Soil Moisture Retrieval Using SMAP L-Band Radiometer and RISAT-1 C-Band SAR Data in the Paddy Dominated Tropical Region of India

National Aeronautics and Space Administration's soil moisture active-passive (SMAP) mission potential to produce high-resolution soil moisture suffered adversely due to its L-band synthetic-aperture radar (SAR) failure. Other satellite-based L-/C-band SAR observations can be used within the SMAP active-passive algorithm. In this article, we evaluated the capability of ingesting ISRO's Radar Imaging Satellite-1 (RISAT-1) C-band SAR observations in the SMAP active-passive algorithm to obtain soil moisture at 1, 3, and 9 km over the agricultural region dominant by paddy that experiences seasonal flooding. We also improved the SMAP mission active-passive algorithm with a dynamic surface water bodies (ponding conditions) masking approach using the native RISAT-1 observations. The article shows that the use of surface water masks helps in mitigating the negative impact of surface water bodies in the active-passive disaggregation process. The SMAP-RISAT soil moisture retrievals at 1 and 3 km resolutions are found to have high unbiased root-mean-square error (ubRMSE) greater than 0.06 m(3)/m(3) during very wet and high vegetative conditions. However, at low and moderate soil moisture states, the ubRMSE is below 0.06 m(3)/m(3). Comparison of soil moisture retrievals at 9 km resolution with upscaled ground-based soil moisture measurements shows ubRMSE less than 0.04 m(3)/m(3). This article is a precursor for estimating soil moisture for the upcoming RISAT-1A dataset over India. The findings will further help in the implementation of a microwave active-passive algorithm to retrieve soil moisture for future satellite missions involving radiometer-SAR instruments, and challenging geophysical conditions (i.e., dynamic surface water bodies).

Automated summary

This article evaluates the integration of ISRO's RISAT-1 C-band SAR observations in the SMAP active-passive algorithm to improve soil moisture retrievals in agricultural regions, specifically those prone to seasonal flooding. The study shows that using surface water masks can help mitigate negative impacts of dynamic surface water bodies in the algortihm and improve soil moisture estimations.