:::{figure} assets/3_PAR_SARImaging.gif :alt: NISAR Imaging Animation
NISAR Imaging Animation: https://assets.science.nasa.gov/content/dam/science/missions/nisar/nisar-jpl/multimedia/3_PAR_SARImaging.mp4 :::
:::{warning} This Cookbook is under active development
The workflows in this Cookbook were initially created using proxy NISAR test data, available here: https://science.nasa.gov/mission/nisar/sample-data/
Some workflows have been updated to use newly available production NISAR data, others have not. Those still needing work have been temporarily removed from the table of contents but remain in the repository.
Upcoming cookbook goals include:
- more GCOV backscatter notebooks
- GCOV PolSAR notebooks
- GSLC notebooks (custom InSAR generation)
- GUNW notebooks
We welcome community contributions! :::
NISAR (NASA-ISRO Synthetic Aperture Radar) is a satellite mission designed to observe Earth's dynamic processes using microwave remote sensing. It's L-band and S-band measurements provide consistent global observations, regardless of cloud cover or daylight.
After learning to access and visualize NISAR L-band data in this cookbook, you will be ready to begin applying NISAR imagery to a wide range of Earth science applications, including ecosystem monitoring, hydrology, cryosphere studies, natural hazards, and surface change analysis.
The NISAR mission will provide one of the most comprehensive global radar datasets ever collected, offering new opportunities to study the dynamic Earth. This cookbook is designed to help a wide range of users, including students, researchers, and communities, to begin working with NISAR’s multiple products.
By introducing clear, hands-on examples, we hope to make the mission’s powerful data resources simple, approachable and usable. Our goal is to help you build the skills needed to explore NISAR data with confidence and apply NISAR’s capabilities to your own scientific, environmental, or community-focused questions.
Lewandowski, Alex. White, Julia.
(contribute)=
This project is open source and open development. See the contributor's guide to learn how to join the project and make your first contribution.
Gain access to the data and set up required software environments for the included workflows.
Learn how to search, access, and work with GCOV data.
Learn how to search and download GCOV data.
Learn how to load, work with, and create visualizations with GCOV data.
Notebooks focusing on NISAR GCOV backscatter.
Links to additional NISAR and NISAR GCOV resources.
We welcome contributions! Learn how to raise issues, make updates, and add content to this cookbook.
You can run the notebooks on a JupyterHub such as OpenSARLab or on your local machine.
-
Sign up for an OpenScienceLab account and request access to OpenSARLab.
-
Navigate to
~/Data_Recipe_Jupyter_Books/NISAR_Cookbookto explore and execute the notebooks.
If you are interested in running this material locally on your computer, you will need to follow this workflow:
-
Install
Pixiusing one of the following options if not already installed:- macOS / Linux:
curl -fsSL https://pixi.sh/install.sh | bash- Windows (PowerShell):
iwr -useb https://pixi.sh/install.ps1 | iex
- Download the pixi installer
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Restart your terminal after installing
Pixiso it is on your PATH. -
Clone the
https://github.com/ASFOpenSARlab/NISAR_Cookbookrepository:git clone https://github.com/ASFOpenSARlab/NISAR_Cookbook.git
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Move into the
NISAR_Cookbookdirectorycd NISAR_Cookbook -
Install the
Pixienvironment needed to execute the notebooks and launch JupyterLab with a single command:pixi run -e isce3 lab:::{tip} Navigate the NISAR Cookbook from its table of contents
Click the "JB" tab on the left-hand sidebar if you wish to navigate the NISAR Cookbook using a table of contents instead of the file browser.
This feature is provided by the jb-toc JupyterLab extension. :::