Abstract
A Bayesian approach for soil moisture change detection under different roughness conditions is proposed in this letter. The main objective of this approach is to exploit the changes in backscattering signals and relate them to soil moisture variations over agricultural fields by considering also the possible changes in the radar signal due to roughness variability. The method is trained and tested on two data sets acquired during SMEX' 02 experiment. One data set considers AirSAR P-band data for which the soil can be considered bare and the second data set considers the correspondent L-band data for which the influence of vegetation cannot be considered negligible. The results indicate that the approach is able to detect soil moisture changes both for P-band and L-band data. In case of L-band one main problem is indeed the presence of vegetation which reduces the backscattering coefficients dynamics.