Using NASA Earth data with Python APIs

Course Description

The session follows on directly from the Python - Numpy & Pandas course and provides hands-on experience accessing & using NASA Earthdata Cloud i.e., freely available satellite data—through Pythonic APIs. We’ll examine environmental risk scenarios (e.g., floods, wildfires) over various defined geographical regions of interest using cloud-based infrastructure and data. The main goals are:

• to highlight strategies for quantitative analysis through “data-proximate computing” (i.e., using cloud-compute resources with distributed data);
• to build a robust foundation for generic cloud-based Jupyer/Python workflows using cloud-based infrastructure and NASA’s publicly available Earth data; and
• to empower participants to adapt and remix examples for their own region-specific contexts.

This session builds on the use of Python/NumPy/Pandas from the first, providing a quick, non-comprehensive overview of using Xarray, Rasterio, Hvplot, & Geoviews for manipulating and visualizing geospatial data. This work has been developed as part of NASA’s Open Science and Transform to Open Science (TOPS) initiatives.