Using NASA EarthData Cloud & Python to Model Climate Risks
summary
Hands-on pipeline that actually logs into NASA EarthData Cloud (netrc credentials), queries the STAC catalog, and stacks remote granules to compute area affected by events. Uses Xarray, Rasterio, Hvplot and pystac/RioXarray to filter by cloud cover, select bands, and count pixels (30 m pixels, 3660×3660 tiles, 12‑day revisit cadence, 255 as missing). Example run pulls ~828 granules for a region, resamples dates, and visualizes wildfire spread after downloading and stacking assets.
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chapters (8)
- 0:00Introduction
- 4:48NASA EarthData Cloud project
- 7:17Climate data analysis tools
- 27:20Data visualization with HoloViz
- 48:30NASA OPERA data products
- 57:11Land surface disturbance data
- 1:04:24Pystac API for data discovery
- 1:17:13Case study and conclusion
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