Soil moisture estimation underneath crop cover using high incidence angle C-band Sentinel-1 SAR data

SAR's return signal sensitivity to dielectric constant and penetration through vegetation makes it ideal for large-scale soil moisture studies. High incidence angle SAR data is especially useful for understanding crop characteristics. However, interpreting the return signal requires considering both crop characteristics and underlying soil moisture. In this study, an attempt has been made to assess the potential of high incidence angle C-band SAR data for soil moisture retrieval by incorporating effects of crop on Radar signal using water cloud model. The simulated VV backscatter from water cloud model by considering Leaf Area Index (LAI) and plant water content as vegetation descriptors has shown Root Mean Square Error (RMSE) of 1.28 dB with actual VV backscatter. Later, a two-layer feedforward neural network comprising one hidden layer with sigmoid neurons has been considered to develop retrieval models. It is observed that the ANN with input of high incidence angle C-band VV backscatter over wheat crop has soil moisture retrieval performance with correlation coefficient (R) and RMSE of 0.55 and 9.18 m3/m3 respectively. Another ANN model is developed to incorporate effects of crop on Radar signal by using VH backscatter, Radar Vegetation Index (RVI) and two-way attenuation factor derived from water cloud model along with VV backscatter. The overall performance of this model has been observed with R and RMSE of 0.77 and 5.81 m3/m3 respectively. The study results indicate that the high incidence angle SAR data may be considered for soil moisture retrieval underneath crop cover by effectively incorporating the crop effects on Radar signal. This may further extend to develop a model to extract crop biophysical parameters as well as soil moisture from Sentinel-1 SAR data.