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A case for natural colour imagery from geostationary satellites, and an approximation for the GOES-R ABI

Miller, Steven D., Schmidt, Christopher C., Schmit, Timothy J., Hillger, Donald W.
International journal of remote sensing 2012 v.33 no.13 pp. 3999-4028
color, experts, image analysis, moderate resolution imaging spectroradiometer, photography, reflectance, satellites, wavelengths
‘Natural’ (or ‘true’) colour imagery, so-called for its qualitative likeness to colour photography, is one of the most visually intuitive and readily communicable forms of satellite information. It is constructed by combining solar reflectance measurements from three narrow spectral bands defining the red, green and blue wavelengths of visible light. Natural colour facilitates the interpretation of multiple components in the complex earth/atmosphere scene and, therefore, it is widely used by experts and non-experts alike to visualize many forms of geophysical phenomena. Although sensors on board low-Earth-orbiting (LEO) satellites have long-demonstrated the superior quality of natural colour imagery over various other ‘false colour’ renditions, similar capabilities currently do not exist on sensors operating in geostationary orbits that offer distinct advantages over LEO in terms of high temporal refresh. The Advanced Baseline Imager (ABI) of the next-generation Geostationary Operational Environmental Satellite (GOES)-R series will include the blue and red bands, but is missing the 0.55 μm green band necessary for producing natural colour. The emphases of this article are twofold. First, we consider the merits of natural colour imagery from the standpoints of both science and operational users, and the philosophical roadblocks of a system definition process that seems inherently ill-equipped to consider qualitative user requirements. Second, we present a mitigation strategy for GOES-R ABI that entails synthesizing the missing ABI green band information via its correlation with spectrally adjacent available bands, with a first-order account for surface type dependencies. The technique is developed, demonstrated and evaluated here using Moderate-resolution Imaging Spectroradiometer (MODIS) data.