Assessment of Interpolation Errors of CYGNSS Soil Moisture Estimations

High spatiotemporal soil moisture (SM) is essential for many meteorological, hydrological, and agricultural applications studies. Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) provides a promising opportunity high-resolution SM retrievals. NASA’s Cyclone System (CYGNSS) recent GNSS-R application that offers relatively high spatial temporal resolution observations from earth’s surface. However, the quasi-random sampling of land surface by CYGNSS constellation circumvents obtaining fully observed daily predictions at resolutions. Spatial interpolation techniques may fill this gap provide covered estimation. errors need to be assessed when applied 9-km CYGNSS-based estimations. In article, we conduct error analysis using Soil Moisture Active Passive (SMAP) Enhanced L3 Radiometer Daily product, sampled observation locations. The results indicate overall (RMSE) was 0.013 m $^3$ $^{-3}$ over SMAP’s recommended grids. addition, sparse are directly interpolated. achieved show interpolated values have similar performance metrics validated with SMAP gridded product as well networks.