Estimation of biophysical parameters from optical remote-sensing images with high-order residues

Robust estimation of biophysical parameters in large geographical areas from remote-sensing images represents an important methodological issue. A possible approach to this problem consists in modeling and correcting the systematic errors (residues) generated by an estimator trained to approximate the relationship between the remote-sensing measurements and the biophysical parameter of interest. In this paper, we propose to extend this approach by capturing information from residues of higher order to refine further the approximated model. The proposed technique was applied to the problem of estimating water quality parameters with a particular focus on the estimation of the chlorophyll concentration. Two data sets and two regression methods (based on Support vector Machines (SVM) and Multilayer Perceptron (MLP) neural networks) were considered for the experimental phase. The obtained results point out that the exploitation of residues of order smaller or equal than two can improve the estimation accuracy while, above this order, overfitting problems may appear.