Parameterization of vegetation backscatter in radar-based, soil moisture estimation

Abstract

The Integral Equation Model (IEM) was previously used in conjunction with an inversion model to retrieve soil moisture using multifrequency and multipolarization data from Spaceborne Imaging Radar C-band (SIR-C) and X-band Synthetic Aperture Radar (X-SAR). Convergence rates well above 90%, and small RMS errors were attained, for both vegetated and bare soil areas, using radar data collected during Washita 1994. However, the IEM was originally developed to describe the scattering from bare soil surfaces only, and, therefore, vegetation backscatter effects are not explicitly incorporated in the model. In this study, the problem is addressed by introducing a simple, semiempirical, vegetation scattering parameterization to the multifrequency, soil moisture inversion algorithm. The parameterization was formulated in the framework of the water–cloud model and relies on the concept of a land-cover (land-use)-based dimensionless vegetation correlation length to represent the spatial variability of vegetation across the landscape and radar-shadow effects (vegetation layovers). An application of the modified inversion model to the Washita 1994 data lead to a decrease of 32% in the RMSE, while the correlation coefficient between ground-based and SAR-derived soil moisture estimates improved from 0.84 to 0.95.