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The main aim of this paper is to show the implementation and application of downscaling cokriging for super-resolution image mapping. By super-resolution, we mean increasing the spatial resolution of satellite sensor images where the pixel size to be predicted is smaller than the pixel size of the empirical image with the finest spatial resolution. It is assumed that coregistered images with different spatial and spectral resolutions of the same scene are available. The main advantages of cokriging are that it takes into account the correlation and cross correlation of images, it accounts for the different supports (i.e., pixel sizes), it can explicitly take into account the point spread function of the sensor, and it has the property of prediction coherence. In addition, ancillary images (topographic maps, thematic maps, etc.) as well as sparse experimental data could be included in the process. The main problem is that super-resolution cokriging requires several covariances and cross covariances, some of which are not empirically accessible (i.e., from the pixel values of the images). In the adopted solution, the fundamental concept is that of covariances and cross-covariance models with point support. Once the set of point-support models is estimated using linear systems theory, any pixel-support covariance and cross covariance can be easily obtained by regularization. We show the performance of the method using Landsat Enhanced Thematic Mapper Plus images.