Abstract
The unresolved topographic expression of surfaces, surface roughness (SR), is a fundamental surface property that conveys useful information for a wide range of Earth and planetary sciences. Yet, this information is difficult to measure remotely because most spaceborne imagers have resolutions on the order of meters to hundreds of meters and SR can vary significantly below these scales. One way to measure SR is to exploit differential shadowing in stereo images, and in particular, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with its nadir (3N) and backward-looking (3B) near-infrared channels. We have proposed a simple ratio of land-leaving radiance in those two channels as a measure of relative SR at scales <15 m/pixel. This “two-look” relative SR measure is simple, robust, and insensitive to atmospheric conditions, and thus ASTER 3B/3N ratio images are suggested as a useful and readily accessible tool for photo-interpretation. Moreover, one could calibrate the ratio data to physical parameters, such as RMS height, and translate to SR maps at 15 m/pixel resolution. Two calibration schemes enable this translation: empirical calibration against independent in situ roughness measurements and model-based calibration against forward simulations of two-look ratios from very high-resolution (<5 mm) digital elevation models of natural surfaces, measured with a ground-based light detection and ranging system. Here, we focus on the latter scheme that enables construction of calibration curves for any given viewing and illumination geometries encountered by ASTER. ASTER now provides a global archive of images, and the two-look approach with ASTER stereo data enables a unique quantitative mapping capability of SR at 15 m/pixel spatial resolution for almost anywhere on Earth.
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Mushkin, A., Gillespie, A. (2010). Using ASTER Stereo Images to Quantify Surface Roughness. In: Ramachandran, B., Justice, C., Abrams, M. (eds) Land Remote Sensing and Global Environmental Change. Remote Sensing and Digital Image Processing, vol 11. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6749-7_20
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