,,,,,,,,,\n33,906,group.1um/fe3+_hematite.fine.gr.ws161,Hematite WS161 W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite_WS161_BECKb_AREF.html,1,sprlb06,,,,,,0.79,,,,,,,,,,,,,\n34,3558,group.1um/fe3+_smectite_nontronite,Nontronite NG-1.a W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Nontronite_NG-1.a_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n35,5328,group.1um/fe2+generic_brd.br5a_actinolite,Actinolite-Hornfels BR93-5a W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Actinolite-Hornfels_BR93-5a_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n36,846,group.1um/fe2+fe3+mix_with_hematite_br5b,Magnetite_skarn BR93-5B W1R1Hb AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Magnetite_skarn_BR93-5B_BECKb_AREF.html,1,sprlb06,,,,,,0.17,,,,,,,,,,,,,\n37,5334,group.1um/fe2+generic_brd.br22c_actinolite,Actinolite-Tremolit BR93-22C W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Actinolite-Tremolit_BR93-22C_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n38,1656,group.1um/fe2+generic_br33a_bioqtzmonz_epidote,Epidote BR93-33a W1R1Bb AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Epidote_BR93-33a_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n39,5490,group.1um/fe2+generic_brd.br36a_chlorite,Biotite-Chlorite_Mx BR93-36A W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Biotite-Chlorite_Mx_BR93-36A_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n40,5460,group.1um/fe2+fe3+_hematite_weathering,Basalt_weathered BR93-43 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Basalt_weathered_BR93-43_BECKb_AREF.html,1,splib06,,,,,,0.01,,,,,,,,,,,,,\n41,5454,group.1um/fe2+generic_basalt_br46b,Basalt_fresh BR93-46B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Basalt_fresh_BR93-46B_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n42,840,group.1um/fe3+_hematite.lg.gr.br25a,Hematite_Coatd_Qtzt BR93-25A W1R1Hb AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite_Coatd_Qtzt_BR93-25A_BECKa_AREF.html,1,sprlb06,,,,,,0.01,,,,,,,,,,,,,\n43,6174,group.1um/fe3+_hematite.med.gr.br25b,Hematite_Coatd_Qtz BR93-25B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite_Coatd_Qtz_BR93-25B_BECKa_AREF.html,1,splib06,,,,,,0.05,,,,,,,,,,,,,\n44,816,group.1um/fe3+_hematite.lg.gr.br25c,Hematite_Coatd_Qtzt BR93-25C W1R1Hb AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite_Coatd_Qtzt_BR93-25C_BECKa_AREF.html,1,sprlb06,,,,,,0.69,,,,,,,,,,,,,\n45,3468,group.1um/fe3+_hematite.nano.BR34b2,Nanohematite BR93-34B2 W1R1BbS,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Nanohematite_BR93-34B2_BECKb_AREF.html,1,splib06,,,,,,0.1,,,,,,,,,,,,,\n46,1026,group.1um/fe3+_hematite-nano+goethite-fg,nHematit+fg-Goethit 34B2+MPC W1R1Hb,No available USGS Spectral Library 7 Description Reference,1,sprlb06,,,,0.1,,0.05,,,,,,,,,,,,,\n47,918,group.1um/fe3+_hematite.nano.BR34b2b,Nanohematite FBR93-34B2b ed1 W1R1Hb,No available USGS Spectral Library 7 Description Reference,1,sprlb06,,,,,,0.1,,,,,,,,,,,,,\n48,822,group.1um/fe3+_hematite.lg.gr.br34c,Hematite_Coatd_Qtz BR93-34C W1R1Ha AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hematite_Coatd_Qtz_BR93-34C_BECKa_AREF.html,1,sprlb06,,,,,,0.63,,,,,,,,,,,,,\n49,6210,group.1um/fe3+_sulfate_jarosite_br34a2,Jarosite_on_Qtzite BR93-34A2 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Jarosite_on_Qtzite_BR93-34A2_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n50,5340,group.1um/fe2+generic_broad_br60b,Actinolite_Dolomit BR93-60B W1R1BbS,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Actinolite_Dolomit_BR93-60B_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n51,6036,group.1um/fe2+generic_vbroad_br20,Phlogopite_Sand_Mix BR93-20 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Phlogopite_Sand_Mix_BR93-20_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n52,6942,group.1um/fe2+fe3+_water_RTsludge,Renyolds_TnlSldgWet SM93-15w W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Renyolds_TnlSldgWet_SM93-15w_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n53,660,group.1um/fe2+_chlorite.clinochlor,Clinochlore GDS158 Flagst W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Clinochlore_GDS158_Flagstaff_ASDNGb_AREF.html,1,sprlb06,,,,,,,,,,,,,,,,,,,\n54,1200,group.1um/fe2+_chlorite.Felow.clinochlor,Clinochlore NMNH83369 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Clinochlore_NMNH83369_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n55,4890,group.1um/fe2+_chlorite.thuringite,Thuringite SMR-15.c 32um W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Thuringite_SMR-15.c_32um_NIC4aa_RREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n56,720,group.1um/copper_carbonate_azurite,Azurite WS316 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Azurite_WS316_BECKa_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n57,3990,group.1um/fe3+copper-hydroxide_pitchlimonite,Pitch_Limonite GDS104 Cu W1R1B?,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pitch_Limonite_GDS104_Cu_BECKu_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n58,2964,group.1um/copper_carbonate_malachite,Malachite HS254.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Malachite_HS254.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n59,5496,group.1um/copper_sulfate_bluefflor,Blue_Efflorscnt_Min SU93-300 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Blue_Efflorscnt_Min_SU93-300_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,-1,,,,,\n60,6594,group.1um/copper_precipitate_greenslime,Green_Slime SM93-14A Summitv W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Green_Slime_SM93-14A_Summitv_BECKa_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n61,3696,group.1um/fe2+_olivine-lrg-gr,Olivine HS285.4B Fo80 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Olivine_HS285.4B_Fo80_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n62,3666,group.1um/fe2+_olivine-fine-gr,Olivine GDS71.a Fo91 65um W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Olivine_GDS71.a_Fo91_65um_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n63,4278,group.1um/carbonate_rhodochrosite,Rhodochrosite HS338.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Rhodochrosite_HS338.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n64,4560,group.1um/fe2+generic_carbonate_siderite1,Siderite HS271.3B W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Siderite_HS271.3B_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n65,4362,group.1um/fe2+generic_amphibole_riebeckite,Riebeckite NMNH122689 Amph W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Riebeckite_NMNH122689_Amph_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n66,1386,group.1um/fe3+fe2+_sulfate_coquimbite,Coquimbite GDS22 W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Coquimbite_GDS22_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,-1,,,,,\n67,1374,group.1um/fe3+fe2+_sulfate_copiapite,Copiapite GDS21 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Copiapite_GDS21_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,-1,,,,,\n68,1134,group.1um/copper_chrysocolla,Chrysocolla HS297.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chrysocolla_HS297.3B_ASDFRb_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n69,3498,group.1um/ree_neodymium_oxide,Neodymium_Oxide GDS34 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Neodymium_Oxide_GDS34_BECKa_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n70,4410,group.1um/ree_samarium_oxide,Samarium_Oxide GDS36 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Samarium_Oxide_GDS36_BECKa_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n71,708,group.1um/fe2+generic_axinite,Axinite HS342.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Axinite_HS342.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n72,4728,group.1um/fe2+generic_staurolite,Staurolite HS188.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Staurolite_HS188.3B_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n73,216,group.1um/fe2+generic_almandine,Almandine WS479 Garnet W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Almandine_WS479_Garnet_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n74,678,group.1um/fe2+_pyroxene_augite,Augite NMNH120049 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Augite_NMNH120049_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n75,810,group.1um/fe2+_pyroxene.bronzite,Bronzite HS9.3B Pyroxene W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Bronzite_HS9.3B_Pyroxene_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n76,858,group.1um/fe2+generic_sulfate_butlerite,Butlerite GDS25 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Butlerite_GDS25_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n77,882,group.1um/fe2+_feldspar.bytownite,Bytownite HS106.3B Plagio W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Bytownite_HS106.3B_Plagio_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n78,1002,group.1um/sulfide_copper_chalcopyrite,Chalcopyrite HS431.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chalcopyrite_HS431.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,-1,,,,\n79,1122,group.1um/fe2+_chromite,Chromite HS281.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chromite_HS281.3B_ASDFRc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n80,1164,group.1um/sulfide_cinnabar,Cinnabar HS133.3B W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cinnabar_HS133.3B_BECKc_AREF.html,1,splib06,,,,,,,,,,,,,,,-1,,,,\n81,1458,group.1um/copper_oxide_cuprite,Cuprite HS127.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cuprite_HS127.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n82,6156,group.1um/fe3+mn_desert.varnish1,Desert_Varnish GDS141 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Desert_Varnish_GDS141_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,-1,\n83,6162,group.1um/fe3+mn_desert.varnish2,Desert_Varnish GDS78A Rhy W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Desert_Varnish_GDS78A_Rhy_BECKa_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,-1,\n84,1644,group.1um/fe2+_pyroxene_enstatite,Enstatite NMNH128288 W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Enstatite_NMNH128288_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n85,2088,group.1um/fe2+_pyroxene_hedenbergite,Hedenbergite NMNH119197 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hedenbergite_NMNH119197_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n86,2796,group.1um/sulfate_lazurite,Lazurite HS418.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Lazurite_HS418.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,-1,,,,,\n87,2928,group.1um/fe3+_maghemite,Maghemite GDS81 Syn (M-3) W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Maghemite_GDS81_Syn_(M-3)_BECKb_AREF.html,1,splib06,,,,,,0.85,,,,,,,,,,,,,\n88,2940,group.1um/magnetite,Magnetite HS195.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Magnetite_HS195.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n89,4158,group.1um/mn_oxide_pyrolusite,Pyrolusite HS138.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pyrolusite_HS138.3B_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n90,3042,group.1um/fe2+_feldspar_microcline,Microcline HS103.3B Feldspar W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Microcline_HS103.3B_Feldspar_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n91,132,group.1um/fe2+_feldspar_albite,Albite HS143.3B Plagioclase W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Albite_HS143.3B_Plagioclase_BECKc_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n92,3852,group.1um/fe2+_feldspar_orthoclase,Orthoclase NMNH142137 Fe W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Orthoclase_NMNH142137_Fe_BECKb_AREF.html,1,splib06,,,,,,,,,,,-1,,,,,,,,\n93,3882,group.1um/inosilicate_pectolite,Pectolite NMNH94865.a W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pectolite_NMNH94865.a_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n94,4314,group.1um/mn2+_rhodonite,Rhodonite NMNHC6148 >250um W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Rhodonite_NMNHC6148_gt250um_BECKb_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n95,4776,group.1um/sulfur,Sulfur GDS94 Reagent W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Sulfur_GDS94_Reagent_BECKa_AREF.html,1,splib06,,,,,,,,,,,,,,,,,,,\n96,7128,group.2um/organic_vegetation-dry-grass-long,Dry_Long_Grass AV87-2 W1R1Ba,No available USGS Spectral Library 7 Description Reference,2,splib06,,,,,,,,,,,,,,,,,-1,,\n97,7248,group.2um/organic_vegetation-dry-grass-golden,Grass_Golden_Dry GDS480 W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Grass_Golden_Dry_GDS480_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,-1,,\n98,6912,group.2um/organic_vegetation-dry-wood,Plywood GDS365 Fresh Pine W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plywood_GDS365_Fresh_Pine_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,-1,,\n99,6864,group.2um/organic_plastic-tarp1,Plastic_Tarp GDS340 Blu_Wovn W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_Tarp_GDS340_Blu_Wovn_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n100,6876,group.2um/organic_plastic-vinyl,Plastic_Vinyl GDS372 White W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_Vinyl_GDS372_White_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n101,6558,group.2um/organic_fiberglass-roofing,Fiberglass GDS337 Grn Roofng W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Fiberglass_GDS337_Grn_Roofng_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n102,6936,group.2um/organic_plastic-polystyrene,Polystyrene GDS345 BluInsul W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Polystyrene_GDS345_BluInsul_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n103,6816,group.2um/organic_plastic-pete,Plastic_PETE GDS380 Clear W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_PETE_GDS380_Clear_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n104,6810,group.2um/organic_plastic-pete2,Plastic_PETE GDS379 TrnslBrn W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_PETE_GDS379_TrnslBrn_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n105,6714,group.2um/organic_plastic-hdpe.1,Plastic_HDPE GDS393 GlosWhTr W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_HDPE_GDS393_GlosWhTr_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n106,6672,group.2um/organic_paint-1,Painted_Aluminum GDS333 LgGr W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Painted_Aluminum_GDS333_LgGr_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n107,6834,group.2um/organic_plastic-pete3,Plastic_PETE GDS383 Clrbluis W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_PETE_GDS383_Clrbluis_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n108,6810,group.2um/organic_plastic-pete4,Plastic_PETE GDS379 TrnslBrn W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_PETE_GDS379_TrnslBrn_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,-1,,,\n109,300,group.2um/sulfate_kalun150c,Alunite GDS97 K Syn (150C) W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_GDS97_K_Syn_(150C)_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n110,294,group.2um/sulfate_kalun250c,Alunite GDS96 K Syn (250C) W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_GDS96_K_Syn_(250C)_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n111,324,group.2um/sulfate_kalun450c,Alunite RES-2 K Syn (450C) W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_RES-2_K_Syn_(450C)_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n112,288,group.2um/sulfate_naalun150c,Alunite GDS95 Na Syn (150C) W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_GDS95_Na_Syn_(150C)_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n113,336,group.2um/sulfate_naalun300c,Alunite RES-4 Na Syn (300C) W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_RES-4_Na_Syn_(300C)_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n114,330,group.2um/sulfate_naalun450c,Alunite RES-3 Na Syn (450C) W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_RES-3_Na_Syn_(450C)_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n115,246,group.2um/sulfate_na82alun100c,Alunite GDS82 Na82 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_GDS82_Na82_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n116,5382,group.2um/sulfate+kaolingrp_natroalun+dickite,Alunite+Dickite MV99-6-26b W1R1Fc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite+Dickite_MV99-6-26b_ASDFRc_AREF.html,2,splib06,,,,,,,,0.485,,,,,,-1,,,,,\n117,252,group.2um/sulfate_na63alun300c,Alunite GDS83 Na63 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_GDS83_Na63_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n118,342,group.2um/sulfate_na40alun400c,Alunite RES-9 Summitv (400C) W2R4Nb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_RES-9_Summitv_(400C)_ASDNGb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n119,276,group.2um/sulfate_alunNa03,Alunite GDS84 Na03 W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_GDS84_Na03_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n120,348,group.2um/sulfate_alunNa56450c,Alunite RES10 NA56% Syn 450C W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_RES10_NA56percent_Syn_450C_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n121,354,group.2um/sulfate_alunNa78.450c,Alunite RES12 NA78% Syn 450C W2R4NaIS,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_RES12_NA78percent_Syn_450C_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n122,5394,group.2um/sulfate_alun35K65Na.low,Alunite0.35K+.65Na CU91-217H W2R4Nb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite0.35K+.65Na_CU91-217H_ASDFRb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n123,5418,group.2um/sulfate_alun73K27Na.low,Alunite0.73K+.27Na CU91-217D W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite0.73K+.27Na_CU91-217D_NIC4a_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n124,5412,group.2um/sulfate_alun66K34Na.low,Alunite0.66K+.34Na CU91-217A W2R4Nb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite0.66K+.34Na_CU91-217A_NIC4b_RREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n125,5976,group.2um/micagrp_muscovite-med-Al,Muscovite CU91-250A med Al W2R4NbS,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite.5+MuscCU91-250A_AMX4_BECKa_AREF.html,2,splib06,,,,,,,0.59,,,,,,,,,,,,\n126,1038,group.2um/calcite.33+kaol.33+mus-AMXr2,Calcite.33+Kaol.33+Mus AMXr2 W1R1Ba,No available USGS Spectral Library 7 Description Reference,2,sprlb06,0.33,,,,,,0.33,0.33,,,,,,,,,,,\n127,654,group.2um/micagrp_muscovite-medhigh-Al,Muscovite GDS113 Ruby W1R1Bb splib06 3348,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Muscovite_GDS113_Ruby_ASDNGa_AREF.html,2,sprlb06,,,,,,,1,,,,,,,,,,,,\n128,3876,group.2um/micagrp_paragonite,Paragonite GDS109 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Paragonite_GDS109_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,-1\n129,714,group.2um/micagrp_illite,Illite IMt-1.b <2um W1R1BaS,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Illite_IMt-1.b_lt2um_ASDNGa_AREF.html,2,sprlb06,,,,,,,0.99,,,,,,,,,,,,\n130,726,group.2um/micagrp_illite.gds4,Illite GDS4.2 Marblehead_WI W1R1H_,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Illite_GDS4.2_Marblehead_ASDNGb_AREF.html,2,sprlb06,,,,,,,0.76,,,,,,,,,,,,\n131,3306,group.2um/micagrp_muscovite-low-Al,Muscovite CU93-1 low-Al Phyl W2R4Nb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Muscovite_CU93-1_low-Al_Phyl_NIC4b_RREF.html,2,splib06,,,,,,,0.22,,,,,,,,,,,,\n132,3372,group.2um/micagrp_muscoviteFerich,Muscovite GDS116 Tanzania W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Muscovite_GDS116_Tanzania_BECKa_AREF.html,2,splib06,,,,,,,1,,,,,,,,,,,,\n133,606,group.2um/kaolgrp_kaolinite_wxl,Kaolinite CM9 (wxl) W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kaolinite_CM9_ASDNGb_AREF.html,2,sprlb06,,,,,,,,0.97,,,,,,,,,,,\n134,672,group.2um/kaolgrp_kaolinite_pxl,Kaolinite KGa-2 (pxl) W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kaolinite_KGa-2_(pxl)_ASDNGb_AREF.html,2,sprlb06,,,,,,,,1,,,,,,,,,,,\n135,684,group.2um/kaolgrp_halloysite,Halloysite NMNH106237 W1R1Ha,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Halloysite_NMNH106237_ASDNGa_AREF.html,2,sprlb06,,,,,,,,0.99,,,,,,,,,,,\n136,3462,group.2um/kaolgrp_nacrite,Nacrite GDS88 s06crj3a=c,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Nacrite_GDS88_BECKc_AREF.html,2,splib06,,,,,,,,-1,,,,,,,,,,,\n137,708,group.2um/kaolgrp_dickite,Dickite NMNH106242 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Dickite_NMNH106242_ASDNGb_AREF.html,2,sprlb06,,,,,,,,-1,,,,,,,,,,,\n138,618,group.2um/sulfate_gypsum,Gypsum HS333.3B (Selenite) W1R1Ha,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Gypsum_HS333.3B_(Selenite)_ASDNGa_AREF.html,2,sprlb06,,,,,0.995,,,,,,,,,,,,,,\n139,5868,group.2um/kaolin.5+smect.5,Kaolin_Smect H89-FR-2 .5Kaol W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kaolin_Smect_H89-FR-2_.5Kaol_BECKb_AREF.html,2,splib06,,,,,,,,0.5,,,,,,,,,,,\n140,5880,group.2um/kaolin.3+smect.7,Kaolin_Smect H89-FR-5 .3Kaol W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kaolin_Smect_H89-FR-5_.3Kaol_BECKb_AREF.html,2,splib06,,,,,,,,0.3,,,,,,,,,,,\n141,588,group.2um/smectite_montmorillonite_na_highswelling,Montmorillonite SWy-1 W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Montmorillonite_SWy-1_ASDNGb_AREF.html,2,sprlb06,,,,,,,,,0.84,,,,,,,,,,\n142,54,group.2um/smectite_montmorillonite_fe_swelling,Montmorillonite_Fe GDS759A,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,0.83,,,,,,,,,,\n143,600,group.2um/smectite_montmorillonite_ca_swelling,Montmorillonite SAz-1 W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Montmorillonite_SAz-1_ASDNGb_AREF.html,2,sprlb06,,,,,,,,,0.99,,,,,,,,,,\n144,60,group.2um/sioh_hydrated_basaltic_glass,HyBasaltGlassMLUT03-9CMarP&T,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,,,,,,\n145,66,group.2um/sulfate_kieserite,Kieserite Batch4 GDS671,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,-1,,,,,\n146,150,group.2um/sulfate_szomolnokite,Szomolnokite NMNH104303 UT,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,-1,,,,,\n147,192,group.2um/sulfate_szomolnokite_heated,Szomolnokite GDS874 Heat275C,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,-1,,,,,\n148,96,group.2um/perchlorate_mg,Perchlorate_Mg GDS851.2 220K,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,,,,,,\n149,144,group.2um/perchlorate_na,Perchlorate_Na GDS857 230C r06crj3a=_,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,,,,,,\n150,5466,group.2um/smectite_beidellite_gds123,Beidellite+Montmor GDS123 =b,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Beidellite+Montmor_GDS123_ASDFRb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,-1\n151,5478,group.2um/smectite_beidellite_gds124,Beidellite+Montmor GDS124 =b,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Beidellite_GDS124_ASDFRb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,-1\n152,990,group.2um/sioh_chalcedony,Chalcedony CU91-6A W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chalcedony_CU91-6A_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n153,1488,group.2um/hydroxide_diaspore,Diaspore HS416.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Diaspore_HS416.3B_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n154,4188,group.2um/pyrophyllite,Pyrophyllite PYS1A fine gr W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pyrophyllite_PYS1A_gt250um_ASDNGa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n155,6054,group.2um/kaol.75+pyroph.25,Pyrophyl.25+wxlKaol.75 AMX17,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pyrophyl.25+wxlKaol.75_AMX17_BECKb_AREF.html,2,splib06,,,,,,,,0.73,,,,,,,,,,,\n156,6060,group.2um/pyroph.5+mont0.5,Pyrophyl.50+Ca-Mont.50 AMX16 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pyrophyl.50+Ca-Mont.50_AMX16_BECKa_AREF.html,2,splib06,,,,,,,,,0.42,,,,,,,,,,\n157,5358,group.2um/pyroph.5+alunit.5,Alun366+.50PyroPYS1A GDS222 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alun366+.50PyroPYS1A_GDS222_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n158,5370,group.2um/alunite+pyrophyl,Alunite+Pyrophyl SD1093A W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite+Pyrophyl_SD1093A_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n159,6048,group.2um/pyroph+tr.musc,Pyrophyl+Muscovite JH_PYRM1 W1R1Fb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pyrophyl+Muscovite_JH_PYRM1_ASDFRb_AREF.html,2,splib06,,,,,,,0.04,,,,,,,,,,,,\n160,5988,group.2um/musc+pyroph,Muscovite+Pyrophyl JH_PYRP1 W1R1Fb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Muscovite+Pyrophyl_JH_PYRP1_ASDFRb_AREF.html,2,splib06,,,,,,,0.15,,,,,,,,,,,,\n161,5352,group.2um/alunite+musc+pyroph,Alun0.3+Musc0.4+Pyro0.3 AMX1 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alun0.3+Musc0.4+Pyro0.3_AMX1_BECKa_AREF.html,2,splib06,,,,,,,0.4,,,,,,,,,,,,\n162,5406,group.2um/alunite.5+kaol.5,Alunite0.5+Kaol_KGa-1 AMX3 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite0.5+Kaol_KGa-1_AMX3_BECKb_AREF.html,2,splib06,,,,,,,,0.475,,,,,,,,,,,\n163,5910,group.2um/kaol.75+alun.25,Kaolwxl.75+Alun_HS295 AMX14 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kaolwxl.75+Alun_HS295_AMX14_BECKb_AREF.html,2,splib06,,,,,,,,0.73,,,,,,,,,,,\n164,5820,group.2um/Kalun+kaol.intmx,Kalun+kaol+gth mv2-ar3 W1R1Fb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kalun+kaol+gth_mv2-ar3_ASDFRb_AREF.html,2,splib06,,,,,,,,0.4,,,,,,,,,,,\n165,5364,group.2um/Na-alun+kaol.intmx,Alun_Na+Kaol+Hemat MV00-11a W1R1Fc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alun_Na+Kaol+Hemat_MV00-11a_ASDFRc_AREF.html,2,splib06,,,,,,,,0.32,,,,,,,,,,,\n166,5832,group.2um/kaolin.5+muscov.medAl,Kaol.5+MuscCU91-250A AMX13 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kaol.5+MuscCU91-250A_AMX13_BECKb_AREF.html,2,splib06,,,,,,,0.295,0.485,,,,,,,,,,,\n167,5838,group.2um/kaolin.5+muscov.medhighAl,Kaol_Wxl+0.5Musc_Ruby AMX12 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kaol_Wxl+0.5Musc_Ruby_AMX12_BECKa_AREF.html,2,splib06,,,,,,,0.5,0.485,,,,,,,,,,,\n168,870,group.2um/kaolin+musc.intimat,Kaol+Musc_intimate CU93-5C W1R1Ha AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Kaol+Muscov_intimate_CU93-5C_ASDNGa_AREF.html,2,sprlb06,,,,,,,0.1,0.485,,,,,,,,,,,\n169,876,group.2um/musc+jarosite.intimat,Muscov+Jaros CU93-314 coatng W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Muscov+Jaros_CU93-314_coatng_ASDNGb_AREF.html,2,sprlb06,,,,,,,0.27,,,,,,,,,,,,\n170,5748,group.2um/sulfate-mix_gypsum+jar+illite.intmix,Gyp+jar+ill BRCM1 Marysvale W1R1Fb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Gyp+jar+ill_BRCM1_Marysvale_ASDFRb_AREF.html,2,splib06,,,,,0.33,,0.33,,,,,,,,,,,,\n171,5766,group.2um/sulfate-mix_gyp+jar+musc.amix,Gyp.5+Kjar.3+hAlmsc.2 AMX24 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Gyp.5+Kjar.3+hAlmsc.2_AMX24_BECKa_AREF.html,2,splib06,,,,,0.497,,0.2,,,,,,,,,,,,\n172,5760,group.2um/sulfate-mix_gyp+jar+musc+dick.amix,Gyp.4+jr20+msc.2+dic.2 AMX23 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Gyp.4+jr20+msc.2+dic.2_AMX23_BECKa_AREF.html,2,splib06,,,,,0.398,,0.2,,,,,,,,,,,,\n173,5754,group.2um/musc+gyp+jar+dick.amix,Gyp.3+jr.1+msc.4+dic.2 AMX22 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Gyp.3+jr.1+msc.4+dic.2_AMX22_BECKa_AREF.html,2,splib06,,,,,0.298,,0.4,,,,,,,,,,,,\n174,5670,group.2um/dick+musc+gyp+jar.amix,Dic.4+msc.3+gyp.2+jr.1 AMX20 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Dic.4+msc.3+gyp.2+jr.1_AMX20_BECKa_AREF.html,2,splib06,,,,,0.199,,0.3,,,,,,,,,,,,\n175,5388,group.2um/alunite.5+musc.5,Alunite.5+MuscCU91-250A AMX4 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite.5+MuscCU91-250A_AMX4_BECKa_AREF.html,2,splib06,,,,,,,0.295,,,,,,,,,,,,\n176,5346,group.2um/alunite.33+kaol.33+musc.33,Alun.33+Kaol.33+Musc.33 AMX2 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alun.33+Kaol.33+Musc.33_AMX2_BECKa_AREF.html,2,splib06,,,,,,,0.195,0.313,,,,,,,,,,,\n177,5982,group.2um/muscovite+chlorite,Muscovite+Chlorite CU91-253D W2R4Na,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Muscovite+Chlorite_CU91-253D_NIC4a_RREF.html,2,splib06,,0.03,,,,,0.53,,,,,,,,,,,,\n178,786,group.2um/smectite_ammonillsmec,Ammonio-Illite/Smectit GDS87 W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Ammonio-Illite-Smect_GDS87_BECKb_AREF.html,2,sprlb06,,,,,,,0.5,,,,,,,,,,,,\n179,846,group.2um/feldspar_buddingtonite_ammonium,Buddingtonite GDS85 D-206 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Buddingtonite_GDS85_D-206_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n180,5502,group.2um/feldspar_buddington.namont,Buddingtnt+Na-Mont CU93-260B W2R4Nb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Buddingtnt+Na-Mont_CU93-260B_NIC4b_RREF.html,2,splib06,,,,,,,,,0.1,,,,,,,,,,\n181,5502,group.2um/feldspar_buddington.namont2,Buddingtnt+Na-Mont CU93-260B W2R4Nb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Buddingtnt+Na-Mont_CU93-260B_NIC4b_RREF.html,2,splib06,,,,,,,,,0.1,,,,,,,,,,\n182,5436,group.2um/sulfate_ammonalunite,Alunite_NH4+Jaro NMNH145596A W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Alunite_NH4+Jaro_NMNH145596A_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n183,390,group.2um/sulfate_ammonjarosite,Ammonio-Jarosite SCR-NHJ W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Ammonio-Jarosite_SCF-NHJ_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n184,612,group.2um/carbonate_dolomite,Dolomite HS102.3B W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Dolomite_HS102.3B_ASDNGb_AREF.html,2,sprlb06,,,0.996,,,,,,,,,,,,,,,,\n185,72,group.2um/actinolite,Actinolite NMNHR16485 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Actinolite_NMNHR16485_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n186,402,group.2um/amphibole,Amphibole NMNH78662 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Amphibole_NMNH78662_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n187,774,group.2um/micagrp_biotite,Biotite HS28.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Biotite_HS28.3B_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,-1\n188,912,group.2um/micagrp_biotite-phlogopite,Biotite-phlogopite P23-5 Sam W1R1H_,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,,,,,,-1\n189,5664,group.2um/chlorite-skarn,Chlorite_Serpentine BR93-22B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chlorite_Serpentine_BR93-22B_BECKb_AREF.html,2,splib06,,0.61,,,,,,,,,,,,,,,,,\n190,1080,group.2um/chlorite,Chlorite SMR-13.b 60-104um W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chlorite_SMR-13.b_60-104um_BECKa_AREF.html,2,splib06,,1,,,,,,,,,,,,,,,,,\n191,252,group.2um/prehnite+.50chlorite,Prehnite+.50Chlorite AMIX,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,0.5,,,,,,,,,,,,,,,,,\n192,258,group.2um/prehnite+.67chlorite,Prehnite+.67Chlorite AMIX,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,0.67,,,,,,,,,,,,,,,,,\n193,264,group.2um/prehnite+.75chlorite,Prehnite+.75Chlorite AMIX,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,0.75,,,,,,,,,,,,,,,,,\n194,276,group.2um/carbonate_aragonite,Aragonite GDS898 Moracco,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,-1,,,,,,\n195,660,group.2um/chlorite_clinochlore,Clinochlore GDS158 Flagst W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Clinochlore_GDS158_Flagstaff_ASDNGb_AREF.html,2,sprlb06,,1,,,,,,,,,,,,,,,,,\n196,1212,group.2um/chlorite_clinochlore.nmnh83369,Clinochlore NMNH83369 =bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Clinochlore_NMNH83369_BECKb_AREF.html,2,splib06,,1,,,,,,,,,,,,,,,,,\n197,648,group.2um/chlorite_clinochlore.fe.gds157,Clinochlore_Fe GDS157.b W1R1Ha,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Clinochlore_Fe_GDS157.b_ASDFRa_AREF.html,2,sprlb06,,1,,,,,,,,,,,,,,,,,\n198,1260,group.2um/chlorite_clinochlore.fe.sc-cca-1,Clinochlore_Fe SC-CCa-1.b =aa_,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Clinochlore_Fe_SC-CCa-1.b_BECKa_AREF.html,2,splib06,,1,,,,,,,,,,,,,,,,,\n199,1338,group.2um/chlorite_cookeite-car-1.a,Cookeite CAr-1.a 104-150um =bb_,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cookeite_CAr-1.a_104-150um_BECKb_AREF.html,2,splib06,,0.94,,,,,,,,,,,,,,,,,\n200,1368,group.2um/chlorite_cookeite-car-1.c,Cookeite CAr-1.c <30um =cc_,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cookeite_CAr-1.c_lt30um_BECKb_AREF.html,2,splib06,,0.81,,,,,,,,,,,,,,,,,\n201,4872,group.2um/chlorite_thuringite,Thuringite SMR-15.b 32um W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Thuringite_SMR-15.b_80um_BECKa_AREF.html,2,splib06,,1,,,,,,,,,,,,,,,,,\n202,1668,group.2um/epidote,Epidote GDS26.a 75-200um W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Epidote_GDS26.a_75-200um_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n203,2274,group.2um/hornblende,Hornblende_Fe HS115.3B W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hornblende_Fe_HS115.3B_BECKc_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n204,2538,group.2um/sulfate_jarosite-Na,Jarosite GDS24 Na W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Jarosite_GDS24_Na_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n205,2568,group.2um/sulfate_jarosite-K,Jarosite GDS99 K 200C Syn W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Jarosite_GDS99_K_200C_Syn_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n206,2604,group.2um/sulfate_jarosite-lowT,Jarosite SJ-1 H3O 10-20% W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Jarosite_SJ-1_H3O_10-20percent_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n207,3930,group.2um/phlogopite,Phlogopite HS23.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Phlogopite_HS23.3B_BECKa_AREF.html,2,splib06,,,,,,,-1,,,,,,,,,,,,\n208,5142,group.2um/tremolite.or.talc,Tremolite HS18.3 W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Tremolite_HS18.3_BECKc_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n209,4800,group.2um/talc.crsgrnd,Talc GDS23 74-250um fr W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Talc_GDS23_ASDNGa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n210,4830,group.2um/talc.fngrnd,Talc WS659 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Talc_WS659_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n211,5538,group.2um/talc+calcite.parkcity,Calcite+Talc PC99-1G W1R1Fc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite+Talc_PC99-1G_ASDFRc_AREF.html,2,splib06,0.5,,,,,,,,,,,,,,,,,,\n212,5682,group.2um/talc+carbonate.parkcity,Dolomit+Calcite+Talc PC99-1E W1R1Fc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Dolomit+Calcite+Talc_PC99-1E_ASDFRc_AREF.html,2,splib06,0.33,,0.33,,,,,,,,,,,,,,,,\n213,5568,group.2um/carbonate_calcite+0.2Ca-mont,Calcite.8+Ca-Montmor.2 AMX15 W1R1BbS,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite.8+Ca-Montmor.2_AMX15_BECKb_AREF.html,2,splib06,0.796,,,,,,,,0.17,,,,,,,,,,\n214,5922,group.2um/carbonate_magnesite,Magnesite+Hydroma HS47.3B W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Magnesite+Hydromag_HS47.3B_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,-1,,,,,,\n215,666,group.2um/carbonate_calcite,Calcite WS272 W1R1Ha,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite_WS272_ASDNGa_AREF.html,2,sprlb06,0.995,,,,,,,,,,,,,,,,,,\n216,5532,group.2um/carbonate_calcite+dolomite.5,Calcite+Dolomite.5 AMX8 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite.5+Dolomite.5_AMX8_BECKb_AREF.html,2,splib06,0.497,,0.498,,,,,,,,,,,,,,,,\n217,5694,group.2um/carbonate_dolo+.5ca-mont,Dolomite.50+Ca-Montmor AMX10 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Dolomite.5+Ca-Mont.5_AMX10_BECKb_AREF.html,2,splib06,,,0.5,,,,,,0.42,,,,,,,,,,\n218,5514,group.2um/calcite.25+dolom.25+Na-mont.5,Calc.25+dolo.25+mont.5 AMX18 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calc.25+Dolo.25+Na-Mon_AMX18_BECKb_AREF.html,2,splib06,0.249,,0.249,,,,,,0.42,,,,,,,,,,\n219,5508,group.2um/carbonate_calcite.25+dolom.25+Ca-mont.5,Calc.25+Dolo.25+Ca-Mon AMX7 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calc.25+Dolo.25+Ca-Mont_AMX7_BECKb_AREF.html,2,splib06,0.249,,0.249,,,,,,0.42,,,,,,,,,,\n220,4560,group.2um/carbonate_siderite,Siderite HS271.3B W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Siderite_HS271.3B_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,-1,,,,,,\n221,3558,group.2um/smectite_nontronite_swelling,Nontronite NG-1.a W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Nontronite_NG-1.a_BECKb_AREF.html,2,splib06,,,,,,,,,-1,,,,,,,,,,\n222,696,group.2um/clay_hectorite,Hectorite SHCa-1.Ac-B W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hectorite_SHCa-1.Ac-B_BECKb_AREF.html,2,sprlb06,,,,,,,,,-1,,,,,,,,,,\n223,702,group.2um/saponite.or.talc,Saponite SapCa-1.AcB W1R1Hb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Saponite_SapCa-1.AcB_BECKb_AREF.html,2,sprlb06,,,,,,,,,-1,,,,,,,,,,\n224,690,group.2um/sepiolite,Sepiolite SepNev-1.AcB W1R1Ha,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Sepiolite_SepNev-1.AcB_BECKa_AREF.html,2,sprlb06,,,,,,,,,,,,,,,,,,,\n225,5574,group.2um/calcite+0.2Na-mont,Calcite.80+Mont_Swy-1 GDS212 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite.80+Mont_Swy-1_GDS212_BECKa_AREF.html,2,splib06,0.796,,,,,,,,0.17,,,,,,,,,,\n226,5526,group.2um/calcite+0.5Ca-mont,Calcite+.50Ca-Mont AMX6 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite.5+.Ca-Mont.5_AMX6_BECKb_AREF.html,2,splib06,0.497,,,,,,,,0.42,,,,,,,,,,\n227,5520,group.2um/carbonate_calcite+0.3muscovite,Calcite+.33Muscov AMX5 Ruby W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite+.33Muscov_AMX5_Ruby_BECKa_AREF.html,2,splib06,0.667,,,,,,0.33,,,,,,,,,,,,\n228,30,group.2um/organic_drygrass+.17Na-mont,Grass_dry+0.17%Na-Mont AMX35 W1R1Ba AREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Grass_dry.83+.17NaMont_AMX35_BECKb_AREF.html,2,sprlb06,,,,,,,,,0.14,,,,,,,,,,\n229,5688,group.2um/carbonate_dolomite.5+Na-mont.5,Dolomite.5+Na-mont.5 AMX21 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Dolomite.5+Na-Mont.5_AMX21_BECKb_AREF.html,2,splib06,,,0.498,,,,,,0.42,,,,,,,,,,\n230,5544,group.2um/carbonate_smectite_calcite.33+Ca-mont.67,Calcite.33+Ca-mont.67 AMX19 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite.33+Ca-Mont.67_AMX19_BECKb_AREF.html,2,splib06,0.328,,,,,,,,0.66,,,,,,,,,,\n231,5580,group.2um/carbonate_calcite+0.2kaolwxl,Calcite.80wt+Kaol_CM9 GDS213 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite.80wt+Kaol_CM9_GDS213_BECKa_AREF.html,2,splib06,0.796,,,,,,,0.19,,,,,,,,,,,\n232,5562,group.2um/carbonate_calcite0.7+kaol0.3,Calcite.7+Kaolwxl.3 AMX9 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Calcite.7+Kaolin-wxl.3_AMX9_BECKa_AREF.html,2,splib06,0.696,,,,,,,0.19,,,,,,,,,,,\n233,1314,group.2um/phyllosilicate.clintonite,Clintonite NMNH126553 W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Clintonite_NMNH126553_BECKc_AREF.html,2,splib06,,,,,,,-1,,,,,,,,,,,,\n234,3870,group.2um/palygorskite,Palygorskite PFL-1 Attapulg W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Palygorskite_PFL-1_Attapulg_BECKb_AREF.html,2,splib06,,,,,,,-1,,,,,,,,,,,,\n235,5298,group.2um/zoisite,Zoisite HS347.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Zoisite_HS347.3B_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n236,1626,group.2um/elbaite,Elbaite NMNH94217-1.c <74 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Elbaite_NMNH94217-1.c_lt74_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n237,1824,group.2um/hydroxide_gibbsite,Gibbsite WS214 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Gibbsite_WS214_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n238,3546,group.2um/nitrate_niter,Niter GDS43 (K-Saltpeter) W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Niter_GDS43_(K-Saltpeter)_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n239,5040,group.2um/topaz,Topaz Mt._Antero_#5 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Topaz_Mt_Antero_%235_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n240,4278,group.2um/carbonate_rhodochrosite,Rhodochrosite HS338.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Rhodochrosite_HS338.3B_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,-1,,,,,,\n241,5268,group.2um/carbonate_witherite,Witherite HS273.3B W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Witherite_HS273.3B_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,-1,,,,,,\n242,4026,group.2um/prehnite,Prehnite GDS613.a <60um =aa_,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Prehnite_GDS613.a_lt60um_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n243,3126,group.2um/prehnite+muscovite,Mizzonite BM1931 12 Scapolte W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Mizzonite_BM1931-12_Scapolte_BECKc_AREF.html,2,splib06,,,,,,,0.1,,,,,,,,,,,,\n244,3978,group.2um/borate_pinnoite,Pinnoite NMNH123943 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Pinnoite_NMNH123943_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n245,3996,group.2um/polyhalite,Polyhalite NMNH92669-4 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Polyhalite_NMNH92669-4_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n246,4368,group.2um/rivadavite,Rivadavite NMNH170164 Amph W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Rivadavite_NMNH170164_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n247,2298,group.2um/howlite,Howlite GDS155 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Howlite_GDS155_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n248,5190,group.2um/ulexite,Ulexite HS441.3B W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Ulexite_HS441.3B_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n249,6114,group.2um/white.crust.starkeyite,White_Crust LV30 starkeyite W1R1Fc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/White_Crust_LV30_starkeyite_ASDFRc_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n250,414,group.2um/zeolite_analcime,Analcime GDS1 Zeolite W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Analcime_GDS1_Zeolite_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n251,834,group.2um/hydroxide_brucite,Brucite HS247.3B W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Brucite_HS247.3B_BECKc_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n252,942,group.2um/chloride_carnallite,Carnallite NMNH98011 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Carnallite_NMNH98011_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n253,1320,group.2um/borate_colemanite,Colemanite GDS143 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Colemanite_GDS143_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n254,1470,group.2um/datolite,Datolite HS442.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Datolite_HS442.3B_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n255,2310,group.2um/hydrogrossular,Hydrogrossular NMNH120555 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Hydrogrossular_NMNH120555_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n256,2862,group.2um/micagrp_lepidolite,Lepidolite NMNH88526-1 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Lepidolite_NMNH88526-1_BECKa_AREF.html,2,splib06,,,,,,,-1,,,,,,,,,,,,\n257,2976,group.2um/micagrp_margarite,Margarite GDS106 W1R1Bc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Margarite_GDS106_BECKc_AREF.html,2,splib06,,,,,,,-1,,,,,,,,,,,,\n258,3012,group.2um/sulfate_mascagnite,Mascagnite GDS65.b (fr) W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Mascagnite_GDS65.b_(fr)_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n259,3114,group.2um/sulfate_mirabilite-wet_soil,Mirabilite GDS150 Na2SO4 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Mirabilite_GDS150_Na2SO4_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n260,3474,group.2um/zeolite_natrolite,Natrolite HS169.3B Zeolite W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Natrolite_HS169.3B_Zeolite_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n261,4338,group.2um/richterite,Richterite NMNH150800 HCl W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Richterite_NMNH150800_HCl_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n262,1152,group.2um/serpentine_chrysotile.coarse-fibrous,Chrysotile ML99-12A Coar Fib W1R1Fb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chrysotile_ML99-12A_Coar_Fib_ASDFRb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n263,1158,group.2um/serpentine_chrysotile.med-fine-fibrous,Chrysotile ML99-12C Fine Fib W1R1Fb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chrysotile_ML99-12C_Fine_Fib_ASDFRb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n264,1146,group.2um/serpentine_chrysotile.fine-fibrous,Chrysotile HS323.1B W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chrysotile_HS323.1B_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n265,1440,group.2um/serpentine_cronstedtite,Cronstedtite M3542 =bb_,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cronstedtite_M3542_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n266,4380,group.2um/micagrp_illite.roscoelite,Roscoelite EN124 W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Roscoelite_EN124_BECKb_AREF.html,2,splib06,,,,,,,0.15,,,,,,,,,,,,\n267,4764,group.2um/carbonate_strontianite,Strontianite HS272.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Strontianite_HS272.3B_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,-1,,,,,,\n268,4788,group.2um/sulfate_syngenite,Syngenite GDS139 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Syngenite_GDS139_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,-1,,,,,\n269,1134,group.2um/chrysocolla,Chrysocolla HS297.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Chrysocolla_HS297.3B_ASDFRb_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n270,720,group.2um/carbonate_azurite,Azurite WS316 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Azurite_WS316_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,-1,,,,,,\n271,2964,group.2um/carbonate_malachite,Malachite HS254.3B W1R1Bb,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Malachite_HS254.3B_BECKb_AREF.html,2,splib06,,,,,,,,,,,,,-1,,,,,,\n272,6522,group.2um/cyanide-CdK,Cyanide_Cadmium_Potassium W2R4Na RREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cyanide_Cadmium_Potassium_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n273,6528,group.2um/cyanide-NiK,Cyanide_Nickel_Potassium W2R4Na RREF,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cyanide_Nickel_Potassium_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n274,6534,group.2um/cyanide-trihydrate,Cyanide_Trihydrate CT1 W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cyanide_Trihydrate_CT1_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n275,6552,group.2um/cyanide-KFe,Cyanide_Potassium_Ferro W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cyanide_Potassium_Ferro_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n276,6540,group.2um/cyanide-ZnK,Cyanide_Zinc_Potassium W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Cyanide_Zinc_Potassium_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n277,6690,group.2um/organic_plastic.grnhs.roof,Plastic GGA-54 Grnhouse Roof W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Plastic_GGA-54_Grnhouse_Roof_BECKa_AREF.html,2,splib06,,,,,,,,,,,,,,,1,-1,,,\n278,6606,group.2um/organic_benzene+montswy,Montmorill+Benzene SWy 1ml W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Montmorill+Benzene_SWy_1ml_BECKa_AREF.html,2,splib06,,,,,,,,,0.756,,,,,,,-1,,,\n279,6624,group.2um/organic_trichlor+montswy,Montmorill+Trichlor SWy 1ml W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Montmorill+Trichlor_SWy_1ml_BECKa_AREF.html,2,splib06,,,,,,,,,0.756,,,,,,,-1,,,\n280,6618,group.2um/organic_toluene+montswy,Montmorill+Toluene SWy .5ml W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Montmorill+Toluene_SWy_.5ml_BECKa_AREF.html,2,splib06,,,,,,,,,0.798,,,,,,,-1,,,\n281,6630,group.2um/organic_unleaded.gas+montswy,Montmorill+Unleaded_Gas SWy W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Montmorill+Unleaded_Gas_SWy_BECKa_AREF.html,2,splib06,,,,,,,,,0.756,,,,,,,-1,,,\n282,6612,group.2um/organic_tce+montswy,Montmorill+TCE SWy saturated W1R1Ba,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Montmorill+TCE_SWy_saturated_BECKa_AREF.html,2,splib06,,,,,,,,,0.756,,,,,,,-1,,,\n283,5310,group.2um/zunyite,Zunyite GDS-241B <150um W1R1B?,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Zunyite_GDS241B_lt150um_BECKu_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n284,7008,group.2um/sulfate-mix_gypsum.trace.dust+debris-WTC01-2,WTC_Dust_Debris WTC01-2 W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/WTC_Dust_Debris_WTC01-2_ASDFRa_AREF.html,2,splib06,,,,,0.1,,,,,,,,,,,-1,,,\n285,7014,group.2um/sulfate-mix_gypsum.trace.dust+debris-WTC01-28,WTC_Dust_Debris WTC01-28 W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/WTC_Dust_Debris_WTC01-28_ASDFRa_AREF.html,2,splib06,,,,,0.1,,,,,,,,,,,-1,,,\n286,6498,group.2um/chrysotile.gypsum.wtc01-8,Coated_Steel_Girder WTC01-8 W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Coated_Steel_Girder_WTC01-8_ASDFRa_AREF.html,2,splib06,,,,,0.1,,,,,,,,,,,-1,,,\n287,4008,group.2um/portlandite,Portlandite GDS525 Ca(OH)2 W1R1Fa,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Portlandite_GDS525_Ca(OH)2_ASDFRa_AREF.html,2,splib06,,,,,,,,,,,,,,,,,,,\n288,234,group.2um/nitrate_potassium,Potassium Nitrate GDS43,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,,,,,,\n289,240,group.2um/nitrate_sodium,Sodium Nitrate GDS883,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,,,,,,\n290,624,group.2um/micagrp_vermiculite_GDS458,Vermiculite GDS458 W1R1Hc,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,0.16,,,,,,,,,\n291,630,group.2um/micagrp_vermiculite_ALB4SA00,Vermiculite ALB4SA00 W1R1Hc,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,0.26,,,,,,,,,\n292,636,group.2um/micagrp_vermiculite_WS682,Vermiculite WS682 W1R1Hc,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,0.68,,,,,,,,,\n293,642,group.2um/micagrp_vermiculite_WS681,Vermiculite WS681 W1R1Hc,https://crustal.usgs.gov/speclab/data/HTMLmetadata/Vermiculite_WS681_BECKc_AREF.html,2,sprlb06,,,,,,,,,,0.42,,,,,,,,,\n294,1008,group.2um/methane-gas-2.37um-experimental,CO2+O2+CH4 TRANSM ModtranZ20 r06crj3a=,No available USGS Spectral Library 7 Description Reference,2,sprlb06,,,,,,,,,,,,,,,,,,," }, { "path": "data/results_urls.txt", "content": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MIN_001_20230427T173309_2311711_010.nc\nhttps://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230804T191650_2321613_007/EMIT_L2B_MIN_001_20230804T191650_2321613_007.nc\nhttps://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230808T173953_2322012_011/EMIT_L2B_MIN_001_20230808T173953_2322012_011.nc\n" }, { "path": "data/rgb_browse_urls.txt", "content": "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230427T173309_2311711_010/EMIT_L2A_RFL_001_20230427T173309_2311711_010.png\nhttps://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230804T191650_2321613_007/EMIT_L2A_RFL_001_20230804T191650_2321613_007.png\nhttps://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230808T173953_2322012_011/EMIT_L2A_RFL_001_20230808T173953_2322012_011.png\n" }, { "path": "data/sample_coords.csv", "content": "ID,Category,Latitude,Longitude\n0,1,-39.94,-62.36\n1,1,-39.75,-61.74\n2,3,-40,-62.1\n3,2,-39.89,-61.85\n4,3,-39.38,-62.03\n" }, { "path": "guides/Getting_EMIT_Data_using_EarthData_Search.md", "content": "# Getting Started with Earth Surface Mineral Dust Source Investigation (EMIT) data in Earthdata Search\n\n**Note:** Users must have [NASA Earthdata Login](https://urs.earthdata.nasa.gov/home) to download data\n\n## Step 1. Go to [Earthdata Search](https://search.earthdata.nasa.gov/search) and Login\n\nClick **Earthdata Login** in the upper right portion of the Earthdata Search Landing Page\n\nEnter your Earthdata Login credentials (username/password) \n\n![Earthdata Landing Page](https://i.imgur.com/CMzS6kA.jpeg)\n\n## Step 2. Finding EMIT Data\n\n1. In the top left portion of the screen type in \"EMIT\" (quotes will limit results to an exact match). This will search for all available EMIT collections.\n\n![Earthdata Landing Page](https://i.imgur.com/UeuG3bd.jpeg)\n\n2. Currently the LP DAAC manages two EMIT collections.\nEMIT L2A Estimated Surface Reflectance and Uncertainty and Masks 60m V001 and EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60m V001.\n\n![Earthdata Landing Page](https://i.imgur.com/j2zWRR2.png)\n\nSelect the collection of granules that you want to view. If you are unsure which collection to choose the ***View Collection Details*** offers more information about each data product. The current state of the collection includes a very limited data release with only a handful of scenes from select locations.\n\n## Step 3. Download EMIT Data\n\nTo view the scenes and location, click on the image to focus it on the map the location is indicated by a green box. A preview of the scene is shown on the right.\n\n1. If you are interested in downloading all EMIT scenes discovered by Earthdata search, select the ***Download All*** icon at the bottom of the search results page.\n\n![Earthdata Landing Page](https://i.imgur.com/YjcAZvU.png)\n\n2. If you are interested in only a single scene, click the download arrow on the card (or list row) for the select scene. You will then be able to download the select files related to that image.\n\n![Earthdata Landing Page](https://i.imgur.com/cdOsgm3.png)\n\nYou can also access S3 information (e.g., AWS region, bucket, temporary credentials for S3 access, and file names)\n\n![Earthdata Landing Page](https://i.imgur.com/Y4jVdD0.png)\n\n3. To download multiple granules, click on the green + symbol to add files to the project. Click on the green button on the bottom of the page that says **Download**. This will take you to the options page for customizing the download.\n\n![Earthdata Landing Page](https://i.imgur.com/ouESYWJ.png)\n\n## Step 4. Data Access\n\nOn the next page click the **Direct Download** option and click the green **Download Data** on the bottom left side of the page.\n\n![Earthdata Landing Page](https://i.imgur.com/NuoENO8.png)\n\n## Step 5. Download Status\n\n![Earthdata Landing Page](https://i.imgur.com/T3sbhau.png)\n\nThe final page of instructions show the download links for data access in the cloud. You should see four tabs: Download Files, AWS S3 Access, Download Script, and Browse Imagery.\n\nThe **Download Files** tab provides the *https://* links for downloading the files locally\n\nThe **AWS S3 Access** tab provides the *s3://* links, which is what we would use to access the data directly in region (us-west-2) within the AWS cloud. \n\n---\n\n**Please visit our [FAQ](https://forum.earthdata.nasa.gov/) page if you have general questions or visit on the [Earthdata Forum](https://forum.earthdata.nasa.gov/) to ask your own question.**\n\n---\n\n## Contact Info \n\nEmail: \nVoice: +1-866-573-3222 \nOrganization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \nWebsite: \nDate last modified: 06-05-2024 \n\n¹Work performed under USGS contract 140G0121D0001 for NASA contract NNG14HH33I. \n" }, { "path": "guides/Streaming_cloud_optimized_geotiffs_using_QGIS.md", "content": "# Streaming NASA Earthdata Cloud-Optimized Geotiffs using QGIS\n\n**Note:** Users must have [NASA Earthdata Login](https://urs.earthdata.nasa.gov/home) to stream data.\n\nQGIS can be used to stream NASA Earthdata cloud-optimized geotiff files. To do this QGIS uses https and the vsicurl virtual file system handler. This guide will show you how to configure QGIS to do this. There are 4 steps needed.\n\n1. Create a `.netrc` file\n2. Add Custom Environment Variables\n3. Restart QGIS\n4. Add Data\n\n## 1. Create a .netrc file\n\nUnder the hood QGIS uses gdal and vsicurl to stream data via https. For NASA Earthdata, this requires a `.netrc` file to store your Earthdata login credentials. If you already have one, you can skip this step. This file should be located in your home directory. Choose one of the methods below to create the file and enter your credentials.\n\n### A. Manual Set Up\n\n- Download the [.netrc template file](https://github.com/nasa/LPDAAC-Data-Resources/tree/main/data/.netrc) and save it in your *home/user/* directory, where *user* is your personal user directory. For example: `C:\\Users\\user\\.netrc` or `home/user/.netrc.` \n- Open the `.netrc` file in a text editor and replace with your NASA Earthdata Login username and with your NASA Earthdata Login password.\n\nAfter editing, the file should look something like this:\n\n![Example .netrc 1](../img/example_netrc1.png)\n\nor you can also have everything on a single line separated by spaces, like:\n\n![example .netrc 2](../img/example_netrc2.png)\n\n### B. Command Line \n\n**For Linux/MacOS:**\n\nTo Create a .netrc file, enter the following in the command line, replacing and with your NASA Earthdata username and password. This will create a file in your home directory or append your NASA credentials to an existing file.\n\n```bash\necho \"machine urs.earthdata.nasa.gov login password \" >>~/.netrc\n```\n\n**For Windows:**\n\nTo Create a .netrc file, enter the following in the command line, replacing and with your NASA Earthdata username and password. This will create a file in your home directory or append your NASA credentials to an existing file.\n\n```cmd\necho machine urs.earthdata.nasa.gov login password >> %userprofile%\\.netrc\n```\n\nYou can verify that the file is correct by opening with a text editor. It should look like an example in one of the figures above.\n\n## 2. Environment Settings\n\nNext, while still in the **Settings** menu, select **System** on the left-hand side, then scroll down to **Environment**. Select the check box and enter the Variables and Values in the table below by clicking the plus sign to add a new variable. These variables, set up a place for cookies to be stored and read from, prevent GDAL from reading all files in the directory, specify the extensions GDAL is allowed to access over HTTPS using the vsicurl virtual file system handler, and allow use of unsafe SSL connections (use with caution).\n\n|Variable|Value|\n| ----------- | ----------- |\n|GDAL_HTTP_COOKIEFILE|~/cookies.txt|\n|GDAL_HTTP_COOKIEJAR|~/cookies.txt|\n|GDAL_DISABLE_READDIR_ON_OPEN|EMPTY_DIR|\n|CPL_VSIL_CURL_ALLOWED_EXTENSIONS|TIF|\n|GDAL_HTTP_UNSAFESSL|YES|\n\n> **Note: These settings may affect streaming from other data sources.**\n\n![Environment Settings](../img/environment_settings.png)\n\n## 3. Restart QGIS\n\nRestart QGIS to load the new environment settings.\n\n## 4. Add Some Data\n\nTo add some raster data, select the add raster data button from the toolbar, select 'Protocol: HTTP(S), cloud, etc' as the source type, then enter a URI for a cloud-optimized geotiff file and press the add button. For example:\n\n`https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BCH4ENH.001/EMIT_L2B_CH4ENH_001_20240423T074559_2411405_018/EMIT_L2B_CH4ENH_001_20240423T074559_2411405_018.tif`\n\n![Add raster data](../img/add_data.png)\n\nThis should add the data to your map, and will work with any NASA Earthdata hosted cloud-optimized geotiff file.\n\n![Added Scene](../img/example_scene.png)\n\n>**If these instructions do not work, please verify that your `.netrc` file has the correct username and password, and is formatted as shown in [Section 1](#1-create-a-netrc-file).**\n\n## Contact Info \n\nEmail: \nVoice: +1-866-573-3222 \nOrganization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \nWebsite: \nDate last modified: 06-25-2024 \n\n¹Work performed under USGS contract 140G0121D0001 for NASA contract NNG14HH33I. \n" }, { "path": "python/how-tos/How_to_Convert_to_ENVI.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How To: Convert EMIT .nc to .envi\\n\",\n \"\\n\",\n \"There are currently 2 similar methods to convert the EMIT netCDF4 files to `.envi` format. Note these only support L1B Radiance, L1B Obs, L2A Reflectance, L2A Reflectance Uncertainty, or L2A Mask to .envi. They do not yet support the L2B Mineral or L2B Mineral Uncertainty products.\\n\",\n \"1. The `write_envi` function in EMIT tools. This function is still being developed but will currently:\\n\",\n \" - Write a GLT output to use for orthocorrection later \\n\",\n \" - Functions from `emit_tools` can be used beforehand to orthorectify if so desired\\n\",\n \"2. The `reformat.py` script available in the [emit-sds/emit-utils](https://github.com/emit-sds/emit-utils) repository can be used to convert EMIT netCDF files (as delivered to the LP DAAC) to ENVI files. This script also can apply the included GLT to orthorectify the image if desired.\\n\",\n \"\\n\",\n \"This jupyter notebook walks through how to use both methods to provide users with programmatic routes to accomplish their EMIT reformatting workflows.\\n\",\n \"\\n\",\n \"**Requirements:**\\n\",\n \"+ A NASA [Earthdata Login](https://urs.earthdata.nasa.gov/) account is required to download EMIT data \\n\",\n \"+ Selected the `emit_tutorials` environment as the kernel for this notebook.\\n\",\n \" + For instructions on setting up the environment, follow the the `setup_instructions.md` included in the `/setup/` folder of the repository. \\n\",\n \"\\n\",\n \"**Learning Objectives**\\n\",\n \"+ How to use the `write_envi` function from `emit_tools` module to convert an EMIT netCDF4 to a `.envi` file.\\n\",\n \"+ How to use the `reformat.py` function from the `emit-utils` repository to convert an EMIT netCDF4 to a `.envi` file.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Setup\\n\",\n \"\\n\",\n \"Import packages\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import earthaccess\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Authenticate using Earthdata Login and Download the required Granules\\n\",\n \"\\n\",\n \"Login to your NASA Earthdata account and create a `.netrc` file using the `login` function from the `earthaccess` library. If you do not have an Earthdata Account, you can create one [here](https://urs.earthdata.nasa.gov/home). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this notebook we will download the files necessary using `earthaccess`. You can also access the data in place or stream it, but this can slow due to the file sizes. Provide a URL for an EMIT L2A Reflectance granule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"url = 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get an HTTPS Session using your earthdata login, set a local path to save the file, and download the granule asset - This may take a while, the reflectance file is approximately 1.8 GB. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get Https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_fsspec_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"granule_asset_id = url.split('/')[-1]\\n\",\n \"# Define Local Filepath\\n\",\n \"fp = f'../../data/{granule_asset_id}'\\n\",\n \"# Download the Granule Asset if it doesn't exist\\n\",\n \"if not os.path.isfile(fp):\\n\",\n \" fs.download(url, fp)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now lets create an output folder where we will save the `.envi` files.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"outpath = '../../data/envi' \\n\",\n \"if not os.path.exists(outpath):\\n\",\n \" os.makedirs(outpath)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Method 1: Using `write_envi` from the `emit_tools` module.\\n\",\n \"\\n\",\n \"Import the necessary packages for this method. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"import emit_tools as et\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Open the granule using the `emit_xarray` function. We can orthorectify here if so desired.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = et.emit_xarray(fp, ortho=True)\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, write the dataset as an `.envi` output. If we chose not to orthorectify, you can include a `glt` file to orthorectify later.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"et.write_envi(ds, outpath, overwrite=False, extension='.img', interleave='BIL', glt_file=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Method 2: Using reformat.py from emit-utils\\n\",\n \"\\n\",\n \"### 2.1 Clone and Install emit-utils\\n\",\n \"\\n\",\n \"Clone the [emit-utils](https://github.com/emit-sds/emit-utils) repository.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"!git clone https://github.com/emit-sds/emit-utils.git ../emit_utils/\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This will copy the `emit-utils` repository to a folder within this repository. \\n\",\n \"\\n\",\n \"After you have copied it, use `pip` package manager to install the directory as a package to ensure you have all of the dependencies and be used in the command line. \\n\",\n \"\\n\",\n \"> **This requires that some dependencies already be installed to work properly on Windows. If you have created the Python environment described in the [setup instructions](../../setup/setup_instructions.md) it should work.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"!pip install --editable ../emit_utils\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After successfully installing `emit-utils`, you can use the scripts contained within as part of your workflows. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Executing the Reformat Script\\n\",\n \"\\n\",\n \"Before calling the `reformat.py` script, make sure you have an output directory for the `.envi` files that will be produced.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"outpath = '../../data/envi' \\n\",\n \"if not os.path.exists(outpath):\\n\",\n \" os.makedirs(outpath)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, execute the `reformat.py` script contained in the emit-utils repository. When executing this script, provide the path to the `.nc` file, followed by the directory to place the `.envi` files in. If you wish to apply the GLT or orthorectify, include `--orthorectify` as an argument.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"!python ../emit_utils/emit_utils/reformat.py ../../data/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc ../../data/envi/ --orthorectify\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This will orthorectify the image, create an ENVI header, and save it in `.envi` format inside the `../data/envi` folder.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 07-06-2023 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"ee32eb811eece122811842209709e899a392a6a8deb39746eb88e988164e27f9\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/how-tos/How_to_Direct_S3_Access.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How to: Use Direct S3 Access to work with EMIT Data\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"EMIT datasets are available through NASA's Earthdata Cloud. NASA Earthdata on Cloud is always free and accessible via either HTTPS or direct S3 bucket access. With direct S3 access, you can bring your \\\"code to the data\\\", making your processing faster and scalable. Direct S3 access to NASA Earthdata on Cloud is only available if your Amazon Web Services (AWS) instance is set up in the `us-west-2` region. This notebook explains how to utilize direct S3 access to EMIT data. \\n\",\n \"\\n\",\n \"**Requirements**\\n\",\n \"\\n\",\n \"+ A NASA [Earthdata Login](https://urs.earthdata.nasa.gov/) account is required to download EMIT data\\n\",\n \"+ A configured `.netrc` file to access NASA Earth Data\\n\",\n \" + This will be configured in the notebook using the `earthaccess` python library. \\n\",\n \"+ Selected the `emit_tutorials` environment as the kernel for this notebook.\\n\",\n \" + For instructions on setting up the environment, follow the `setup_instructions.md` included in the `/setup/` folder of the repository. \\n\",\n \"+ **xarray v2022.12.0** or newer to read s3 files multiple times when open with s3fs\\n\",\n \" + Version can be checked in Python using `xarray.__version__`\\n\",\n \" + To update, activate your environment and enter `pip install xarray==2022.12.0` in command line.\\n\",\n \"\\n\",\n \"**Learning Objectives** \\n\",\n \"+ How to use Direct S3 Access to EMIT Data\\n\",\n \"+ How to add this functionality to any notebook from this repository\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import the required packages.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Import Packages\\n\",\n \"import os\\n\",\n \"import sys\\n\",\n \"import requests\\n\",\n \"import s3fs\\n\",\n \"import netCDF4 as nc\\n\",\n \"from osgeo import gdal\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import hvplot.xarray\\n\",\n \"import holoviews as hv\\n\",\n \"import earthaccess\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from emit_tools import emit_xarray\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Overview of s3 Access\\n\",\n \"\\n\",\n \"NASA Earthdata Cloud data in S3 can be directly accessed via temporary credentials; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region. Direct S3 access is achieved by passing NASA supplied temporary credentials (token) to AWS so we can interact with S3 objects from applicable Earthdata Cloud buckets. Your NASA Earthdata login credentials can easily be managed using the `earthaccess` library. We can create the necessary `.netrc` file that stores the Earthdata Login credentials locally with the following code cell. Enter your username and password if prompted, these will be used to sign into NASA Earthdata and retrieve the necessary temporary credentials allowing you to utilize s3 access.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# The persist option creates and stores your username and password in a .netrc file\\n\",\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For now, each NASA DAAC has different AWS credentials endpoints. Below are some of the credential endpoints to various DAACs:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"s3_cred_endpoint = {\\n\",\n \" 'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',\\n\",\n \" 'gesdisc': 'https://data.gesdisc.earthdata.nasa.gov/s3credentials',\\n\",\n \" 'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',\\n\",\n \" 'ornldaac': 'https://data.ornldaac.earthdata.nasa.gov/s3credentials',\\n\",\n \" 'ghrcdaac': 'https://data.ghrc.earthdata.nasa.gov/s3credentials'\\n\",\n \"}\\n\",\n \"s3_cred_endpoint\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a function to make a request to an endpoint for temporary credentials. Remember, each DAAC has their own endpoint and credentials are not usable for cloud data from other DAACs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Define Function \\n\",\n \"def get_temp_creds(provider):\\n\",\n \" return requests.get(s3_cred_endpoint[provider]).json()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Get Credentials\\n\",\n \"temp_creds_req = get_temp_creds('lpdaac')\\n\",\n \"#temp_creds_req\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Set up an s3fs Session for Direct Access\\n\",\n \"\\n\",\n \"`s3fs` sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the temporary credentials we recieved from our temporary credentials endpoint.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Pass Authentication to s3fs\\n\",\n \"fs_s3 = s3fs.S3FileSystem(anon=False, \\n\",\n \" key=temp_creds_req['accessKeyId'], \\n\",\n \" secret=temp_creds_req['secretAccessKey'], \\n\",\n \" token=temp_creds_req['sessionToken'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Below we specify the s3 URL to the data asset in Earthdata Cloud. This URL can be found via Earthdata Search or programmatically through the CMR and CMR-STAC APIs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Define S3 URL \\n\",\n \"s3_url = 's3://lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Open with the netCDF file using the s3fs package, then load the cloud asset into an xarray dataset, or use directly with `emit_xarray` function from `emit_tools`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Open s3 url\\n\",\n \"fp = fs_s3.open(s3_url, mode='rb')\\n\",\n \"# Open dataset with xarray\\n\",\n \"ds = xr.open_dataset(fp) #Note this only opens the root group (reflectance)\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Open and Orthorectify\\n\",\n \"ds = emit_xarray(fp, ortho=True)\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"#Plot Spatially\\n\",\n \"ds.reflectance.data[ds.reflectance.data == -9999] = np.nan # Mask out fill_values\\n\",\n \"ds.sel(wavelengths=650, method='nearest').hvplot.image(cmap='viridis', aspect = 'equal', frame_width=500, rasterize=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"#Plot Spectra at a Location\\n\",\n \"ds['reflectance'].data[:,:,ds['good_wavelengths'].data==0] = np.nan # Mask out bad bands\\n\",\n \"ds.sel(longitude=-61.833,latitude=-39.710,method='nearest').hvplot.line(y='reflectance',x='wavelengths', color='black', frame_width=400)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## S3 Access for any Notebook in this Repository\\n\",\n \"\\n\",\n \"Add the two code blocks below to any how-to or tutorial notebook, by replacing the block that sets the local filepath(s) as `fp` with the two blocks below. The first block imports the additional packages required and retrieves temporary s3 credentials. The second uses `s3fs` to open the desired s3 URL and create an object readable by `xarray`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import requests\\n\",\n \"import s3fs\\n\",\n \"import earthaccess\\n\",\n \"\\n\",\n \"# Use Earthaccess to Manage NASA Earthdata Credentials and create .netrc if necessary\\n\",\n \"earthaccess.login(persist=True)\\n\",\n \"\\n\",\n \"# Get Temporary Credentials/Token\\n\",\n \"temp_creds_req = requests.get('https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials').json() # use lpdaac credential endpoint for EMIT data\\n\",\n \"\\n\",\n \"# Create s3fs session\\n\",\n \"fs_s3 = s3fs.S3FileSystem(anon=False, \\n\",\n \" key=temp_creds_req['accessKeyId'], \\n\",\n \" secret=temp_creds_req['secretAccessKey'], \\n\",\n \" token=temp_creds_req['sessionToken'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set s3 url and open\\n\",\n \"s3_url = 's3://lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc' # S3 URL to L2A Reflectance File used throughout tutorial\\n\",\n \"#s3_url_mask = 's3://lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_MASK_001_20220903T163129_2224611_012.nc' # Only used for Quality How-to\\n\",\n \"fp = fs_s3.open(s3_url, mode='rb')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now you should be able to proceed as normal through the other Jupyter notebooks at the step opening the file with `emit_xarray` from the `emit_tools` module or `xarray.open_dataset` depending on the notebook.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 08-03-2023 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3292b2aceff7d39327a7519422d4180a7c9b133202090f26e797e3dd8f2c7877\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/how-tos/How_to_Extract_Area.ipynb", "content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How to: Extracting EMIT Spectra using a Shapefile/GeoJSON\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"In this notebook we will open a netCDF4 file from the Earth Surface Minteral Dust Source Investigation (EMIT) as an `xarray.Dataset`. We will then extract extract or clip to an area using a `.geojson` file (will also work with shapefile). The workflows outlined here will work with reflectance L2A or radiance L1B data.\\n\",\n \"\\n\",\n \"**Requirements:**\\n\",\n \"+ A NASA [Earthdata Login](https://urs.earthdata.nasa.gov/) account is required to download EMIT data \\n\",\n \"+ Selected the `emit_tutorials` environment as the kernel for this notebook.\\n\",\n \" + For instructions on setting up the environment, follow the the `setup_instructions.md` included in the `/setup/` folder of the repository. \\n\",\n \"\\n\",\n \"**Learning Objectives** \\n\",\n \"- How to open and EMIT Dataset as an `xarray.Dataset`\\n\",\n \"- How to extract values or clip an EMIT dataset to a region of interest\\n\",\n \"- How to write a new netCDF4 output using the clipped data\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import the required Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Import Packages\\n\",\n \"import os\\n\",\n \"import earthaccess\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"from osgeo import gdal\\n\",\n \"import rasterio as rio\\n\",\n \"import rioxarray as rxr\\n\",\n \"import hvplot.xarray\\n\",\n \"import hvplot.pandas\\n\",\n \"import holoviews as hv\\n\",\n \"import geopandas as gp\\n\",\n \"import sys\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from emit_tools import emit_xarray\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Login to your NASA Earthdata account and create a `.netrc` file using the `login` function from the `earthaccess` library. If you do not have an Earthdata Account, you can create one [here](https://urs.earthdata.nasa.gov/home). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this notebook we will download the files necessary using `earthaccess`. You can also access the data in place or stream it, but this can slow due to the file sizes. Provide a URL for an EMIT L2A Reflectance granule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"url = 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc'\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get an HTTPS Session using your earthdata login, set a local path to save the file, and download the granule asset - This may take a while, the reflectance file is approximately 1.8 GB. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get requests https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"granule_asset_id = url.split('/')[-1]\\n\",\n \"# Define Local Filepath\\n\",\n \"fp = f'../../data/{granule_asset_id}'\\n\",\n \"# Download the Granule Asset if it doesn't exist\\n\",\n \"if not os.path.isfile(fp):\\n\",\n \" with fs.get(url,stream=True) as src:\\n\",\n \" with open(fp,'wb') as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Open the file downloaded and defined as `fp`. To do this, we will use the `emit_xarray` function from the `emit_tools` module. This module contains a few helpful functions that can be used with EMIT data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"ds = emit_xarray(fp, ortho=True)\\n\",\n \"ds\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using the `read_file()` function from `geopandas`, read in the `.geojson` file containing the polygon you wish to extract.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"shape = gp.read_file('../../data/isla_gaviota.geojson')\\n\",\n \"shape\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First, mask out fill-values by setting them to `np.nan`, then plot the polygon we've loaded overlayed on a plot of the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Set fill values to NaN\\n\",\n \"ds.reflectance.data[ds.reflectance.data == -9999] = np.nan\\n\",\n \"# Select band and plot with polygon\\n\",\n \"ds_850 = ds.sel(wavelengths=850,method='nearest')\\n\",\n \"ds_850.hvplot.image(cmap='viridis', frame_height=600, frame_width=600, geo=True, crs='EPSG:4326').opts(title=f\\\"Reflectance at {ds_850.wavelengths.data:.3f} ({ds_850.wavelengths.units})\\\")*shape.hvplot(color='#d95f02', alpha=0.5, geo=True, crs='EPSG:4326')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the `clip` function from `rasterio` to clip the dataset to polygons from the `geopandas.geodataframe`. Setting `all_touched` to `True` will include pixels that intersected with the edges of the polygon. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"clipped = ds.rio.clip(shape.geometry.values,shape.crs, all_touched=True)\\n\",\n \"clipped\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To view the clipped image, select a band from the `clipped` dataset and plot it spatially.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"clipped_850 = clipped.sel(wavelengths=850,method='nearest')\\n\",\n \"clipped_850.hvplot.image(cmap='viridis', frame_height=600, geo=True, tiles='ESRI').opts(\\n\",\n \" title=f'Reflectance at {clipped_850.wavelengths.data:.3f} ({clipped_850.wavelengths.units})',\\n\",\n \" xlabel='Longitude', ylabel='Latitude')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can save the clipped `xarray.Dataset` as a netCDF4 output that can be reopened using the `xarray.open_dataset` function. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"clipped.to_netcdf('../../data/clipped_data.nc')\\n\",\n \"# Example for Opening \\n\",\n \"#ds = xr.open_dataset('../../data/clipped_data.nc')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 03-13-2024 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3292b2aceff7d39327a7519422d4180a7c9b133202090f26e797e3dd8f2c7877\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/how-tos/How_to_Extract_Points.ipynb", "content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How to: Extracting EMIT Spectra at Specified Coordinates\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"In this notebook we will open a netCDF4 file from the Earth Surface Minteral Dust Source Investigation (EMIT) as an `xarray.Dataset`. We will then extract the spectra at point coordinates from a `.csv` as a dataframe, then save and plot the data.\\n\",\n \"\\n\",\n \"**Requirements:**\\n\",\n \"+ A NASA [Earthdata Login](https://urs.earthdata.nasa.gov/) account is required to download EMIT data \\n\",\n \"+ Selected the `emit_tutorials` environment as the kernel for this notebook.\\n\",\n \" + For instructions on setting up the environment, follow the the `setup_instructions.md` included in the `/setup/` folder of the repository. \\n\",\n \"\\n\",\n \"**Learning Objectives**\\n\",\n \"+ How to open an EMIT file as an `xarray.Dataset`\\n\",\n \"+ How to extract spectra at coordinates listed in a `.csv` file \"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import the required Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import Packages\\n\",\n \"import sys\\n\",\n \"import os\\n\",\n \"import earthaccess\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import xarray as xr\\n\",\n \"import hvplot.pandas\\n\",\n \"import hvplot.xarray\\n\",\n \"import holoviews as hv\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from emit_tools import emit_xarray\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Login to your NASA Earthdata account and create a `.netrc` file using the `login` function from the `earthaccess` library. If you do not have an Earthdata Account, you can create one [here](https://urs.earthdata.nasa.gov/home). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this notebook we will download the files necessary using `earthaccess`. You can also access the data in place or stream it, but this can slow due to the file sizes. Provide a URL for an EMIT L2A Reflectance granule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"url = 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc'\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get an HTTPS Session using your earthdata login, set a local path to save the file, and download the granule asset - This may take a while, the reflectance file is approximately 1.8 GB.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get requests https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"granule_asset_id = url.split('/')[-1]\\n\",\n \"# Define Local Filepath\\n\",\n \"fp = f'../../data/{granule_asset_id}'\\n\",\n \"# Download the Granule Asset if it doesn't exist\\n\",\n \"if not os.path.isfile(fp):\\n\",\n \" with fs.get(url,stream=True) as src:\\n\",\n \" with open(fp,'wb') as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Open the file downloaded and defined as `fp`. To do this, we will use the `emit_tools` module which contains a few helpful functions that can be used with EMIT data. Use the `ortho=True` option to orthorectify the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = emit_xarray(fp, ortho=True)\\n\",\n \"ds\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now open the `.csv` included in the `/data/` directory as a `pandas.dataframe`.\\n\",\n \"\\n\",\n \"> Note: The category values here are arbitrary and included as an example of an additional column users may want.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"points = pd.read_csv('../../data/sample_coords.csv')\\n\",\n \"points\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Make a plot to visualize the points we're going to select on the dataset. Here we use the reflectance values at 850nm as our basemap. To mask fill-values, assign them to `np.nan`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Assign fill-values to NaN\\n\",\n \"ds.reflectance.data[ds.reflectance.data == -9999] = np.nan\\n\",\n \"# Select a single band and plot points on top\\n\",\n \"ds.sel(wavelengths=850,method='nearest').hvplot.image(cmap='greys', frame_height=600, frame_width=600, geo=True, crs='EPSG:4326').opts(title=f\\\"Example Points\\\")*\\\\\\n\",\n \"points.hvplot.points(x='Longitude',y='Latitude', by='ID', color=hv.Cycle('Dark2'), geo=True, crs='EPSG:4326')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set the `points` dataframe index as `ID` to utilize our existing point ID's as an index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"points = points.set_index(['ID'])\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Convert the dataframe to an `xarray.Dataset`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xp = points.to_xarray()\\n\",\n \"xp\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Select the data from our EMIT dataset using the Latitude and Longitude coordinates from our point dataset, then convert the output to a `pandas.dataframe`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"extracted = ds.sel(latitude=xp.Latitude,longitude=xp.Longitude, method='nearest').to_dataframe()\\n\",\n \"extracted\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The output is a longform dataframe using the `'ID'` field as an index. This is missing our `'Category'` column from our original dataframe. Use the `pd.join` function to add the `'Category'` column to our dataset using `'ID'` as an index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = extracted.join(points['Category'], on=['ID'])\\n\",\n \"df\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we have a dataframe containing our initial data, in addition to the extracted point data. This a a good place to save an output as a `.csv`. Go ahead and do that below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df.to_csv('../../data/example_out.csv')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can use our dataframe to plot the reflectance data we extracted, but first, mask the reflectance values of `-0.01`, which represent deep water vapor absorption regions. To do this, assign values where the reflectance = `-0.01` to `np.nan`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Data values are slightly different than -0.01 because of floating point precision, which requires a tolerance to be set\\n\",\n \"df['reflectance'] = df['reflectance'].where(abs(df['reflectance'] - (-0.01)) > 1e-8, np.nan)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Plot the data using `hvplot`. We can use `by=` to separate the reflectances by their `ID`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df.hvplot(x='wavelengths',y='reflectance', by=['ID'], color=hv.Cycle('Dark2'), frame_height=400, frame_width=600).opts(title='Example Points - Reflectance', xlabel='Wavelengths (nm)',ylabel='Reflectance')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 03-13-2024 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3292b2aceff7d39327a7519422d4180a7c9b133202090f26e797e3dd8f2c7877\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/how-tos/How_to_Orthorectify.ipynb", "content": "{\n \"cells\": [\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How To: Orthorectify EMIT Data\\n\",\n \"\\n\",\n \">**Warning: This notebook uses a lot of memory, if using 2i2c, you will need a 29.7 GB, 3.75 CPU instance.**\\n\",\n \"\\n\",\n \"EMIT Data is provided in non-orthorectified format to reduce data size. The `location` group contains latitude and longitude values of each pixel as well as a geometric lookup table (GLT) that can be used to orthocorrect the imagery. The GLT is an array that provides relative downtrack and crosstrack reference locations from the raw scene to facilitate fast projection of the dataset.\\n\",\n \"\\n\",\n \"This notebook will walk through using the `emit_tools` module to do the orthorectification process as well as a in depth walkthrough of the process.\\n\",\n \"\\n\",\n \"If you are planning to convert the EMIT `netCDF4` files to `.envi` format, the `reformat.py` available in the [emit-sds/emit-utils](https://github.com/emit-sds/emit-utils) repository can be used to orthorectify the imagery during the conversion process.\\n\",\n \"\\n\",\n \"**Requirements:**\\n\",\n \"+ A NASA [Earthdata Login](https://urs.earthdata.nasa.gov/) account is required to download EMIT data \\n\",\n \"+ Selected the `emit_tutorials` environment as the kernel for this notebook.\\n\",\n \" + For instructions on setting up the environment, follow the the `setup_instructions.md` included in the `/setup/` folder of the repository. \\n\",\n \"\\n\",\n \"**Learning Objectives**\\n\",\n \"+ How to open an EMIT file as an `xarray.Dataset`\\n\",\n \"+ How to orthorectify an EMIT file\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Using emit_xarray to Orthorectify\\n\",\n \"\\n\",\n \"Import the required Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import Packages\\n\",\n \"import os\\n\",\n \"import earthaccess\\n\",\n \"import netCDF4 as nc\\n\",\n \"from osgeo import gdal\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import hvplot.xarray\\n\",\n \"import holoviews as hv\\n\",\n \"import sys\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from emit_tools import emit_xarray\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Login to your NASA Earthdata account and create a `.netrc` file using the `login` function from the `earthaccess` library. If you do not have an Earthdata Account, you can create one [here](https://urs.earthdata.nasa.gov/home). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this notebook we will download the files necessary using `earthaccess`. You can also access the data in place or stream it, but this can slow due to the file sizes. Provide a URL for an EMIT L2A Reflectance granule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"url = 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc'\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get an HTTPS Session using your earthdata login, set a local path to save the file, and download the granule asset - This may take a while, the reflectance file is approximately 1.8 GB. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get requests https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"granule_asset_id = url.split('/')[-1]\\n\",\n \"# Define Local Filepath\\n\",\n \"fp = f'../../data/{granule_asset_id}'\\n\",\n \"# Download the Granule Asset if it doesn't exist\\n\",\n \"if not os.path.isfile(fp):\\n\",\n \" with fs.get(url,stream=True) as src:\\n\",\n \" with open(fp,'wb') as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the `emit_xarray` function imported from the `emit_tools` module to read in the data.\\n\",\n \"\\n\",\n \"The `emit_xarray` function will open and and place data from the groups of an EMIT dataset into an `xarray.Dataset`. It includes an `ortho` option which is set to `False` by default. This function reads the root group, `location` group, and `sensor_band_parameters` groups from the EMIT dataset. It then flattens all of the variables contained within those 3 groups into a single `xarray_dataset`. When `ortho=True`, it uses the GLT layers to build a lat/lon grid and places the values from the `reflectance` root group into the grid. The lat and lon grid is used as dimensional coordinates in the output `xarray.dataset` while the `wavelengths` and `fwhm` from the `sensor_band_parameters` group are used as non-dimensional coordinates. The `reflectance` data from the root group of the netCDF is then orthorectified and added as a variable in the `xarray.Dataset`.\\n\",\n \"\\n\",\n \"Read in a dataset using the `emit_xarray` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = emit_xarray(fp, ortho=True)\\n\",\n \"ds\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's visualize a red band. First assign fill-values to `np.nan`, then select and plot a band using the `sel` function from `xarray` and the `hvplot` library. When we orthorectify (place data on a grid and rotate it) we end up with a bunch of extra pixels that have no data. These are filled with -9999, which will distort our visualization unless we assign them to `np.nan` or mask them.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Assign fill-values to nan\\n\",\n \"ds.reflectance.data[ds.reflectance.data == -9999] = np.nan\\n\",\n \"# Get red band and plot example\\n\",\n \"ds.sel(wavelengths=650,method='nearest').hvplot.image(cmap='viridis', aspect = 'equal', frame_width=500, rasterize=True)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The orthorectified dataset can also be saved as a netCDF4 output that can be reopened using the `xarray.open_dataset` function if so desired using the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds.to_netcdf('../../data/example_emit_ortho_out.nc')\\n\",\n \"# Example for Opening \\n\",\n \"# ds = xr.open_dataset('../data/example_emit_ortho_out.nc')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now open a dataset with `ortho=False`. Notice that the data is not on a Lat/Lon grid, instead each downtrack and crosstrack pixel center is geotagged with a latitude and longitude.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = emit_xarray(fp,ortho=False)\\n\",\n \"ds\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see the difference in structure and the plotted data if the dataset is orthorectified or not. It's also not necessary to mask fill-values generated by the orthorectification process.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get red band and plot example\\n\",\n \"ds.sel(wavelengths=850,method='nearest').hvplot.image(cmap='viridis', aspect = 'equal', frame_width=500, rasterize=True)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Detailed Explanation of Orthorectification and xarray.Dataset Construction\\n\",\n \"\\n\",\n \"As mentioned, the orthorectification process for EMIT data utilizes a premade geometric lookup table (GLT) that is included in the `location` group of the file. `glt_x` and `glt_y` contain the pixel indices from the raw dataset to construct an orthocorrected array. These indices data can be pulled from the raw dataset to populate an orthocorrected array using numpy broadcasting.\\n\",\n \"\\n\",\n \"To do this, first read in the `location` group data. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"loc = xr.open_dataset(fp, engine = 'h5netcdf', group='location')\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, build a numpy array using the GLT arrays from the `location` group. Use the `stack` function to stack the x and y GLT arrays and use the function `nan_to_num` to set the NaN values to the `GLT_NODATA_VALUE`, which is 0 for EMIT datasets. After doing this, you can check the shape of the array to see the expected height and width in pixels of the image after orthorectification.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"GLT_NODATA_VALUE=0\\n\",\n \"glt_array = np.nan_to_num(np.stack([loc['glt_x'].data,loc['glt_y'].data],axis=-1),nan=GLT_NODATA_VALUE).astype(int)\\n\",\n \"glt_array.shape\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we have a GLT array, we need to open and create a numpy array from the reflectance dataset. Open the dataset using `xarray` and assign the data to an array. Here you can check the shape of the array to see the dimensions of the uncorrected data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = xr.open_dataset(fp,engine = 'h5netcdf')\\n\",\n \"ds_array = ds['reflectance'].data\\n\",\n \"ds_array.shape\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have both arrays, we need to assign a fill value for positions that have no data. We can then construct an empty array with the dimensions we desire (GLT shape) and populate it with values from the dataset. To do this we first use np.zeros to create an array of all zeros then add the `fill_value` to the array of zeros to change the zeros to the `fill_value`.\\n\",\n \"\\n\",\n \"Next we can build an array of valid locations by omitting the portions of the array containing the `GLT_NODATA_VALUES`.\\n\",\n \"\\n\",\n \"After that, change the indexing of the GLT array which is one based to zero based by subtracting 1 from them.\\n\",\n \"\\n\",\n \"Lastly, we can use broadcasting/indexing to pull through the values we want from the dataset into our new output array.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Build Output Dataset\\n\",\n \"fill_value = -9999\\n\",\n \"out_ds = np.zeros((glt_array.shape[0], glt_array.shape[1], ds_array.shape[-1]), dtype=np.float32) + fill_value\\n\",\n \"valid_glt = np.all(glt_array != GLT_NODATA_VALUE, axis=-1)\\n\",\n \"# Adjust for One based Index\\n\",\n \"glt_array[valid_glt] -= 1 \\n\",\n \"# Use indexing/broadcasting to populate array cells with 0 values\\n\",\n \"out_ds[valid_glt, :] = ds_array[glt_array[valid_glt, 1], glt_array[valid_glt, 0], :]\\n\",\n \"out_ds.shape\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After this step, we have an array of values that is orthorectified, but if we want to have a grid of lat/lon values we still need to calculate them using the geotransform. We can retrieve this information from the dataset attributes (`ds.attrs['geotransform']`).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"GT = ds.geotransform\\n\",\n \"GT\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The geotransform consists of 2 formulas with 6 coefficients used to calculate the xy-grid to project the dataset. \\n\",\n \"\\n\",\n \"`x_geo = GT[0] + x * GT[1] + y * GT[2]` \\n\",\n \"\\n\",\n \"`y_geo = GT[3] + x * GT[4] + y * GT[5]`\\n\",\n \"\\n\",\n \"`GT[0]` - The x-coordinate of the upper-left corner of the upper-left pixel \\n\",\n \"`GT[1]` - W-E pixel width \\n\",\n \"`GT[2]` - Row rotation (zero for North up images) \\n\",\n \"`GT[3]` - The y-coordinate of the upper-left corner of the upper-left pixel \\n\",\n \"`GT[4]` - Column rotation (zero for North up images) \\n\",\n \"`GT[5]` - N-S pixel height (negative value for a north-up image) \\n\",\n \"\\n\",\n \"Create empty arrays for the x and y (lon and lat) based on the dimensions of the dataset.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create Array for Lat and Lon and fill\\n\",\n \"dim_x = loc.glt_x.shape[1]\\n\",\n \"dim_y = loc.glt_x.shape[0]\\n\",\n \"lon = np.zeros(dim_x)\\n\",\n \"lat = np.zeros(dim_y)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we have an empty array with the correct dimensions, we can populate it with values using the geotransform formula to build a lat/lon grid for plotting the orthorectified data. We can remove the `GT[2]` and `GT[4]` terms from the equation since our orthorectified image is North up. We also want to shift the geotransform by half a pixel since we want to build an array of pixel centers. We do this by adding half a pixel width to `GT[0]` and `GT[3]`. Calclutate the latitude and longitude for each row (x_dim) and column (y_dim) of the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for x in np.arange(dim_x):\\n\",\n \" x_geo = (GT[0]+0.5*GT[1]) + x * GT[1]\\n\",\n \" lon[x] = x_geo\\n\",\n \"for y in np.arange(dim_y):\\n\",\n \" y_geo = (GT[3]+0.5*GT[5]) + y * GT[5]\\n\",\n \" lat[y] = y_geo\\n\",\n \"\\n\",\n \"lat, lon\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As a last step we can place this and remaining information from the EMIT dataset, such as attributes and wavelengths into an `xarray.Dataset` if desired. To do this, build dictionaries for the coordinates and variables components of the dataset. In this example, the variable would be `reflectance`. First, we'll want to read in the `sensor_band_parameters` group to get the wavelength and fwhm info, then build the dictionaries.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"wvl = xr.open_dataset(fp, engine = 'h5netcdf', group='sensor_band_parameters')\\n\",\n \"coords = {'lat':(['lat'],lat), 'lon':(['lon'],lon), **wvl.variables} ## ** upacks the existing dictionary from the wvl dataset.\\n\",\n \"data_vars = {'reflectance':(['lat','lon','bands'], out_ds)}\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now build an `xarray.Dataset` using the dictionaries, then add attributes to the dataset from the non-orthocorrected data. We also can add the crs using the `rio.write_crs` function, to allow `rasterio` and `rioxarray` to recognize the CRS for future use.\\n\",\n \"\\n\",\n \">Note: the wavelength and fwhm are included as non-dimensional coordinates because they are independent of Lat/Lon.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"out_xr = xr.Dataset(data_vars=data_vars, coords=coords, attrs= ds.attrs)\\n\",\n \"out_xr['reflectance'].attrs = ds['reflectance'].attrs\\n\",\n \"out_xr.coords['lat'].attrs = loc['lat'].attrs\\n\",\n \"out_xr.coords['lon'].attrs = loc['lon'].attrs\\n\",\n \"out_xr.rio.write_crs(ds.spatial_ref,inplace=True) # Add CRS in easily recognizable format\\n\",\n \"out_xr\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we have an orthorectified dataset with coordinates that can be plotted. We can also mask the `fill_values` of -9999 to make a clearer figure.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"out_xr['reflectance'].data[out_xr['reflectance'].data == fill_value] = np.nan\\n\",\n \"out_xr.sel(bands=63).hvplot.image(cmap='viridis', aspect = 'equal', frame_width=500, rasterize=True)\"\n ]\n },\n {\n \"attachments\": {},\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 05-30-2023 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3292b2aceff7d39327a7519422d4180a7c9b133202090f26e797e3dd8f2c7877\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/how-tos/How_to_find_EMIT_data_using_CMR_API.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How to: Find and Access EMIT Data\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"This notebook will explain how to access Earth Surface Minteral Dust Source Investigation (EMIT) data programmaticly using NASA's CMR API. The Common Metadata Repository (CMR) is a metadata system that catalogs Earth Science data and associated metadata records. The CMR Application Programming Interface (API) provides programatic search capabilities through CMR's vast metadata holdings using various parameters and keywords. When querying NASA's CMR, there is a limit of 1 million granules matched and only 2000 granules returned per page. \\n\",\n \"\\n\",\n \"**Requirements:**\\n\",\n \"+ A NASA [Earthdata Login](https://urs.earthdata.nasa.gov/) account is required to download EMIT data \\n\",\n \"+ Selected the `emit_tutorials` environment as the kernel for this notebook.\\n\",\n \" + For instructions on setting up the environment, follow the the `setup_instructions.md` included in the `/setup/` folder of the repository. \\n\",\n \"\\n\",\n \"**Learning Objectives** \\n\",\n \"- How to find EMIT data using NASA's CMR API\\n\",\n \"- How to download programmatically \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import the required packages\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import requests\\n\",\n \"import earthaccess\\n\",\n \"import pandas as pd\\n\",\n \"import datetime as dt\\n\",\n \"import geopandas\\n\",\n \"from shapely.geometry import MultiPolygon, Polygon, box\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Obtaining the Concept ID\\n\",\n \"\\n\",\n \"NASA EarthData's unique ID for this dataset (called Concept ID) is needed for searching the dataset. The dataset Digital Object Identifier or DOI can be used to obtain the Concept ID. DOIs can be found by clicking the `Citation` link on the LP DAAC's [EMIT Product Pages](https://lpdaac.usgs.gov/product_search/?query=emit&view=cards&sort=title).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"doi = '10.5067/EMIT/EMITL2ARFL.001'# EMIT L2A Reflectance\\n\",\n \"\\n\",\n \"# CMR API base url\\n\",\n \"cmrurl='https://cmr.earthdata.nasa.gov/search/' \\n\",\n \"\\n\",\n \"doisearch = cmrurl + 'collections.json?doi=' + doi\\n\",\n \"concept_id = requests.get(doisearch).json()['feed']['entry'][0]['id']\\n\",\n \"print(concept_id)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is the unique NASA-given concept ID for the EMIT L2A Reflectance dataset, which can be used to retrieve relevant files (or granules).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Searching using CMR API\\n\",\n \"\\n\",\n \"When searching the CMR API, users can provide spatial bounds and date-time ranges to narrow their search. These spatial bounds can be either, points, a bounding box, or a polygon. \\n\",\n \"\\n\",\n \"Specify start time and dates and reformat them to the structure necessary for searching CMR.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Temporal Bound - Year, month, day. Hour, minutes, and seconds (ZULU) can also be included \\n\",\n \"start_date = dt.datetime(2022, 9, 3)\\n\",\n \"end_date = dt.datetime(2022, 9, 3, 23, 23, 59) \\n\",\n \"\\n\",\n \"# CMR formatted start and end times\\n\",\n \"dt_format = '%Y-%m-%dT%H:%M:%SZ'\\n\",\n \"temporal_str = start_date.strftime(dt_format) + ',' + end_date.strftime(dt_format)\\n\",\n \"print(temporal_str)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The CMR API only allows 2000 results to be shown at a time. Using `page_num` allows a user to loop through the search result pages. The sections below walk through using Points, Bounding Boxes, and Polygons to spatially constrain a search made using the CMR API. \\n\",\n \"\\n\",\n \"### Search using Points\\n\",\n \"\\n\",\n \"To search using a point we specify a latitude and longitude.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Search using a Point\\n\",\n \"\\n\",\n \"lon = -62.1123\\n\",\n \"lat = -39.89402\\n\",\n \"point_str = str(lon) +','+ str(lat)\\n\",\n \"\\n\",\n \"page_num = 1\\n\",\n \"page_size = 2000 # CMR page size limit\\n\",\n \"\\n\",\n \"granule_arr = []\\n\",\n \"\\n\",\n \"while True:\\n\",\n \" \\n\",\n \" # defining parameters\\n\",\n \" cmr_param = {\\n\",\n \" \\\"collection_concept_id\\\": concept_id, \\n\",\n \" \\\"page_size\\\": page_size,\\n\",\n \" \\\"page_num\\\": page_num,\\n\",\n \" \\\"temporal\\\": temporal_str,\\n\",\n \" \\\"point\\\":point_str\\n\",\n \" }\\n\",\n \"\\n\",\n \" granulesearch = cmrurl + 'granules.json'\\n\",\n \" response = requests.post(granulesearch, data=cmr_param)\\n\",\n \" granules = response.json()['feed']['entry']\\n\",\n \" \\n\",\n \" if granules:\\n\",\n \" for g in granules:\\n\",\n \" granule_urls = ''\\n\",\n \" granule_poly = ''\\n\",\n \" \\n\",\n \" # read cloud cover\\n\",\n \" cloud_cover = g['cloud_cover']\\n\",\n \" \\n\",\n \" # reading bounding geometries\\n\",\n \" if 'polygons' in g:\\n\",\n \" polygons= g['polygons']\\n\",\n \" multipolygons = []\\n\",\n \" for poly in polygons:\\n\",\n \" i=iter(poly[0].split (\\\" \\\"))\\n\",\n \" ltln = list(map(\\\" \\\".join,zip(i,i)))\\n\",\n \" multipolygons.append(Polygon([[float(p.split(\\\" \\\")[1]), float(p.split(\\\" \\\")[0])] for p in ltln]))\\n\",\n \" granule_poly = MultiPolygon(multipolygons)\\n\",\n \" \\n\",\n \" # Get https URLs to .nc files and exclude .dmrpp files\\n\",\n \" granule_urls = [x['href'] for x in g['links'] if 'https' in x['href'] and '.nc' in x['href'] and '.dmrpp' not in x['href']]\\n\",\n \" # Add to list\\n\",\n \" granule_arr.append([granule_urls, cloud_cover, granule_poly])\\n\",\n \" \\n\",\n \" page_num += 1\\n\",\n \" else: \\n\",\n \" break\\n\",\n \"print(granule_arr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Search using a bounding box\\n\",\n \"For this we'll use a bounding box along the coast of Argentina with a bottom left corner of -62.1123 Longitude, -39.89402 Latitude, and a top right corner of -61.70801 Longitude and -39.57769 Latitude.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Search Using a Bounding Box\\n\",\n \"bound = (-62.1123, -39.89402, -61.70801, -39.57769) \\n\",\n \"bound_str = ','.join(map(str,bound))\\n\",\n \"\\n\",\n \"page_num = 1\\n\",\n \"page_size = 2000 # CMR page size limit\\n\",\n \"\\n\",\n \"granule_arr = []\\n\",\n \"\\n\",\n \"while True:\\n\",\n \" \\n\",\n \" # defining parameters\\n\",\n \" cmr_param = {\\n\",\n \" \\\"collection_concept_id\\\": concept_id, \\n\",\n \" \\\"page_size\\\": page_size,\\n\",\n \" \\\"page_num\\\": page_num,\\n\",\n \" \\\"temporal\\\": temporal_str,\\n\",\n \" \\\"bounding_box[]\\\":bound_str\\n\",\n \" }\\n\",\n \"\\n\",\n \" granulesearch = cmrurl + 'granules.json'\\n\",\n \" response = requests.post(granulesearch, data=cmr_param)\\n\",\n \" granules = response.json()['feed']['entry']\\n\",\n \" \\n\",\n \" if granules:\\n\",\n \" for g in granules:\\n\",\n \" granule_urls = ''\\n\",\n \" granule_poly = ''\\n\",\n \" \\n\",\n \" # read cloud cover\\n\",\n \" cloud_cover = g['cloud_cover']\\n\",\n \" \\n\",\n \" # reading results bounding geometries\\n\",\n \" if 'polygons' in g:\\n\",\n \" polygons= g['polygons']\\n\",\n \" multipolygons = []\\n\",\n \" for poly in polygons:\\n\",\n \" i=iter(poly[0].split (\\\" \\\"))\\n\",\n \" ltln = list(map(\\\" \\\".join,zip(i,i)))\\n\",\n \" multipolygons.append(Polygon([[float(p.split(\\\" \\\")[1]), float(p.split(\\\" \\\")[0])] for p in ltln]))\\n\",\n \" granule_poly = MultiPolygon(multipolygons)\\n\",\n \" \\n\",\n \" # Get https URLs to .nc files and exclude .dmrpp files\\n\",\n \" granule_urls = [x['href'] for x in g['links'] if 'https' in x['href'] and '.nc' in x['href'] and '.dmrpp' not in x['href']]\\n\",\n \" # Add to list\\n\",\n \" granule_arr.append([granule_urls, cloud_cover, granule_poly])\\n\",\n \" \\n\",\n \" page_num += 1\\n\",\n \" else: \\n\",\n \" break\\n\",\n \"print(granule_arr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Search a Polygon\\n\",\n \"\\n\",\n \"A polygon can also be used to spatially search using the CMR API. A shapefile, geojson, or other format can be opened as a geopandas dataframe, then reformatted to a geojson format to be sent as a parameter in the CMR search. Note that very complex shapefiles must be simplified, there is a 5000 coordinate limit.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Search using a Polygon\\n\",\n \"polygon = geopandas.read_file('../../data/isla_gaviota.geojson')\\n\",\n \"geojson = {\\\"shapefile\\\": (\\\"isla_gaviota.geojson\\\", polygon.geometry.to_json(), \\\"application/geo+json\\\")}\\n\",\n \"\\n\",\n \"page_num = 1\\n\",\n \"page_size = 2000 # CMR page size limit\\n\",\n \"\\n\",\n \"granule_arr = []\\n\",\n \"\\n\",\n \"while True:\\n\",\n \" \\n\",\n \" # defining parameters\\n\",\n \" cmr_param = {\\n\",\n \" \\\"collection_concept_id\\\": concept_id, \\n\",\n \" \\\"page_size\\\": page_size,\\n\",\n \" \\\"page_num\\\": page_num,\\n\",\n \" \\\"temporal\\\": temporal_str,\\n\",\n \" \\\"simplify-shapefile\\\": 'true' # this is needed to bypass 5000 coordinates limit of CMR\\n\",\n \" }\\n\",\n \"\\n\",\n \" granulesearch = cmrurl + 'granules.json'\\n\",\n \" response = requests.post(granulesearch, data=cmr_param, files=geojson)\\n\",\n \" granules = response.json()['feed']['entry']\\n\",\n \" \\n\",\n \" if granules:\\n\",\n \" for g in granules:\\n\",\n \" granule_urls = ''\\n\",\n \" granule_poly = ''\\n\",\n \" \\n\",\n \" # read granule title and cloud cover\\n\",\n \" granule_name = g['title']\\n\",\n \" cloud_cover = g['cloud_cover']\\n\",\n \" \\n\",\n \" # reading bounding geometries\\n\",\n \" if 'polygons' in g:\\n\",\n \" polygons= g['polygons']\\n\",\n \" multipolygons = []\\n\",\n \" for poly in polygons:\\n\",\n \" i=iter(poly[0].split (\\\" \\\"))\\n\",\n \" ltln = list(map(\\\" \\\".join,zip(i,i)))\\n\",\n \" multipolygons.append(Polygon([[float(p.split(\\\" \\\")[1]), float(p.split(\\\" \\\")[0])] for p in ltln]))\\n\",\n \" granule_poly = MultiPolygon(multipolygons)\\n\",\n \" \\n\",\n \" # Get https URLs to .nc files and exclude .dmrpp files\\n\",\n \" granule_urls = [x['href'] for x in g['links'] if 'https' in x['href'] and '.nc' in x['href'] and '.dmrpp' not in x['href']]\\n\",\n \" # Add to list\\n\",\n \" granule_arr.append([granule_urls, cloud_cover, granule_poly])\\n\",\n \" \\n\",\n \" page_num += 1\\n\",\n \" else: \\n\",\n \" break\\n\",\n \" \\n\",\n \"print(granule_arr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"> Note: At the time this tutorial was made, all 3 searches, point, bounding box, and polygon should result in the same assets being returned.\\n\",\n \"\\n\",\n \"### Creating a Dataframe with the resulting Links\\n\",\n \"\\n\",\n \"A `pandas.dataframe` can be used to store the download URLs and geometries of each file. The EMIT L2A Reflectance and Uncertainty and Mask collection contains 3 assets per granule (reflectance, reflectance uncertainty, and masks). We can see when printing this list, that there are three assets that correspond to a single polygon. For the next step we will place these into a dataframe and 'explode' the dataframe to place each of these in a separate row. If we only want a subset of these assets, we can filter them out. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# creating a pandas dataframe\\n\",\n \"cmr_results_df = pd.DataFrame(granule_arr, columns=[\\\"asset_url\\\", \\\"cloud_cover\\\", \\\"granule_poly\\\"])\\n\",\n \"# Drop granules with empty geometry - if any exist\\n\",\n \"cmr_results_df = cmr_results_df[cmr_results_df['granule_poly'] != '']\\n\",\n \"# Expand so each row contains a single url \\n\",\n \"cmr_results_df = cmr_results_df.explode('asset_url')\\n\",\n \"# Name each asset based on filename\\n\",\n \"cmr_results_df.insert(0,'asset_name', cmr_results_df.asset_url.str.split('/',n=-1).str.get(-1))\\n\",\n \"\\n\",\n \"cmr_results_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"At this stage we can filter based on the assets that we want or the cloud cover. For this example lets say we are only interested in the Reflectance and the Mask. To filter by asset, we can match strings included in the asset name. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"cmr_results_df = cmr_results_df[cmr_results_df.asset_name.str.contains('_RFL_') | cmr_results_df.asset_name.str.contains('MASK')]\\n\",\n \"cmr_results_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After filtering down to the assets you want, you can output a text file with the asset urls or save the entire dataframe, then use a utility such as wget or the DAAC Data Download Tool to download the files. To download you will need to set up NASA Earthdata Login authentication using a .netrc file. \\n\",\n \"\\n\",\n \"Save the asset urls to a textfile in the `/data/` folder.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Save text file of asset urls\\n\",\n \"cmr_results_dfs = cmr_results_df[:-1].drop_duplicates(subset=['asset_url']) # Remove any duplicates\\n\",\n \"cmr_results_df.to_csv('../../data/emit_asset_urls.txt', columns = ['asset_url'], index=False, header = False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Downloading Files using the list of URLS/Text File\\n\",\n \"\\n\",\n \"To download the files using Python, you can run the cell below. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define input filepath\\n\",\n \"url_list_filepath = \\\"../../data/emit_asset_urls.txt\\\"\\n\",\n \"# Define output directory\\n\",\n \"output_directory = \\\"../../data/\\\"\\n\",\n \"\\n\",\n \"# Open Text file\\n\",\n \"with open(url_list_filepath, \\\"r\\\") as file:\\n\",\n \" file_list = file.read().splitlines()\\n\",\n \" file.close()\\n\",\n \"\\n\",\n \"# EDL Authentication/Create .netrc if necessary\\n\",\n \"earthaccess.login(persist=True)\\n\",\n \"# Get requests https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"for url in file_list:\\n\",\n \" granule_asset_id = url.split(\\\"/\\\")[-1]\\n\",\n \" # Define Local Filepath\\n\",\n \" fp = f\\\"{output_directory}{granule_asset_id}\\\"\\n\",\n \" # Download the Granule Asset if it doesn't exist\\n\",\n \" print(f\\\"Downloading {granule_asset_id}...\\\")\\n\",\n \" if not os.path.isfile(fp):\\n\",\n \" with fs.get(url, stream=True) as src:\\n\",\n \" with open(fp, \\\"wb\\\") as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64 * 1024 * 1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To download using wget, use the following in the command line.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"!wget -P ../../data/ -i ../../data/emit_asset_urls.txt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 03-22-2024 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3292b2aceff7d39327a7519422d4180a7c9b133202090f26e797e3dd8f2c7877\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/how-tos/How_to_find_and_access_EMIT_data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How to: Find and Access EMIT Data\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"There are currently 4 ways to find EMIT data:\\n\",\n \"\\n\",\n \"1. [EarthData Search](https://search.earthdata.nasa.gov/search)\\n\",\n \"2. [NASA's CMR API](https://www.earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/cmr) (`earthaccess` uses this)\\n\",\n \"3. [NASA's CMR-STAC API](https://cmr.earthdata.nasa.gov/search/site/docs/search/stac)\\n\",\n \"3. [Visions Open Access Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/)\\n\",\n \"\\n\",\n \"This notebook will explain how to access Earth Surface Mineral Dust Source Investigation (EMIT) data programmaticly using the [earthaccess python library](https://github.com/nsidc/earthaccess). `earthaccess` is an easy to use library that reduces finding and downloading or streaming data over https or s3 to only a few lines of code. `earthaccess` searches NASA's Common Metadata Repository (CMR), a metadata system that catalogs Earth Science data and associated metadata records, then can be used to download granules or generate lists granule search result URLs.\\n\",\n \"\\n\",\n \"**Requirements:**\\n\",\n \"- A NASA [Earthdata Login](https://urs.earthdata.nasa.gov/) account is required to download EMIT data \\n\",\n \"- *No Python setup requirements if connected to the workshop cloud instance!*\\n\",\n \"- **Local Only** Set up Python Environment - See **setup_instructions.md** in the `/setup/` folder to set up a local compatible Python environment\\n\",\n \"\\n\",\n \"**Learning Objectives** \\n\",\n \"- How to get information about data collections using `earthaccess`\\n\",\n \"- How to search and access EMIT data using `earthaccess`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Setup\\n\",\n \"Import the required packages\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import earthaccess\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import geopandas as gp\\n\",\n \"from shapely.geometry.polygon import orient\\n\",\n \"import xarray as xr\\n\",\n \"import sys\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from emit_tools import emit_xarray\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Authentication\\n\",\n \"\\n\",\n \"`earthaccess` creates and leverages Earthdata Login tokens to authenticate with NASA systems. Earthdata Login tokens expire after a month. To retrieve a token from Earthdata Login, you can either enter your username and password each time you use `earthaccess`, or use a `.netrc` file. A `.netrc` file is a configuration file that is commonly used to store login credentials for remote systems. If you don't have a `.netrc` or don't know if you have one or not, you can use the `persist` argument with the `login` function below to create or update an existing one, then use it for authentication.\\n\",\n \"\\n\",\n \"If you do not have an Earthdata Account, you can create one [here](https://urs.earthdata.nasa.gov/home). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"auth = earthaccess.login(persist=True)\\n\",\n \"print(auth.authenticated)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you receive a message that your token has expired, use `refresh_tokens()` like below to generate a new one.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# auth.refresh_tokens\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Searching for Collections\\n\",\n \"\\n\",\n \"The EMIT mission produces several collections or datasets available via the LP DAAC cloud archive.\\n\",\n \"\\n\",\n \"To view what's available, we can use the `search_datasets` function and with the `keyword` and and `provider` arguments. The `provider` is the data location, in this case `LPCLOUD`. Specifying the provider isn't necessary, but the \\\"emit\\\" keyword can be found in metadata for some other datasets, and additional collections may be returned.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Retrieve Collections\\n\",\n \"collections = earthaccess.search_datasets(provider='LPCLOUD', keyword='emit')\\n\",\n \"# Print Quantity of Results\\n\",\n \"print(f'Collections found: {len(collections)}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you print the `collections` object you can explore all of the json metadata.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# # Print collections\\n\",\n \"# collections\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also create a list of the `short-name`, `concept-id`, and `version` of each result collection using list comprehension. These fields are important for specifying and searching for data within collections. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"collections_info = [\\n\",\n \" {\\n\",\n \" 'short_name': c.summary()['short-name'],\\n\",\n \" 'collection_concept_id': c.summary()['concept-id'],\\n\",\n \" 'version': c.summary()['version'],\\n\",\n \" 'entry_title': c['umm']['EntryTitle']\\n\",\n \" }\\n\",\n \" for c in collections\\n\",\n \"]\\n\",\n \"pd.set_option('display.max_colwidth', 150)\\n\",\n \"collections_info = pd.DataFrame(collections_info)\\n\",\n \"collections_info\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The collection `concept-id` is the best way to search for data within a collection, as this is unique to each collection. The `short-name` can be used as well, however the `version` should be passed as well as there can be multiple versions available with the same short name. After finding the collection you want to search, you can use the `concept-id` to search for granules within that collection.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Searching for Granules\\n\",\n \"\\n\",\n \"A `granule` can be thought of as a unique spatiotemporal grouping within a collection. To search for `granules`, we can use the `search_data` function from `earthaccess` and provide the arguments for our search. Its possible to specify search products using several criteria shown in the table below:\\n\",\n \"\\n\",\n \"|dataset origin and location|spatio temporal parameters|dataset metadata parameters|\\n\",\n \"|:---|:---|:---|\\n\",\n \"|archive_center|bounding_box|concept_id\\n\",\n \"|data_center|temporal|entry_title\\n\",\n \"|daac|point|keyword\\n\",\n \"|provider|polygon|version\\n\",\n \"|cloud_hosted|line|short_name\\n\",\n \"\\n\",\n \"### Point Search\\n\",\n \"\\n\",\n \"In this case, we specify the `shortname`, `point` coordinates, `temporal` range, and min and max `cloud_cover` percentages, as well as `count`, which limits the maximum number of results returned. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Search example using a Point\\n\",\n \"results = earthaccess.search_data(\\n\",\n \" short_name='EMITL2ARFL',\\n\",\n \" point=(-62.1123,-39.89402),\\n\",\n \" temporal=('2022-09-03','2022-09-04'),\\n\",\n \" cloud_cover=(0,90),\\n\",\n \" count=100\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Bounding Box Search\\n\",\n \"\\n\",\n \"You can also use a bounding box to search. To do this we will first open a geojson file containing our region of interest (ROI) then simplify it to a bounding box by getting the bounds and putting them into a Python object called a tuple. We will use the `total_bounds` property to get the bounding box of our ROI, and add that to a Python tuple, which is the expected data type for the bounding_box parameter `earthaccess` `search_data`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"geojson = gp.read_file('../../data/isla_gaviota.geojson')\\n\",\n \"geojson.geometry\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bbox = tuple(list(geojson.total_bounds))\\n\",\n \"bbox\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can search for granules using the a bounding box.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Search example using bounding box\\n\",\n \"results = earthaccess.search_data(\\n\",\n \" short_name='EMITL2ARFL',\\n\",\n \" bounding_box=bbox,\\n\",\n \" temporal=('2022-09-03','2022-09-04'),\\n\",\n \" cloud_cover=(0,90),\\n\",\n \" count=100\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"### Polygon Search\\n\",\n \"\\n\",\n \"A polygon can also be used to search. For a simple polygon without holes we can take the geojson we opened and grab the coordinates of the exterior ring vertices and place them in a list. Note that this list of vertices must be in **counter-clockwise order** to be accepted by the `search_data` function. If necessary, the external ring vertices of your polygon can be reordered using the `orient` function from the shapely library.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Orient External Ring Vertices\\n\",\n \"oriented = orient(geojson.geometry[0], sign=1.0)\\n\",\n \"# Create List of External Ring vertices coordinates\\n\",\n \"polygon = list(oriented.exterior.coords)\\n\",\n \"polygon\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With this list of coordinate pairs we can use the `polygon` parameter for our search. \\n\",\n \"> Note that we overwrote the `results` object, because for all 3 types spatial search, the `results` are the same for this example.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Search Example using a Polygon\\n\",\n \"results = earthaccess.search_data(\\n\",\n \" short_name='EMITL2ARFL',\\n\",\n \" polygon=polygon,\\n\",\n \" temporal=('2022-09-03','2022-09-04'),\\n\",\n \" cloud_cover=(0,90),\\n\",\n \" count=100\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Working with Search Results\\n\",\n \"\\n\",\n \"All three of these examples will have the same result, since the spatiotemporal parameters fall within the same single granule. Results is a `list`, so we can use an index to view a single result.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"result = results[0]\\n\",\n \"result\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also retrieve specific metadata for a result using `.keys()` since this object also acts as a dictionary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"result.keys()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Look at each of the keys to see what is available.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"result['meta']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"result['size']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `umm` metadata contains a lot of fields, so instead of printing the entire object, we can just look at the keys. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"result['umm'].keys()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One important piece of info here is the Look at the cloud cover percentage.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"result['umm']['CloudCover']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Another of note is the `AdditionalAttributes` key, which contains other useful information about the EMIT granule, like solar zenith and azimuth.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"result['umm']['AdditionalAttributes']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From here, we can do other things, such as convert the results to a `pandas` dataframe, or filter down your results further using string matching and list comprehension.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.json_normalize(results)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Downloading or Streaming Data\\n\",\n \"\\n\",\n \"After we have our results, there are 2 ways we an work with the data:\\n\",\n \"\\n\",\n \"1. Download All Assets\\n\",\n \"2. Selectively Download Assets\\n\",\n \"3. Access in place / Stream the data. \\n\",\n \"\\n\",\n \"To download the data we can simply use the download function. This will retrieve all assets associated with a granule, and is nice if you plan to work with the data in this way and need all of the assets included with the product. For the EMIT L2A Reflectance, this includes the Uncertainty and Masks files.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# earthaccess.download(results, '../../data/')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we want to stream the data or further filter the assets for download we want to first create a list of URLs nested by granule using list comprehesion.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"emit_results_urls = [granule.data_links() for granule in results]\\n\",\n \"emit_results_urls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can also split these into results for specific assets or filter out an asset using the following. In this example, we only want to access or download reflectance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"filtered_asset_links = []\\n\",\n \"# Pick Desired Assets - Use underscores to aid in stringmatching of the filenames (_RFL_, _RFLUNCERT_, _MASK_)\\n\",\n \"desired_assets = ['_RFL_']\\n\",\n \"# Step through each sublist (granule) and filter based on desired assets.\\n\",\n \"for n, granule in enumerate(emit_results_urls):\\n\",\n \" for url in granule: \\n\",\n \" asset_name = url.split('/')[-1]\\n\",\n \" if any(asset in asset_name for asset in desired_assets):\\n\",\n \" filtered_asset_links.append(url)\\n\",\n \"filtered_asset_links\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we have our filtered list, we can stream the reflectance asset or download it. Start an https session then open it to stream the data, or download to save the file.\\n\",\n \"\\n\",\n \"#### Stream Data\\n\",\n \"\\n\",\n \"This may take a while to load the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get Https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_fsspec_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"url = filtered_asset_links[0]\\n\",\n \"granule_asset_id = url.split('/')[-1]\\n\",\n \"# Define Local Filepath\\n\",\n \"fp = fs.open(url)\\n\",\n \"# Open with `emit_xarray` function\\n\",\n \"ds = emit_xarray(fp)\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Download Filtered \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get requests https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"for url in filtered_asset_links:\\n\",\n \" granule_asset_id = url.split('/')[-1]\\n\",\n \" # Define Local Filepath\\n\",\n \" fp = f'../../data/{granule_asset_id}'\\n\",\n \" # Download the Granule Asset if it doesn't exist\\n\",\n \" if not os.path.isfile(fp):\\n\",\n \" with fs.get(url,stream=True) as src:\\n\",\n \" with open(fp,'wb') as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 11-06-2024 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"lpdaac\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/how-tos/How_to_use_EMIT_Quality_data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# How to: Use EMIT Quality Data\\n\",\n \"\\n\",\n \"There are two quality layers associated with L2A Reflectance Product contained within the L2A Mask file. The `mask` variable contains 6 binary flag bands (1-5, 8) which should be excluded from analysis, and two data bands (6,7).\\n\",\n \"\\n\",\n \"The second `band_mask` variable indicates whether or not any given wavelength of any given pixel is interpolated. Interpolation occurs either due to a focal plane array bad pixel, or from saturation. This data is provided as a packed unsigned integer array with 36 elements.\\n\",\n \"\\n\",\n \"**Requirements:**\\n\",\n \"+ A NASA [Earthdata Login](https://urs.earthdata.nasa.gov/) account is required to download EMIT data \\n\",\n \"+ Selected the `emit_tutorials` environment as the kernel for this notebook.\\n\",\n \" + For instructions on setting up the environment, follow the the `setup_instructions.md` included in the `/setup/` folder of the repository. \\n\",\n \"\\n\",\n \"**Learning Objectives**\\n\",\n \"+ How to build a mask using the quality flags from an EMIT L2A Mask file\\n\",\n \"+ How to build a mask by unpacking the `band_mask` data\\n\",\n \"+ How to apply the band and quality masks to another EMIT file.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Using EMIT Quality Flag Data\\n\",\n \"Import the required Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import Packages\\n\",\n \"import os\\n\",\n \"import earthaccess\\n\",\n \"import netCDF4 as nc\\n\",\n \"from osgeo import gdal\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import hvplot.xarray\\n\",\n \"import holoviews as hv\\n\",\n \"import sys\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"import emit_tools\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Login to your NASA Earthdata account and create a `.netrc` file using the `login` function from the `earthaccess` library. If you do not have an Earthdata Account, you can create one [here](https://urs.earthdata.nasa.gov/home). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this notebook we will download the files necessary using `earthaccess`. You can also access the data in place or stream it, but this can slow due to the file sizes. Provide URLs for an EMIT L2A Reflectance and L2A Mask.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"urls = ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_MASK_001_20220903T163129_2224611_012.nc']\\n\",\n \"# Get requests https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"for url in urls:\\n\",\n \" granule_asset_id = url.split('/')[-1]\\n\",\n \" # Define Local Filepath\\n\",\n \" fp = f'../../data/{granule_asset_id}'\\n\",\n \" # Download the Granule Asset if it doesn't exist\\n\",\n \" if not os.path.isfile(fp):\\n\",\n \" with fs.get(url,stream=True) as src:\\n\",\n \" with open(fp,'wb') as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set a filepath for the reflectance and mask files downloaded.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fp = '../../data/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc'\\n\",\n \"fp_mask = '../../data/EMIT_L2A_MASK_001_20220903T163129_2224611_012.nc'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The most efficient way to utilize the mask is to apply it before orthorectification because the orthorectified datasets take up more space. To apply a mask using the L2A Mask file, we want to open it, specify which bands to use in construction of a mask, and then apply the mask.\\n\",\n \"\\n\",\n \"To do this, first take a look at what each band will mask by reading in the `sensor_band_parameters` group from the mask file as an `xarray.dataset` then converting to a dataframe.\\n\",\n \"\\n\",\n \">Note: In the user guide, the bands are indexed as 1-8 not 0-7 as used here.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mask_parameters_ds = xr.open_dataset(fp_mask,engine = 'h5netcdf', group='sensor_band_parameters')\\n\",\n \"mask_key = mask_parameters_ds['mask_bands'].to_dataframe()\\n\",\n \"mask_key\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Visualizing the mask\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's start by reviewing some of the content of these bands, and seeing what's available.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from matplotlib import pyplot as plt\\n\",\n \"import matplotlib.gridspec as gridspec\\n\",\n \"fig = plt.figure(figsize=(20,50))\\n\",\n \"gs = gridspec.GridSpec(ncols=3, nrows=len(mask_key), figure=fig)\\n\",\n \"\\n\",\n \"ds = emit_tools.emit_xarray(fp, ortho = False)\\n\",\n \"mask_ds = emit_tools.emit_xarray(fp_mask, ortho=False)\\n\",\n \"\\n\",\n \"rgb_inds = np.array([np.nanargmin(abs(ds['wavelengths'].values - x)) for x in [650, 560, 470]])\\n\",\n \"rgb = ds['reflectance'].values[:,:,rgb_inds] # subset RGB\\n\",\n \"rgb[rgb < 0] = np.nan\\n\",\n \"rgb -= np.nanpercentile(rgb,2,axis=(0,1))[np.newaxis,np.newaxis,:] # scale from 2-95 %\\n\",\n \"rgb /= np.nanpercentile(rgb,95,axis=(0,1))[np.newaxis,np.newaxis,:]\\n\",\n \"\\n\",\n \"for _n in range(int(len(mask_key)/2)):\\n\",\n \" ax = fig.add_subplot(gs[_n, 0])\\n\",\n \" plt.imshow(rgb);\\n\",\n \" plt.axis('off')\\n\",\n \" plt.title('RGB')\\n\",\n \" \\n\",\n \" ax = fig.add_subplot(gs[_n, 1])\\n\",\n \" md = mask_ds['mask'].values[...,_n]\\n\",\n \" md[np.isnan(rgb[...,0])] = np.nan\\n\",\n \" plt.imshow(md);\\n\",\n \" plt.axis('off')\\n\",\n \" plt.title(mask_key['mask_bands'][_n])\\n\",\n \" \\n\",\n \" ax = fig.add_subplot(gs[_n, 2])\\n\",\n \" md = mask_ds['mask'].values[...,_n+int(len(mask_key)/2)]\\n\",\n \" md[np.isnan(rgb[...,0])] = np.nan\\n\",\n \" plt.imshow(md);\\n\",\n \" plt.axis('off')\\n\",\n \" plt.title(mask_key['mask_bands'][_n+int(len(mask_key)/2)])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Some of these bands are direct masks (Cloud, Dilated, Currus, Water, Spacecraft), and some (AOD550 and H2O (g cm-2)) are information calculated during the L2A reflectance retrieval that may be used as additional screening, depending on the application. The final mask that the EMIT mission will use for its minerological applications is shown as the Aggreged Flag - but not all users might want this particular mask.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's take a closer look at some of those bands with additional information, that could be used either to screen specific content out or that might be used as signal:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"md = mask_ds['mask'].values[:,:,list(mask_key['mask_bands']).index('AOD550')]\\n\",\n \"md[np.isnan(rgb[...,0])] = np.nan\\n\",\n \"plt.imshow(md, vmin=np.nanpercentile(md,2),vmax=np.nanpercentile(md,98));\\n\",\n \"plt.title('AOD550')\\n\",\n \"plt.colorbar()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fig = plt.figure()\\n\",\n \"md = mask_ds['mask'].values[:,:,list(mask_key['mask_bands']).index('H2O (g cm-2)')]\\n\",\n \"md[np.isnan(rgb[...,0])] = np.nan\\n\",\n \"plt.imshow(md, vmin=np.nanpercentile(md,2),vmax=np.nanpercentile(md,98));\\n\",\n \"plt.title('H2O (g cm-2)')\\n\",\n \"plt.colorbar()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Applying the Mask\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mask_key\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The above dataframe shows exactly what each band contained within the file represents/will mask. For this example we will use flags 0,1,3, and 4 to remove any potential clouds and any artefacts caused by the space station. This can be done using the `quality_mask` function from the `emit_tools` module. This function combines the requested flags into a single mask and returns it as an array.\\n\",\n \"\\n\",\n \"Select the bands to use.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"flags = [0,1,3]\\n\",\n \"flags\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now create the mask using the `quality_mask` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mask = emit_tools.quality_mask(fp_mask,flags)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To see the regions of the unorthocorrected image that will be masked we can plot the mask array.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"flags = [0,1,3,4]\\n\",\n \"mask = emit_tools.quality_mask(fp_mask,flags)\\n\",\n \"fig = plt.figure(figsize=(15,15))\\n\",\n \"gs = gridspec.GridSpec(ncols=2, nrows=2, figure=fig)\\n\",\n \"\\n\",\n \"ax = fig.add_subplot(gs[0, 0])\\n\",\n \"plt.imshow(rgb)\\n\",\n \"plt.scatter(1200,1200,c='red',marker='+')\\n\",\n \"\\n\",\n \"ax = fig.add_subplot(gs[0, 1])\\n\",\n \"plt.imshow(mask)\\n\",\n \"plt.scatter(1200,1200,c='red',marker='+')\\n\",\n \"\\n\",\n \"ax = fig.add_subplot(gs[1, :])\\n\",\n \"plt.plot(ds['wavelengths'],ds['reflectance'].values[1200,1200,:])\\n\",\n \"plt.xlabel('Wavelengths [nm]')\\n\",\n \"plt.ylabel('Reflectance')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have a mask to apply, we can use it as the `qmask` parameter in the `emit_xarray` function. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = emit_tools.emit_xarray(fp, ortho=True, qmask=mask)\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This `ds` dataset is now orthorectified and the specified mask has been applied. Visualize the output using a plot of a red band (650 nm).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds.reflectance.data[ds.reflectance.data == -9999] = np.nan\\n\",\n \"ds.sel(wavelengths=650, method='nearest').hvplot.image(cmap='viridis', aspect = 'equal', frame_width=500, rasterize=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The modified `xarray.Dataset` can also be saved as a netCDF4 output that can be reopened using the `xarray.open_dataset` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds.to_netcdf('../../data/example_quality_nc_out.nc')\\n\",\n \"# Example for Opening \\n\",\n \"# ds = xr.open_dataset('../data/example_quality_nc_out.nc')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Using EMIT Band Mask Data\\n\",\n \"\\n\",\n \"The EMIT L2A Mask file also contains `band_mask` data, which indicates whether or not any given wavelength of any given pixel is interpolated. Interpolation occurs either due to a focal plane array bad pixel, or from saturation. This data comes as a packed unsigned integer array with 36 elements.\\n\",\n \"\\n\",\n \"Unpack the data an using the `band_mask` function from the `emit_tools` module. This function will unpack the data and create an array that can be used to mask the bands/pixels when added as an input into the `emit_xarray` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bmask = emit_tools.band_mask(fp_mask)\\n\",\n \"bmask.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can quickly plot an example of a band where some pixels have been interpolated (band 234).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from matplotlib import pyplot as plt\\n\",\n \"plt.imshow(bmask[:,:,234])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or more helpfully, plot a representation of the crosstrack of the detector array to see where interpolation is occuring. If your research depends on spectral features contained within these interpolated crosstrack region, you may want to mask them out.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fig = plt.figure(figsize=(20,10))\\n\",\n \"\\n\",\n \"plt.imshow(bmask[0,...].T)\\n\",\n \"plt.xlabel('Crosstrack Element')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 07-03-2023 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3292b2aceff7d39327a7519422d4180a7c9b133202090f26e797e3dd8f2c7877\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/modules/emit_tools.py", "content": "\"\"\"\nThis Module has the functions related to working with an EMIT dataset. This includes doing things\nlike opening and flattening the data to work in xarray, orthorectification, and extracting point and area samples.\n\nAuthor: Erik Bolch, ebolch@contractor.usgs.gov \n\nLast Updated: 05/09/2024\n\nTO DO: \n- Rework masking functions to be more flexible\n- Update format to match AppEEARS outputs\n\"\"\"\n\n# Packages used\nimport netCDF4 as nc\nimport os\nfrom spectral.io import envi\nfrom osgeo import gdal\nimport numpy as np\nimport math\nfrom skimage import io\nimport pandas as pd\nimport geopandas as gpd\nimport xarray as xr\nimport rasterio as rio\nimport rioxarray as rxr\nimport s3fs\nfrom rioxarray.merge import merge_arrays\nfrom fsspec.implementations.http import HTTPFile\n\n\ndef emit_xarray(filepath, ortho=False, qmask=None, unpacked_bmask=None):\n \"\"\"\n This function utilizes other functions in this module to streamline opening an EMIT dataset as an xarray.Dataset.\n\n Parameters:\n filepath: a filepath to an EMIT netCDF file\n ortho: True or False, whether to orthorectify the dataset or leave in crosstrack/downtrack coordinates.\n qmask: a numpy array output from the quality_mask function used to mask pixels based on quality flags selected in that function. Any non-orthorectified array with the proper crosstrack and downtrack dimensions can also be used.\n unpacked_bmask: a numpy array from the band_mask function that can be used to mask band-specific pixels that have been interpolated.\n\n Returns:\n out_xr: an xarray.Dataset constructed based on the parameters provided.\n\n \"\"\"\n # Grab granule filename to check product\n\n if type(filepath) == s3fs.core.S3File:\n granule_id = filepath.info()[\"name\"].split(\"/\", -1)[-1].split(\".\", -1)[0]\n elif type(filepath) == HTTPFile:\n granule_id = filepath.path.split(\"/\", -1)[-1].split(\".\", -1)[0]\n else:\n granule_id = os.path.splitext(os.path.basename(filepath))[0]\n\n # Read in Data as Xarray Datasets\n engine, wvl_group = \"h5netcdf\", None\n\n ds = xr.open_dataset(filepath, engine=engine)\n loc = xr.open_dataset(filepath, engine=engine, group=\"location\")\n\n # Check if mineral dataset and read in groups (only ds/loc for minunc)\n\n if \"L2B_MIN_\" in granule_id:\n wvl_group = \"mineral_metadata\"\n elif \"L2B_MINUNC\" not in granule_id:\n wvl_group = \"sensor_band_parameters\"\n\n wvl = None\n\n if wvl_group:\n wvl = xr.open_dataset(filepath, engine=engine, group=wvl_group)\n\n # Building Flat Dataset from Components\n data_vars = {**ds.variables}\n\n # Format xarray coordinates based upon emit product (no wvl for mineral uncertainty)\n coords = {\n \"downtrack\": ([\"downtrack\"], ds.downtrack.data),\n \"crosstrack\": ([\"crosstrack\"], ds.crosstrack.data),\n **loc.variables,\n }\n\n product_band_map = {\n \"L2B_MIN_\": \"name\",\n \"L2A_MASK_\": \"mask_bands\",\n \"L1B_OBS_\": \"observation_bands\",\n \"L2A_RFL_\": \"wavelengths\",\n \"L1B_RAD_\": \"wavelengths\",\n \"L2A_RFLUNCERT_\": \"wavelengths\",\n }\n\n # if band := product_band_map.get(next((k for k in product_band_map.keys() if k in granule_id), 'unknown'), None):\n # coords['bands'] = wvl[band].data\n\n if wvl:\n coords = {**coords, **wvl.variables}\n\n out_xr = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n out_xr.attrs[\"granule_id\"] = granule_id\n\n if band := product_band_map.get(\n next((k for k in product_band_map.keys() if k in granule_id), \"unknown\"), None\n ):\n if \"minerals\" in list(out_xr.dims):\n out_xr = out_xr.swap_dims({\"minerals\": band})\n out_xr = out_xr.rename({band: \"mineral_name\"})\n else:\n out_xr = out_xr.swap_dims({\"bands\": band})\n\n # Apply Quality and Band Masks, set fill values to NaN\n for var in list(ds.data_vars):\n if qmask is not None:\n out_xr[var].data[qmask == 1] = -9999\n if unpacked_bmask is not None:\n out_xr[var].data[unpacked_bmask == 1] = -9999\n\n if ortho is True:\n out_xr = ortho_xr(out_xr)\n out_xr.attrs[\"Orthorectified\"] = \"True\"\n\n return out_xr\n\n\n# Function to Calculate the center of pixel Lat and Lon Coordinates of the GLT grid\ndef get_pixel_center_coords(ds):\n \"\"\"\n This function calculates the gridded latitude and longitude pixel centers for the dataset using the geotransform and GLT arrays.\n\n Parameters:\n ds: an emit dataset opened with emit_xarray function\n\n Returns:\n x_geo, y_geo: longitude and latitude pixel centers of glt (gridded data)\n\n \"\"\"\n # Retrieve GLT\n GT = ds.geotransform\n # Get Shape of GLT\n dim_x = ds.glt_x.shape[1]\n dim_y = ds.glt_y.shape[0]\n # Build Arrays containing pixel centers\n x_geo = (GT[0] + 0.5 * GT[1]) + np.arange(dim_x) * GT[1]\n y_geo = (GT[3] + 0.5 * GT[5]) + np.arange(dim_y) * GT[5]\n\n return x_geo, y_geo\n\n\n# Function to Apply the GLT to an array\ndef apply_glt(ds_array, glt_array, fill_value=-9999, GLT_NODATA_VALUE=0):\n \"\"\"\n This function applies the GLT array to a numpy array of either 2 or 3 dimensions.\n\n Parameters:\n ds_array: numpy array of the desired variable\n glt_array: a GLT array constructed from EMIT GLT data\n\n Returns:\n out_ds: a numpy array of orthorectified data.\n \"\"\"\n\n # Build Output Dataset\n if ds_array.ndim == 2:\n ds_array = ds_array[:, :, np.newaxis]\n out_ds = np.full(\n (glt_array.shape[0], glt_array.shape[1], ds_array.shape[-1]),\n fill_value,\n dtype=np.float32,\n )\n valid_glt = np.all(glt_array != GLT_NODATA_VALUE, axis=-1)\n\n # Adjust for One based Index - make a copy to prevent decrementing multiple times inside ortho_xr when applying the glt to elev\n glt_array_copy = glt_array.copy()\n glt_array_copy[valid_glt] -= 1\n out_ds[valid_glt, :] = ds_array[\n glt_array_copy[valid_glt, 1], glt_array_copy[valid_glt, 0], :\n ]\n return out_ds\n\n\ndef ortho_xr(ds, GLT_NODATA_VALUE=0, fill_value=-9999):\n \"\"\"\n This function uses `apply_glt` to create an orthorectified xarray dataset.\n\n Parameters:\n ds: an xarray dataset produced by emit_xarray\n GLT_NODATA_VALUE: no data value for the GLT tables, 0 by default\n fill_value: the fill value for EMIT datasets, -9999 by default\n\n Returns:\n ortho_ds: an orthocorrected xarray dataset.\n\n \"\"\"\n # Build glt_ds\n\n glt_ds = np.nan_to_num(\n np.stack([ds[\"glt_x\"].data, ds[\"glt_y\"].data], axis=-1), nan=GLT_NODATA_VALUE\n ).astype(int)\n\n # List Variables\n var_list = list(ds.data_vars)\n\n # Remove flat field from data vars - the flat field is only useful with additional information before orthorectification\n if \"flat_field_update\" in var_list:\n var_list.remove(\"flat_field_update\")\n\n # Create empty dictionary for orthocorrected data vars\n data_vars = {}\n\n # Extract Rawspace Dataset Variable Values (Typically Reflectance)\n for var in var_list:\n raw_ds = ds[var].data\n var_dims = ds[var].dims\n # Apply GLT to dataset\n out_ds = apply_glt(raw_ds, glt_ds, GLT_NODATA_VALUE=GLT_NODATA_VALUE)\n\n # Update variables - Only works for 2 or 3 dimensional arays\n if raw_ds.ndim == 2:\n out_ds = out_ds.squeeze()\n data_vars[var] = ([\"latitude\", \"longitude\"], out_ds)\n else:\n data_vars[var] = ([\"latitude\", \"longitude\", var_dims[-1]], out_ds)\n\n del raw_ds\n\n # Calculate Lat and Lon Vectors\n lon, lat = get_pixel_center_coords(\n ds\n ) # Reorder this function to make sense in case of multiple variables\n\n # Apply GLT to elevation\n elev_ds = apply_glt(ds[\"elev\"].data, glt_ds)\n\n # Delete glt_ds - no longer needed\n del glt_ds\n\n # Create Coordinate Dictionary\n coords = {\n \"latitude\": ([\"latitude\"], lat),\n \"longitude\": ([\"longitude\"], lon),\n **ds.coords,\n } # unpack to add appropriate coordinates\n\n # Remove Unnecessary Coords\n for key in [\"downtrack\", \"crosstrack\", \"lat\", \"lon\", \"glt_x\", \"glt_y\", \"elev\"]:\n del coords[key]\n\n # Add Orthocorrected Elevation\n coords[\"elev\"] = ([\"latitude\", \"longitude\"], np.squeeze(elev_ds))\n\n # Build Output xarray Dataset and assign data_vars array attributes\n out_xr = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n\n del out_ds\n # Assign Attributes from Original Datasets\n for var in var_list:\n out_xr[var].attrs = ds[var].attrs\n out_xr.coords[\"latitude\"].attrs = ds[\"lat\"].attrs\n out_xr.coords[\"longitude\"].attrs = ds[\"lon\"].attrs\n out_xr.coords[\"elev\"].attrs = ds[\"elev\"].attrs\n\n # Add Spatial Reference in recognizable format\n out_xr.rio.write_crs(ds.spatial_ref, inplace=True)\n\n return out_xr\n\n\ndef quality_mask(filepath, quality_bands):\n \"\"\"\n This function builds a single layer mask to apply based on the bands selected from an EMIT L2A Mask file.\n\n Parameters:\n filepath: an EMIT L2A Mask netCDF file.\n quality_bands: a list of bands (quality flags only) from the mask file that should be used in creation of mask.\n\n Returns:\n qmask: a numpy array that can be used with the emit_xarray function to apply a quality mask.\n \"\"\"\n # Open Dataset\n mask_ds = xr.open_dataset(filepath, engine=\"h5netcdf\")\n # Open Sensor band Group\n mask_parameters_ds = xr.open_dataset(\n filepath, engine=\"h5netcdf\", group=\"sensor_band_parameters\"\n )\n # Print Flags used\n flags_used = mask_parameters_ds[\"mask_bands\"].data[quality_bands]\n print(f\"Flags used: {flags_used}\")\n # Check for data bands and build mask\n if any(x in quality_bands for x in [5, 6]):\n err_str = f\"Selected flags include a data band (5 or 6) not just flag bands\"\n raise AttributeError(err_str)\n else:\n qmask = np.sum(mask_ds[\"mask\"][:, :, quality_bands].values, axis=-1)\n qmask[qmask > 1] = 1\n return qmask\n\n\ndef band_mask(filepath):\n \"\"\"\n This function unpacks the packed band mask to apply to the dataset. Can be used manually or as an input in the emit_xarray() function.\n\n Parameters:\n filepath: an EMIT L2A Mask netCDF file.\n packed_bands: the 'packed_bands' dataset from the EMIT L2A Mask file.\n\n Returns:\n band_mask: a numpy array that can be used with the emit_xarray function to apply a band mask.\n \"\"\"\n # Open Dataset\n mask_ds = xr.open_dataset(filepath, engine=\"h5netcdf\")\n # Open band_mask and convert to uint8\n bmask = mask_ds.band_mask.data.astype(\"uint8\")\n # Print Flags used\n unpacked_bmask = np.unpackbits(bmask, axis=-1)\n # Remove bands > 285\n unpacked_bmask = unpacked_bmask[:, :, 0:285]\n # Check for data bands and build mask\n return unpacked_bmask\n\n\ndef write_envi(\n xr_ds,\n output_dir,\n overwrite=False,\n extension=\".img\",\n interleave=\"BIL\",\n glt_file=False,\n):\n \"\"\"\n This function takes an EMIT dataset read into an xarray dataset using the emit_xarray function and then writes an ENVI file and header. Does not work for L2B MIN.\n\n Parameters:\n xr_ds: an EMIT dataset read into xarray using the emit_xarray function.\n output_dir: output directory\n overwrite: overwrite existing file if True\n extension: the file extension for the envi formatted file, .img by default.\n glt_file: also create a GLT ENVI file for later use to reproject\n\n Returns:\n envi_ds: file in the output directory\n glt_ds: file in the output directory\n\n \"\"\"\n # Check if xr_ds has been orthorectified, raise exception if it has been but GLT is still requested\n if (\n \"Orthorectified\" in xr_ds.attrs.keys()\n and xr_ds.attrs[\"Orthorectified\"] == \"True\"\n and glt_file == True\n ):\n raise Exception(\"Data is already orthorectified.\")\n\n # Typemap dictionary for ENVI files\n envi_typemap = {\n \"uint8\": 1,\n \"int16\": 2,\n \"int32\": 3,\n \"float32\": 4,\n \"float64\": 5,\n \"complex64\": 6,\n \"complex128\": 9,\n \"uint16\": 12,\n \"uint32\": 13,\n \"int64\": 14,\n \"uint64\": 15,\n }\n\n # Get CRS/geotransform for creation of Orthorectified ENVI file or optional GLT file\n gt = xr_ds.attrs[\"geotransform\"]\n mapinfo = (\n \"{Geographic Lat/Lon, 1, 1, \"\n + str(gt[0])\n + \", \"\n + str(gt[3])\n + \", \"\n + str(gt[1])\n + \", \"\n + str(gt[5] * -1)\n + \", WGS-84, units=Degrees}\"\n )\n\n # This creates the coordinate system string\n # hard-coded replacement of wkt crs could probably be improved, though should be the same for all EMIT datasets\n csstring = '{ GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]] }'\n # List data variables (typically reflectance/radiance)\n var_names = list(xr_ds.data_vars)\n\n # Loop through variable names\n for var in var_names:\n # Define output filename\n output_name = os.path.join(output_dir, xr_ds.attrs[\"granule_id\"] + \"_\" + var)\n\n nbands = 1\n if len(xr_ds[var].data.shape) > 2:\n nbands = xr_ds[var].data.shape[2]\n\n # Start building metadata\n metadata = {\n \"lines\": xr_ds[var].data.shape[0],\n \"samples\": xr_ds[var].data.shape[1],\n \"bands\": nbands,\n \"interleave\": interleave,\n \"header offset\": 0,\n \"file type\": \"ENVI Standard\",\n \"data type\": envi_typemap[str(xr_ds[var].data.dtype)],\n \"byte order\": 0,\n \"data ignore value\": -9999,\n }\n\n for key in list(xr_ds.attrs.keys()):\n if key == \"summary\":\n metadata[\"description\"] = xr_ds.attrs[key]\n elif key not in [\"geotransform\", \"spatial_ref\"]:\n metadata[key] = f\"{{ {xr_ds.attrs[key]} }}\"\n\n # List all variables in dataset (including coordinate variables)\n meta_vars = list(xr_ds.variables)\n\n # Add band parameter information to metadata (ie wavelengths/obs etc.)\n for m in meta_vars:\n if m == \"wavelengths\" or m == \"radiance_wl\":\n metadata[\"wavelength\"] = np.array(xr_ds[m].data).astype(str).tolist()\n elif m == \"fwhm\" or m == \"radiance_fwhm\":\n metadata[\"fwhm\"] = np.array(xr_ds[m].data).astype(str).tolist()\n elif m == \"good_wavelengths\":\n metadata[\"good_wavelengths\"] = (\n np.array(xr_ds[m].data).astype(int).tolist()\n )\n elif m == \"observation_bands\":\n metadata[\"band names\"] = np.array(xr_ds[m].data).astype(str).tolist()\n elif m == \"mask_bands\":\n if var == \"band_mask\":\n metadata[\"band names\"] = [\n \"packed_bands_\" + bn\n for bn in np.arange(285 / 8).astype(str).tolist()\n ]\n else:\n metadata[\"band names\"] = (\n np.array(xr_ds[m].data).astype(str).tolist()\n )\n if \"wavelength\" in list(metadata.keys()) and \"band names\" not in list(\n metadata.keys()\n ):\n metadata[\"band names\"] = metadata[\"wavelength\"]\n\n # Add CRS/mapinfo if xarray dataset has been orthorectified\n if (\n \"Orthorectified\" in xr_ds.attrs.keys()\n and xr_ds.attrs[\"Orthorectified\"] == \"True\"\n ):\n metadata[\"coordinate system string\"] = csstring\n metadata[\"map info\"] = mapinfo\n\n # Write Variables as ENVI Output\n envi_ds = envi.create_image(\n envi_header(output_name), metadata, ext=extension, force=overwrite\n )\n mm = envi_ds.open_memmap(interleave=\"bip\", writable=True)\n\n dat = xr_ds[var].data\n\n if len(dat.shape) == 2:\n dat = dat.reshape((dat.shape[0], dat.shape[1], 1))\n\n mm[...] = dat\n\n # Create GLT Metadata/File\n if glt_file == True:\n # Output Name\n glt_output_name = os.path.join(\n output_dir, xr_ds.attrs[\"granule_id\"] + \"_\" + \"glt\"\n )\n\n # Write GLT Metadata\n glt_metadata = metadata\n\n # Remove Unwanted Metadata\n glt_metadata.pop(\"wavelength\", None)\n glt_metadata.pop(\"fwhm\", None)\n\n # Replace Metadata\n glt_metadata[\"lines\"] = xr_ds[\"glt_x\"].data.shape[0]\n glt_metadata[\"samples\"] = xr_ds[\"glt_x\"].data.shape[1]\n glt_metadata[\"bands\"] = 2\n glt_metadata[\"data type\"] = envi_typemap[\"int32\"]\n glt_metadata[\"band names\"] = [\"glt_x\", \"glt_y\"]\n glt_metadata[\"coordinate system string\"] = csstring\n glt_metadata[\"map info\"] = mapinfo\n\n # Write GLT Outputs as ENVI File\n glt_ds = envi.create_image(\n envi_header(glt_output_name), glt_metadata, ext=extension, force=overwrite\n )\n mmglt = glt_ds.open_memmap(interleave=\"bip\", writable=True)\n mmglt[...] = np.stack(\n (xr_ds[\"glt_x\"].values, xr_ds[\"glt_y\"].values), axis=-1\n ).astype(\"int32\")\n\n\ndef envi_header(inputpath):\n \"\"\"\n Convert a envi binary/header path to a header, handling extensions\n Args:\n inputpath: path to envi binary file\n Returns:\n str: the header file associated with the input reference.\n \"\"\"\n if (\n os.path.splitext(inputpath)[-1] == \".img\"\n or os.path.splitext(inputpath)[-1] == \".dat\"\n or os.path.splitext(inputpath)[-1] == \".raw\"\n ):\n # headers could be at either filename.img.hdr or filename.hdr. Check both, return the one that exists if it\n # does, if not return the latter (new file creation presumed).\n hdrfile = os.path.splitext(inputpath)[0] + \".hdr\"\n if os.path.isfile(hdrfile):\n return hdrfile\n elif os.path.isfile(inputpath + \".hdr\"):\n return inputpath + \".hdr\"\n return hdrfile\n elif os.path.splitext(inputpath)[-1] == \".hdr\":\n return inputpath\n else:\n return inputpath + \".hdr\"\n\n\ndef spatial_subset(ds, gdf):\n \"\"\"\n Uses a geodataframe containing polygon geometry to clip the GLT of an emit dataset read with emit_xarray, then uses the min/max downtrack and crosstrack\n indices to subset the extent of the dataset in rawspace, masking areas outside the provided spatial geometry. Uses rioxarray's clip function.\n\n Parameters:\n ds: an emit dataset read into xarray using the emit_xarray function.\n gdf: a geodataframe.\n\n Returns:\n clipped_ds: an xarray dataset clipped to the extent of the provided geodataframe that can be orthorectified with ortho_xr.\n \"\"\"\n # Reformat the GLT\n lon, lat = get_pixel_center_coords(ds)\n data_vars = {\n \"glt_x\": ([\"latitude\", \"longitude\"], ds.glt_x.data),\n \"glt_y\": ([\"latitude\", \"longitude\"], ds.glt_y.data),\n }\n coords = {\n \"latitude\": ([\"latitude\"], lat),\n \"longitude\": ([\"longitude\"], lon),\n \"ortho_y\": ([\"latitude\"], ds.ortho_y.data),\n \"ortho_x\": ([\"longitude\"], ds.ortho_x.data),\n }\n glt_ds = xr.Dataset(data_vars=data_vars, coords=coords, attrs=ds.attrs)\n glt_ds.rio.write_crs(glt_ds.spatial_ref, inplace=True)\n\n # Clip the emit glt\n clipped = glt_ds.rio.clip(gdf.geometry.values, gdf.crs, all_touched=True)\n # Get the clipped geotransform\n clipped_gt = np.array(\n [float(i) for i in clipped[\"spatial_ref\"].GeoTransform.split(\" \")]\n )\n\n valid_gltx = clipped.glt_x.data > 0\n valid_glty = clipped.glt_y.data > 0\n # Get the subset indices, -1 to convert to 0-based\n subset_down = [\n int(np.min(clipped.glt_y.data[valid_glty]) - 1),\n int(np.max(clipped.glt_y.data[valid_glty]) - 1),\n ]\n subset_cross = [\n int(np.min(clipped.glt_x.data[valid_gltx]) - 1),\n int(np.max(clipped.glt_x.data[valid_gltx]) - 1),\n ]\n\n # print(subset_down, subset_cross)\n\n crosstrack_mask = (ds.crosstrack >= subset_cross[0]) & (\n ds.crosstrack <= subset_cross[-1]\n )\n\n downtrack_mask = (ds.downtrack >= subset_down[0]) & (\n ds.downtrack <= subset_down[-1]\n )\n\n # Mask Areas outside of crosstrack and downtrack covered by the shape\n clipped_ds = ds.where((crosstrack_mask & downtrack_mask), drop=True)\n # Replace Full dataset geotransform with clipped geotransform\n clipped_ds.attrs[\"geotransform\"] = clipped_gt\n\n # Drop unnecessary vars from dataset\n clipped_ds = clipped_ds.drop_vars([\"glt_x\", \"glt_y\", \"downtrack\", \"crosstrack\"])\n\n # Re-index the GLT to the new array\n glt_x_data = np.maximum(clipped.glt_x.data - subset_cross[0], 0)\n glt_y_data = np.maximum(clipped.glt_y.data - subset_down[0], 0)\n\n clipped_ds = clipped_ds.assign_coords(\n {\n \"glt_x\": ([\"ortho_y\", \"ortho_x\"], np.nan_to_num(glt_x_data)),\n \"glt_y\": ([\"ortho_y\", \"ortho_x\"], np.nan_to_num(glt_y_data)),\n }\n )\n clipped_ds = clipped_ds.assign_coords(\n {\n \"downtrack\": (\n [\"downtrack\"],\n np.arange(0, clipped_ds[list(ds.data_vars.keys())[0]].shape[0]),\n ),\n \"crosstrack\": (\n [\"crosstrack\"],\n np.arange(0, clipped_ds[list(ds.data_vars.keys())[0]].shape[1]),\n ),\n }\n )\n\n clipped_ds.attrs[\"subset_downtrack_range\"] = subset_down\n clipped_ds.attrs[\"subset_crosstrack_range\"] = subset_cross\n\n return clipped_ds\n\n\ndef is_adjacent(scene: str, same_orbit: list):\n \"\"\"\n This function makes a list of scene numbers from the same orbit as integers and checks\n if they are adjacent/sequential.\n \"\"\"\n scene_nums = [int(scene.split(\".\")[-2].split(\"_\")[-1]) for scene in same_orbit]\n return all(b - a == 1 for a, b in zip(scene_nums[:-1], scene_nums[1:]))\n\n\ndef merge_emit(datasets: dict, gdf: gpd.GeoDataFrame):\n \"\"\"\n A function to merge xarray datasets formatted using emit_xarray. This could probably be improved,\n lots of shuffling data around to keep in xarray and get it to merge properly. Note: GDF may only work with a\n single geometry.\n \"\"\"\n nested_data_arrays = {}\n # loop over datasets\n for dataset in datasets:\n # create dictionary of arrays for each dataset\n\n # create dictionary of 1D variables, which should be consistent across datasets\n one_d_arrays = {}\n\n # Dictionary of variables to merge\n data_arrays = {}\n # Loop over variables in dataset including elevation\n for var in list(datasets[dataset].data_vars) + [\"elev\"]:\n # Get 1D for this variable and add to dictionary\n if not one_d_arrays:\n # These should be an array describing the others (wavelengths, mask_bands, etc.)\n one_dim = [\n item\n for item in list(datasets[dataset].coords)\n if item not in [\"latitude\", \"longitude\", \"spatial_ref\"]\n and len(datasets[dataset][item].dims) == 1\n ]\n # print(one_dim)\n for od in one_dim:\n one_d_arrays[od] = datasets[dataset].coords[od].data\n\n # Update format for merging - This could probably be improved\n da = datasets[dataset][var].reset_coords(\"elev\", drop=False)\n da = da.rename({\"latitude\": \"y\", \"longitude\": \"x\"})\n if len(da.dims) == 3:\n if any(item in list(da.coords) for item in one_dim):\n da = da.drop_vars(one_dim)\n da = da.drop_vars(\"elev\")\n da = da.to_array(name=var).squeeze(\"variable\", drop=True)\n da = da.transpose(da.dims[-1], da.dims[0], da.dims[1])\n # print(da.dims)\n if var == \"elev\":\n da = da.to_array(name=var).squeeze(\"variable\", drop=True)\n data_arrays[var] = da\n nested_data_arrays[dataset] = data_arrays\n\n # Transpose the nested arrays dict. This is horrible to read, but works to pair up variables (ie mask) from the different granules\n transposed_dict = {\n inner_key: {\n outer_key: inner_dict[inner_key]\n for outer_key, inner_dict in nested_data_arrays.items()\n }\n for inner_key in nested_data_arrays[next(iter(nested_data_arrays))]\n }\n\n # remove some unused data\n del nested_data_arrays, data_arrays, da\n\n # Merge the arrays using rioxarray.merge_arrays()\n merged = {}\n for _var in transposed_dict:\n merged[_var] = merge_arrays(\n list(transposed_dict[_var].values()),\n bounds=gdf.unary_union.bounds,\n nodata=-9999,\n )\n\n # Create a new xarray dataset from the merged arrays\n # Create Merged Dataset\n merged_ds = xr.Dataset(data_vars=merged, coords=one_d_arrays)\n # Rename x and y to longitude and latitude\n merged_ds = merged_ds.rename({\"y\": \"latitude\", \"x\": \"longitude\"})\n del transposed_dict, merged\n return merged_ds\n\n\ndef ortho_browse(url, glt, spatial_ref, geotransform, white_background=True):\n \"\"\"\n Use an EMIT GLT, geotransform, and spatial ref to orthorectify a browse image. (browse images are in native resolution)\n \"\"\"\n # Read Data\n data = io.imread(url)\n # Orthorectify using GLT and transpose so band is first dimension\n if white_background == True:\n fill = 255\n else:\n fill = 0\n ortho_data = apply_glt(data, glt, fill_value=fill).transpose(2, 0, 1)\n coords = {\n \"y\": (\n [\"y\"],\n (geotransform[3] + 0.5 * geotransform[5])\n + np.arange(glt.shape[0]) * geotransform[5],\n ),\n \"x\": (\n [\"x\"],\n (geotransform[0] + 0.5 * geotransform[1])\n + np.arange(glt.shape[1]) * geotransform[1],\n ),\n }\n ortho_data = ortho_data.astype(int)\n ortho_data[ortho_data == -1] = 0\n # Place in xarray.datarray\n da = xr.DataArray(ortho_data, dims=[\"band\", \"y\", \"x\"], coords=coords)\n da.rio.write_crs(spatial_ref, inplace=True)\n return da\n" }, { "path": "python/modules/tutorial_utils.py", "content": "\"\"\"\nThis module contains various functions used within the Exploring EMIT\nL2B CH4 plume complexes notebook for searching, wrangling data and visualizations.\n\nAuthor: Erik Bolch, ebolch@contractor.usgs.gov\nLast Updated: 2024-02-16\n\"\"\"\n\n# Imports\nfrom typing import List, Union\nimport re\nimport pandas as pd\nimport geopandas as gpd\nimport shapely\nimport earthaccess\nimport asyncio\nimport aiohttp\n\n\ndef convert_bounds(bbox, invert_y=False):\n \"\"\"\n Helper method for changing bounding box representation to leaflet notation\n\n ``(lon1, lat1, lon2, lat2) -> ((lat1, lon1), (lat2, lon2))``\n \"\"\"\n x1, y1, x2, y2 = bbox\n if invert_y:\n y1, y2 = y2, y1\n return ((y1, x1), (y2, x2))\n\n\ndef flattent_column_names(df: pd.DataFrame) -> pd.DataFrame:\n df.columns = [\n re.sub(\"([A-Z]+)\", r\"_\\1\", col.split(\".\")[-1]).lower() for col in df.columns\n ]\n return df\n\n\ndef get_shapely_object(\n result: earthaccess.search.DataGranule,\n) -> Union[shapely.geometry.base.BaseGeometry, None]:\n \"\"\"\n Retrieve the coordinates from the umm metadata and convert to a shapely geometry.\n \"\"\"\n shape = None\n try:\n geo = result[\"umm\"][\"SpatialExtent\"][\"HorizontalSpatialDomain\"][\"Geometry\"]\n keys = geo.keys()\n if \"BoundingRectangles\" in keys:\n bounding_rectangle = geo[\"BoundingRectangles\"][0]\n # Create bbox tuple\n bbox_coords = (\n bounding_rectangle[\"WestBoundingCoordinate\"],\n bounding_rectangle[\"SouthBoundingCoordinate\"],\n bounding_rectangle[\"EastBoundingCoordinate\"],\n bounding_rectangle[\"NorthBoundingCoordinate\"],\n )\n # Create shapely geometry from bbox\n shape = shapely.geometry.box(*bbox_coords, ccw=True)\n elif \"GPolygons\" in keys:\n points = geo[\"GPolygons\"][0][\"Boundary\"][\"Points\"]\n # Create shapely geometry from polygons\n shape = shapely.geometry.Polygon(\n [[p[\"Longitude\"], p[\"Latitude\"]] for p in points]\n )\n else:\n raise ValueError(\n \"Provided result does not contain bounding boxes/polygons or is incompatible.\"\n )\n except Exception as e:\n pass\n return shape\n\n\ndef list_metadata_fields(results: List[earthaccess.search.DataGranule]) -> List[str]:\n metadata_fields = list(flattent_column_names(pd.json_normalize(results)).columns)\n return metadata_fields\n\n\ndef results_to_geopandas(\n results: List[earthaccess.search.DataGranule],\n fields: List[str] = [],\n) -> gpd.GeoDataFrame:\n \"\"\"\n Convert the results of an earthaccess search into a geodataframe using some default fields.\n Add additional ones with the fields kwarg.\n \"\"\"\n default_fields = [\n \"size\",\n \"concept_id\",\n \"dataset-id\",\n \"native-id\",\n \"provider-id\",\n \"_related_urls\",\n \"_single_date_time\",\n \"_beginning_date_time\",\n \"_ending_date_time\",\n \"geometry\",\n ]\n results_df = pd.json_normalize(list(results), errors=\"ignore\")\n\n results_df = flattent_column_names(results_df)\n if len(fields) == 0:\n fields = default_fields\n else:\n fields = list(set(fields + default_fields))\n\n results_df = results_df.drop(\n columns=[col for col in results_df.columns if col not in fields]\n )\n\n results_df[\"_related_urls\"] = results_df[\"_related_urls\"].apply(\n lambda links: [\n link\n for link in links\n if link[\"Type\"]\n in [\n \"GET DATA\",\n \"GET DATA VIA DIRECT ACCESS\",\n \"GET RELATED VISUALIZATION\",\n ]\n ]\n )\n # Create shapely polygons for result\n geometries = [\n get_shapely_object(results[index]) for index in results_df.index.to_list()\n ]\n # Convert to GeoDataframe\n gdf = gpd.GeoDataFrame(results_df, geometry=geometries, crs=\"EPSG:4326\")\n return gdf\n" }, { "path": "python/tutorials/Exploring_EMIT_L2A_Reflectance.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exploring L2A Reflectance\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"In this notebook we will open a Level 2A (L2A) Reflectance product file (netcdf) from Earth Surface Mineral Dust Source Investigation (EMIT). We will inspect the structure and plot the spectra of individual pixels and spatial coverage of a single band. Next, we will orthorectify the imagery using the included geometry lookup table (GLT). Finally, we will take advantage of the `holoviews streams` module to build an interactive plot.\\n\",\n \"\\n\",\n \"**Background**\\n\",\n \"\\n\",\n \"The EMIT instrument is an imaging spectrometer that measures light in visible and infrared wavelengths. These measurements display unique spectral signatures that correspond to the composition on the Earth's surface. The EMIT mission focuses specifically on mapping the composition of minerals to better understand the effects of mineral dust throughout the Earth system and human populations now and in the future. More details about EMIT and its associated products can be found in the **README.md** and on the [EMIT website](https://earth.jpl.nasa.gov/emit/).\\n\",\n \"\\n\",\n \"The L2A Reflectance Product contains estimated surface reflectance. Surface reflectance is the fraction of incoming solar radiation reflected Earth's surface. Materials reflect proportions of radiation differently based upon their chemical composition. This means that reflectance information can be used to determine the composition of a target. In this guide you will learn how to plot a band from the L2A reflectance spatially and look at the spectral curve associated with individual pixels.\\n\",\n \"\\n\",\n \"**References**\\n\",\n \"- Leith, Alex. 2023. Hyperspectral Notebooks. Jupyter Notebook. Auspatious. https://github.com/auspatious/hyperspectral-notebooks/tree/main\\n\",\n \"\\n\",\n \"**Requirements** \\n\",\n \" - Set up Python Environment - See **setup_instructions.md** in the `/setup/` folder\\n\",\n \"\\n\",\n \"**Learning Objectives** \\n\",\n \"- How to open an EMIT `.nc` file as an `xarray.Dataset`\\n\",\n \"- Apply the Geometry Lookup Table (GLT) to orthorectify the image.\\n\",\n \"- How to plot the spectra of pixels\\n\",\n \"- How to plot specific bands as images\\n\",\n \"- How to make an interactive plot to visualize spectra\\n\",\n \"\\n\",\n \"**Tutorial Outline** \\n\",\n \"\\n\",\n \"1.1 Setup \\n\",\n \"1.2 Opening The Data \\n\",\n \"1.3 Plotting Data - Non-Orthorectified \\n\",\n \"1.4 Orthorectification \\n\",\n \"1.5 Plotting Data - Orthorectified \\n\",\n \"1.6 Saving Orthorectified Data \\n\",\n \"1.7 Interactive Plots \\n\",\n \"\\n\",\n \"![Interactive Plot](../../img/interactive_plots.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.1 Setup\\n\",\n \"\\n\",\n \"Import the required Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import earthaccess\\n\",\n \"import os\\n\",\n \"import warnings\\n\",\n \"import csv\\n\",\n \"from osgeo import gdal\\n\",\n \"import numpy as np\\n\",\n \"import math\\n\",\n \"import rasterio as rio\\n\",\n \"import xarray as xr\\n\",\n \"import holoviews as hv\\n\",\n \"import hvplot.xarray\\n\",\n \"import netCDF4 as nc\\n\",\n \"\\n\",\n \"# This will ignore some warnings caused by holoviews\\n\",\n \"warnings.simplefilter('ignore') \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Login to your NASA Earthdata account and create a `.netrc` file using the `login` function from the `earthaccess` library. If you do not have an Earthdata Account, you can create one [here](https://urs.earthdata.nasa.gov/home). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this notebook we will download the files necessary using `earthaccess`. You can also access the data in place or stream it, but this can slow due to the file sizes. Provide a URL for an EMIT L2A Reflectance granule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"url = 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2ARFL.001/EMIT_L2A_RFL_001_20220903T163129_2224611_012/EMIT_L2A_RFL_001_20220903T163129_2224611_012.nc'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get an HTTPS Session using your earthdata login, set a local path to save the file, and download the granule asset - This may take a while, the reflectance file is approximately 1.8 GB.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Get requests https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"granule_asset_id = url.split('/')[-1]\\n\",\n \"# Define Local Filepath\\n\",\n \"fp = f'../../data/{granule_asset_id}'\\n\",\n \"# Download the Granule Asset if it doesn't exist\\n\",\n \"if not os.path.isfile(fp):\\n\",\n \" with fs.get(url,stream=True) as src:\\n\",\n \" with open(fp,'wb') as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.2 Opening EMIT Data \\n\",\n \"\\n\",\n \"EMIT L2A Reflectance Data are distributed in a non-orthocorrected spatially raw NetCDF4 (.nc) format consisting of the data and its associated metadata. Inside the L2A Reflectance `.nc` file there are 3 groups. Groups can be thought of as containers to organize the data. \\n\",\n \"\\n\",\n \"1. The root group that can be considered the main dataset contains the reflectance data described by the downtrack, crosstrack, and bands dimensions. \\n\",\n \"2. The `sensor_band_parameters` group containing the wavelength center and the full-width half maximum (FWHM) of each band. \\n\",\n \"3. The `location` group contains latitude and longitude values at the center of each pixel described by the crosstrack and downtrack dimensions, as well as a geometry lookup table (GLT) described by the ortho_x and ortho_y dimensions. The GLT is an orthorectified image (EPSG:4326) consisting of 2 layers containing downtrack and crosstrack indices. These index positions allow us to quickly project the raw data onto this geographic grid.\\n\",\n \"\\n\",\n \"To access the `.nc` file we will use the `netCDF4` and `xarray` libraries. The `netCDF4` library will be used to explore thee data structure, then we will use `xarray` to work with the data. `xarray` is a python package for working with labelled multi-dimensional arrays. It provides a data model where data, dimensions, and attributes together in an easily interpretable way. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"ds_nc = nc.Dataset(fp)\\n\",\n \"ds_nc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_nc['location']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From this output, we can see the `reflectance` variable, and the `sensor_band_parameters` and `location` groups. We can also see the dimensions, their sizes, and file metadata.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have a better understanding of the structure of the file, read the EMIT data as an `xarray.Dataset` and preview it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"ds = xr.open_dataset(fp)\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This `xarray` dataset only contains the `reflectance` variable and attributes metadata, not the data from the other groups in the file. This is because `xarray` only supports reading non-hierarchical (flat) datasets, meaning that when loading a NetCDF into an `xarray.Dataset`, only the root group is added. The other groups will have to be read into `xarray` separately. We can list them using the `netCDF4` library to get the group names, then use that to add them to new `xarray` datasets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"ds_nc.groups.keys()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we know the other group names, read the `sensor_band_parameters` and `location` groups into their own `xarray` datasets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"wvl = xr.open_dataset(fp,group='sensor_band_parameters')\\n\",\n \"wvl\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"loc = xr.open_dataset(fp,group='location')\\n\",\n \"loc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We could merge all 3 datasets, but since `sensor_band_parameters` and `location` describe various aspects of the `reflectance` variable we can simply add them as coordinates, along with a downtrack and crosstrack dimension to describe the reflectance data array. This will allow us to utilize some additional features of `xarray`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"# Create coordinates and an index for the downtrack and crosstrack dimensions, then unpack the variables from the wvl and loc datasets and set them as coordinates for ds\\n\",\n \"ds = ds.assign_coords({'downtrack':(['downtrack'], ds.downtrack.data),'crosstrack':(['crosstrack'],ds.crosstrack.data), **wvl.variables, **loc.variables})\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Another step we can take is to swap the 'bands' dimension with wavelengths. Doing this will allow us to index based on the wavelength of the band, and remove 'bands' as a dimension. We can do this since bands is just a 3rd dimension that will is defined based on the 'sensor_band_parameters' group (i.e. 'wavelengths' for reflectance, or 'mask_bands' for mask data).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = ds.swap_dims({'bands':'wavelengths'})\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we have an `xarray.Dataset` containing all of the information from EMIT netCDF file. Since these datasets are large, we can go ahead and delete objects we won't be using to conserve memory.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"del wvl\\n\",\n \"del loc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.3 Visualizing Spectra - Non-Orthorectified\\n\",\n \"\\n\",\n \"Pick a random downtrack and crosstrack location. Here we chose 660, 370 (downtrack,crosstrack). Next use the `sel()` function from `xarray` and the `hvplot.line()` functions to first select the spatial position and then plot a line showing the reflectance at that location. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"example = ds['reflectance'].sel(downtrack=660,crosstrack=370)\\n\",\n \"example.hvplot.line(y='reflectance',x='wavelengths', color='black', frame_height=400, frame_width=600)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see some flat regions in the spectral curve around 1320-1440 nm and 1770-1970 nm. These are where water absoption features in these regions were removed. Typically this data is noisy due to the moisture present in the atmosphere; therefore, these spectral regions offer little information about targets and can be excluded from calculations. \\n\",\n \"\\n\",\n \"We can set reflectance values where the `good_wavelenghts` is 0 (these will have a reflectance of -0.1) to `np.nan` do mask them out and improve visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"ds['reflectance'].data[:,:,ds['good_wavelengths'].data==0] = np.nan\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Plot the filtered reflectance values using the same downtrack and crosstrack position as above.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"ds['reflectance'].sel(downtrack=660,crosstrack=370).hvplot.line(y='reflectance',x='wavelengths', color='black', frame_height=400, frame_width=600)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Without these data we can better interpret the spectral curve and `hvplot` will do a better job automatically scaling our axes.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also plot the data spatially. Since we changed our dimension and index to `wavelengths` we can use the `sel()` function to spectrally subset to the wavelength nearest to 850nm in the NIR, then plot the data spatially using `hvplot.image()` to view the reflectance at 850nm of each pixel across the acquired region.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"refl850 = ds.sel(wavelengths=850, method='nearest')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"refl850.hvplot.image(cmap='viridis', aspect = 'equal', frame_width=720).opts(title=f\\\"{refl850.wavelengths.values:.3f} {refl850.wavelengths.units}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.4 Orthorectification\\n\",\n \"\\n\",\n \"The 'real' orthorectifation process has already been done for EMIT data. Here we are using the crosstrack and downtrack indices contained in the GLT to place our spatially raw reflectance data a into geographic grid with the `ortho_x` and `ortho_y` dimensions. As previously mentioned a Geometry Lookup Table (GLT) is included in the `location` group of the netCDF4 file. Applying the GLT will orthorectify the data and give us Latitude and Longitude positional information.\\n\",\n \"\\n\",\n \"Before using the GLT to orthorectify the data, examine the `location` group from the dataset by reading it into `xarray`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"loc = xr.open_dataset(fp,group='location')\\n\",\n \"loc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that each downtrack and crosstrack position has a latitude, longitude, and elevation, and the ortho_x and ortho_y data make up glt_x and glt_y arrays with a different shape. These arrays contain crosstrack and downtrack index values to quickly reproject the data. We will use these indexes to build an array of 2009x2353x285 (lat,lon,bands), filling it with the data from the reflectance dataset. \\n\",\n \"\\n\",\n \"Go ahead and remove this dataset. We will use a function in the provided `emit_tools` module to orthorectify the data and place it into an `xarray.Dataset`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"del loc\\n\",\n \"del example\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Import the `emit_tools` module and call use the help function to see how it can be used.\\n\",\n \"\\n\",\n \"> Note: This function currently works with L1B Radiance and L2A Reflectance Data.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from emit_tools import emit_xarray\\n\",\n \"help(emit_xarray)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that the `emit_xarray` function will automatically apply the GLT to orthorectify the data unless `ortho = False`. The function will also apply masks if desired during construction of the output `xarray.Dataset`. EMIT L2A Masks files provides a quality mask and a band_mask indicating if values were interpolated. For more about masking, see the [How_to_use_EMIT_Quality_data.ipynb](../how-tos/How_to_use_EMIT_Quality_data.ipynb). \\n\",\n \"\\n\",\n \"Use the `emit_xarray` function to read in and orthorectify the L2A reflectance data. \\n\",\n \"\\n\",\n \">**For a detailed walkthrough of the orthorectification process using the GLT see section 2 of the How_to_Orthorectify.ipynb in the how-tos folder.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_geo = emit_xarray(fp, ortho=True)\\n\",\n \"ds_geo\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"non_ortho_fig = ds.sel(wavelengths=850, method='nearest').hvplot.image(cmap='Viridis', aspect = 'equal',frame_height=600).opts(\\n\",\n \" title=f\\\"Reflectance at {refl850.wavelengths.values:.3f} {refl850.wavelengths.units}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When we orthorectify the scene, locations in the grid without data will be filled with the default fill value of -9999. To improve visualizations we can assign these locations to `np.nan` to mask them (make them transparent).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_geo.reflectance.data[ds_geo.reflectance.data == -9999] = np.nan\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_geo.sel(wavelengths=850, method='nearest').hvplot.image(cmap='Viridis', geo=True, tiles='ESRI', alpha=0.8, frame_height=600).opts(\\n\",\n \" title=f\\\"Reflectance at {refl850.wavelengths.values:.3f} {refl850.wavelengths.units} (Orthorectified)\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.5 Plotting Data - Orthorectified\\n\",\n \"\\n\",\n \"Now that the data has been orthorectified, plot the georeferenced dataset using the same single wavelength (850nm) as above alongside the uncorrected image. We an also plot the orthorectified data against an imagery tile using the `geo=True` and `tiles=` parameters instead of `aspect='equal'`. Any tile source available in `geoviews` should work here. This will change the axis names, but that can be fixed by adding them manually in the `opts`, like below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(ds_geo.sel(wavelengths=850, method='nearest').hvplot.image(cmap='Viridis', geo=True, tiles='ESRI', alpha=0.8, frame_height=600).opts(\\n\",\n \" title=f\\\"Reflectance at {refl850.wavelengths.values:.3f} {refl850.wavelengths.units} (Orthorectified)\\\") +\\\\\\n\",\n \" ds.sel(wavelengths=850, method='nearest').hvplot.image(cmap='Viridis', aspect = 'equal',frame_height=600).opts(\\n\",\n \" title=f\\\"Reflectance at {refl850.wavelengths.values:.3f} {refl850.wavelengths.units}\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see that the orthorectification step placed the data on a geogrpahic grid that matches pretty well with ESRI tiles. Now that we have a better idea of what the target area looks like, we can also plot the spectra using the georeferenced data. First, filter out the water absorption bands like we did earlier. By limiting the third dimension of the array to `good_wavelengths`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_geo['reflectance'].data[:,:,ds_geo['good_wavelengths'].data==0] = np.nan\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now, plot the spectra at the Lat/Lon coordinates provided below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"point = ds_geo.sel(longitude=-61.833,latitude=-39.710,method='nearest')\\n\",\n \"point.hvplot.line(y='reflectance',x='wavelengths', color='black', frame_height=400, frame_width=600).opts(\\n\",\n \" title = f'Latitude = {point.latitude.values.round(3)}, Longitude = {point.longitude.values.round(3)}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.6 Writing an Orthorectified Output\\n\",\n \"\\n\",\n \"At this point, the `ds_geo` orthorectified EMIT data can also be written as a flattened netCDF4 output that can be read using the `xarray.open_dataset` function, if desired. Before doing this, we can transpose the dimension order so the bands are the first dimension so that the data is readable by software like QGIS. This file will be larger than the original EMIT granule since it has been orthorectified. If we do write an output file, the format will be such that it can be read in using `xarray`.\\n\",\n \"\\n\",\n \"Transpose the dimensions order and write an output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Transpose dimensions\\n\",\n \"# ds_geo = ds_geo.transpose('wavelengths','latitude','longitude')\\n\",\n \"# ds_geo.to_netcdf('../../data/geo_ds_out.nc')\\n\",\n \"\\n\",\n \"# Example for Opening \\n\",\n \"# ds = xr.open_dataset('../data/geo_ds_out.nc')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1.7 Interactive Spatial and Spectral Plots\\n\",\n \"\\n\",\n \"Combining the Spatial and Spectral information into a single visualization can be a powerful tool for exploring and inspecting imaging spectroscopy data. Using the `streams` module from `Holoviews` we can link a spatial map to a plot of spectra.\\n\",\n \"\\n\",\n \"We could plot a single band image as we previously have, but using a multiband image, like an RGB may help infer what targets we're examining. Build an RGB image following the steps below.\\n\",\n \"\\n\",\n \"Select bands to represent red (650 nm), green (560 nm), and blue (470 nm) by finding the nearest to a wavelength chosen to represent that color.\\n\",\n \"\\n\",\n \"> Note that if subsetting by bands like this example, it is more memory efficient to subset before orthorectifying. Instead of using `ortho=True` in the `emit_xarray` function, select bands first, then apply the orthorectification using the `ortho_xr` function from `emit_tools.py` (requires a separate import).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rgb = ds_geo.sel(wavelengths=[650, 560, 470], method='nearest')\\n\",\n \"rgb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, write a function to scale the values using a gamma correction. Without applying this scaling the majority of the image would be very dark, with the reflectance data being skewed by the few pixels with very high reflectance. \\n\",\n \"> Note: This has no impact on analysis or data, just visualizing the RGB map. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Function to adjust gamma across all bands - adjust brightness\\n\",\n \"def gamma_adjust(rgb_ds, bright=0.2, white_background=False):\\n\",\n \" array = rgb_ds.reflectance.data\\n\",\n \" gamma = math.log(bright)/math.log(np.nanmean(array)) # Create exponent for gamma scaling - can be adjusted by changing 0.2 \\n\",\n \" scaled = np.power(array,gamma).clip(0,1) # Apply scaling and clip to 0-1 range\\n\",\n \" if white_background == True:\\n\",\n \" scaled = np.nan_to_num(scaled, nan = 1) # Assign NA's to 1 so they appear white in plots\\n\",\n \" rgb_ds.reflectance.data = scaled\\n\",\n \" return rgb_ds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rgb = gamma_adjust(rgb, white_background=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have an RGB dataset, use it to build our spatial plot.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"map = rgb.hvplot.rgb(x='longitude', y='latitude', bands='wavelengths', aspect = 'equal', frame_height=500)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To visualize the spectral and spatial data side-by-side, we use the `Point Draw` tool from the `holoviews` library. \\n\",\n \"\\n\",\n \"Define a limit to the quantity of points and spectra we will plot, a list of colors to cycle through, and an initial point. We use the input from the `PointerXY`to show the spectra where our mouse cursor is. Then use the input from the `Tap` function to provide clicked x and y positions on the `map`. These retrieve spectra from the dataset at those coordinates. \\n\",\n \"\\n\",\n \"Click in the RGB image to add spectra to the plot. You can also click and hold the mouse button then drag previously place points. To remove a point click and hold the mouse button down, then press the backspace key.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set Point Limit\\n\",\n \"POINT_LIMIT = 10\\n\",\n \"\\n\",\n \"# Set up Color Cycling\\n\",\n \"color_cycle = hv.Cycle('Category20')\\n\",\n \"colors = [color_cycle[i] for i in range(5)]\\n\",\n \"\\n\",\n \"# Get center coordinates of image\\n\",\n \"xmid = ds_geo.longitude.values[int(len(ds_geo.longitude) / 2)]\\n\",\n \"ymid = ds_geo.latitude.values[int(len(ds_geo.latitude) / 2)]\\n\",\n \"\\n\",\n \"#\\n\",\n \"first_point = ([xmid], [ymid], [0])\\n\",\n \"points = hv.Points(first_point, vdims='id')\\n\",\n \"points_stream = hv.streams.PointDraw(\\n\",\n \" data=points.columns(),\\n\",\n \" source=points,\\n\",\n \" drag=True,\\n\",\n \" num_objects=POINT_LIMIT,\\n\",\n \" styles={'fill_color': color_cycle.values[1:POINT_LIMIT+1], 'line_color': 'gray'}\\n\",\n \")\\n\",\n \"\\n\",\n \"posxy = hv.streams.PointerXY(source=map, x=xmid, y=ymid)\\n\",\n \"clickxy = hv.streams.Tap(source=map, x=xmid, y=ymid)\\n\",\n \"\\n\",\n \"# Function to build spectral plot of clicked location to show on hover stream plot\\n\",\n \"def click_spectra(data):\\n\",\n \" coordinates = []\\n\",\n \" if data is None or not any(len(d) for d in data.values()):\\n\",\n \" coordinates.append(clicked_points[0][0], clicked_points[1][0])\\n\",\n \" else:\\n\",\n \" coordinates = [c for c in zip(data['x'], data['y'])]\\n\",\n \" \\n\",\n \" plots = []\\n\",\n \" for i, coords in enumerate(coordinates):\\n\",\n \" x, y = coords\\n\",\n \" data = ds_geo.sel(longitude=x, latitude=y, method=\\\"nearest\\\")\\n\",\n \" plots.append(\\n\",\n \" data.hvplot.line(\\n\",\n \" y=\\\"reflectance\\\",\\n\",\n \" x=\\\"wavelengths\\\",\\n\",\n \" color=color_cycle,\\n\",\n \" label=f\\\"{i}\\\"\\n\",\n \" )\\n\",\n \" )\\n\",\n \" points_stream.data[\\\"id\\\"][i] = i\\n\",\n \" return hv.Overlay(plots)\\n\",\n \"\\n\",\n \"def hover_spectra(x,y):\\n\",\n \" return ds_geo.sel(longitude=x,latitude=y,method='nearest').hvplot.line(y='reflectance',x='wavelengths',\\n\",\n \" color='black', frame_width=400)\\n\",\n \"# Define the Dynamic Maps\\n\",\n \"click_dmap = hv.DynamicMap(click_spectra, streams=[points_stream])\\n\",\n \"hover_dmap = hv.DynamicMap(hover_spectra, streams=[posxy])\\n\",\n \"\\n\",\n \"# Plot the Map and Dynamic Map side by side\\n\",\n \"hv.Layout(hover_dmap*click_dmap + map * points).cols(2).opts(\\n\",\n \" hv.opts.Points(active_tools=['point_draw'], size=10, tools=['hover'], color='white', line_color='gray'),\\n\",\n \" hv.opts.Overlay(show_legend=False, show_title=False, fontscale=1.5, frame_height=480)\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After selecting a number of points we can build a dictionary of points and spectra, then export the spectra to a `.csv` file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"data = points_stream.data\\n\",\n \"wavelengths = ds_geo.wavelengths.values\\n\",\n \"\\n\",\n \"rows = [[\\\"id\\\", \\\"x\\\", \\\"y\\\"] + [str(i) for i in wavelengths]]\\n\",\n \" \\n\",\n \"for p in zip(data['x'], data['y'], data['id']):\\n\",\n \" x, y, i = p\\n\",\n \" spectra = ds_geo.sel(longitude=x, latitude=y, method=\\\"nearest\\\").reflectance.values\\n\",\n \" row = [i, x, y] + list(spectra)\\n\",\n \" rows.append(row)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('../../data/interactive_plot_data.csv', 'w', newline='') as f:\\n\",\n \" writer = csv.writer(f)\\n\",\n \" writer.writerows(rows)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 11-27-2023 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n },\n \"vscode\": {\n \"interpreter\": {\n \"hash\": \"3292b2aceff7d39327a7519422d4180a7c9b133202090f26e797e3dd8f2c7877\"\n }\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/tutorials/Finding_EMIT_L2B_Data.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Finding EMIT L2B Data\\n\",\n \"\\n\",\n \"**Summary**\\n\",\n \"\\n\",\n \"This notebook demonstrates how to search for Earth Mineral dust source Investigation (EMIT) L2B Estimated Mineral Identification and Band Depth and Uncertainty (EMITL2BMIN) data. This data is available in the NASA Earthdata Cloud, to download or stream, and can be found by querying the NASA Earthdata Common Metadata Repository (CMR) API, or using the [Earthdata Search]() interface. In this example we will use the `earthaccess` Python library, which abstracts the NASA CMR API, providing an easy way to search and review results programatically, and manages authentication credentials to simplify the user experience when streaming or downloading data.\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"**Background**\\n\",\n \"\\n\",\n \"The EMIT instrument is an imaging spectrometer that measures light in visible and infrared wavelengths. These measurements display unique spectral signatures that correspond to the composition on the Earth's surface. The EMIT mission focuses specifically on mapping the composition of minerals to better understand the effects of mineral dust throughout the Earth system and human populations now and in the future. More details about EMIT and its associated products can be found in the **README.md** and on the [EMIT website](https://earth.jpl.nasa.gov/emit/).\\n\",\n \"\\n\",\n \"The [EMITL2BMIN](https://doi.org/10.5067/EMIT/EMITL2BMIN.001) data product provides estimated mineral identification, band depths and uncertainty in a spatially raw, non-orthocorrected format. Two spectral groups, which correspond to different regions of the spectra, are identified independently and often co-occur are used to identify minerals. These estimates are generated using the [Tetracorder system](https://www.usgs.gov/publications/tetracorder-user-guide-version-44?_gl=1*1eoj33d*_ga*MTU3MTA3ODgxNS4xNjQ5MTg1MDgx*_ga_0YWDZEJ295*MTY4NjkyNTg0Mi40NC4xLjE2ODY5MjU4NzMuMC4wLjA.)([code](https://github.com/PSI-edu/spectroscopy-tetracorder)) and are based on [EMITL2ARFL](https://doi.org/10.5067/EMIT/EMITL2ARFL.001) reflectance values. The product also consists of an EMIT_L2B_MINUNCERT file, which provides band depth uncertainty estimates calculated using surface Reflectance Uncertainty values from the [EMITL2ARFL](https://doi.org/10.5067/EMIT/EMITL2ARFL.001) data product. The band depth uncertainties are presented as standard deviations, and the fit score for each mineral identification is also provided as the coefficient of determination (r2) of the match between the continuum normalized library reference and the continuum normalized observed spectrum. Associated metadata indicates the name and reference information for each identified mineral, and additional information about aggregating minerals into different categories, and the code used for product generation is available in the [emit-sds-l2b repository](https://github.com/emit-sds/emit-sds-l2b).\\n\",\n \"\\n\",\n \"**Disclaimer**\\n\",\n \"\\n\",\n \"The [EMIT_L2B_MIN](https://doi.org/10.5067/EMIT/EMITL2BMIN.001) product is generated to support the EMIT mission objectives of constraining the sign of dust related radiative forcing. Ten mineral types are the core focus of this work: Calcite, Chlorite, Dolomite, Goethite, Gypsum, Hematite, Illite+Muscovite, Kaolinite, Montmorillonite, and Vermiculite. A future product will aggregate these results for use in Earth System Models. Additional minerals are included in this product for transparency but were not the focus of this product. Further validation is required to use these additional mineral maps, particularly in the case of resource exploration. Similarly, the separation of minerals with similar spectral features, such as a fine-grained goethite and hematite, is an area of active research. The results presented here are an initial offering, but the precise categorization is likely to evolve over time, and the limits of what can and cannot be separated on the global scale is still being explored. The user is encouraged to read the [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1659/EMITL2B_ATBD_v1.pdf) for more details. \\n\",\n \"\\n\",\n \"**Requirements**\\n\",\n \"\\n\",\n \" - [NASA Earthdata Account](https://urs.earthdata.nasa.gov/home). This is free and can be set up quickly. \\n\",\n \" - *No Python setup requirements if connected to the workshop cloud instance!* \\n\",\n \" - **Local Only** Set up Python Environment - See **setup_instructions.md** in the `/setup/` folder to set up a local compatible Python environment \\n\",\n \"\\n\",\n \"**Learning Objectives**\\n\",\n \"- How to get information about data collections using `earthaccess`\\n\",\n \"- How to query for EMIT L2B Mineralogy data using spatiotemporal parameters\\n\",\n \"- How to create a geodataframe from the search results\\n\",\n \"- How to further filter and save results URLs to a list.\\n\",\n \"\\n\",\n \"**Tutorial Outline**\\n\",\n \"\\n\",\n \"1. Setup \\n\",\n \"2. Searching for EMIT L2B Mineralogy Data\\n\",\n \"3. Advanced Filtering\\n\",\n \"4. Visualizing Data\\n\",\n \"5. Creating a List of Results and Asset URLs\\n\",\n \"6. Streaming or Downloading Data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Setup\\n\",\n \"\\n\",\n \"Import the required Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import required libraries\\n\",\n \"import os\\n\",\n \"import sys\\n\",\n \"import folium\\n\",\n \"import earthaccess\\n\",\n \"import warnings\\n\",\n \"import folium.plugins\\n\",\n \"import pandas as pd\\n\",\n \"import geopandas as gpd\\n\",\n \"import math\\n\",\n \"\\n\",\n \"from branca.element import Figure\\n\",\n \"from IPython.display import display\\n\",\n \"from shapely import geometry\\n\",\n \"from skimage import io\\n\",\n \"from datetime import timedelta\\n\",\n \"from shapely.geometry.polygon import orient\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"import matplotlib.cm as cm\\n\",\n \"\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from tutorial_utils import list_metadata_fields, results_to_geopandas, convert_bounds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.2 NASA Earthdata Login Credentials\\n\",\n \"\\n\",\n \"To download or stream NASA data you will need an Earthdata account, you can create one [here](https://urs.earthdata.nasa.gov/home). Searching We will use the `login` function from the `earthaccess` library for authentication before downloading at the end of the notebook. This function can also be used to create a local `.netrc` file if it doesn't exist or add your login info to an existing `.netrc` file. If no Earthdata Login credentials are found in the `.netrc` you'll be prompted for them. This step is not necessary to conduct searches but is needed to download or stream data.\\n\",\n \"\\n\",\n \"## 2. Searching for EMIT L2B Mineralogy Data\\n\",\n \"\\n\",\n \"To find data we will use the [`earthaccess` Python library](https://github.com/nsidc/earthaccess). `earthaccess` searches [NASA Common Metadata Repository (CMR) API](), a metadata system that catalogs Earth Science data and associated metadata records. The results can then be used to download granules or generate lists of granule search result URLs.\\n\",\n \"\\n\",\n \"Using `earthaccess` we can search based on the attributes of a granule, which can be thought of as a spatiotemporal scene from an instrument containing multiple assets (ex: Reflectance, Reflectance Uncertainty, Masks for the EMIT L2A Reflectance Collection, and EMIT ). When conducting a search we can provide a product, in this case the mineralogy product, a date-time range, and spatial constraints. This process can also be used with other EMIT products, other NASA collections.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Querying for Datasets\\n\",\n \"\\n\",\n \"Our first step in searching for data is determining which collection (e.g. EMIT L2A Estimated Surface Reflectance Uncertainty and Masks, EMIT L2B Estimated Mineral Identification and Band Depth and Uncertainty) we want to search for. The best way to do this is using the collection `short_name` (e.g. EMITL2ARFL, EMITL2BMIN) or `concept-id`. In rare cases, the `short_name` of two collections can be the same, so we will use the `concept-id` which is a unique identifier for each collection. To find the `concept-id` we can search using some keywords.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{\\n\",\n \" \\\"meta\\\": {\\n\",\n \" \\\"concept-id\\\": \\\"C2408034484-LPCLOUD\\\",\\n\",\n \" \\\"granule-count\\\": 87550,\\n\",\n \" \\\"provider-id\\\": \\\"LPCLOUD\\\"\\n\",\n \" },\\n\",\n \" \\\"umm\\\": {\\n\",\n \" \\\"ShortName\\\": \\\"EMITL2BMIN\\\",\\n\",\n \" \\\"EntryTitle\\\": \\\"EMIT L2B Estimated Mineral Identification and Band Depth and Uncertainty 60 m V001\\\",\\n\",\n \" \\\"Version\\\": \\\"001\\\"\\n\",\n \" }\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"meta\\\": {\\n\",\n \" \\\"concept-id\\\": \\\"C2748097305-LPCLOUD\\\",\\n\",\n \" \\\"granule-count\\\": 1748,\\n\",\n \" \\\"provider-id\\\": \\\"LPCLOUD\\\"\\n\",\n \" },\\n\",\n \" \\\"umm\\\": {\\n\",\n \" \\\"ShortName\\\": \\\"EMITL2BCH4ENH\\\",\\n\",\n \" \\\"EntryTitle\\\": \\\"EMIT L2B Methane Enhancement Data 60 m V001\\\",\\n\",\n \" \\\"Version\\\": \\\"001\\\"\\n\",\n \" }\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"meta\\\": {\\n\",\n \" \\\"concept-id\\\": \\\"C2748088093-LPCLOUD\\\",\\n\",\n \" \\\"granule-count\\\": 1285,\\n\",\n \" \\\"provider-id\\\": \\\"LPCLOUD\\\"\\n\",\n \" },\\n\",\n \" \\\"umm\\\": {\\n\",\n \" \\\"ShortName\\\": \\\"EMITL2BCH4PLM\\\",\\n\",\n \" \\\"EntryTitle\\\": \\\"EMIT L2B Estimated Methane Plume Complexes 60 m V001\\\",\\n\",\n \" \\\"Version\\\": \\\"001\\\"\\n\",\n \" }\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"meta\\\": {\\n\",\n \" \\\"concept-id\\\": \\\"C2872578364-LPCLOUD\\\",\\n\",\n \" \\\"granule-count\\\": 402,\\n\",\n \" \\\"provider-id\\\": \\\"LPCLOUD\\\"\\n\",\n \" },\\n\",\n \" \\\"umm\\\": {\\n\",\n \" \\\"ShortName\\\": \\\"EMITL2BCO2ENH\\\",\\n\",\n \" \\\"EntryTitle\\\": \\\"EMIT L2B Carbon Dioxide Enhancement Data 60 m V001\\\",\\n\",\n \" \\\"Version\\\": \\\"001\\\"\\n\",\n \" }\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"meta\\\": {\\n\",\n \" \\\"concept-id\\\": \\\"C2867824144-LPCLOUD\\\",\\n\",\n \" \\\"granule-count\\\": 173,\\n\",\n \" \\\"provider-id\\\": \\\"LPCLOUD\\\"\\n\",\n \" },\\n\",\n \" \\\"umm\\\": {\\n\",\n \" \\\"ShortName\\\": \\\"EMITL2BCO2PLM\\\",\\n\",\n \" \\\"EntryTitle\\\": \\\"EMIT L2B Estimated Carbon Dioxide Plume Complexes 60 m V001\\\",\\n\",\n \" \\\"Version\\\": \\\"001\\\"\\n\",\n \" }\\n\",\n \" },\\n\",\n \" {\\n\",\n \" \\\"meta\\\": {\\n\",\n \" \\\"concept-id\\\": \\\"C2408752948-LPCLOUD\\\",\\n\",\n \" \\\"granule-count\\\": 1,\\n\",\n \" \\\"provider-id\\\": \\\"LPCLOUD\\\"\\n\",\n \" },\\n\",\n \" \\\"umm\\\": {\\n\",\n \" \\\"ShortName\\\": \\\"EMITL3ASA\\\",\\n\",\n \" \\\"EntryTitle\\\": \\\"EMIT L3 Aggregated Mineral Spectral Abundance and Uncertainty 0.5 Deg V001\\\",\\n\",\n \" \\\"Version\\\": \\\"001\\\"\\n\",\n \" }\\n\",\n \" }]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# EMIT Collection Query\\n\",\n \"emit_collection_query = earthaccess.collection_query().keyword('EMIT L2B Mineral')\\n\",\n \"emit_collection_query.fields(['ShortName','EntryTitle','Version']).get()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From this list of results we can see that the `concept-id` for the desired mineral product is `C2408034484-LPCLOUD`. We can use this to define one of our search arguments.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"concept_id = 'C2408034484-LPCLOUD'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Define Temporal Range\\n\",\n \"\\n\",\n \"For our date range, we'll look at all EMIT data collected over 2023. The `date_range` can be specified as a pair of dates, start and end (up to, not including).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"date_range = ('2023-01-01','2024-01-01')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Define Spatial Region of Interest\\n\",\n \"\\n\",\n \"For this example, our spatial region of interest will be the area around Cuprite, NV. A location where there have been several previous mineralogy studies. We can define this region using a rectangular polygon. If you want to make a polygon for a different region, you can use a tool like [geojson.io](http://geojson.io/).\\n\",\n \"\\n\",\n \"Open the `geojson` as a `geodataframe`, and check the coordinate reference system (CRS) of the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\\n\",\n \"Name: WGS 84\\n\",\n \"Axis Info [ellipsoidal]:\\n\",\n \"- Lat[north]: Geodetic latitude (degree)\\n\",\n \"- Lon[east]: Geodetic longitude (degree)\\n\",\n \"Area of Use:\\n\",\n \"- name: World.\\n\",\n \"- bounds: (-180.0, -90.0, 180.0, 90.0)\\n\",\n \"Datum: World Geodetic System 1984 ensemble\\n\",\n \"- Ellipsoid: WGS 84\\n\",\n \"- Prime Meridian: Greenwich\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"roi_gdf = gpd.read_file('../../data/cuprite_bbox.geojson')\\n\",\n \"roi_gdf.crs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" geometry\\n\",\n \"0 POLYGON ((-117.24309 37.59129, -117.24309 37.5...\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"roi_gdf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can see this `geodataframe` consists of a single polygon which we want to include in our search, but the geometry is the only information contained in the file, so lets add a column for the site name, and set the value to \\\"Cuprite\\\".\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"roi_gdf['Name'] = 'Cuprite'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" geometry Name\\n\",\n \"0 POLYGON ((-117.24309 37.59129, -117.24309 37.5... Cuprite\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"roi_gdf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can visualize the ROI using the `folium` library and the `explore` function from `geopandas`. First, we'll create a helper function. Then we will create a new map, using Google Maps tiles as our basemap, and add the polygon to the map. We'll also use our `convert_bounds` helper function to limit the map view to roughly the extent of the polygon.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Function to convert a bounding box for use in leaflet notation\\n\",\n \"\\n\",\n \"def convert_bounds(bbox, invert_y=False):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Helper method for changing bounding box representation to leaflet notation\\n\",\n \"\\n\",\n \" ``(lon1, lat1, lon2, lat2) -> ((lat1, lon1), (lat2, lon2))``\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" x1, y1, x2, y2 = bbox\\n\",\n \" if invert_y:\\n\",\n \" y1, y2 = y2, y1\\n\",\n \" return ((y1, x1), (y2, x2))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = Figure(width=\\\"750px\\\", height=\\\"375px\\\")\\n\",\n \"map1 = folium.Map(tiles='https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}', attr='Google')\\n\",\n \"fig.add_child(map1)\\n\",\n \"\\n\",\n \"# Add roi geodataframe\\n\",\n \"roi_gdf.explore(\\n\",\n \" \\\"Name\\\",\\n\",\n \" popup=True,\\n\",\n \" categorical=True,\\n\",\n \" cmap='Set3',\\n\",\n \" style_kwds=dict(opacity=0.7, fillOpacity=0.4),\\n\",\n \" name=\\\"Regions of Interest\\\",\\n\",\n \" m=map1\\n\",\n \")\\n\",\n \"\\n\",\n \"map1.add_child(folium.LayerControl())\\n\",\n \"map1.fit_bounds(bounds=convert_bounds(roi_gdf.unary_union.bounds))\\n\",\n \"display(fig)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In our `earthaccess` search, we will use the `polygon` argument to find where this geometry intersects with the footprint of the EMIT scenes. To do this, we need to create a list of exterior polygon vertices in counter-clockwise order to submit in our search.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[(-117.24309240198033, 37.59129385913785),\\n\",\n \" (-117.24309240198033, 37.50102626452812),\\n\",\n \" (-117.14631968357332, 37.50102626452812),\\n\",\n \" (-117.14631968357332, 37.59129385913785),\\n\",\n \" (-117.24309240198033, 37.59129385913785)]\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Use orient to place vertices in counter-clockwise order\\n\",\n \"roi = orient(roi_gdf.geometry[0], sign = 1.0)\\n\",\n \"# Put the exterior coordinates in a list\\n\",\n \"roi = list(roi.exterior.coords)\\n\",\n \"roi\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After we have all of the pieces: *spatial extent*, *temporal range*, and *concept-id*, we can perform a search. Note that we are limiting our search 500 results using the `count` argument, which doesn't matter here.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Granules found: 9\\n\"\n ]\n }\n ],\n \"source\": [\n \"results = earthaccess.search_data(\\n\",\n \" concept_id=concept_id,\\n\",\n \" polygon=roi,\\n\",\n \" temporal=date_range,\\n\",\n \" count=500\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Our search returned 9 results, which we can convert to a `geodataframe` for further filtering and analysis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[Collection: {'ShortName': 'EMITL2BMIN', 'Version': '001'}\\n\",\n \" Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -116.86429595947266, 'Latitude': 37.96258544921875}, {'Longitude': -117.72748565673828, 'Latitude': 37.362998962402344}, {'Longitude': -117.27104949951172, 'Latitude': 36.70587921142578}, {'Longitude': -116.4078598022461, 'Latitude': 37.30546569824219}, {'Longitude': -116.86429595947266, 'Latitude': 37.96258544921875}]}}]}}}\\n\",\n \" Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2023-01-29T21:14:07Z', 'EndingDateTime': '2023-01-29T21:14:19Z'}}\\n\",\n \" Size(MB): 75.91049480438232\\n\",\n \" Data: ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230129T211407_2302914_008/EMIT_L2B_MIN_001_20230129T211407_2302914_008.nc', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230129T211407_2302914_008/EMIT_L2B_MINUNCERT_001_20230129T211407_2302914_008.nc'],\\n\",\n \" Collection: {'ShortName': 'EMITL2BMIN', 'Version': '001'}\\n\",\n \" Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -117.79698181152344, 'Latitude': 38.333797454833984}, {'Longitude': -118.24942016601562, 'Latitude': 37.6685905456543}, {'Longitude': -117.468505859375, 'Latitude': 37.1374397277832}, {'Longitude': -117.01606750488281, 'Latitude': 37.80264663696289}, {'Longitude': -117.79698181152344, 'Latitude': 38.333797454833984}]}}]}}}\\n\",\n \" Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2023-04-27T17:32:57Z', 'EndingDateTime': '2023-04-27T17:33:09Z'}}\\n\",\n \" Size(MB): 101.56163311004639\\n\",\n \" Data: ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173257_2311711_009/EMIT_L2B_MIN_001_20230427T173257_2311711_009.nc', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173257_2311711_009/EMIT_L2B_MINUNCERT_001_20230427T173257_2311711_009.nc'],\\n\",\n \" Collection: 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-117.02520751953125, 'Latitude': 37.83479690551758}]}}]}}}\\n\",\n \" Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2023-10-23T18:39:17Z', 'EndingDateTime': '2023-10-23T18:39:29Z'}}\\n\",\n \" Size(MB): 100.52127742767334\\n\",\n \" Data: ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20231023T183917_2329612_010/EMIT_L2B_MIN_001_20231023T183917_2329612_010.nc', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20231023T183917_2329612_010/EMIT_L2B_MINUNCERT_001_20231023T183917_2329612_010.nc'],\\n\",\n \" Collection: {'ShortName': 'EMITL2BMIN', 'Version': '001'}\\n\",\n \" Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -117.53460693359375, 'Latitude': 38.284488677978516}, {'Longitude': -118.40948486328125, 'Latitude': 37.68711471557617}, {'Longitude': -117.95744323730469, 'Latitude': 37.025081634521484}, {'Longitude': -117.08256530761719, 'Latitude': 37.62245559692383}, {'Longitude': -117.53460693359375, 'Latitude': 38.284488677978516}]}}]}}}\\n\",\n \" Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2023-12-01T20:10:58Z', 'EndingDateTime': '2023-12-01T20:11:10Z'}}\\n\",\n \" Size(MB): 101.65311431884766\\n\",\n \" Data: ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20231201T201058_2333513_006/EMIT_L2B_MIN_001_20231201T201058_2333513_006.nc', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20231201T201058_2333513_006/EMIT_L2B_MINUNCERT_001_20231201T201058_2333513_006.nc'],\\n\",\n \" Collection: {'ShortName': 'EMITL2BMIN', 'Version': '001'}\\n\",\n \" Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -116.26719665527344, 'Latitude': 39.1148796081543}, {'Longitude': -117.70616149902344, 'Latitude': 38.172176361083984}, {'Longitude': -117.26689147949219, 'Latitude': 37.50168228149414}, {'Longitude': -115.82792663574219, 'Latitude': 38.44438552856445}, {'Longitude': -116.26719665527344, 'Latitude': 39.1148796081543}]}}]}}}\\n\",\n \" Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2023-12-01T20:11:10Z', 'EndingDateTime': '2023-12-01T20:11:31Z'}}\\n\",\n \" Size(MB): 170.77763843536377\\n\",\n \" Data: ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20231201T201110_2333513_007/EMIT_L2B_MIN_001_20231201T201110_2333513_007.nc', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20231201T201110_2333513_007/EMIT_L2B_MINUNCERT_001_20231201T201110_2333513_007.nc']]\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Advanced Filtering\\n\",\n \"\\n\",\n \"Now that we have some results, we will place them into a geodataframe that includes links to browse imagery and the files, so we can do some more advanced filtering of the data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"List the metadata fields available in the search results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['size',\\n\",\n \" 'concept-type',\\n\",\n \" 'concept-id',\\n\",\n \" 'revision-id',\\n\",\n \" 'native-id',\\n\",\n \" 'collection-concept-id',\\n\",\n \" 'provider-id',\\n\",\n \" 'format',\\n\",\n \" 'revision-date',\\n\",\n \" '_beginning_date_time',\\n\",\n \" '_ending_date_time',\\n\",\n \" '_granule_ur',\\n\",\n \" '_additional_attributes',\\n\",\n \" '_gpolygons',\\n\",\n \" '_provider_dates',\\n\",\n \" '_short_name',\\n\",\n \" '_version',\\n\",\n \" '_pgename',\\n\",\n \" '_pgeversion',\\n\",\n \" '_related_urls',\\n\",\n \" '_cloud_cover',\\n\",\n \" '_day_night_flag',\\n\",\n \" '_archive_and_distribution_information',\\n\",\n \" '_production_date_time',\\n\",\n \" '_platforms',\\n\",\n \" '_url',\\n\",\n \" '_name',\\n\",\n \" '_version']\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list_metadata_fields(results)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Some datasets have unique metadata that we can choose to include when we use our `results_to_geopandas` function from the `tutorial_utils.py` module to create a geodataframe. Below is a list of default fields. We can also include additional fields by passing them as a list to the `fields` argument.\\n\",\n \"\\n\",\n \"default_fields = [ \\n\",\n \" \\\"size\\\", \\n\",\n \" \\\"concept_id\\\", \\n\",\n \" \\\"dataset-id\\\", \\n\",\n \" \\\"native-id\\\", \\n\",\n \" \\\"provider-id\\\", \\n\",\n \" \\\"_related_urls\\\", \\n\",\n \" \\\"_single_date_time\\\", \\n\",\n \" \\\"_beginning_date_time\\\", \\n\",\n \" \\\"_ending_date_time\\\", \\n\",\n \" \\\"geometry\\\", \\n\",\n \" ]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For example, `_cloud_cover` is not always available. We can add it to the default fields of this function by adding it to a `fields` argument in list form.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results_gdf = results_to_geopandas(results, fields=['_cloud_cover'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Add an index column so we can reference it using the `explore` function from `geopandas`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Specify index so we can reference it with gdf.explore()\\n\",\n \"results_gdf['index']=results_gdf.index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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sizenative-idprovider-id_beginning_date_time_ending_date_time_related_urls_cloud_covergeometryindex
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1101.561633EMIT_L2B_MIN_001_20230427T173257_2311711_009LPCLOUD2023-04-27T17:32:57Z2023-04-27T17:33:09Z[{'URL': 'https://data.lpdaac.earthdatacloud.n...21POLYGON ((-117.79698 38.33380, -118.24942 37.6...1
2100.931341EMIT_L2B_MIN_001_20230427T173309_2311711_010LPCLOUD2023-04-27T17:33:09Z2023-04-27T17:33:21Z[{'URL': 'https://data.lpdaac.earthdatacloud.n...8POLYGON ((-117.09042 37.85598, -117.54974 37.1...2
3101.904746EMIT_L2B_MIN_001_20230804T191650_2321613_007LPCLOUD2023-08-04T19:16:50Z2023-08-04T19:17:02Z[{'URL': 'https://data.lpdaac.earthdatacloud.n...4POLYGON ((-117.15681 38.27825, -118.03522 37.6...3
4102.344337EMIT_L2B_MIN_001_20230808T173953_2322012_011LPCLOUD2023-08-08T17:39:53Z2023-08-08T17:40:05Z[{'URL': 'https://data.lpdaac.earthdatacloud.n...6POLYGON ((-116.72791 37.98892, -117.59428 37.3...4
5101.627942EMIT_L2B_MIN_001_20231023T183905_2329612_009LPCLOUD2023-10-23T18:39:05Z2023-10-23T18:39:17Z[{'URL': 'https://data.lpdaac.earthdatacloud.n...74POLYGON ((-117.73664 38.31432, -118.18502 37.6...5
6100.521277EMIT_L2B_MIN_001_20231023T183917_2329612_010LPCLOUD2023-10-23T18:39:17Z2023-10-23T18:39:29Z[{'URL': 'https://data.lpdaac.earthdatacloud.n...32POLYGON ((-117.02521 37.83480, -117.48288 37.1...6
7101.653114EMIT_L2B_MIN_001_20231201T201058_2333513_006LPCLOUD2023-12-01T20:10:58Z2023-12-01T20:11:10Z[{'URL': 'https://data.lpdaac.earthdatacloud.n...65POLYGON ((-117.53461 38.28449, -118.40948 37.6...7
8170.777638EMIT_L2B_MIN_001_20231201T201110_2333513_007LPCLOUD2023-12-01T20:11:10Z2023-12-01T20:11:31Z[{'URL': 'https://data.lpdaac.earthdatacloud.n...53POLYGON ((-116.26720 39.11488, -117.70616 38.1...8
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" size native-id provider-id \\\\\\n\",\n \"0 75.910495 EMIT_L2B_MIN_001_20230129T211407_2302914_008 LPCLOUD \\n\",\n \"1 101.561633 EMIT_L2B_MIN_001_20230427T173257_2311711_009 LPCLOUD \\n\",\n \"2 100.931341 EMIT_L2B_MIN_001_20230427T173309_2311711_010 LPCLOUD \\n\",\n \"3 101.904746 EMIT_L2B_MIN_001_20230804T191650_2321613_007 LPCLOUD \\n\",\n \"4 102.344337 EMIT_L2B_MIN_001_20230808T173953_2322012_011 LPCLOUD \\n\",\n \"5 101.627942 EMIT_L2B_MIN_001_20231023T183905_2329612_009 LPCLOUD \\n\",\n \"6 100.521277 EMIT_L2B_MIN_001_20231023T183917_2329612_010 LPCLOUD \\n\",\n \"7 101.653114 EMIT_L2B_MIN_001_20231201T201058_2333513_006 LPCLOUD \\n\",\n \"8 170.777638 EMIT_L2B_MIN_001_20231201T201110_2333513_007 LPCLOUD \\n\",\n \"\\n\",\n \" _beginning_date_time _ending_date_time \\\\\\n\",\n \"0 2023-01-29T21:14:07Z 2023-01-29T21:14:19Z \\n\",\n \"1 2023-04-27T17:32:57Z 2023-04-27T17:33:09Z \\n\",\n \"2 2023-04-27T17:33:09Z 2023-04-27T17:33:21Z \\n\",\n \"3 2023-08-04T19:16:50Z 2023-08-04T19:17:02Z \\n\",\n \"4 2023-08-08T17:39:53Z 2023-08-08T17:40:05Z \\n\",\n \"5 2023-10-23T18:39:05Z 2023-10-23T18:39:17Z \\n\",\n \"6 2023-10-23T18:39:17Z 2023-10-23T18:39:29Z \\n\",\n \"7 2023-12-01T20:10:58Z 2023-12-01T20:11:10Z \\n\",\n \"8 2023-12-01T20:11:10Z 2023-12-01T20:11:31Z \\n\",\n \"\\n\",\n \" _related_urls _cloud_cover \\\\\\n\",\n \"0 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 99 \\n\",\n \"1 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 21 \\n\",\n \"2 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 8 \\n\",\n \"3 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 4 \\n\",\n \"4 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 6 \\n\",\n \"5 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 74 \\n\",\n \"6 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 32 \\n\",\n \"7 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 65 \\n\",\n \"8 [{'URL': 'https://data.lpdaac.earthdatacloud.n... 53 \\n\",\n \"\\n\",\n \" geometry index \\n\",\n \"0 POLYGON ((-116.86430 37.96259, -117.72749 37.3... 0 \\n\",\n \"1 POLYGON ((-117.79698 38.33380, -118.24942 37.6... 1 \\n\",\n \"2 POLYGON ((-117.09042 37.85598, -117.54974 37.1... 2 \\n\",\n \"3 POLYGON ((-117.15681 38.27825, -118.03522 37.6... 3 \\n\",\n \"4 POLYGON ((-116.72791 37.98892, -117.59428 37.3... 4 \\n\",\n \"5 POLYGON ((-117.73664 38.31432, -118.18502 37.6... 5 \\n\",\n \"6 POLYGON ((-117.02521 37.83480, -117.48288 37.1... 6 \\n\",\n \"7 POLYGON ((-117.53461 38.28449, -118.40948 37.6... 7 \\n\",\n \"8 POLYGON ((-116.26720 39.11488, -117.70616 38.1... 8 \"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"results_gdf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Filter the results geodataframe by cloud cover. We'll use a cloud cover of 10% as our threshold.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Filter Results\\n\",\n \"results_gdf = results_gdf[results_gdf['_cloud_cover'] < 10]\\n\",\n \"results_gdf.reset_index(drop=True, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Visualize the filtered results by iterating over the rows of the geodataframe and adding the geometry to the map. We do this instead of the `explore` function, so we add separate layers for each, allowing use to add or remove them using the `LayerControl` widget.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\ebolch\\\\AppData\\\\Local\\\\Temp\\\\1\\\\ipykernel_16232\\\\2570979738.py:13: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\\n\",\n \" cmap = cm.get_cmap('Set3')\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Set up Figure and Basemap tiles\\n\",\n \"fig = Figure(width=\\\"1080px\\\",height=\\\"540\\\")\\n\",\n \"map1 = folium.Map(tiles=None)\\n\",\n \"folium.TileLayer(tiles='https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}',name='Google Satellite', attr='Google', overlay=True).add_to(map1)\\n\",\n \"folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png',\\n\",\n \" name='ESRI World Imagery',\\n\",\n \" attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',\\n\",\n \" overlay='True').add_to(map1)\\n\",\n \"fig.add_child(map1)\\n\",\n \"\\n\",\n \"\\n\",\n \"# Create a color map for the results\\n\",\n \"cmap = cm.get_cmap('Set3')\\n\",\n \"n = len(results_gdf['native-id'].unique())\\n\",\n \"colors = [cmap(i) for i in range(n)]\\n\",\n \"colors = [cm.colors.rgb2hex(color) for color in colors]\\n\",\n \"\\n\",\n \"# Add Search Results by Row\\n\",\n \"for index, row in results_gdf.iterrows():\\n\",\n \" color = colors[index % len(colors)]\\n\",\n \" folium.GeoJson(row.geometry, name = row['native-id'],style_function=lambda feature, color=color: {'color': color, 'fillColor': color}).add_to(map1)\\n\",\n \"\\n\",\n \"folium.GeoJson(roi_gdf,\\n\",\n \" name='Cuprite_ROI',\\n\",\n \" ).add_to(map1)\\n\",\n \"\\n\",\n \"# Zoom to Data\\n\",\n \"map1.fit_bounds(bounds=convert_bounds(results_gdf.unary_union.bounds))\\n\",\n \"# Add Layer controls\\n\",\n \"map1.add_child(folium.LayerControl(collapsed=False))\\n\",\n \"display(fig)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"View the related urls for the first result. We can see that there are multiple assets available for each result, including the mineralogy data, uncertainty data, and browse images, as well as multiple ways to access the data, https or s3 links.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[{'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MIN_001_20230427T173309_2311711_010.nc',\\n\",\n \" 'Description': 'Download EMIT_L2B_MIN_001_20230427T173309_2311711_010.nc',\\n\",\n \" 'Type': 'GET DATA'},\\n\",\n \" {'URL': 's3://lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MIN_001_20230427T173309_2311711_010.nc',\\n\",\n \" 'Description': 'This link provides direct download access via S3 to the granule',\\n\",\n \" 'Type': 'GET DATA VIA DIRECT ACCESS'},\\n\",\n \" {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MINUNCERT_001_20230427T173309_2311711_010.nc',\\n\",\n \" 'Description': 'Download EMIT_L2B_MINUNCERT_001_20230427T173309_2311711_010.nc',\\n\",\n \" 'Type': 'GET DATA'},\\n\",\n \" {'URL': 's3://lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MINUNCERT_001_20230427T173309_2311711_010.nc',\\n\",\n \" 'Description': 'This link provides direct download access via S3 to the granule',\\n\",\n \" 'Type': 'GET DATA VIA DIRECT ACCESS'},\\n\",\n \" {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MIN_001_20230427T173309_2311711_010.png',\\n\",\n \" 'Description': 'Download EMIT_L2B_MIN_001_20230427T173309_2311711_010.png',\\n\",\n \" 'Type': 'GET RELATED VISUALIZATION'},\\n\",\n \" {'URL': 's3://lp-prod-public/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MIN_001_20230427T173309_2311711_010.png',\\n\",\n \" 'Description': 'This link provides direct download access via S3 to the granule',\\n\",\n \" 'Type': 'GET RELATED VISUALIZATION'}]\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"results_gdf._related_urls[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can use a function to return the asset URLs for a given result. This function will return a dictionary with the asset names as keys and the URLs as values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_asset_url(row,asset, key='Type',value='GET DATA'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Retrieve a url from the list of dictionaries for a row in the _related_urls column.\\n\",\n \" Asset examples: CH4PLM, CH4PLMMETA, RFL, MASK, RFLUNCERT \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Add _ to asset so string matching works\\n\",\n \" asset = f\\\"_{asset}_\\\"\\n\",\n \" # Retrieve URL matching parameters\\n\",\n \" for _dict in row['_related_urls']:\\n\",\n \" if _dict.get(key) == value and asset in _dict['URL'].split('/')[-1]:\\n\",\n \" return _dict['URL']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Apply the function for to the results geodataframe to get the asset URLs for each result for the `L2B_MIN` asset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MIN_001_20230427T173309_2311711_010.nc',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230804T191650_2321613_007/EMIT_L2B_MIN_001_20230804T191650_2321613_007.nc',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230808T173953_2322012_011/EMIT_L2B_MIN_001_20230808T173953_2322012_011.nc']\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Iterate over rows in the plm_gdf and get the mineral urls and store them in a list\\n\",\n \"min_urls = results_gdf.apply(lambda row: get_asset_url(row, asset='L2B_MIN'), axis=1).tolist()\\n\",\n \"min_urls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can repeat this for the uncertainty URLs.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MINUNCERT_001_20230427T173309_2311711_010.nc',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230804T191650_2321613_007/EMIT_L2B_MINUNCERT_001_20230804T191650_2321613_007.nc',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230808T173953_2322012_011/EMIT_L2B_MINUNCERT_001_20230808T173953_2322012_011.nc']\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"min_unc_urls = results_gdf.apply(lambda row: get_asset_url(row, asset='L2B_MINUNCERT'), axis=1).tolist()\\n\",\n \"min_unc_urls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With some knowledge of how the granules and assets are neamed, we can grab the rgb browse images to get an idea of what the location looks like. First retrieve the browse images for the mineral product. These, show the mineral band depth only. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230427T173309_2311711_010/EMIT_L2B_MIN_001_20230427T173309_2311711_010.png',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230804T191650_2321613_007/EMIT_L2B_MIN_001_20230804T191650_2321613_007.png',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2BMIN.001/EMIT_L2B_MIN_001_20230808T173953_2322012_011/EMIT_L2B_MIN_001_20230808T173953_2322012_011.png']\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"min_png = results_gdf.apply(lambda row: get_asset_url(row, asset='L2B_MIN', value='GET RELATED VISUALIZATION'), axis=1).tolist()\\n\",\n \"min_png\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With some slight changes to these, we can retrieve the RGB browse images from the L2A Reflectance product.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230427T173309_2311711_010/EMIT_L2A_RFL_001_20230427T173309_2311711_010.png',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230804T191650_2321613_007/EMIT_L2A_RFL_001_20230804T191650_2321613_007.png',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/EMITL2ARFL.001/EMIT_L2A_RFL_001_20230808T173953_2322012_011/EMIT_L2A_RFL_001_20230808T173953_2322012_011.png']\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Replace Collection ID\\n\",\n \"rgb_urls = [s.replace('EMITL2BMIN', 'EMITL2ARFL') for s in min_png]\\n\",\n \"# Update Product and Asset Names\\n\",\n \"rgb_urls = [s.replace('EMIT_L2B_MIN', 'EMIT_L2A_RFL') for s in rgb_urls]\\n\",\n \"# Change file extension\\n\",\n \"#rgb_urls = [s.replace('.nc', '.png') for s in rgb_urls]\\n\",\n \"rgb_urls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Visualize the RGB browse images to get an idea for what the area we are investigating looks like.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cols = 3\\n\",\n \"rows = math.ceil(len(results_gdf)/cols)\\n\",\n \"fig, ax = plt.subplots(rows, cols, figsize=(12,12))\\n\",\n \"ax = ax.flatten()\\n\",\n \"\\n\",\n \"for _n, index in enumerate(results_gdf.index.to_list()):\\n\",\n \" img = io.imread(rgb_urls[index])\\n\",\n \" ax[_n].imshow(img)\\n\",\n \" ax[_n].set_title(f\\\"Index: {index} - {results_gdf['native-id'][index]}\\\", fontsize=8)\\n\",\n \" ax[_n].axis('off')\\n\",\n \"plt.tight_layout()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The black line in the third scene is caused by the on-board cloud masking.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Saving Lists of Results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can save our lists of results URLs as a text file for later use, either to download the data, or stream it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('../../data/rgb_browse_urls.txt', 'w') as f:\\n\",\n \" for line in rgb_urls:\\n\",\n \" f.write(f\\\"{line}\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('../../data/results_urls.txt', 'w') as f:\\n\",\n \" for line in min_urls:\\n\",\n \" f.write(f\\\"{line}\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"with open('../../data/min_uncert_urls.txt', 'w') as f:\\n\",\n \" for line in min_unc_urls:\\n\",\n \" f.write(f\\\"{line}\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Streaming or Downloading Data\\n\",\n \"For the workshop, we will stream the data, but either method can be used, and each has trade-offs based on the internet speed, storage space, or use case. The EMIT files are very large due to the number of bands, so operations can take some time if streaming with a slower internet connection. Since the workshop is hosted in a Cloud workspace, we can stream the data directly to the workspace.\\n\",\n \"\\n\",\n \"### 6.1 Streaming Data Workflow\\n\",\n \"For an example of streaming both netCDF please see [Working with EMIT L2B Mineralogy.ipynb](Working_with_EMIT_L2B_Mineralogy.ipynb).\\n\",\n \"\\n\",\n \"If you plan to stream the data, you can stop here and move to the next notebook.\\n\",\n \"\\n\",\n \"### 6.2 Downloading Data Workflow\\n\",\n \"To download the scenes, we can use the earthaccess library to authenticate then download the files.\\n\",\n \"\\n\",\n \"First, log into Earthdata using the login function from the earthaccess library. The persist=True argument will create a local .netrc file if it doesn’t exist, or add your login info to an existing .netrc file. If no Earthdata Login credentials are found in the .netrc you’ll be prompted for them. As mentioned in section 1.2, this step is not necessary to conduct searches, but is needed to download or stream data.\\n\",\n \"\\n\",\n \"The outputs saved in section 5 can be downloading by uncommenting and running the following cells.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# # Authenticate using earthaccess\\n\",\n \"# earthaccess.login(persist=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# # Open Text File and Read Lines\\n\",\n \"# file_list = ['../../data/rgb_browse_urls.txt','../../data/results_urls.txt']\\n\",\n \"# urls = []\\n\",\n \"# for file in file_list:\\n\",\n \"# with open(file) as f:\\n\",\n \"# urls.extend([line.rstrip('\\\\n') for line in f])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# # Get requests https Session using Earthdata Login Info\\n\",\n \"# fs = earthaccess.get_requests_https_session()\\n\",\n \"# # Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"# for url in urls:\\n\",\n \"# granule_asset_id = url.split('/')[-1]\\n\",\n \"# # Define Local Filepath\\n\",\n \"# fp = f'../../data/{granule_asset_id}'\\n\",\n \"# # Download the Granule Asset if it doesn't exist\\n\",\n \"# if not os.path.isfile(fp):\\n\",\n \"# with fs.get(url,stream=True) as src:\\n\",\n \"# with open(fp,'wb') as dst:\\n\",\n \"# for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \"# dst.write(chunk)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 06-28-2024 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract 140G0121D0001 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"lpdaac_vitals\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "python/tutorials/Generating_Methane_Spectral_Fingerprint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Generating Methane Spectral Fingerprint\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"In this notebook, we'll examine a EMIT L1B At-Sensor Calibrated Radiance ([EMITL1BRAD](https://lpdaac.usgs.gov/products/emitl1bradv001/)) scene to visualize the spectra for a sample point inside a methane plume, and two sample points outside of methane plumes. To highlight the spectral differences we will look at an in-plume divided by out-of-plume ratio to get an example of the spectral signature or fingerprint of methane and visually compare this to the target methane signature.\\n\",\n \"\\n\",\n \"**Background**\\n\",\n \"\\n\",\n \"Methane is a major contributor to atmospheric radiative forcing because its more efficient at trapping radiation than other greenhouse gases, like carbon dioxide. Because the lifetime of methane in the atmosphere is only about 10 years, reducing methane emissions offers an effective way to curb anthropogenic contributions to atmospheric radiative forcing.\\n\",\n \"\\n\",\n \"The EMIT instrument is an imaging spectrometer that measures light in visible and infrared wavelengths. These measurements display unique spectral signatures that correspond to chemical composition. Although the primary mission focuses on mapping the mineral composition of Earth's surface, the EMIT instrument has also been used to successfully map methane point source emissions within its target mask using these unique spectral signatures. More details about EMIT and its associated products can be found in the **README.md** and on the [EMIT website](https://earth.jpl.nasa.gov/emit/).\\n\",\n \"\\n\",\n \"The EMITL1BRAD product provides at-sensor calibrated radiance values along with observation data in a spatially raw, non-orthocorrected format. This product is used to estimate the EMIT L2B Methane Enhancement Data ([EMITL2BCH4ENH](https://lpdaac.usgs.gov/products/emitl2bch4enhv001/)) product. The EMITL2BCH4ENH product is created using an adaptive matched filter to search each pixel's radiance spectrum for deviations that are characteristic of methane's absorption spectrum, and is only delivered to the LP DAAC for scenes where plume complexes have been identified. To reduce the risk of false positives, all EMITL2BCH4ENH data undergo a manual review process before being designated as a plume complex. For more information on the identification and manual review process, see Section 4.2.2 of the [EMIT GHG Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1696/EMIT_GHG_ATBD_V1.pdf). \\n\",\n \"\\n\",\n \"\\n\",\n \"**References**\\n\",\n \"\\n\",\n \"Andrew K. Thorpe et al., Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space. Sci. Adv.9, eadh2391 (2023). DOI:[10.1126/sciadv.adh2391](https://www.science.org/doi/10.1126/sciadv.adh2391)\\n\",\n \"\\n\",\n \"**Requirements** \\n\",\n \" - Set up Python Environment - See **setup_instructions.md** in the `/setup/` folder\\n\",\n \" - NASA Earthdata Login Account. [Sign Up](https://urs.earthdata.nasa.gov/users/new)\\n\",\n \"\\n\",\n \"**Data Used** \\n\",\n \" - [EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data (EMITL1BRAD)](https://lpdaac.usgs.gov/products/emitl1bradv001/)\\n\",\n \" - [EMIT L2B Methane Enhancement Data (EMITL2BCH4ENH)](https://lpdaac.usgs.gov/products/emitl2arflv001/)\\n\",\n \"\\n\",\n \"\\n\",\n \"**Learning Objectives** \\n\",\n \" - Open and orthorectify an EMITL1BRAD scene\\n\",\n \" - Open and visualize the scene and point data\\n\",\n \" - Extract point data using coordinates in a .csv file\\n\",\n \" - Visualize spectral fingerprint of methane and compare it to the modeled methane signature\\n\",\n \"\\n\",\n \"**Tutorial Outline** \\n\",\n \"\\n\",\n \"1. [**Setup**](#setup)\\n\",\n \"2. [**Opening EMIT Data**](#opening)\\n\",\n \"3. [**Extracting Point Data**](#extracting_points)\\n\",\n \"4. [**Methane Spectral Signature**](#spectral_signature)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Setup\\n\",\n \"\\n\",\n \"Import the necessary Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import sys\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from osgeo import gdal\\n\",\n \"import earthaccess\\n\",\n \"import rasterio as rio\\n\",\n \"import rioxarray as rxr\\n\",\n \"import holoviews as hv\\n\",\n \"import hvplot\\n\",\n \"import hvplot.xarray\\n\",\n \"import hvplot.pandas\\n\",\n \"from skimage import exposure\\n\",\n \"\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from emit_tools import emit_xarray\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Download the EMIT L1B Radiance and L2B Methane Enhancement products for the scene we're going to look at.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"urls = ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL1BRAD.001/EMIT_L1B_RAD_001_20220815T042838_2222703_003/EMIT_L1B_RAD_001_20220815T042838_2222703_003.nc',\\n\",\n \" 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BCH4ENH.001/EMIT_L2B_CH4ENH_001_20220815T042838_2222703_003/EMIT_L2B_CH4ENH_001_20220815T042838_2222703_003.tif']\\n\",\n \"\\n\",\n \"# Authenticate and create an https session\\n\",\n \"earthaccess.login(persist=True)\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"\\n\",\n \"for url in urls:\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \" granule_asset_id = url.split('/')[-1]\\n\",\n \" # Define Local Filepath\\n\",\n \" fp = f'../../data/{granule_asset_id}'\\n\",\n \" # Download the Granule Asset if it doesn't exist\\n\",\n \" if not os.path.isfile(fp):\\n\",\n \" with fs.get(url,stream=True) as src:\\n\",\n \" with open(fp,'wb') as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set the filepath for the radiance and methane enhancement files.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rad_fp = '../../data/EMIT_L1B_RAD_001_20220815T042838_2222703_003.nc'\\n\",\n \"enh_fp = '../../data/EMIT_L2B_CH4ENH_001_20220815T042838_2222703_003.tif'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Opening EMIT Data\\n\",\n \"\\n\",\n \"The EMIT L1B At-Sensor Radiance data is distributed in a non-orthorectified spatially raw netCDF4 (.nc) format consisting of the data and its associated metadata. Inside the L1B file, there are 3 groups. \\n\",\n \"\\n\",\n \"1. The root group that can be considered the main dataset contains the radiance data described by the downtrack, crosstrack, and bands dimensions. \\n\",\n \"2. The sensor_band_parameters group containing the wavelength center and the full-width half maximum (FWHM) of each band. \\n\",\n \"3. The location group contains latitude and longitude values at the center of each pixel described by the crosstrack and downtrack dimensions, as well as a geometry lookup table (GLT) described by the ortho_x and ortho_y dimensions. The GLT is an orthorectified image (EPSG:4326) consisting of 2 layers containing downtrack and crosstrack indices. These index positions allow us to quickly project the raw data onto this geographic grid. \\n\",\n \"\\n\",\n \"This data can be opened using the `netCDF4` and `xarray` libraries, or utilizing functions within the `emit_tools.py` module to organize them into a flattened (no groups) `xarray.Dataset` object. For this notebook, we will use functions from `emit_tools.py` to simplify working with the data. For more about the structure and use of `netCDF4` and `xarray` please see the [Exploring EMIT L2A Reflectance Jupyter Notebook](https://github.com/nasa/EMIT-Data-Resources/blob/main/python/tutorials/Exploring_EMIT_L2A_Reflectance.ipynb).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Open the radiance file using the `emit_xarray` function from the `emit_tools.py` module and orthorecitify it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rad = emit_xarray(rad_fp, ortho=True)\\n\",\n \"rad\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The EMIT L2B Methane Enhancement Data represent an enhancement above background methane concentration for a 1 meter layer in parts-per-million (ppm) meter (m). These units are used rather than ppm because we are unable to measure the vertical extent of plumes. This data is distributed as a single band cloud-optimized geotiff (COG) and it has been orthocorrected. We can open this using the `rioxarray` library to place the data in an `xarray.DataArray` object.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Open the methane enhancement geotiff file using `rioxarray` and squeeze the `band` dimension to remove the extra dimension so our array will only have 2 dimensions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"enh = rxr.open_rasterio(enh_fp).squeeze('band',drop=True) \\n\",\n \"enh\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Extracting Point Data \\n\",\n \"\\n\",\n \"Open the `.csv` file included in the `/data/` directory as a `pandas.DataFrame`. This file contains the latitude and longitude coordinates for three points of interest. Two that are outside of a methane plume, and one that is inside.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Define our Points for In-plume and out-of-plume\\n\",\n \"points = pd.read_csv('../../data/methane_tutorial/methane_inout_points.csv')\\n\",\n \"points\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set the index as the `ID` column.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"points = points.set_index(['ID'])\\n\",\n \"points\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we can use the `sel` function from `xarray` to extract the radiance data at each point in our dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Extract target spectra from dataset\\n\",\n \"point_ds = rad.sel(latitude=points.to_xarray().latitude, longitude=points.to_xarray().longitude, method='nearest')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After extracting, we can convert the data to a `pandas.DataFrame` and join it with our 'in-plume' column from the original dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"point_df = point_ds.to_dataframe().join(points['in-plume'],on=['ID'])\\n\",\n \"point_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"At this point, we can save the data to a `.csv` file for future use.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# point_df.to_csv('../../data/methane_tutorial/point_df.csv')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, visualize these points on an rgb image generated from the radiance file, and the methane enhancement data, just to get a better idea of where these points are located.\\n\",\n \"\\n\",\n \"To do this, first create an RGB data array from the radiance file using the `sel` function to select the bands nearest to the desired wavelengths.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create an RGB from Radiance\\n\",\n \"rgb = rad.sel(wavelengths=[650,560,470], method='nearest')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, use a function to rescale the brightness, so this image is easier to see.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rgb.radiance.data[rgb.radiance.data == -9999] = 0\\n\",\n \"rgb.radiance.data = exposure.rescale_intensity(rgb.radiance.data, in_range='image', out_range=(0,1))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have the necessary pieces to visualize we can make some spatial visualizations using `hvplot`. For the enhancement data, set the fill value of -9999 to `np.nan` to make it transparent.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create RGB Plot\\n\",\n \"rgb_map = rgb.hvplot.rgb(x='longitude',y='latitude',bands='wavelengths',title='RGB Radiance', geo=True, crs='EPSG:4326')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create Methane Enhancement Plot\\n\",\n \"enh.data[enh.data == -9999] = np.nan\\n\",\n \"methane_map = enh.hvplot.image(x='x',y='y',cmap='viridis', geo=True, crs='EPSG:4326', clim=(0,enh.data.max()), title='Methane Enhancement', clabel='ppm m', xlabel='Longitude', ylabel='Latitude')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"point_map = point_df.hvplot.points(x='longitude',y='latitude', color='in-plume', cmap='HighContrast', geo=True, crs='EPSG:4326', hover=False, colorbar=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can combine these plots with an `*` operator to overlay them on the same plot. This does require that all are in the same CRS to display properly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"(rgb_map*point_map) + (methane_map*point_map)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now lets look at the spectra. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"point_df.hvplot.line(x='wavelengths',y='radiance', by=['ID'], color=hv.Cycle('Dark2'), frame_height=400, frame_width=600, title = 'Radiance Spectra, ID 0 is in-plume' , xlabel='Wavelength (nm)', ylabel='Radiance (W/m^2/sr/nm)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Methane Spectral Signature\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's open a file containing the modeled methane signature and visualize it. This is the spectral fingerprint that we are looking for in EMIT data to identify methane plumes. Open our absorption coefficient file using `pandas`, add some column names and set an index.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Open file \\n\",\n \"ch4_ac = pd.read_csv('../../data/methane_tutorial/emit20220815t042838_ch4_target', sep='\\\\s+', header=None)\\n\",\n \"# Add Column Names\\n\",\n \"ch4_ac.columns = ['index','wavelength','value']\\n\",\n \"# Set Index\\n\",\n \"ch4_ac.set_index('index', inplace=True)\\n\",\n \"ch4_ac\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a figure for the absorption coefficient. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ac_plot = ch4_ac.hvplot(x='wavelength',y='value', frame_height=400, frame_width=400, line_color='black', line_width=2, xlim=(2150,2450), ylim=(-1.5,0), xlabel='Wavelength (nm)', title='Methane Absorption Coefficient', ylabel='')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ac_plot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can visualize a similar curve by creating a band ratio of in-plume to out-of-plume spectra. dividing the radiance for a pixel with methane by radiance for a pixel outside the plume to generate a diagnostic spectral fingerprint. This spectral fingerprint is confirmation that EMIT is observing methane enhancements with characteristic methane absorption features, which agree well with the modeled methane signature.\\n\",\n \"\\n\",\n \"This agreement is strong in the example we selected due to the similarity of the spectra without the contribution of methane. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To create the band ratio, first separate our data into in-plume and out-of-plume dataframes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"in_plume = point_df.loc[point_df['in-plume'] == 1].copy()\\n\",\n \"out_plume = point_df.loc[point_df['in-plume'] == 0].copy()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, add a column for the in/out band ratio using the in-plume divided by out-of-plume radiance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"out_plume['band_ratio'] = (in_plume.loc[0,'radiance']/out_plume['radiance'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create an `hvplot` object for our band ratio.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"in_out_plot = out_plume.hvplot(x='wavelengths',y='band_ratio', by=['ID'], color=hv.Cycle('Dark2'), frame_height=400, frame_width=400, xlim=(2150,2450), ylim=(0.85,1.05), ylabel='In Plume/Out of Plume Ratio', xlabel='Wavelength (nm)', title='In Plume/Out of Plume Ratio')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Overlay our absorption coefficient to show similarity between the two within the 2150 and 2450 nanometers (nm) range where methane spectral features are present. We can do this by setting our figure `xlim`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from bokeh.models import GlyphRenderer, LinearAxis, LinearScale, Range1d\\n\",\n \"\\n\",\n \"def overlay_hook(plot, element):\\n\",\n \" # Adds right y-axis\\n\",\n \" p = plot.handles[\\\"plot\\\"]\\n\",\n \" p.extra_y_scales = {\\\"right\\\": LinearScale()}\\n\",\n \" p.extra_y_ranges = {\\\"right\\\": Range1d(-1.5,0)}\\n\",\n \" p.add_layout(LinearAxis(y_range_name=\\\"right\\\"), \\\"right\\\")\\n\",\n \"\\n\",\n \" # find the last line and set it to right\\n\",\n \" lines = [p for p in p.renderers if isinstance(p, GlyphRenderer)]\\n\",\n \" lines[-1].y_range_name = \\\"right\\\"\\n\",\n \"\\n\",\n \"# Create Figure\\n\",\n \"(in_out_plot.opts(ylim=(0.85,0.95)) * ac_plot.opts(color=\\\"k\\\")).opts(hooks=[overlay_hook]).opts(title='In Plume/Out of Plume and Absorption Coefficient') \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can contrast this with our out-of-plume/out-of-plume band ratios to show how similar the in-plume and out-of-plume ratio is to this spectral signature. Create a column in our dataframe for the out-of-plume/out-of-plume band ratio and visualize it overlayed with our methane absorption feature using `hvplot`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"out_plume['out-out'] = (out_plume['radiance'] / out_plume.loc[2,'radiance'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"out_out_plot = out_plume.hvplot(x='wavelengths',y='out-out', by=['ID'], color=hv.Cycle('Dark2'), frame_height=400, frame_width=400, xlim=(2150,2450), ylim=(0.85,1.05), ylabel='Out/Out 2 Ratio', xlabel='Wavelength (nm)', title='Out of Plume/Out of Plume 2 Ratio')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from bokeh.models import GlyphRenderer, LinearAxis, LinearScale, Range1d\\n\",\n \"\\n\",\n \"def overlay_hook(plot, element):\\n\",\n \" # Adds right y-axis\\n\",\n \" p = plot.handles[\\\"plot\\\"]\\n\",\n \" p.extra_y_scales = {\\\"right\\\": LinearScale()}\\n\",\n \" p.extra_y_ranges = {\\\"right\\\": Range1d(-1.5,0)}\\n\",\n \" p.add_layout(LinearAxis(y_range_name=\\\"right\\\"), \\\"right\\\")\\n\",\n \"\\n\",\n \" # find the last line and set it to right\\n\",\n \" lines = [p for p in p.renderers if isinstance(p, GlyphRenderer)]\\n\",\n \" lines[-1].y_range_name = \\\"right\\\"\\n\",\n \"\\n\",\n \"# Create Figure\\n\",\n \"(out_out_plot * ac_plot.opts(color=\\\"k\\\")).opts(hooks=[overlay_hook]).opts(title='Out of Plume/Out of Plume and Absorption Coefficient') \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This highlights the similarity of the in-plume/out-of-plume band ratio to the modeled methane signature, as opposed to a band ratio where methane is not present. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info:\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: https://lpdaac.usgs.gov/ \\n\",\n \"Date last modified: 03-13-2024 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"lpdaac_vitals_test\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.13\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "python/tutorials/Visualizing_Methane_Plume_Timeseries.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Visualizing Methane Plume Timeseries\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"In this notebook, we'll conduct a search and visualize the available methane plume complex observations detected by the Earth Mineral Dust Source Investigation (EMIT) instrument, then select a region of interest (ROI) that shows several plumes that appear to be from the same source. We will then visualize a time series of plume data for the ROI, calculate the integrated methane enhancement, or mass of methane observed in each plume. Lastly, to add more context to the plume timeseries, we will look at all EMIT observations over the target regions and determine if there were factors such as clouds limiting plume detection, or simply no methane emissions during additional overpasses.\\n\",\n \"\\n\",\n \"> **NOTE**: Throughout this notebook, several complex functions and workflows are used to process and visualize data. These can be found in the `emit_tools.py` and `tutorial_utils.py` modules.\\n\",\n \"\\n\",\n \"**Background**\\n\",\n \"\\n\",\n \"Methane is major contributor to atmospheric radiative forcing because its more efficient at trapping radiation than other greenhouse gases, like carbon dioxide. Because the lifetime of methane in the atmosphere is only about 10 years, reducing methane emissions offers an effective way to curb anthropogenic contributions to atmospheric radiative forcing.\\n\",\n \"\\n\",\n \"The EMIT instrument is an imaging spectrometer that measures light in visible and infrared wavelengths. These measurements display unique spectral signatures that correspond to chemical composition. Although the primary mission focuses on mapping the mineral composition of Earth's surface, the EMIT instrument has also been used to successfully map methane point source emissions within its target mask using the unique spectral fingerprint of methane. More details about EMIT and its associated products can be found in the **README.md** and on the [EMIT website](https://earth.jpl.nasa.gov/emit/).\\n\",\n \"\\n\",\n \"The L2B Estimated Methane Plume Complexes ([EMITL2BCH4PLM](https://doi.org/10.5067/EMIT/EMITL2BCH4PLM.001)) product provides delineated methane plume complexes in parts per million meter (ppm m) along with associated uncertainties. This product is a plume specific subset of the EMIT L2B Methane Enhancement Data ([EMITL2BCH4ENH](https://doi.org/10.5067/EMIT/EMITL2BCH4ENH.001)) product. Each EMITL2BCH4PLM granule is sized to a specific plume complex but may cross multiple EMITL2BCH4ENH granules. A list of source EMITL2BCH4ENH granules is included in the GeoTIFF file metadata as well as the GeoJSON file. Each EMITL2BCH4PLM granule contains two files: one Cloud Optimized GeoTIFF (COG) file at a spatial resolution of 60 meters (m) and one GeoJSON file. The EMITL2BCH4PLM COG file contains a raster image of a methane plume complex extracted from EMITL2BCH4ENH v001 data. The EMITL2BCH4PLM GeoJSON file contains a vector outline of the plume complex, a list of source scenes, coordinates of the maximum enhancement values with associated uncertainties. \\n\",\n \"\\n\",\n \"The EMITL2BCH4ENH product only includes granules where methane plume complexes have been identified. To reduce the risk of false positives, all EMITL2BCH4ENH data undergo a manual review (or identification and confirmation) process before being designated as a plume complex. For more information on the manual review process, see Section 4.2.2 of the [EMIT GHG Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1696/EMIT_GHG_ATBD_V1.pdf). \\n\",\n \"\\n\",\n \"\\n\",\n \"**References**\\n\",\n \"\\n\",\n \"Andrew K. Thorpe et al., Attribution of individual methane and carbon dioxide emission sources using EMIT observations from space. Sci. Adv.9, eadh2391 (2023). DOI:[10.1126/sciadv.adh2391](https://www.science.org/doi/10.1126/sciadv.adh2391)\\n\",\n \"\\n\",\n \"**Requirements** \\n\",\n \" - Set up Python Environment - See **setup_instructions.md** in the `/setup/` folder\\n\",\n \" - NASA Earthdata Login Account. [Sign Up](https://urs.earthdata.nasa.gov/users/new)\\n\",\n \"\\n\",\n \"**Data Used** \\n\",\n \" - [EMIT L2B Estimated Methane Plume Complexes (EMITL2BCH4PLM)](https://doi.org/10.5067/EMIT/EMITL2BCH4PLM.001)\\n\",\n \" - [EMIT L2A Estimated Surface Reflectance and Uncertainty and Masks (EMITL2ARFL)](https://doi.org/10.5067/EMIT/EMITL2ARFL.001)\\n\",\n \"\\n\",\n \"**Learning Objectives** \\n\",\n \" - Search for EMIT L2B Estimated Methane Plume Complexes\\n\",\n \" - Visualize search results on a map\\n\",\n \" - Retrieve and visualize the EMIT L2B Estimated Methane Plume Complexes Metadata\\n\",\n \" - Select a region of interest and build a timeseries of plume data\\n\",\n \" - Further investigate plume detection by looking at browse images and quality information\\n\",\n \"\\n\",\n \"**Tutorial Outline** \\n\",\n \"\\n\",\n \"1. [**Setup**](#setup)\\n\",\n \"2. [**Search for EMIT L2B Estimated Methane Plume Complexes**](#search)\\n\",\n \"3. [**Creating a Timeseries from Plume Data**](#plume-timeseries)\\n\",\n \"4. [**Further investigation into plume detection**](#plume-detection)\\n\",\n \"5. [**Calculating the Integrated Mass Enhancement for Plumes**](#ime)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Set up\\n\",\n \"\\n\",\n \"Import the necessary Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Import required libraries\\n\",\n \"import sys\\n\",\n \"import os\\n\",\n \"import glob\\n\",\n \"import requests\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import xarray as xr\\n\",\n \"from osgeo import gdal\\n\",\n \"import geopandas as gpd\\n\",\n \"\\n\",\n \"from datetime import datetime\\n\",\n \"import folium\\n\",\n \"import earthaccess\\n\",\n \"import folium.plugins\\n\",\n \"import rioxarray as rxr\\n\",\n \"\\n\",\n \"import hvplot.xarray\\n\",\n \"import hvplot.pandas\\n\",\n \"\\n\",\n \"from branca.element import Figure\\n\",\n \"from IPython.display import display\\n\",\n \"from shapely.geometry.polygon import orient\\n\",\n \"from shapely.geometry import Point\\n\",\n \"\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"from emit_tools import emit_xarray, ortho_xr, ortho_browse\\n\",\n \"from tutorial_utils import list_metadata_fields, results_to_geopandas, convert_bounds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Login using `earthaccess` and create a `.netrc` file if necessary. This file will store your NASA Earthdata Login credentials and use them to authenticate when necessary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All of the data we use or save will go into the `methane_tutorial` directory, so we can go ahead and define that filepath now, relative to this notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"methane_dir = '../../data/methane_tutorial/'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Search for EMIT L2B Estimated Methane Plume Complexes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use `earthaccess` to find all EMIT L2B Estimated Methane Plume Complexes (EMITL2BCH4PLM) data available from 2023. Define the date range, and concept-ids (unique product identifier) for the EMIT products that we want to search for, but leave the spatial arguments like `polygon` and `bbox` empty so we can preview detected methane plumes globally. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Data Collections for our search, using a dictionary\\n\",\n \"concept_ids = {'plumes':'C2748088093-LPCLOUD', 'reflectance':'C2408750690-LPCLOUD'}\\n\",\n \"# Define Date Range\\n\",\n \"date_range = ('2023-01-01','2023-12-31')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"results = earthaccess.search_data(\\n\",\n \" concept_id=concept_ids['plumes'],\\n\",\n \" temporal=date_range,\\n\",\n \" count=2000\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Convert the results to a `geopandas.GeoDataFrame` using a function from our `tutorial_utils` module. This gives a nice way to organize and visualize the search results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"gdf = results_to_geopandas(results)\\n\",\n \"gdf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The function includes default fields, but you can add more with the `fields` argument. To see all of the metadata available use the `list_metadata_fields` function imported from the `tutorial_utils.py` module.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"jupyter\": {\n \"source_hidden\": true\n }\n },\n \"outputs\": [],\n \"source\": [\n \"list_metadata_fields(results)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Add an index column to the dataframe to include it in the tooltips for our visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Specify index so we can reference it with gdf.explore()\\n\",\n \"gdf['index'] = gdf.index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set up Figure and Basemap tiles\\n\",\n \"fig = Figure(width=\\\"1080px\\\",height=\\\"540\\\")\\n\",\n \"map1 = folium.Map(tiles=None)\\n\",\n \"folium.TileLayer(tiles='https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}',name='Google Satellite', attr='Google', overlay=True).add_to(map1)\\n\",\n \"folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png',\\n\",\n \" name='ESRI World Imagery',\\n\",\n \" attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',\\n\",\n \" overlay='True').add_to(map1)\\n\",\n \"fig.add_child(map1)\\n\",\n \"# Add Search Results gdf\\n\",\n \"gdf.explore(\\\"_single_date_time\\\",\\n\",\n \" categorical=True,\\n\",\n \" style_kwds={\\\"fillOpacity\\\":0.1,\\\"width\\\":2},\\n\",\n \" name=\\\"EMIT L2B CH4PLM\\\",\\n\",\n \" tooltip=[\\n\",\n \" \\\"index\\\",\\n\",\n \" \\\"native-id\\\",\\n\",\n \" \\\"_single_date_time\\\",\\n\",\n \" ],\\n\",\n \" m=map1,\\n\",\n \" legend=False\\n\",\n \")\\n\",\n \"\\n\",\n \"# Zoom to Data\\n\",\n \"map1.fit_bounds(bounds=convert_bounds(gdf.unary_union.bounds))\\n\",\n \"# Add Layer controls\\n\",\n \"map1.add_child(folium.LayerControl(collapsed=False))\\n\",\n \"display(fig)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this example we'll chose a region that looks like it has a several plumes emitting from the same source, a landfill in Jordan. We could create a simple bounding box around our target region by using the plumes that extend furthest in the cardinal directions to generate a bounding box around the region that we can use in our upcoming analysis. \\n\",\n \"\\n\",\n \"However, to simplify things we will import an existing `geojson` as a `GeoDataFrame` with a bounding box around this region because the plume indices may change, which results in inconsistent outputs. A commented out cell below as included as an example of how the `GeoDataFrame` was created before being written to `geojson`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# # Select a list of plumes to create geometry we can use for a spatial subset\\n\",\n \"# plumes = [146,198,243]\\n\",\n \"# bbox = gdf.loc[plumes].geometry.unary_union.envelope\\n\",\n \"# bbox = orient(bbox, sign=1)\\n\",\n \"# plume_bbox = gpd.GeoDataFrame({\\\"name\\\":['plume_bbox'], \\\"geometry\\\":[bbox]},crs=gdf.crs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Open the predefined `geojson` of our plume bounding box as a `GeoDataFrame` then visualize on our `folium` figure.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Open the geojson with our plume bbox\\n\",\n \"plume_bbox = gpd.read_file(f'{methane_dir}/plume_bbox.geojson')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set up Figure and Basemap tiles\\n\",\n \"fig = Figure(width=\\\"1080px\\\",height=\\\"540\\\")\\n\",\n \"map1 = folium.Map(tiles=None)\\n\",\n \"folium.TileLayer(tiles='https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}',name='Google Satellite', attr='Google', overlay=True).add_to(map1)\\n\",\n \"folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png',\\n\",\n \" name='ESRI World Imagery',\\n\",\n \" attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',\\n\",\n \" overlay='True').add_to(map1)\\n\",\n \"fig.add_child(map1)\\n\",\n \"# Add Search Results gdf\\n\",\n \"plume_bbox.explore(\\\"name\\\",\\n\",\n \" name='Plume BBox',\\n\",\n \" style_kwds={\\\"fillOpacity\\\":0,\\\"width\\\":2},\\n\",\n \" m=map1,\\n\",\n \" legend=False)\\n\",\n \"\\n\",\n \"gdf.explore(\\\"_single_date_time\\\",\\n\",\n \" categorical=True,\\n\",\n \" style_kwds={\\\"fillOpacity\\\":0.1,\\\"width\\\":2},\\n\",\n \" name=\\\"EMIT L2B CH4PLM\\\",\\n\",\n \" tooltip=[\\n\",\n \" \\\"index\\\",\\n\",\n \" \\\"native-id\\\",\\n\",\n \" \\\"_single_date_time\\\",\\n\",\n \" ],\\n\",\n \" m=map1,\\n\",\n \" legend=False\\n\",\n \")\\n\",\n \"# Zoom to Data\\n\",\n \"map1.fit_bounds(bounds=convert_bounds(plume_bbox.unary_union.bounds))\\n\",\n \"# Add Layer controls\\n\",\n \"map1.add_child(folium.LayerControl(collapsed=False))\\n\",\n \"display(fig)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Subset our geodataframe of global plumes to only those that intersect our bounding box.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_gdf = gdf[gdf.geometry.intersects(plume_bbox.geometry[0])]\\n\",\n \"plm_gdf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's examine the `_related_urls` column in our geodataframe. This column contains various links to assets related to each plume.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_gdf['_related_urls'].iloc[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can write a function to return the asset `URL` for a given asset and row in our dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_asset_url(row,asset, key='Type',value='GET DATA'):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Retrieve a url from the list of dictionaries for a row in the _related_urls column.\\n\",\n \" Asset examples: CH4PLM, CH4PLMMETA, RFL, MASK, RFLUNCERT \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Add _ to asset so string matching works\\n\",\n \" asset = f\\\"_{asset}_\\\"\\n\",\n \" # Retrieve URL matching parameters\\n\",\n \" for _dict in row['_related_urls']:\\n\",\n \" if _dict.get(key) == value and asset in _dict['URL'].split('/')[-1]:\\n\",\n \" return _dict['URL']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Write another function to make a request to retrieve the json metadata for a given plume, using our `get_asset_url` function to select the correct URL from the dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Function to fetch CH4 Plume Metadata\\n\",\n \"# Speed could be improved here by using asyncio/aiohttp\\n\",\n \"def fetch_ch4_metadata(row):\\n\",\n \" response = requests.get(get_asset_url(row, 'CH4PLMMETA'))\\n\",\n \" json = response.json()\\n\",\n \" return json['features'][0]['properties']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fetch_ch4_metadata(plm_gdf.iloc[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Retrieve additional plume metadata contained in the EMIT L2B Estimated Methane Plume Complexes (EMITL2BCH4PLM) data product, which contains the maximum enhancement value, the uncertainty of the plume complex, and the list of source scenes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Apply the function to each row and convert the result to a DataFrame\\n\",\n \"plm_meta = plm_gdf.apply(fetch_ch4_metadata, axis=1).apply(pd.Series)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_meta\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can add the points with highest methane concentration to our visualization.\\n\",\n \"\\n\",\n \"Create an index column, as we did for the plumes, then convert the latitude and longitude of max concentration to a shapely `Point` object and add it to our `GeoDataFrame`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Specify index so we can reference it with gdf.explore()\\n\",\n \"plm_meta['index'] = plm_meta.index\\n\",\n \"# Add Geometry and convert to geodataframe\\n\",\n \"plm_meta['geometry'] = plm_meta.apply(lambda row: Point(row['Longitude of max concentration'], row['Latitude of max concentration']), axis=1)\\n\",\n \"plm_meta = gpd.GeoDataFrame(plm_meta, geometry='geometry', crs='EPSG:4326')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_meta\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now add this to our visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set up Figure and Basemap tiles\\n\",\n \"fig = Figure(width=\\\"1080px\\\",height=\\\"540\\\")\\n\",\n \"map1 = folium.Map(tiles=None)\\n\",\n \"folium.TileLayer(tiles='https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}',name='Google Satellite', attr='Google', overlay=True).add_to(map1)\\n\",\n \"folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png',\\n\",\n \" name='ESRI World Imagery',\\n\",\n \" attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',\\n\",\n \" overlay='True').add_to(map1)\\n\",\n \"fig.add_child(map1)\\n\",\n \"# Add Search Results gdf\\n\",\n \"plume_bbox.explore(\\\"name\\\",\\n\",\n \" name='Plume BBox',\\n\",\n \" style_kwds={\\\"fillOpacity\\\":0,\\\"width\\\":2},\\n\",\n \" m=map1,\\n\",\n \" legend=False)\\n\",\n \"\\n\",\n \"plm_gdf.explore(\\\"index\\\",\\n\",\n \" categorical=True,\\n\",\n \" style_kwds={\\\"fillOpacity\\\":0.1,\\\"width\\\":2},\\n\",\n \" name=\\\"EMIT L2B CH4PLM\\\",\\n\",\n \" tooltip=[\\n\",\n \" \\\"index\\\",\\n\",\n \" \\\"native-id\\\",\\n\",\n \" \\\"_single_date_time\\\",\\n\",\n \" ],\\n\",\n \" m=map1,\\n\",\n \" legend=False\\n\",\n \")\\n\",\n \"\\n\",\n \"plm_meta.explore(\\\"index\\\",\\n\",\n \" categorical=True,\\n\",\n \" style_kwds={\\\"fillOpacity\\\":0.1,\\\"width\\\":2},\\n\",\n \" name=\\\"Location of Max Concentration (ppm m)\\\",\\n\",\n \" tooltip=[\\n\",\n \" \\\"DAAC Scene Names\\\",\\n\",\n \" \\\"UTC Time Observed\\\",\\n\",\n \" \\\"Max Plume Concentration (ppm m)\\\",\\n\",\n \" \\\"Concentration Uncertainty (ppm m)\\\",\\n\",\n \" \\\"Orbit\\\"\\n\",\n \" ],\\n\",\n \" m=map1,\\n\",\n \" legend=False\\n\",\n \")\\n\",\n \"# Zoom to Data\\n\",\n \"map1.fit_bounds(bounds=convert_bounds(plume_bbox.unary_union.bounds))\\n\",\n \"# Add Layer controls\\n\",\n \"map1.add_child(folium.LayerControl(collapsed=False))\\n\",\n \"display(fig)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Creating a Timeseries from Plume Data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can visualize a timeseries of these plumes that appear to be from the same source. To do this we'll generate a list of the COG URLs for the plumes, then use rioxarray to stream the data and build a timeseries dataset based on the timestamps in the filenames.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the `get_asset_url` function to retrieve the CH4PLM asset URLs by applying it to our dataframe and converting the output to a list.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Iterate over rows in the plm_gdf and get the CH4PLM URLs and store them in a list\\n\",\n \"plm_urls = plm_gdf.apply(lambda row: get_asset_url(row, asset='CH4PLM'), axis=1).tolist()\\n\",\n \"plm_urls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have a list of COG urls we can set our `gdal` configuration options to pass our NASA Earthdata login credentials when we access each COG.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# GDAL configurations used to successfully access LP DAAC Cloud Assets via vsicurl \\n\",\n \"gdal.SetConfigOption('GDAL_HTTP_COOKIEFILE','~/cookies.txt')\\n\",\n \"gdal.SetConfigOption('GDAL_HTTP_COOKIEJAR', '~/cookies.txt')\\n\",\n \"gdal.SetConfigOption('GDAL_DISABLE_READDIR_ON_OPEN','EMPTY_DIR')\\n\",\n \"gdal.SetConfigOption('CPL_VSIL_CURL_ALLOWED_EXTENSIONS','TIF')\\n\",\n \"gdal.SetConfigOption('GDAL_HTTP_UNSAFESSL', 'YES')\\n\",\n \"gdal.SetConfigOption('GDAL_HTTP_MAX_RETRY', '10')\\n\",\n \"gdal.SetConfigOption('GDAL_HTTP_RETRY_DELAY', '0.5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To build our timeseries we will start by opening all the necessary data. Loop over our list of URLs, open each plume, merge plumes acquired at the same time, and store them in a dictionary where keys correspond to the acquisition time and values are the plume data in an `xarray.DataArray`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_ts_dict = {}\\n\",\n \"# Set max retries for vsicurl errors\\n\",\n \"max_retries=5\\n\",\n \"# Iterate over plm urls\\n\",\n \"for url in plm_urls:\\n\",\n \" # retrieve acquisition time from url\\n\",\n \" acquisition_time = url.split('/')[-1].split('.')[-2].split('_')[-2]\\n\",\n \" # list plumes identified in same scene if there are any\\n\",\n \" same_scene = [url for url in plm_urls if acquisition_time in url.split('/')[-1].split('.')[-2].split('_')[-2]]\\n\",\n \" to_merge = []\\n\",\n \" # prevent duplicate processing of plumes from the same scene\\n\",\n \" if acquisition_time not in list(plm_ts_dict.keys()):\\n\",\n \" # Open and merge plumes identified from each scene\\n\",\n \" for _plm in same_scene:\\n\",\n \" print(f\\\"Opening {_plm.split('/')[-1]}\\\")\\n\",\n \" # Open COG and squeeze band dimension\\n\",\n \" plm = rxr.open_rasterio(_plm).squeeze('band', drop=True)\\n\",\n \" # Add to list of plumes to merge\\n\",\n \" to_merge.append(plm)\\n\",\n \" # Merge plumes and add to timeseries\\n\",\n \" plm_ts_dict[acquisition_time] = rxr.merge.merge_arrays(to_merge) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have a plume object for each date in our timeseries, we need to put them all on a common grid so they are spatially aligned before we can stack them along the time dimension.\\n\",\n \"\\n\",\n \"To do this, we will find the minimum and maximum bounds of each dataarray in our dictionary, then create a common grid to reproject to.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from typing import List\\n\",\n \"def create_common_grid(data_arrays: List[xr.DataArray]) -> xr.DataArray:\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Create a common grid for a list of xarray DataArrays, matching the resolution of the first data array.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Initial Bounds for common grid\\n\",\n \" minx = miny = float('inf')\\n\",\n \" maxx = maxy = float('-inf')\\n\",\n \"\\n\",\n \" for array in data_arrays:\\n\",\n \" left, bottom, right, top = array.rio.bounds()\\n\",\n \" minx = min(minx, left)\\n\",\n \" miny = min(miny, bottom)\\n\",\n \" maxx = max(maxx, right)\\n\",\n \" maxy = max(maxy, top)\\n\",\n \"\\n\",\n \" bounds = (minx, miny, maxx, maxy)\\n\",\n \"\\n\",\n \" res = data_arrays[0].rio.resolution()\\n\",\n \" crs = data_arrays[0].rio.crs\\n\",\n \" nodata = data_arrays[0].rio.nodata\\n\",\n \"\\n\",\n \" # Calculate new raster shape using the new extent, maintaining the original resolution\\n\",\n \" height = int(np.ceil((bounds[3] - bounds[1]) / abs(res[1])))\\n\",\n \" width = int(np.ceil((bounds[2] - bounds[0]) / abs(res[0])))\\n\",\n \" data = np.full((height,width),nodata)\\n\",\n \" coords = {'y':(['y'],np.arange(bounds[1], bounds[3], abs(res[1]))),\\n\",\n \" 'x':(['x'],np.arange(bounds[0], bounds[2], abs(res[0])))}\\n\",\n \" common_grid = xr.DataArray(data, coords=coords)\\n\",\n \" common_grid.rio.write_crs(crs, inplace=True)\\n\",\n \" return(common_grid)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"common_grid = create_common_grid(list(plm_ts_dict.values()))\\n\",\n \"common_grid\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reproject each of the plume dataarrays to the common grid.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_ts_dict = {key: value.rio.reproject_match(common_grid) for key, value in plm_ts_dict.items()}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we have all of our plumes on a standard grid, we can concatenate them along a time dimension to create a timeseries. Create an xarray variable called 'time' from our dictionary keys, then use `xarray.concat` to concatenate all of our plumes along the time dimension.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_time = xr.Variable('time', [datetime.strptime(t,'%Y%m%dT%H%M%S') for t in list(plm_ts_dict.keys())])\\n\",\n \"plm_time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_ts_ds = xr.concat(list(plm_ts_dict.values()), dim=plm_time)\\n\",\n \"plm_ts_ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set our no_data values to `np.nan` to make sure they are transparent for improved visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_ts_ds.data[plm_ts_ds.data == -9999] = np.nan\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Plot the plume time series.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_ts_plot = plm_ts_ds.hvplot.image(x='x',y='y',geo=True, tiles='ESRI', crs='EPGS:4326', cmap='inferno',clim=(0,np.nanmax(plm_ts_ds.data)),clabel=f'Methane Concentation ({plm_ts_ds.Units})', frame_width=600, frame_height=600, rasterize=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_ts_plot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Calculating the IME for each plume\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The integrated mass enhancement (IME), a sum of the mass of methane present in each plume is calculated by summing the mass of methane present in all plume pixels. The IME in kilograms (kg) can be estimated by using the equation below which includes the mixing ratio length per pixel (ppmm), the area of the pixel (m^2), where 22.4 is the volume of 1 mole of gas at standard temperature and pressure (STP) in liters, and 0.01604 is the molar mass of methane in kg/mol.\\n\",\n \"\\n\",\n \"The IME can be combined with a plume length and windspeed to estimate emissions in units of kg per hour, but in this tutorial, we will not calculate emissions, rather keep things simple and calculate the IME for each plume and observe how these values change over time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"$$\\\\ kg\\\\ \\\\ (per \\\\ \\\\ pixel) = \\\\frac{pixel \\\\ \\\\ value \\\\ \\\\ ppm \\\\cdot m}{1} \\\\frac{1}{1 \\\\cdot 10^6 \\\\ \\\\ ppm} \\\\frac {60 \\\\ \\\\ m \\\\cdot 60 \\\\ \\\\ m} {1} \\\\frac {1000 \\\\ \\\\ L} {m^3} \\\\frac {1 \\\\ \\\\ mol} {22.4 \\\\ \\\\ L} \\\\frac {0.01604 \\\\ \\\\ kg} {1 \\\\ \\\\ mol}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can write this as a function for each pixel, then apply it to the entire timeseries to calculate the IME for each plume.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calc_ime(plume_da):\\n\",\n \" molar_volume = 22.4 # L/mol at STP\\n\",\n \" molar_mass_ch4 = 0.01604 #kg/mol\\n\",\n \"\\n\",\n \" kg = plume_da * (1/1e6) * (60*60) * (1000) * (1/molar_volume) * molar_mass_ch4\\n\",\n \" ime = np.nansum(kg)\\n\",\n \" return ime\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Apply the function along the 'x' and 'y' dimensions\\n\",\n \"ime_ts = xr.apply_ufunc(calc_ime, plm_ts_ds, input_core_dims=[['y', 'x']], vectorize=True)\\n\",\n \"ime_ts.name = 'value'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ime_plot = ime_ts.hvplot.scatter(x='time',y='value', title='Observed Methane IME over 2023', color='black', xticks=list(ime_ts.time.data), rot=90, grid=True, xlabel='Date Observed', ylabel='kg')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ime_plot\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Further investigation into plume detection\\n\",\n \"\\n\",\n \"The timeseries shown above doesn't necessarily give us a full a full picture methane emissions at the landfill. In addition to cases where no emissions were observed, gaps in data can result from absence of observations due the variable revisit period of the ISS, or clouds. To add more context to this plume timeseries, we will look at all EMIT acquisitions over the target regions and try to determine if there were factors affecting plume detection, or simply no methane emissions during those for any other overpass.\\n\",\n \"\\n\",\n \"For this search we'll want to restrict our search to a smaller ROI than our bounding box. We want to pick something smaller more centered around the source of the emission, so we avoid retrieving irrelevant data, for example an overpass barely crossing the corner of our large bounding box. To do this we will use the maximum concentration points from our plumes to create a smaller ROI that is likely to include a methane plume if one is present.\\n\",\n \"\\n\",\n \"Create a new polygon using the maximum concentration points from our plumes as vertices, then orienting the points in counter-clockwise order so they are compatible with an `earthaccess` search. This is just a simple example approach, there are several ways we could define a smaller ROI using the plume data or ancillary information.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"max_conc_poly = plm_meta.geometry.unary_union.envelope\\n\",\n \"max_conc_poly = orient(max_conc_poly, sign=1.0)\\n\",\n \"max_conc_gdf = gpd.GeoDataFrame({\\\"name\\\":['max_points'], \\\"geometry\\\":[max_conc_poly]},crs=plm_meta.crs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"roi = list(max_conc_gdf.geometry[0].exterior.coords)\\n\",\n \"roi\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now conduct a search for reflectance data over our ROI and convert the results to a `geopandas.GeoDataFrame`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rfl_results = earthaccess.search_data(\\n\",\n \" concept_id=concept_ids['reflectance'],\\n\",\n \" polygon=roi,\\n\",\n \" temporal= date_range, #('2023-03-01','2023-03-31')\\n\",\n \" count=2000\\n\",\n \")\\n\",\n \"rfl_gdf = results_to_geopandas(rfl_results)\\n\",\n \"rfl_gdf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From the results we can see we have 20 scenes that intersect our ROI. We can visualize the footprints of these scenes to gain some insight into coverage over our ROI. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set up Figure and Basemap tiles\\n\",\n \"fig = Figure(width=\\\"1080px\\\",height=\\\"540\\\")\\n\",\n \"map1 = folium.Map(tiles=None)\\n\",\n \"folium.TileLayer(tiles='https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}',name='Google Satellite', attr='Google', overlay=True).add_to(map1)\\n\",\n \"folium.TileLayer(tiles='https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png',\\n\",\n \" name='ESRI World Imagery',\\n\",\n \" attr='Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community',\\n\",\n \" overlay='True').add_to(map1)\\n\",\n \"fig.add_child(map1)\\n\",\n \"\\n\",\n \"# Add Search Reflectance Scenes with no CH4\\n\",\n \"rfl_gdf.explore(color='red',\\n\",\n \" style_kwds={\\\"fillOpacity\\\":0,\\\"width\\\":2},\\n\",\n \" name=\\\"Scenes with no CH4 Plumes\\\",\\n\",\n \" tooltip=[\\n\",\n \" \\\"native-id\\\",\\n\",\n \" \\\"_beginning_date_time\\\",\\n\",\n \" ],\\n\",\n \" m=map1,\\n\",\n \" legend=False)\\n\",\n \"\\n\",\n \"# Add Plume BBox to Map\\n\",\n \"max_conc_gdf.explore(m=map1,\\n\",\n \" name='Plumes Bounding Box',\\n\",\n \" legend=False)\\n\",\n \"\\n\",\n \"# Zoom to Data\\n\",\n \"map1.fit_bounds(bounds=convert_bounds(rfl_gdf.unary_union.bounds))\\n\",\n \"# Add Layer controls\\n\",\n \"map1.add_child(folium.LayerControl(collapsed=False))\\n\",\n \"display(fig)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"From this we can see that there are several scenes that intersect with our ROI, but likely have no relevant information since they only cover a small portion or corner of the ROI.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can use a similar process as with the methane product to construct a time series to better understand the data gathered on each overpass. To do this, we will use the browse imagery and the masks included in the EMITL2ARFL product. The by default the mask files and browse images are not orthorectified, so we must do that as part of our workflow.\\n\",\n \"\\n\",\n \"First, get the urls for the browse images and masks for each scene in our `rfl_gdf` search results using the `get_asset_urls` function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"png_urls = rfl_gdf.apply(lambda row: get_asset_url(row, asset='RFL', value='GET RELATED VISUALIZATION'), axis=1).tolist()\\n\",\n \"png_urls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mask_urls = rfl_gdf.apply(lambda row: get_asset_url(row, asset='MASK'), axis=1).tolist()\\n\",\n \"mask_urls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can write these as a text file so we don't need to search again, although we will use the `rfl_gdf` GeoDataFrame later in the tutorial. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# # Save URL List\\n\",\n \"# with open(f'{methane_dir}rfl_mask_urls.txt', 'w') as f:\\n\",\n \"# for line in mask_urls:\\n\",\n \"# f.write(f\\\"{line}\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since the mask files are not chunked, its quicker to download them to do the processing.\\n\",\n \"\\n\",\n \"Login with `earthaccess` and download these files.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\\n\",\n \"# Get requests https Session using Earthdata Login Info\\n\",\n \"fs = earthaccess.get_requests_https_session()\\n\",\n \"# Retrieve granule asset ID from URL (to maintain existing naming convention)\\n\",\n \"for url in mask_urls:\\n\",\n \" granule_asset_id = url.split('/')[-1]\\n\",\n \" # Define Local Filepath\\n\",\n \" fp = f'{methane_dir}{granule_asset_id}'\\n\",\n \" # Download the Granule Asset if it doesn't exist\\n\",\n \" if not os.path.isfile(fp):\\n\",\n \" with fs.get(url,stream=True) as src:\\n\",\n \" with open(fp,'wb') as dst:\\n\",\n \" for chunk in src.iter_content(chunk_size=64*1024*1024):\\n\",\n \" dst.write(chunk)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For each of these scenes we want to open EMIT L2A Mask data, then subset spatially and select only the variable we want, in this case we'll use the `Cloud flag` from the `masks` dataarray. There are other flags, such as a cirrus mask, dilated cloud flag, spacecraft flag, water flag, and and aerosol optical depth. We could potentially use some of these as well to inform our decisions about plume detection, but will stick to the `Cloud flag` in this example for simplicity.\\n\",\n \"\\n\",\n \"As we do this, we will also use the GLT included in the mask file to orthorectify our RGB browse image. We can do this because the browse png files are in the native resolution and can be broadcast onto an orthorectified grid using the GLT. We will use the `reproject_match` function to reproject the data on a `common_grid`, which will automatically clip the data to the `common_grid` extent.\\n\",\n \"\\n\",\n \"First, get the filepaths for our downloaded mask data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# List the downloaded files\\n\",\n \"fps = glob.glob(f'{methane_dir}*.nc')\\n\",\n \"# Make sure only files listed are also in the mask_urls list of files downloaded\\n\",\n \"fns = [os.path.basename(fp) for fp in fps if any(os.path.basename(fp) in url.split('/')[-1] for url in mask_urls)]\\n\",\n \"fns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a function to loop through our files, orthorectifying the mask and browse image, clipping and reprojecting to our predefined `common_grid`, and finally saving outputs as a COG.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def process_scenes(fns, outdir, common_grid):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" This function will process a list of EMIT Mask scenes, selecting the cloud flag, orthorectifying the mask and browse image, then reprojecting both to a common grid and saving an output.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" for fn in fns:\\n\",\n \" # Get Granule Asset ID for First Adjacent Scene (may only be one)\\n\",\n \" granule_asset_id = fn.split('.')[-2]\\n\",\n \" # Set Output Path\\n\",\n \" outpath_mask = f\\\"{outdir}{granule_asset_id}_cloud_flag.tif\\\"\\n\",\n \" outpath_browse = f\\\"{outdir}{granule_asset_id}_ortho_browse.tif\\\"\\n\",\n \" # Check if the file exists\\n\",\n \" if not os.path.isfile(outpath_mask):\\n\",\n \" # Open Mask Dataset\\n\",\n \" emit_ds = emit_xarray(f'{methane_dir}{fn}', ortho=False)\\n\",\n \" # Retrieve GLT, spatial_ref, and geotransform to use on browse image\\n\",\n \" glt = np.nan_to_num(np.stack([emit_ds[\\\"glt_x\\\"].data, emit_ds[\\\"glt_y\\\"].data], axis=-1),nan=0).astype(int)\\n\",\n \" spatial_ref = emit_ds.spatial_ref\\n\",\n \" gt = emit_ds.geotransform\\n\",\n \" # Select browse image url corresponding to the scene\\n\",\n \" png_url = [url for url in png_urls if fn.split('.')[-2].split('_')[-3] in url][0]\\n\",\n \" # Orthorectify browse and mask\\n\",\n \" rgb = ortho_browse(png_url, glt, spatial_ref, gt, white_background=True)\\n\",\n \" emit_ds = ortho_xr(emit_ds)\\n\",\n \" # Select only mask array and desired quality flag and reproject to match our chosen extent\\n\",\n \" mask_da = emit_ds['mask'].sel(mask_bands='Cloud flag')\\n\",\n \" # Drop elevation\\n\",\n \" mask_da = mask_da.drop_vars('elev')\\n\",\n \" mask_da.name = 'Cloud flag'\\n\",\n \" mask_da.data = np.nan_to_num(mask_da.data, nan=-9999)\\n\",\n \" mask_da = mask_da.rio.reproject_match(common_grid, nodata=-9999)\\n\",\n \" #mask_da.rio.write_nodata(np.nan, inplace=True)\\n\",\n \" # Reproject rgb\\n\",\n \" rgb = rgb.rio.reproject_match(common_grid, nodata=255) # 255 for white background\\n\",\n \" # Write cog outputs \\n\",\n \" mask_da.rio.to_raster(outpath_mask,driver=\\\"COG\\\")\\n\",\n \" rgb.rio.to_raster(outpath_browse,driver=\\\"COG\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run the function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"process_scenes(fns, methane_dir, common_grid)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a list of the processed files to use in creation of a timeseries. We'll use a similar process to what we did for the plumes, adding a `time` variable to our datasets and concatenating.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mask_files = sorted(glob.glob(f'{methane_dir}*cloud_flag.tif'))\\n\",\n \"mask_files\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rgb_files = sorted(glob.glob(f'{methane_dir}*ortho_browse.tif'))\\n\",\n \"rgb_files\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Build a time index from the filenames.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def time_index_from_filenames(file_names,datetime_pos):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Helper function to create a pandas DatetimeIndex\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return [datetime.strptime(f.split('_')[datetime_pos], '%Y%m%dT%H%M%S') for f in file_names]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mask_time = xr.Variable('time', time_index_from_filenames(mask_files, -5))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Open and concatenate our datasets along the time dimension, then assign fill_values to `np.nan` to make those sections of the data transparent in our visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"quality_ts_da = xr.concat([rxr.open_rasterio(f).squeeze('band', drop=True).rio.reproject_match(common_grid) for f in mask_files], dim=mask_time)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a plot object for the quality timeseries, first setting the quality mask values representing no clouds (0) or no data (-9999) to `np.nan` so they will be transparent in our visualization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"quality_ts_da.data[quality_ts_da.data < 1] = np.nan\\n\",\n \"quality_ts_map = quality_ts_da.hvplot.image(x='x',y='y',cmap='greys',groupby='time',clim=(0,1),geo=True,frame_height=400)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"RGB images are a good way to add something more visually understandable than just the mask layers. Follow the same process as above to build an RGB timeseries, then plot it with the bounding box and plume extents.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rgb_ts_ds = xr.concat([rxr.open_rasterio(f).rio.reproject_match(common_grid) for f in rgb_files], dim=mask_time)\\n\",\n \"rgb_ts_ds.data[rgb_ts_ds.data == -1] = 255\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rgb_ts_map = rgb_ts_ds.hvplot.rgb(x='x',y='y', bands='band',groupby='time',geo=True, frame_height=400, crs='EPSG:4326')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lastly, add a new column to our plume geodataframe named `time` so we are using the same naming convention and can layer our plume polygons from our filtered search on top of our quality data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plm_gdf['time'] = pd.to_datetime(plm_gdf.loc[:,'_single_date_time'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because we've set our RGB to display as white where there is no data, and our cloud mask where no clouds are present as transparent, we can identify any areas with no data over our ROI by white/transparent, and cloudy areas as black. The larger black lines/rectangles are representative of onboard cloud masking, where no data was downlinked due to a high volume of clouds detected.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"rgb_ts_map*quality_ts_map*plm_gdf.hvplot(groupby='time', geo=True, line_color='red', fill_color=None)*max_conc_gdf.hvplot(color='red',crs='EPSG:4326',fill_color=None, line_color='yellow')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With this information, we can build a dataframe assigning a category to each of the dates where there was no plume detected and add this information to our IME timeseries figure.\\n\",\n \"\\n\",\n \"Create a dataframe of dates where no plume was detected by finding the dates where there are no plumes in our `mask_time` arrays by removing timestamps from our `plm_time` array.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"no_plm_time = np.setdiff1d(mask_time.data,plm_time.data)\\n\",\n \"no_plm_time\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Build a dataframe out of these dates where there were no plumes detected\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Build a dataframe\\n\",\n \"no_plm_df = pd.DataFrame({'time':no_plm_time})\\n\",\n \"no_plm_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Next, categorize them based on the visualizations we made and remove any times where there wasn't a good observation of our area of interest.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# add a category column to describe the observation \\n\",\n \"no_plm_df['category'] = 'no_data'\\n\",\n \"no_plm_df.loc[[0,4],'category'] = 'cloud'\\n\",\n \"no_plm_df.loc[7,'category'] = 'no_plume'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"no_plm_df = no_plm_df[~no_plm_df['category'].str.contains('no_data')]\\n\",\n \"no_plm_df['time'] = pd.to_datetime(no_plm_df['time'])\\n\",\n \"no_plm_df.reset_index(drop=True, inplace=True)\\n\",\n \"no_plm_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now merge our IME timeseries dataframe with this one, along the time column to add these observations with different categories to our timeseries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ime_df = pd.merge(ime_ts.to_dataframe(), no_plm_df, on=['time'], how='outer')\\n\",\n \"ime_df = ime_df.sort_values(by='time')\\n\",\n \"ime_df['category']=ime_df['category'].fillna('plume')\\n\",\n \"ime_df['value'] = ime_df['value'].fillna(0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ime_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now plot this IME timeseries alongside our plume timeseries. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ime_timeline = ime_df.hvplot.scatter(x='time',y='value', by='category', size=100, xlabel='Date Observed', ylabel='kg', title='Observed Methane IME over 2023', rot=90,\\n\",\n \" grid=True, frame_height=400, frame_width=800, ylim=(0,11000),xticks=list(ime_df.time))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ime_timeline.opts(legend_position='bottom') + plm_ts_plot.opts(title='Methane Plumes Observed', frame_height=400, frame_width=400)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info:\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: https://lpdaac.usgs.gov/ \\n\",\n \"Date last modified: 10-25-2024 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract G15PD00467 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.14\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "python/tutorials/Working_with_EMIT_L2B_Mineralogy.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28e9239d-5a33-4e90-b1e8-d18bc44f4f45\",\n \"metadata\": {},\n \"source\": [\n \"> **Disclaimer:** The [**EMITL2BMIN**](https://doi.org/10.5067/EMIT/EMITL2BMIN.001) product is generated to support the EMIT mission objectives of constraining the sign of dust related radiative forcing. Ten mineral types are the core focus of this work: Calcite, Chlorite, Dolomite, Goethite, Gypsum, Hematite, Illite+Muscovite, Kaolinite, Montmorillonite, and Vermiculite. The [**EMIT_L3_ASA**]() product contain the aggregate abundance of these minerals at a coarser resolution for use in Earth System Models. Additional minerals are included in the **EMITL2BMIN** product for transparency but were not the focus of this product. Further validation is required to use these additional mineral maps, particularly in the case of resource exploration. Similarly, the separation of minerals with similar spectral features, such as a fine-grained goethite and hematite, is an area of active research. The results presented here are an initial offering, but the precise categorization is likely to evolve over time, and the limits of what can and cannot be separated on the global scale is still being explored. The user is encouraged to read the [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1659/EMITL2B_ATBD_v1.pdf) for more details.\\n\",\n \"\\n\",\n \"# Working with EMIT L2B Mineralogy Data\\n\",\n \"\\n\",\n \"**Summary** \\n\",\n \"\\n\",\n \"In this notebook we will open the [EMIT L2B Estimated Mineral Identification and Band Depth and Uncertainty (EMITL2BMIN)](https://doi.org/10.5067/EMIT/EMITL2BMIN.001) products, find a mineral of interest from the ten mineral types focused on by the EMIT mission, evaluate uncertainty, orthorectify the data, then create an output mask or vector file for the granule.\\n\",\n \"\\n\",\n \"**Background**\\n\",\n \"\\n\",\n \"The EMIT instrument is an imaging spectrometer that measures light in visible and infrared wavelengths. These measurements display unique spectral signatures that correspond to the composition on the Earth's surface. The EMIT mission focuses specifically on mapping the composition of minerals to better understand the effects of mineral dust throughout the Earth system and human populations now and in the future. More details about EMIT and its associated products can be found in the **README.md** and on the [EMIT website](https://earth.jpl.nasa.gov/emit/).\\n\",\n \"\\n\",\n \"The [EMITL2BMIN](https://doi.org/10.5067/EMIT/EMITL2BMIN.001) data product provides estimated mineral identification, band depths and uncertainty in a spatially raw, non-orthocorrected format. Two spectral groups, which correspond to different regions of the spectra, are identified independently and often co-occur are used to identify minerals. These estimates are generated using the [Tetracorder system](https://www.usgs.gov/publications/tetracorder-user-guide-version-44?_gl=1*1eoj33d*_ga*MTU3MTA3ODgxNS4xNjQ5MTg1MDgx*_ga_0YWDZEJ295*MTY4NjkyNTg0Mi40NC4xLjE2ODY5MjU4NzMuMC4wLjA.)([code](https://github.com/PSI-edu/spectroscopy-tetracorder)) and are based on [EMITL2ARFL](https://doi.org/10.5067/EMIT/EMITL2ARFL.001) reflectance values. The product also consists of an EMIT_L2B_MINUNCERT file, which provides band depth uncertainty estimates calculated using surface Reflectance Uncertainty values from the [EMITL2ARFL](https://doi.org/10.5067/EMIT/EMITL2ARFL.001) data product. The band depth uncertainties are presented as standard deviations, and the fit score for each mineral identification is also provided as the coefficient of determination (r2) of the match between the continuum normalized library reference and the continuum normalized observed spectrum. Associated metadata indicates the name and reference information for each identified mineral, and additional information about aggregating minerals into different categories, and the code used for product generation is available in the [emit-sds-l2b repository]().\\n\",\n \"\\n\",\n \"**Disclaimer**\\n\",\n \"\\n\",\n \"The [EMIT_L2B_MIN](https://doi.org/10.5067/EMIT/EMITL2BMIN.001) product is generated to support the EMIT mission objectives of constraining the sign of dust related radiative forcing. Ten mineral types are the core focus of this work: Calcite, Chlorite, Dolomite, Goethite, Gypsum, Hematite, Illite+Muscovite, Kaolinite, Montmorillonite, and Vermiculite. A future product will aggregate these results for use in Earth System Models. Additional minerals are included in this product for transparency but were not the focus of this product. Further validation is required to use these additional mineral maps, particularly in the case of resource exploration. Similarly, the separation of minerals with similar spectral features, such as a fine-grained goethite and hematite, is an area of active research. The results presented here are an initial offering, but the precise categorization is likely to evolve over time, and the limits of what can and cannot be separated on the global scale is still being explored. The user is encouraged to read the [Algorithm Theoretical Basis Document (ATBD)](https://lpdaac.usgs.gov/documents/1659/EMITL2B_ATBD_v1.pdf) for more details.\\n\",\n \"\\n\",\n \"**Requirements** \\n\",\n \" - Set up Python Environment - See **setup_instructions.md** in the `/setup/` folder \\n\",\n \"\\n\",\n \"**Learning Objectives** \\n\",\n \"- How to open an EMIT L2B `.nc` file as an `xarray.Dataset`\\n\",\n \"- Apply the Geometry Lookup Table (GLT) to orthorectify the image.\\n\",\n \"- Find minerals of interest within a granule\\n\",\n \"- Visualize Mineral Identification and Band depth\\n\",\n \"- Evaluate mineral uncertainty\\n\",\n \"- Calculate and Visualize mineral Abundance\\n\",\n \"\\n\",\n \"**Tutorial Outline** \\n\",\n \"\\n\",\n \"1. Setup \\n\",\n \"2. Working with Mineral Identification and Band Depth\\n\",\n \"3. Aggregating and Mineral Abundance\\n\",\n \"4. Exporting to Cloud-Optimized GeoTIFF (COG)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0b6c3019-765d-47b7-8536-5c24ff0288c4\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Setup\\n\",\n \"\\n\",\n \"Import the required Python libraries.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5370bd60-e09c-4ba2-82f7-09697360ff79\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"import earthaccess\\n\",\n \"import geopandas as gp\\n\",\n \"import os\\n\",\n \"import sys\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import xarray as xr\\n\",\n \"import hvplot.xarray\\n\",\n \"import holoviews as hv\\n\",\n \"import panel as pn\\n\",\n \"sys.path.append('../modules/')\\n\",\n \"import emit_tools as et\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7b3798c2\",\n \"metadata\": {},\n \"source\": [\n \"Login to your NASA Earthdata account and create a `.netrc` file using the `login` function from the `earthaccess` library. If you do not have an Earthdata Account, you can create one [here](https://urs.earthdata.nasa.gov/home). \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"909ddf57\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"earthaccess.login(persist=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b94d9b18\",\n \"metadata\": {},\n \"source\": [\n \"For this notebook we will download the files necessary using `earthaccess`. You can also access the data in place or stream it, but this can slow due to the file sizes. Provide a URL for an EMIT L2B Mineral Identification and Band Depth granule.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0b858790\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# List the browse images from the text file output of the previous notebook.\\n\",\n \"min_list = '../../data/results_urls.txt'\\n\",\n \"with open(min_list) as f:\\n\",\n \" min_urls = [line.rstrip('\\\\n') for line in f]\\n\",\n \"min_urls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"57a34e59\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# List the browse images from the text file output of the previous notebook.\\n\",\n \"unc_list = '../../data/min_uncert_urls.txt'\\n\",\n \"with open(unc_list) as f:\\n\",\n \" unc_urls = [line.rstrip('\\\\n') for line in f]\\n\",\n \"unc_urls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"84d63672\",\n \"metadata\": {},\n \"source\": [\n \"Get an HTTPS Session using your earthdata login, set a local path to save the file, and download the granule asset, in this case we are selecting the second scene (index 1) from the list of granules for both the mineral and uncertainty files.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"eb62adee\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"fs = earthaccess.get_fsspec_https_session()\\n\",\n \"fp = fs.open(min_urls[0])\\n\",\n \"fp_un = fs.open(unc_urls[0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d4e3769f\",\n \"metadata\": {},\n \"source\": [\n \"## 1.2 Downloaded Data\\n\",\n \"\\n\",\n \"If you’ve already downloaded the data using the workflow shown in Section 6 of the [Finding EMIT L2B Mineralogy Data](Finding_EMIT_L2B_Mineralogy_Data.ipynb) , you can just set filepaths using the cell below.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"14114c12-c896-43d9-9424-3d015b98a0b5\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"#fp = '../../data/EMIT_L2B_MIN_001_20230427T173309_2311711_010.nc' # Mineral\\n\",\n \"#fp_rgb = '../../data/EMIT_L2A_RFL_001_20230427T173309_2311711_010.png' # RGB\\n\",\n \"#fp_un = '../../data/EMIT_L2B_MINUNCERT_001_20230427T173309_2311711_010.nc' # Mineral Uncertainty\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9c28b8fb\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Working with the L2B Mineral Identification and Band Depth\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"39339147\",\n \"metadata\": {},\n \"source\": [\n \"EMITL2BMIN data are distributed in a non-orthocorrected spatially raw NetCDF4 (.nc) format consisting of the data and its associated metadata. Inside the `.nc` file there are 3 groups. Groups can be thought of as containers to organize the data. \\n\",\n \"\\n\",\n \"1. The root group that can be considered the main dataset contains 4 data variables data described by the downtrack, and crosstrack dimensions. These variables are `group_1_mineral_id`, `group_1_band_depth`, `group_2_mineral_id`, and `group_2_band_depth`. These contain the ID and a band depth for each mineral group. These groups do not correspond to the `.netcdf` file groups, but rather the spectral library groups used to identify the minerals based on which region of the spectra the mineral features correspond to.\\n\",\n \"2. The `mineral_metadata` group containing the spectral library entry name, index, record, group, and url for each entry.\\n\",\n \"3. The `location` group contains latitude and longitude values at the center of each pixel described by the crosstrack and downtrack dimensions, as well as a geometry lookup table (GLT) described by the ortho_x and ortho_y dimensions. The GLT is an orthorectified image (EPSG:4326) consisting of 2 layers containing downtrack and crosstrack indices. These index positions allow us to quickly project the raw data onto this geographic grid.\\n\",\n \"\\n\",\n \"To access the `.nc` file, you can use the `netCDF4`, `xarray` libraries, or fuctions from the `emit_tools.py` library. Here we will use the `emit_xarray` function from this library, which will open and organize the data into an easy to work with `xarray.Dataset` object. We can also pass the `ortho=True` argument to orthorectify the data at this stage, but we will start just examining the data to get a better understanding. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1e97803b-d221-45b0-b388-a851b5fee96a\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [],\n \"source\": [\n \"ds_min = et.emit_xarray(fp)\\n\",\n \"ds_min\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a54c4bd3\",\n \"metadata\": {},\n \"source\": [\n \"If we look at the mineral `index` by printing the first 5 values, we can see that values start with 1. If we look at the minimum values of the mineral IDs we can see these have 0 as a possible value. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6bdd201e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(ds_min.index.data[:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"768344b0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(f'Group_1_minimum:{ds_min.group_1_mineral_id.data.min()} Group_2_minimum: {ds_min.group_2_mineral_id.data.min()}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7c98dfb5\",\n \"metadata\": {},\n \"source\": [\n \"The 0 here represents no match. For convenience, let's make a DataFrame that holds the mineral data, and add that 'No match' reference to it:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"88e8e8bc-1eb6-4b8c-96ee-2c043b8ce9cc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"min_df = pd.DataFrame({x: ds_min[x].values for x in [var for var in ds_min.coords if 'mineral_name' in ds_min[var].dims]})\\n\",\n \"min_df.loc[-1] = {'index': 0, 'mineral_name': 'No_Match', 'record': -1.0, 'url': 'NA', 'group': 1.0, 'library': 'NA', 'spatial_ref': 0}\\n\",\n \"min_df = min_df.sort_index().reset_index(drop=True)\\n\",\n \"min_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e56c228d\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Orthorectification\\n\",\n \"\\n\",\n \"The orthorectifation process has already been done for EMIT data. Here we are just using the crosstrack and downtrack indices contained in the GLT to place our spatially raw mineralogy data a into geographic grid with the `ortho_x` and `ortho_y` dimensions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c444096a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_min = et.ortho_xr(ds_min)\\n\",\n \"ds_min\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fe4aa0c5\",\n \"metadata\": {},\n \"source\": [\n \"We can see from these outputs that the dimensions are now latitude and longitude.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a44790aa\",\n \"metadata\": {},\n \"source\": [\n \"In this example, we'll just work with the group_1 mineral data. We can find the minerals present in the scene by finding unique values in the `group_1_mineral_id`, but first we will replace fill-values introduced during orthorectification with `np.nan`, to omit them from our analysis and improve visualizations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2d2679d1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Assign fill to np.nan\\n\",\n \"for var in ds_min.data_vars:\\n\",\n \" ds_min[var].data[ds_min[var].data == -9999] = np.nan\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3565883f-0d9c-4556-a81f-0adefb971518\",\n \"metadata\": {},\n \"source\": [\n \"## 2.2 Visualize Group 1 Minerals\\n\",\n \"\\n\",\n \"To visualize minerals present, plot Group 1 Minerals using a categorical color set. You can hover over a colored region to see the zero-based mineral id from the spectral library. Note that these values correspond to the 1-based index value.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5debcfaf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_min['group_1_mineral_id'].hvplot.image(cmap='glasbey', geo=True, tiles='ESRI', alpha=0.8,frame_width=750).opts(title='Group 1 Mineral ID')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fafa07ec\",\n \"metadata\": {},\n \"source\": [\n \"This figure shows the minerals present in the scene, but doesn't really quantify how well they matched with the spectral library. For that we can look at the band depth for each mineral. We can build an interactive tool to do this using the `panel` and `hvplot` libraries. This will take a bit of time to load for each selection.\\n\",\n \"\\n\",\n \"Because many minerals are scarce, we'll start by updating the names to include relative fractions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e490efeb-0e98-4a95-8ed4-9167ee5029c3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"g1_min_percent = [np.round(np.sum(ds_min.group_1_mineral_id.data.flatten() == g1min) / np.sum(ds_min.group_1_mineral_id.data.flatten() > 0),2) * 100 for g1min in range(len(min_df))]\\n\",\n \"g1_dropdown_names = [str(g1_min_percent[_x]) + ' %: ' + x for (_x, x) in enumerate(min_df.mineral_name.tolist()) if g1_min_percent[_x] > 0 and x != 'No_Match']\\n\",\n \"g1_dropdown_names = np.array(g1_dropdown_names)[np.argsort([float(x.split(' %:')[0]) for x in g1_dropdown_names])[::-1]].tolist()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b5965751\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Interactive Panel Control For Mineral Band Depth\\n\",\n \"min_select = pn.widgets.Select(name='Mineral Name', options = g1_dropdown_names, value = g1_dropdown_names[0])\\n\",\n \"@pn.depends(min_select)\\n\",\n \"def min_browse(min_select):\\n\",\n \" mask = ds_min['group_1_band_depth'].where(ds_min['group_1_mineral_id'] == min_df['mineral_name'].tolist().index(min_select.split('%: ')[-1]))\\n\",\n \" map = mask.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', alpha=0.8,frame_width=450, clim=(0,np.nanpercentile(mask,98))).opts(title=f'{min_select} Band Depth')\\n\",\n \" return map\\n\",\n \"pn.Row(pn.WidgetBox(min_select),min_browse)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e8320cb8-51b2-4f0a-b549-578b0e975dbd\",\n \"metadata\": {},\n \"source\": [\n \"## 2.3 Visualize Group 2 Minerals\\n\",\n \"\\n\",\n \"We can do the same thing with Group 2 Minerals. Group 2 will show a more diverse set of minerals in this region, including clays and carbonates.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6a9862e8-376d-48c8-8af3-6c7a7941326d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_min['group_2_mineral_id'].hvplot.image(cmap='glasbey', geo=True, tiles='ESRI', alpha=0.8,frame_width=750).opts(title='Group 2 Mineral ID')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b8d3110a-5cd8-474c-99c0-70e9e7ca4021\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"g2_min_percent = [np.round(np.sum(ds_min.group_2_mineral_id.data.flatten() == g1min) / np.sum(ds_min.group_2_mineral_id.data.flatten() > 0),2) * 100 for g1min in range(len(min_df))]\\n\",\n \"g2_dropdown_names = [str(g2_min_percent[_x]) + ' %: ' + x for (_x, x) in enumerate(min_df.mineral_name.tolist()) if g2_min_percent[_x] > 0 and x != 'No_Match']\\n\",\n \"g2_dropdown_names = np.array(g2_dropdown_names)[np.argsort([float(x.split(' %:')[0]) for x in g2_dropdown_names])[::-1]].tolist()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"284282c0-3be6-4a7b-b054-836d3d83ae69\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Interactive Panel Control For Mineral Band Depth\\n\",\n \"min_select_g2 = pn.widgets.Select(name='Mineral Name', options = g2_dropdown_names, value = g2_dropdown_names[0])\\n\",\n \"@pn.depends(min_select_g2)\\n\",\n \"def min_browse_g2(min_select):\\n\",\n \" # print(min_select)\\n\",\n \" mask = ds_min['group_2_band_depth'].where(ds_min['group_2_mineral_id'] == min_df['mineral_name'].tolist().index(min_select.split('%: ')[-1]))\\n\",\n \" map = mask.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', alpha=0.8,frame_width=450, clim=(0,np.nanpercentile(mask,98))).opts(title=f'{min_select} Band Depth')\\n\",\n \" return map\\n\",\n \"pn.Row(pn.WidgetBox(min_select_g2),min_browse_g2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0e5825a2-7865-402f-8e33-cf45a05490fe\",\n \"metadata\": {},\n \"source\": [\n \"## 2.4 Visualizing and filtering with uncertainties\\n\",\n \"\\n\",\n \"Often just as important as visualizing the core mineral detections is displaying the corresponding uncertainties. The EMIT L2B MIN proudct comes with two different uncertainties (repeated for each group):\\n\",\n \"\\n\",\n \"1. Fit - this is how good of a fit the individual mineral detection was, as estimated by the correlation coefficient of the alignment between the continuum removed library reference and the continuum removed observed spectra (after scaling).\\n\",\n \"2. Band depth uncertainty - this is the propogation of the reflectance uncertainty through the band depth calculation.\\n\",\n \"\\n\",\n \"Let's take a look at each of these by loading the relevant datasets:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0a659aac-1e9a-45ae-80e8-42d97f90633b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Open an Orthorectify the Uncertainty Data\\n\",\n \"ds_min_unc = et.emit_xarray(fp_un)\\n\",\n \"ds_min_unc = et.ortho_xr(ds_min_unc)\\n\",\n \"ds_min_unc\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"30c87313-8810-4a29-9358-00e6187d41ce\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"min_select_fit_g2 = pn.widgets.Select(name='Mineral Name', options = g1_dropdown_names, value = g1_dropdown_names[0])\\n\",\n \"@pn.depends(min_select_fit_g2)\\n\",\n \"def min_browse_fit_g2(min_select):\\n\",\n \" mask = ds_min_unc['group_1_fit'].where(ds_min['group_1_mineral_id'] == min_df['mineral_name'].tolist().index(min_select.split('%: ')[-1]))\\n\",\n \" # The fits are scaled down by a factor of two in the current EMIT L2B products, so scale them back up:\\n\",\n \" mask *= 2\\n\",\n \" map = mask.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', alpha=0.8,frame_width=450, clim=(0,np.nanpercentile(mask,98))).opts(title=f'{min_select} Fit')\\n\",\n \" return map\\n\",\n \"pn.Row(pn.WidgetBox(min_select_fit_g2),min_browse_fit_g2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7cd9f2fa\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Aggregating and Mineral Abundance\\n\",\n \"\\n\",\n \"The above visualizations walk through the identification of individual library contituents, and visualize band depths. However, the Tetracorder library used by EMIT contains many substrates that are spectrally distinct, but which may be useful to group together for some science applications. The library also contains many mixtures - both aerial and intimate.\\n\",\n \"\\n\",\n \"To start, the mineral_grouping_matrix from the emit-sds-l2b repository (coppied locally) contains information aggregated from laboratory XRD analyses to attempt to quantify the abundance of different minerals within each constituent. A -1 in the spreadsheet indicates an unknown but non-zero quantity, which in the few cases in the EMIT-10 columns we assume to be 100%. Let's open that spreadsheet and take a look:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9eea13cf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Open Mineral Groupings .csv\\n\",\n \"mineral_groupings = pd.read_csv('../../data/mineral_grouping_matrix_20230503.csv')\\n\",\n \"# The EMIT 10 Minerals are in columns 6 - 17. Columns after 17 are experimental, and we'll drop for this tutorial:\\n\",\n \"mineral_groupings = mineral_groupings.drop([x for _x, x in enumerate(mineral_groupings) if _x >= 17], axis=1)\\n\",\n \"\\n\",\n \"# Retrieve the EMIT 10 Mineral Names from Columns 7-16 (starting with 0) in .csv\\n\",\n \"mineral_names = [x for _x, x in enumerate(list(mineral_groupings)) if _x > 6 and _x < 17]\\n\",\n \"# Use EMIT 10 Mineral Names to Subset .csv to only columns with EMIT 10 mineral_names\\n\",\n \"mineral_abundance_ref = np.array(mineral_groupings[mineral_names])\\n\",\n \"# Replace Some values in the .csv\\n\",\n \"mineral_abundance_ref[np.isnan(mineral_abundance_ref)] = 0\\n\",\n \"mineral_abundance_ref[mineral_abundance_ref == -1] = 1\\n\",\n \"\\n\",\n \"mineral_groupings\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a560c2ea-26f0-4a6c-9673-3ea65feab1eb\",\n \"metadata\": {},\n \"source\": [\n \"# 3.1 Approximating Mineral Spectral Abundance\\n\",\n \"\\n\",\n \"If we make the assumption that the XRD analysis are accurate, and that band-depth scales linearly with abundance, we can approximate the mineral abundance at the surface. It should be noted that both of these assumptions are fraught - XRD analyses break down for some very small grainsize particles, particularly the Iron Oxides (Goethite and Hematite), and band-depth is heavily influenced by particle grain size, and so the abundance-band depth relationship is not linear. We will expand on these details a bit more later, but for now lets take a look at what happens if we hold both of these as true.\\n\",\n \"\\n\",\n \"The first step is to run through each mineral that has a non-nan value in the mineral_groupings dataframe, and add up the sum of each of those within the scene. A little matrix multiplication is all we need to do that. Notably, in the emit-sds/emit-sds-l2b, this functionality is already built into the group_aggregator.py script in an efficient manner...but because the calculation is simple we'll reproduce here for learning purposes.\\n\",\n \"\\n\",\n \"Below, we step through each mineral one at a time, and multiply the band depth by the XRD value for each constintuent in the scene, storing it in a (y,x,band) numpy array, which we can cast into x-array for visualization down the line.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b830cda5-ddef-40c3-a1ca-a4dcd310c3d6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mineral_abundance = np.zeros((ds_min['group_1_band_depth'].shape[0], ds_min['group_1_band_depth'].shape[1], len(mineral_names)))\\n\",\n \"for _m, mineral_name in enumerate(mineral_names):\\n\",\n \" print(f'Calculating {mineral_name}')\\n\",\n \" for _c in range(mineral_groupings.shape[0]):\\n\",\n \" if np.isnan(mineral_groupings[mineral_name][_c]) == False: \\n\",\n \" group = mineral_groupings[\\\"Group\\\"][_c]\\n\",\n \" mineral_abundance[...,_m] += (ds_min[f'group_{group}_mineral_id'].values == mineral_groupings['Index'][_c]) * ds_min[f'group_{group}_band_depth'].values * mineral_abundance_ref[_c][_m]\\n\",\n \" \\n\",\n \" \\n\",\n \"mineral_abundance[np.isnan(mineral_abundance)] = 0\\n\",\n \"mineral_abundance[np.all(mineral_abundance == 0, axis=-1),:] = np.nan\\n\",\n \"\\n\",\n \"# Cast as x-array for consistent visualization\\n\",\n \"mineral_abundance_xarray = xr.merge([xr.DataArray(mineral_abundance[...,_x],\\n\",\n \" name=mineral_names[_x],\\n\",\n \" coords=ds_min['group_1_band_depth'].coords,\\n\",\n \" attrs=ds_min.attrs,) \\n\",\n \" for _x in range(len(mineral_names))])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7445a29b-fcae-4c57-a505-0336f0286590\",\n \"metadata\": {},\n \"source\": [\n \"After which we can show them as a widget:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6ef3cf63-4fa1-4ac9-a43c-cd023769b8ac\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"min_abun_wid = pn.widgets.Select(name='Mineral Abundance', options = mineral_names, value = mineral_names[0])\\n\",\n \"@pn.depends(min_abun_wid)\\n\",\n \"def min_abun_browse(mineral_name):\\n\",\n \" map = mineral_abundance_xarray[mineral_name].hvplot.image(cmap='viridis', geo=True, tiles='ESRI', alpha=0.8,frame_width=450, \\n\",\n \" clim=(0,np.nanpercentile(mineral_abundance_xarray[mineral_name],98))).opts(\\\\\\n\",\n \" title=f'{mineral_name} Spectral Abundance')\\n\",\n \"\\n\",\n \" return map\\n\",\n \"pn.Row(pn.WidgetBox(min_abun_wid),min_abun_browse)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"326609dd-b759-4e73-b00a-32f082ba2e3e\",\n \"metadata\": {},\n \"source\": [\n \"We can also take a look at the histogram distributions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"27283418-47fb-423e-b848-fda2754d0506\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"max_bin_max = np.max([np.nanpercentile(mineral_abundance_xarray[x].values,98) for x in mineral_names])\\n\",\n \"mineral_abundance_xarray.hvplot.hist(y=mineral_names, cmap='glasbey', bins=50, alpha=0.5, legend='left', bin_range=(0.001, max_bin_max), \\n\",\n \" xlabel='Spectral Abundance', ylabel='Pixel Count', title='',frame_width=650)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8904ef13-b867-4723-a289-774c318ffa73\",\n \"metadata\": {},\n \"source\": [\n \"Notably, these abundance fractions do not sum to one. This can either be due to copmlex mixtures that aren't fully captured, or due to the presence of additional materials that do not directly absorb in the VSWIR - most namely quartz and feldspar, but also including organic soil matter, as well as NPV and PV. To illustrate this, let's examine the totals.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fc40f552-8830-4e50-b55c-83b994fb5a4c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"total_mineral_abundance = xr.DataArray(np.nansum(mineral_abundance,axis=-1), \\n\",\n \" name='Total Abundance', \\n\",\n \" coords=ds_min['group_1_band_depth'].coords, \\n\",\n \" attrs=ds_min.attrs,)\\n\",\n \"total_mineral_abundance.hvplot.hist(y=['Total Abundance'], cmap='glasbey', bins=50, alpha=0.5, legend='left', \\n\",\n \" bin_range=(0.001, np.nanpercentile(total_mineral_abundance.values, 99.9)), \\n\",\n \" xlabel='Spectral Abundance', ylabel='Pixel Count', title='',frame_width=650)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0fb01dad\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Exporting To Cloud-Optimized Geotiffs\\n\",\n \"\\n\",\n \"We can select layers from any of the xarray datasets we've created and export them to a Cloud-Optimized Geotiff (COG) using the `rio.to_raster` function from the `rasterio` library. This will allow us to share the data with others or use it in other GIS software.\\n\",\n \"\\n\",\n \"First create an output folder.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"677f044d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Create Out filenames and set folder\\n\",\n \"out_folder = '../../data/output/' # may need to change based on your directory structure\\n\",\n \"# Create out_folder if it does not exist\\n\",\n \"if not os.path.exists(out_folder):\\n\",\n \" os.makedirs(out_folder)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3e201ddd\",\n \"metadata\": {},\n \"source\": [\n \"### 4.1 Mineral ID\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9a83fd5b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set output Filename\\n\",\n \"out_name = f'{ds_min.granule_id}_group_1_mineral_id.tif'\\n\",\n \"# Select Group to Output\\n\",\n \"dat_out = ds_min['group_1_mineral_id']\\n\",\n \"# Replace nan with a fill value\\n\",\n \"dat_out.data = np.nan_to_num(dat_out.data, nan=-9999)\\n\",\n \"# Change datatype for integers/categorical\\n\",\n \"dat_out.data = dat_out.data.astype(int)\\n\",\n \"# Encode nodata value\\n\",\n \"dat_out.rio.write_nodata(-9999, encoded=True, inplace=True)\\n\",\n \"# Write data to COG\\n\",\n \"dat_out.rio.to_raster(raster_path=f'{out_folder}{out_name}', driver='COG')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b8499033\",\n \"metadata\": {},\n \"source\": [\n \"### 4.2 Band Depth\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7f32807f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set output Filename\\n\",\n \"out_name = f'{ds_min.granule_id}_group_1_band_depth.tif'\\n\",\n \"# Select Group to Output\\n\",\n \"dat_out = ds_min['group_1_band_depth']\\n\",\n \"# Replace nan with a fill value\\n\",\n \"dat_out.data = np.nan_to_num(dat_out.data, nan=-9999)\\n\",\n \"# Encode nodata value\\n\",\n \"dat_out.rio.write_nodata(-9999, encoded=True, inplace=True)\\n\",\n \"# Write data to COG\\n\",\n \"dat_out.rio.to_raster(raster_path=f'{out_folder}{out_name}', driver='COG')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f3cbdb50\",\n \"metadata\": {},\n \"source\": [\n \"### 4.3 Abundance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f62e0222\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Set output Filename\\n\",\n \"out_name = f'{mineral_abundance_xarray.granule_id}_Calcite.tif'\\n\",\n \"# Select Layer\\n\",\n \"dat_out = mineral_abundance_xarray['Calcite']\\n\",\n \"# Replace nan with a fill value\\n\",\n \"dat_out.data = np.nan_to_num(dat_out.data, nan=-9999)\\n\",\n \"# Encode nodata value\\n\",\n \"dat_out.rio.write_nodata(-9999, encoded=True, inplace=True)\\n\",\n \"# Write data to COG\\n\",\n \"dat_out.rio.to_raster(raster_path=f'{out_folder}{out_name}', driver='COG')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9996cfae\",\n \"metadata\": {},\n \"source\": [\n \"## Contact Info: \\n\",\n \"\\n\",\n \"Email: LPDAAC@usgs.gov \\n\",\n \"Voice: +1-866-573-3222 \\n\",\n \"Organization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \\n\",\n \"Website: \\n\",\n \"Date last modified: 07-07-2024 \\n\",\n \"\\n\",\n \"¹Work performed under USGS contract 140G0121D0001 for NASA contract NNG14HH33I. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.10.14\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "setup/prerequisites.md", "content": "# Prerequisites\n\nTo follow along during the workshop, or to run through the notebooks contained within the repository using the Openscapes 2i2c Cloud JupyterHub (cloud workspace), the following are required. All software or accounts are free.\n\n1. **Earthdata Login account** \n - Create an Earthdata Login account (if you don't already have one) at \n - Remember your username and password; you will need them to download or access data during the workshop and beyond.\n2. **GitHub username**\n - Create a GitHub account (if you don’t already have one) at . Follow optional [advice on choosing your username](https://happygitwithr.com/github-acct.html)\n - Your GitHub username is used to enable you access to a cloud environment during the workshop. To gain access, please request access to the NASA Openscapes JupyterHub using [this form](https://forms.gle/Su2p7BQhwnf9Fufb8). You will receive an email invitation to join the organization on GitHub. You must join to gain access to the workspace. \n3. **Netrc file**\n - This file is needed to access NASA Earthdata assets from a scripting environment like Python.\n - There are multiple methods to create a .netrc file. For this workshop, `earthaccess` package is used to automatically create a netrc file using your Earthdata login credentials if one does not exist. There are detailed instruction available for creating a .netrc file using other methods [here](https://nasa-openscapes.github.io/2022-Fall-ECOSTRESS-Cloud-Workshop/how-tos/authentication/NASA_Earthdata_Authentication.html).\n4. **Laptop or tablet**\n - Participation in the exercises requires a laptop or tablet. Yes, a tablet works too! All workshop participants will have access to a 2i2c Jupyter Lab instance running in AWS us-west 2. \n" }, { "path": "setup/setup_instructions.md", "content": "# Repository Setup Instructions\n\nThe how-tos and tutorials in this repository require a [NASA Earthdata account](https://urs.earthdata.nasa.gov/), an installation of [Git](https://git-scm.com/downloads), and a compatible Python Environment. Resources in the EMIT-Data-Resources repository have been developed using the Openscapes 2i2c JupyterHub cloud workspace.\n\nFor local Python environment setup we recommend using [mamba](https://mamba.readthedocs.io/en/latest/) to manage Python packages. To install *mamba*, download [miniforge](https://github.com/conda-forge/miniforge) for your operating system. If using Windows, be sure to check the box to \"Add mamba to my PATH environment variable\" to enable use of mamba directly from your command line interface. **Note that this may cause an issue if you have an existing mamba install through Anaconda.** \n\n## Python Environment Setup\n\nThese Python Environments will work for all of the guides, how-to's, and tutorials within this repository, and the [VITALS repository](https://github.com/nasa/VITALS).\n\n1. Using your preferred command line interface (command prompt, terminal, cmder, etc.) navigate to your local copy of the repository, then type the following to create a compatible Python environment.\n\n For Windows:\n\n ```cmd\n mamba create -n lpdaac_vitals -c conda-forge --yes python=3.10 fiona=1.8.22 gdal hvplot geoviews rioxarray rasterio jupyter geopandas earthaccess jupyter_bokeh h5py h5netcdf spectral scikit-image jupyterlab seaborn dask ray-default\n ```\n\n For MacOSX*:\n\n ```cmd\n mamba create -n lpdaac_vitals -c conda-forge --yes python=3.10 gdal=3.7.2 hvplot geoviews rioxarray rasterio geopandas fiona=1.9.4 jupyter earthaccess jupyter_bokeh h5py h5netcdf spectral scikit-image seaborn jupyterlab dask\n ```\n\n >***MacOSX users will need to install \"ray[default]\" separately using pip after creating and activating the environment.** \n\n2. Next, activate the Python Environment that you just created.\n\n ```cmd\n mamba activate lpdaac_vitals \n ```\n\n **After activating the environment if using MacOSX, install the \"ray[default]\" package using pip:**\n\n ```cmd\n pip install ray[default]\n ```\n\n3. Now you can launch Jupyter Notebook to open the notebooks included.\n\n ```cmd\n jupyter notebook \n ```\n\n**Still having trouble getting a compatible Python environment set up? Contact [LP DAAC User Services](https://lpdaac.usgs.gov/lpdaac-contact-us/).** \n\n---\n\n## Contact Info \n\nEmail: \nVoice: +1-866-573-3222 \nOrganization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \nWebsite: \nDate last modified: 06-24-2024 \n\n¹Work performed under USGS contract 140G0121D0001 for NASA contract NNG14HH33I. \n" }, { "path": "setup/workshop_setup.md", "content": "# Cloud Workspace Setup\n\n> If you plan to use this repository with the **Openscapes 2i2c JupyterHub Cloud Workspace** there are no additional setup requirements for the Python environment. All packages needed are included unless specified within a notebook, in which case a cell will be dedicated to installing the necessary Python libraries using the appropriate package manager.\n\nAfter completing the [prerequisites](prerequisites.md) you will have access to the Openscapes 2i2c JupyterHub cloud workspace. [Click here to start JupyterLab](https://workshop.openscapes.2i2c.cloud/). Use your email and the provided password to sign in. This password will be provided in the workshop. If you're interested in using the 2i2c cloud workspace outside of the workshop, please [contact us](#contact-info).\n\nAfter signing in you will be prompted for some server options:\n\n

\n \n

\n\nBe sure to select the radio button for **Python** and a size of **14.8 GB RAM and up to 3.75 CPUs**.\n\nAt this point you can use the terminal to clone the repository.\n\n## Cloning the EMIT-Data-Resources Repository\n\nIf you plan to edit or contribute to the EMIT-Data-Resources repository, we recommend following a fork and pull workflow: first fork the repository, then clone your fork to your local machine, make changes, push changes to your fork, then make a pull request back to the main repository. An example can be found in the [CONTRIBUTING.md](../CONTRIBUTING.md) file.\n\nIf you just want to work with the notebooks or modules, you can simply clone the repository.\n\nTo clone the repository, navigate to the directory where you want to store the repository on your local machine, then type the following:\n\n```cmd\ngit clone https://github.com/nasa/EMIT-Data-Resources.git\n```\n\n## Troubleshooting\n\nWe recommend *Shutting down all kernels* after running each notebook. This will clear the memory used by the previous notebook, and is necessary to run some of the more memory intensive notebooks.\n\n

\n \n

\n\nNo single notebook exceeds roughly the limit using the provided data, but **if you choose to use your own data in the notebook, or have 2 notebooks open and do not shut down the kernel, you may get an out of memory error.** \n\nIf you elect to try this on your own data/ROI, you may need to select a larger server size. This will often happen if you are using the last EMIT scene from an orbit. In some cases those can be almost double the size of a normal scene. Please select the smallest possible.\n\n---\n\n## Contact Info \n\nEmail: \nVoice: +1-866-573-3222 \nOrganization: Land Processes Distributed Active Archive Center (LP DAAC)¹ \nWebsite: \nDate last modified: 05-24-2024 \n\n¹Work performed under USGS contract 140G0121D0001 for NASA contract NNG14HH33I.\n" } ]