Field-scale soil moisture estimation using sentinel-1 GRD SAR data

Soil moisture is a critical land variable that controls the energy and mass balance in land-atmosphere interactions. Spaceborne Syn-thetic Aperture Radar (SAR) sensors offer an efficient way to map and monitor soil moisture because of their sensitivity towards the dielectric and geometric properties of the target. In addition, SAR acquisitions are weather-independent, providing a significant advan-tage over optical imaging during periods of cloud cover. However, vegetation cover makes these processes more complex and influences the interaction of SAR backscatter resulting from combined soil matrix and vegetation cover. Therefore, using SAR data, it is necessary to compensate for vegetation contribution in total backscatter while estimating soil moisture over the vegetated soil surface. This study presents a technique that utilizes a vegetation index derived from SAR data to generate high-resolution soil moisture maps. It is note-worthy that this proposed soil moisture retrieval method uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Hence, the proposed method has a high potential for operational soil moisture monitoring globally. We validated over 34 soil moisture stations of the Texas Soil Observation Network (TxSON) using time-series Sentinel-1 SAR data. The Root Mean Square Error (RMSE) values for estimated volumetric soil moisture are within the range of 0.048 m3 m-3 to 0.055 m3 m-3 with the Pearson correlation coefficient r > 0:79. The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.