A novel hybrid approach to the estimation of biophysical parameters from remotely sensed data

This paper presents a novel hybrid approach to the estimation of biophysical parameters from remotely sensed data. This approach integrates theoretical analytical models and empirical models based on field reference samples to increase the reliability and the accuracy of the estimation. The estimation process is modeled by two terms: the first one expresses the relationship between the input features and the target biophysical variable according a theoretical model based on the physics of the considered problem; the second one corrects the deviation between theoretical model estimates and true target values according to an empirical data-driven model. The latter is derived by exploiting the available (typically few) field reference samples. In this way the robustness and generality of theoretical model based estimates, which stem from the rigorous theoretical foundation, is preserved, while the bias and imprecision (due to simplifications in the analytical formulations of the model with respect to the real estimation process) are reduced. Results achieved for the specific application of soil moisture estimation from microwave remotely sensed data with two different correction strategies are reported. These results show the effectiveness and the potentiality of the proposed integration approach.