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Remote sensing is providing an increasing number of crucial data about Earth. Systematic revisitation time allows the analysis of long time series as well as imagery utilization in the most interesting moments. Nevertheless, the current huge amount of data makes essential the usage of automatic methods to select the best captures, as many of them are not useful because of clouds, shadows, etc. Because of that, one of the characteristics of the more recent missions is the distribution, along with the spectral data, of a large amount of quality ancillary datasets. These datasets can act synergistically in the aim of selecting the best quality images, but the criteria they provide are not always enough. Indeed, these datasets are often used on a per pixel basis and the spatial pattern of the different spectral bands is forgotten, so ignoring the key information they can provide for our goals. With this aim, our work takes one of the most successful instruments in remote sensing, MODIS, and demonstrates, through geostatistical techniques, that the role of the spatial patterns of the spectral bands can effectively improve image selection in a complex (for climate, relief, and vegetation and crop phenology) region of 63,700 km2. The results show that band 01 (red) is the preferred one, as it achieves a 13% higher success than when only using quality bands criteria: a 94% global accuracy (66 true classifications, and only four omissions and one commission error). A second, important finding, is that the geostatistical selection improves results when using any band, except for band 02 (NIR1), which makes our proposal potentially useful for most remote sensing missions. Finally, the method can be executed in a reasonable computing time due to previously developed high-performance computing techniques.
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