MODIS (MODerate resolution Imaging Spectroradiometer) daily surface reflectance data is distributed with one of the most complete quality ancillary data sets. Such amount of quality information is essential for automatically selecting the highest quality MODIS daily images, for example using geostatistical analysis of the image spatial pattern. However, the success of this automatic selection certainly could depend on the spectral information of each MODIS band. This work studies the influence of MODIS spectral bands on the automatic identification of high quality daily images by analyzing their variogram and aiming at the identification of the most suitable spectral band (or band combination) for the spatial characterization of a given geographical region. The analysis tests the influence of each of the reflectance bands of the 2009 MOD09GA Daily Surface Reflectance product and the first component of its Principal Component Analysis over an area of 32 000 km2 , Catalonia (northeast of the Iberian Peninsula). Specifically, the combination of quality data and the variogram analysis allows the detection of different anomalies by the correspondence between the variability among the pixels and the fitted variogram parameters: nugget, sill and range. The variogram analysis is reaffirmed as an extremely useful approach for the automatic selection of high quality images while highlighting the need of high computational techniques for such huge processing. Finally, it reveals that is crucial to select the appropriate spectral band in order to, not only optimize, but substantially improve the automatic selection of remote sensing images using geostatistical analysis based on variogram tools.
Cristina Domingo-Marimon, Lluís Pesquer, Natalia Gómez-Carbajo, María-Teresa Jiménez-Diaz, and Xavier Pons, "On the interest of the spectral bands in the automatic selection of high quality MODIS data through spatial pattern identification," Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270X (Presented at SPIE Remote Sensing: September 13, 2017; Published: 4 October 2017); https://doi.org/10.1117/12.2278596.
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