Satellite data processing can be difficult.

This collection of Jupyter notebooks is meant to help you get started accessing, visualizing, and analyzing OB.DAAC data products with Python. You can learn from these notebooks either by viewing the code and results on this webpage or by downloading the notebook files and running them with JupyterLab. If you plan to run any of these notebooks, please continue reading for information about the Earthdata Cloud and environments in Python.

Python Environments

The notebooks import Python packages that must be installed and discoverable on the host. Please use our environment.yml file to create a Conda environment that satisfies all the dependencies, or otherwise ensure your environment satisifes these dependencies. Note that the environment includes the ipykernel package in case your JupyterLab includes nb_conda_kernels or you want to manually make the environment available to JupyterLab as a kernel.

The Earthdata Cloud icon next to some notebooks indicates they are meant to be run using Amazon Web Services (AWS), which is the cloud provider used for NASA Earthdata Cloud. If you are not set up on AWS, you can still use those notebooks, but will need to download data as descibed below in the Data Access notebook. If you are new to using NASA Earthdata Cloud, the Cloud Cookbook provides a lot of background and resources that are constantly being improved by the NASA-Openscapes community.

Can I use an existing AWS platform?

You may already have access to the AWS platform through your institution or research community. For example, JupyterHubs maintained by Openscapes, Cryo in the Cloud, MAAP, and NASA Goddard's Open Science Studio are running on AWS in the same "us-west-2" region that hosts the NASA Earthdata Cloud. If that is not the case, you may want to learn about getting started with AWS for NASA Earthdata Cloud.