Automatic land and sea surface temperature estimation from remote sensing data

Land surface temperature (LST) and sea surface temperature (SST) are important quantities for many environmental models, and remote sensing is a feasible and promising way to estimate them on a regional and global scale. In order to estimate LST and SST from satellite data many algorithms have been devised, most of which require a-priori information about the surface and the atmosphere. However, the high variability of surface and atmospheric parameters causes these traditional methods to produce significant estimation errors, thus making their application on a global scale critical. A recently proposed approach involves the use of support vector machines (SVMs). Based on satellite data and corresponding in-situ measurements, they generate an approximation of the relation between them, which can be used subsequently to estimate unknown surface temperatures for additional satellite data. Such a strategy requires the user to set several internal parameters. In this paper a method is proposed for automatically setting these parameters to values that lead to minimum estimation errors. This is achieved by minimizing a functional correlated to regression errors (i.e., the "spanbound" upper bound on the leave-one-out error) which can be computed using only the training set, without the need for a further validation set. In order to minimize this functional, the Powell's algorithm is used, because it is applicable also to nondifferentiable functions. Experimental results generated by the proposed method turn out to be very similar to those obtained by cross-validation and by a grid search for the parameter configuration yielding the best test-set accuracy, although with a dramatic reduction in the computational times.