Abstract
The presence of vegetation in agricultural fields affects the accuracy of soil moisture retrieval using synthetic aperture radar (SAR) data. As a result, the estimation of soil moisture using the existing Oh model produces high error values. The magnitude of this error primarily depends upon the nature of crops, crop coverage, and the roughness of the field. Hence, in this study, along with the Oh model, we proposed a novel approach using model-based decomposition to reduce the volume contribution of the vegetation. This proposed method is employed on fallow as well as different crop fields in the summer of 2023 in the Kharagpur region of India using the Sentinel-1 dual polarimetric SAR data. The Root Mean Square Error (RMSE) of the proposed method is ≈25% to 52% lower over different crop types as compared to the existing Oh model. Moreover, the proposed method is also compared with the Chang model, designed to estimate soil moisture in vegetative fields. The proposed method exhibits RMSE that is around ≈10% to 17% lower across various crop kinds, in comparison to the Chang model. Thus, the proposed novel approach, with the advantage of not requiring in situ plant descriptors, will simplify the application of dual polarimetric SAR data for soil moisture estimation in a variety of land-use scenarios.