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In this paper, we address the problem of biophysical parameter estimation from measurements acquired by remote sensors. The objective of this work is to define the robust estimation method, which is characterized by a high accuracy over the whole feature space. In particular, we present a novel estimation technique that is based on the exploitation of the systematic errors (residuals) generated by an estimator trained to approximate the relationship between the remote-sensing measurements and the biophysical parameter of interest. The proposed Residual-Based Estimation (RBE) technique was applied to the problem of estimating water quality parameters, with a particular focus on the measure of concentration of chlorophyll. Experimental results pointed out the effectiveness of the RBE technique, which significantly increased the estimation accuracy with respect to the different kinds of standard neural-network estimators.