An extensive data set, made up of different remote sensing experiments, six carried with a ground-based instrument, a scatterometer, and two with the AIRSAR sensor, Washita '92 and SMEX '02, has been investigated. The aim was to study the feasibility of soil parameters extractions in different environmental conditions and with different sensors.
The extraction algorithm is a combination of Bayesian methodology with theoretical models. The chosen theoretical model is the Integral Equation Model because its range of applicability covers most of experiment surface conditions.
Bayesian methodology allows meaningful and rigorous incorporations of all information sources into the inverse problem solution. The key point is the evaluation of a joint posterior probability density function based on the contemporary knowledge of data sets consisting of soil parameters measurements and the corresponding remotely sensed data. In this study, it is obtained by applying the maximum likelihood principle (MLP).
The inversion procedure has been applied to bare and vegetated fields. The correlation coefficient between measured and estimated dielectric constant values are R = 0.41 and R = 0.81 for bare fields and for C and L band respectively. In the case of the vegetated soils, the correlation coefficients are variable between 0.34 and 0.94, according to the different level of vegetation. It can be noted that the drying phase changes considerably from one part to another of the same field. The in-homogeneity of the fields introduces further errors in the inversion procedure.