Modelling the Error Statistics in Support Vector Regression of Surface Temperature from Infrared Data

Land surface temperature (LST) and sea surface temperature (SST) are important quantities for many hydrological and meteorological models and satellite infrared remote sensing represents a feasible way to map them on global and regional scales. However, in order to integrate temperature estimates into data-assimilation schemes (e.g., in applications such as flood prevention), a further critical input is often represented by the statistics of the temperature regression error. A supervised approach, based on support vector machine (SVM), has recently been developed to estimate LST and SST from satellite radiometry. In this paper, two novel methods are proposed to model the statistics of the SVM regression error occurring on each image sample. This problem has been only recently explored in the SVM literature by developing Bayesian reformulations of SVM regression. The methods proposed in this paper extend this approach by integrating it with either maximum-likelihood or confidence-interval supervised estimators in order to improve the accuracy in modelling the error contribution due to intrinsic data variability (e.g., noise).