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Air surface temperature estimation from satellite thermal infrared time series and pixelwise modeling of the estimation uncertainty through support vector machines

Accurately mapping air temperature near the Earth surface plays a primary role in weather and climate studies and for solar energy planning and production. Remote sensing allows spatially distributed estimates of air temperature to be computed, thus complementing the spatially sparse observations collected by ground stations. In this paper, a novel method for periodic (e.g., daily or monthly) air-temperature estimation from satellite images is proposed. It is based on support vector machines (SVMs) and generalizes, to the case of air temperature, a recently developed SVM-based approach to land and sea surface temperature estimation. Case-specific techniques aimed at computing periodic statistics of air temperature and based on the expectation-maximization algorithm are also integrated in the proposed approach. The method also allows the statistics of the estimation error to be modeled on a pixelwise basis by combining nonstationary stochastic processes and Clark's variance approximation. Experimental results with MSG-SEVIRI and MétéoFrance data acquired over Provence-Alpes-Côte d'Azur (France) are presented.