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This paper demonstrates that a high-resolution reflectivity model used in conjunction with an instrument pointspread function (PSF) can both determine georegistration parameters of coarse resolution sensors and improve the spatial resolution by compositing noncoincident repeat satellite data. To demonstrate this ability, an ideal location is selected and several first principle assumptions are made to simplify the reflectivity model. Twenty-three 1-km advanced very high-resolution radiometer (AVHRR) images are composited by using a Bayesian statistical sampling technique to yield estimates of a simple terrain-based reflectivity model with 180-m resolution. The terrain values are determined from a 90-m resolution digital elevation model (DEM). The Bayesian technique uses the AVHRR data to iteratively determine the most likely values for the model spectral albedos contained within an AVHRR field of view. Model predicted radiances for the repeat AVHRR footprints are computed by integrating model albedo values over the AVHRR PSF. As a first-order verification, simulated AVHRR reflectivities are shown to reconstruct well a smoothly varying prescribed albedo field. Comparisons of the composited real AVHRR image result with Landsat Multi Spectral Scanner (MSS) data show that the model reconstruction resolves surface features, which are not resolved in a single AVHRR image.
Baldwin et al. (1998) studied this question.