CROP BACKSCATTER MODELING AND SOIL MOISTURE ESTIMATION WITH SUPPORT VECTOR REGRESSION

In this paper, we used an improved version of the Tor Vergata radiative transfer model to simulate the backscattering coefficient for the L-band SAR signals over areas covered with vegetation. Fields of winter wheat, maize and sugar beet observed during the AgriSAR2006 campaign were investigated. For maize field, the presence of periodic soil surface profiles played an important role in determining the total backscattering. Soil moisture was also estimated using an inverse algorithm based on a supervised, nonparametric learning technique, u-SVR. u-SVR proved good generalization properties even with a limited number of training samples available. Dependence to the origin of training samples, as well as the influence of different features, was thoroughly considered.