Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Surface soil moisture (SM) retrieval over agricultural areas from polarimetric synthetic aperture radar (PolSAR) has long been restricted by vegetation attenuation, simplified scattering modelling, and limited SAR measurements. This study proposes a modified decomposition framework to retrieve SM multi-incidence multitemporal PolSAR observations. The is constructed combining the X-Bragg model, extended double Fresnel model generalised volume (GVSM). Compared with traditional models, proposed considers depolarisation of dihedral diverse contribution. Under assumption that invariant for observations at two different incidence angles does not change between consecutive measurements, analytical parameter solutions, including dielectric constant crop stem, can be obtained solving multivariable nonlinear equations. applied time series L-band uninhabited aerial vehicle data acquired during Soil Moisture Active Passive Validation Experiment in 2012. In this study, we assess performance comparing inversion results in-situ measurements bean, canola, corn, soybean, wheat winter GVSM Yamaguchi models. Given estimation inherently influenced phenology empirical parameters which are introduced also investigate influence surface angle co-pol phase difference on estimation. Results show provides an accuracy RMSE<6.0% correlation R?0.6 rate larger than 90%. Over fields, 0.8 estimates observed when dominant. Specifically, stem permittivity, retrieved synchronously shows linear relationship biomass plant water content soybean fields. We find priori knowledge angle, adaptive could help improve framework. However, still fully because power ratio potentially ground scattering.