Dancing with the SARs

Intermediate, Advanced. Earth Science.

Use radar data from the NASA-ISRO NISAR mission to track and visualize surface change, such as wetland loss, wildfire scars, earthquakes, farming or glacier motion, somewhere on Earth.

Read the official challenge on spaceappschallenge.org

NISAR products in CMR are labeled provisional or beta; Sentinel-1 OPERA RTC is the validated fallback.

Your first hour

Create an Earthdata Login and run the NISAR GCOV starter for your area of interest.
Preview coverage in the GIBS NISAR layer or ASF Search before downloading any large HDF5 files.
Pick one change story (flooding, burn scar, crop cycle, ground motion) and gather before and after scenes.

Suggested datasets

In order of how useful they are likely to be. These are suggestions, not the official resource list.

NISAR L2 Geocoded Polarimetric Covariance (GCOV), Provisional

NASA ASF DAAC

Works in browser

Map-projected L-band radar backscatter from the NASA-ISRO NISAR mission, the simplest NISAR product for spotting surface change such as flooding, crop growth, forest loss or burn scars. Released as a provisional (pre-validation) product, so treat values with care; a beta version and urgent-response products also exist in CMR.

CMR short name NISAR_L2_GCOV_PROVISIONAL_V1, concept ID C2854338529-ASF, GIBS layer NISAR_L2_Geocoded_Polarimetric_Covariance

NISAR L2 Geocoded Unwrapped Interferogram (GUNW), Provisional

NASA ASF DAAC

Works in browser

Interferograms built from pairs of NISAR radar passes, showing centimeter-scale ground motion from earthquakes, subsidence, landslides or glacier flow. Provisional product; expect large HDF5 files and some InSAR background before using it.

CMR short name NISAR_L2_GUNW_PROVISIONAL_V1, concept ID C2854335566-ASF

OPERA Radiometric Terrain Corrected Backscatter from Sentinel-1 (RTC-S1)

NASA ASF DAAC / JPL OPERA

Works in browser

Analysis-ready C-band radar backscatter from Sentinel-1 as Cloud Optimized GeoTIFFs, validated and much easier to open than raw SAR. A good fallback or comparison layer for NISAR, with a longer archive.

CMR short name OPERA_L2_RTC-S1_V1, concept ID C2777436413-ASF, GIBS layer OPERA_L2_Radiometric_Terrain_Corrected_SAR_Sentinel-1

OPERA Dynamic Surface Water Extent from HLS (DSWx-HLS)

NASA PO.DAAC / JPL OPERA

Works in browser

30 m maps of open and partial surface water derived from Landsat and Sentinel-2, useful for tracking wetland loss, flooding and reservoir change, and for ground-truthing radar water maps.

CMR short name OPERA_L3_DSWX-HLS_V1, concept ID C2617126679-POCLOUD, GIBS layer OPERA_L3_Dynamic_Surface_Water_Extent-HLS

OPERA Land Surface Disturbance Alert from HLS (DIST-ALERT)

NASA LP DAAC / JPL OPERA

Works in browser

30 m alerts flagging where vegetation cover has dropped compared with a historical baseline, which picks up wildfire scars, clearing and storm damage. Pairs well with radar change maps for validation.

CMR short name OPERA_L3_DIST-ALERT-HLS_V1, concept ID C2746980408-LPCLOUD, GIBS layer OPERA_L3_DIST-ALERT-HLS_Color_Index

Starter code

Search NISAR GCOV scenesPython

import earthaccess

# Search needs no login. Bounding box: (west, south, east, north)
results = earthaccess.search_data(
    short_name="NISAR_L2_GCOV_PROVISIONAL_V1",
    bounding_box=(-122.6, 37.0, -121.5, 38.2),  # San Francisco Bay
    temporal=("2025-10-01", "2026-10-04"),
    count=5,
)
print(len(results), "granules")
for g in results:
    print(g["umm"]["GranuleUR"])

# Next step (downloads need a free Earthdata Login):
# earthaccess.login()
# files = earthaccess.download(results[:1], "./nisar")

Try a live search

Ask NASA's Common Metadata Repository for the five newest files in NISAR L2 Geocoded Polarimetric Covariance (GCOV), Provisional. This runs in your browser and needs no login.