Soil moisture retrieval from dual-polarized Sentinel-1 SAR data over agricultural regions using a water cloud model.

The accurate retrieval of soil moisture plays a pivotal role in agriculture, particularly in effective irrigation water management, as it significantly affects crop growth and yield. The present study mainly focuses on the robustly investigated capability of dual-polarized Sentinel-1 SAR-derived vegetation descriptors in the water cloud model (WCM) in surface soil moisture (SSM) retrieval over wheat crops. The vegetation descriptors used in the study are radar vegetation index (RVI), backscattering ratio, polarimetric radar vegetation index (PRVI), dual polarization SAR vegetation index (DPSVI), and dual polarimetric radar vegetation index (DpRVI). The results of the WCM model illustrate that all the models show acceptable results, which confirms that this vegetative descriptor can be useful to estimate the accurate soil moisture over the wheat crop in the study area, except for DPSVI. Furthermore, the results revealed that model performances gradually decrease as the crop enters the complex stages. In summary, the overall finding demonstrates that PRVI outperformed other models in terms of statistical indicators value for calibration (R = 0.728, NSE = 0.727, PBIAS = - 2.67%, and RMSE = 2.985%) and validation (R = 0.728, NSE = 0.684, PBIAS = - 13.666%, and RMSE = 4.106%). Thus, overall results proved that the WCM model has considerable potential to retrieve SSM over wheat crops from Sentinel-1 satellite data. This study will be beneficial for regional water resources managers for proper allocation of irrigation water, effective irrigation management, and enhanced irrigation efficiency within the regions.