Inversion of electromagnetic models for estimating bare soil parameters from radar multifrequency and multipolarization data

In this work we asses the practical feasibility of bare soil parameter estimation using multifrequency SAR polarimetric data. We use both the IEM and a semi-empirical model to simulate the radar measurements. We account for the multidimensional noise which is present in the measured polarimetric covariance matrix and compare different inversion algorithms, namely multivariate regression, maximum likelihood and minimum variance algorithms, and a neural network. The comparison are performed using different sets of radar parameters in order to assess the accuracy achievable by different radar configurations.