Shilpa, K.Suresh Raju, C.Mandal, D.Rao, Y.S.Shetty, A.2026-02-042023Journal of the Indian Society of Remote Sensing, 2023, 51, 5, pp. 949-9620255660Xhttps://doi.org/10.1007/s12524-023-01682-4https://idr.nitk.ac.in/handle/123456789/21904Soil moisture estimation from agriculture fields using SAR measurements is a challenging process owing to the presence of vegetation canopy. In this study, the soil moisture (SM) is retrieved from multi-polarization airborne L- and C-band E-SAR data of different agriculture fields by using the radar parameter, Radar Vegetation Index (RVI). The retrieval methodology employs the semi-empirical Water Cloud Model (WCM) for vegetation-soil system modeling, followed by an inversion algorithm based on a Look Up Table approach. The impact of using different vegetation descriptors, both from in situ measured (Leaf Area Index, Wet Biomass and Vegetation Water Content) and radar derived (L-band RVI and C-band RVI), on the WCM inversion for SM retrieval is examined. The use of the RVI as the vegetation descriptor, which is obtained from C-band data, improves soil moisture retrieval with an RMSE of 7–8% volumetric soil moisture at L-band. © 2023, Indian Society of Remote Sensing.agricultural landalgorithminverse analysispolarizationsatellite datasoil moisturesoil-vegetation interactionsynthetic aperture radar