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The knowledge of air temperature near the Earth's surface plays a relevant role in weather and climate studies as well as in the framework of solar energy management; e.g., for identifying the most suitable locations for a new solar installation or monitoring the performance of existing systems. Remote sensing allows air temperature to be estimated on a spatially distributed basis, thus complementing the spatially sparse observations collected by ground micro-meteorological stations. In this paper, a novel approach to periodic (e.g., daily or monthly) air temperature estimation from satellite images based on support vector machines (SVMs) is proposed. A recently developed SVM-based approach to supervised land and sea surface temperature estimation using satellite images is generalized to the case of air temperature and integrated with case-specific techniques aimed at computing periodic statistics of air temperature using the expectation-maximization algorithm. The method is fully automated and allows the statistics of the estimation error to be modeled on a pixelwise basis. This last result is accomplished by combining nonstationary multidimensional stochastic processes and Clark's variance approximation. The method is experimentally validated with MSG-SEVIRI data acquired over Provence-Alpes-Côte d'Azur (France).

In the context of sea and ocean monitoring from SAR images, this paper illustrates a study of azimuth ambiguities, together with a new approach suggested to solve the problem.The proposed method presents here the added value in application to coastal monitoring and ship detection.This work is developed in the context of the project 1 funded by the Italian Space Agency. The data set for experimentation is made of COSMO-SkyMed images related to Ligurian Sea (Italy).