Preliminary Overpass Level Assessment of Early NISAR 200 m L-Band Soil Moisture: Effects of Quality Control and Sensor Depth
The NASA–ISRO Synthetic Aperture Radar (NISAR) mission provides L-band soil moisture at 200 m, but validating its early products depends on retrieval flags, overpass collocation and sensor depth. We evaluated the beta Level-3 product against seven U.S. in situ networks (October 2025–January 2026) and the provisional product at 21 National Ecological Observatory Network sites (June–July 2026). Beta retrievals with flag 0, which averages all three candidate algorithms, were matched to in situ data within 60 min, merged by orbit and averaged over 0–10 cm sensors, yielding 261 site overpasses from 117 sites. Beta NISAR had a preliminary bias of 0.003 m3 m−3, a root mean square error (RMSE) of 0.081 m3 m−3 (95% interval 0.066–0.098) and R=0.578. Accepting all flag classes increased coverage ninefold and RMSE to 0.096 m3 m−3. On common site overpasses, deeper sensors did not have significantly higher RMSE than 5 cm sensors. The exploratory provisional assessment found a wet bias of 0.110 m3 m−3, concentrated at very dry reference sites, with a pooled R of −0.038 but an anomaly R within sites of 0.259. These estimates rest on few sites and short records; validation of early NISAR products should report flag classes, overpass construction and matched depths and separate pooled from temporal agreement.
Authors
- Dewei Yin
- Changjiang Shi (ORCID: https://orcid.org/0000-0001-5738-7910)
- Cui Zhou (ORCID: https://orcid.org/0000-0002-8523-1064)
- Yaning Yi (ORCID: https://orcid.org/0000-0002-2653-8920)
- Chuanqi Liu (ORCID: https://orcid.org/0009-0003-7835-3252)
- Zhixin Liu
Institutions
- Central South University of Forestry and Technology (CN)
- Chinese Academy of Sciences (CN)
- State Forestry and Grassland Administration (CN)
- Aerospace Information Research Institute (CN)
- University of Chinese Academy of Sciences (CN)
- National Institute of Natural Hazards (CN)
- Minnan Normal University (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2026-11-03
- DOI
- https://doi.org/10.3390/rs18213777
- Primary Topic
- Soil Moisture and Remote Sensing
- Type
- article
- Field-Weighted Citation Impact
- 0.00