Joint Sentinel-1 and SMAP Data Assimilation to Improve Soil Moisture Estimates

Abstract

SMAP (Soil Moisture Active and Passive) radiometer observations at 40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9-km SMAP Level-4 Soil Moisture product. This study demonstrates that adding high-resolution radar observations from Sentinel-1 to the SMAP assimilation can increase the spatio-temporal accuracy of soil moisture estimates. Radar observations were assimilated either separately from or simultaneously with radiometer observations. Assimilation impact was assessed by comparing 3-hourly, 9-km surface and root-zone soil moisture simulations with in situ measurements from 9-km SMAP core validation sites and sparse networks, from May 2015 to December 2016. The Sentinel-1 assimilation consistently improved surface soil moisture, whereas root-zone impacts were mostly neutral. Relatively larger improvements were obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performed best, demonstrating the complementary value of radar and radiometer observations