Jeff Dozier¹ and Thomas H. Painter²

¹Donald Bren School of Environmental Science and Management, University of California, Santa Barbara, California 93106
²National Snow and Ice Data Center, University of Colorado, Boulder, Colorado 80309; email: tpainter@nsidc.org

Annual Review of Earth and Planetary Sciences
Vol. 32: 465-494 (Volume publication date May 2004)
First posted online on March 3, 2004

Abstract
Models of processes in the alpine snow cover fundamentally depend on the spatial distribution of the surface energy balance over areas where topographic variability causes huge differences in the incoming solar radiation and in snow depth because of redistribution by wind. At a spatial scale commensurate with that of the terrain, we want to know which areas are covered by snow, and we want to estimate the snow's spectral albedo, along with other properties such as grain size, contaminants, temperature, liquid water content, and depth or water equivalent. From multispectral and hyperspectral remote sensing at wavelengths from 0.4–15 μm, the retrievable properties include snow-covered area, albedo, grain size, liquid water very near the surface, and temperature. Spectral mixture analysis allows the retrieval of the subpixel variability of snow-covered area, along with the snow's albedo. Remaining research challenges include the remote sensing of absorbing impurities; accounting for variability in the bidirectional-reflectance distribution function and the variability of grain size with depth; retrieving snow cover in forested regions; reconciling field measurements of emissivity with snow properties; and adapting the algorithms to frequent, large-scale processing.

Multispectral and hyperspectral remote sensing of Alpine snow properties (full paper)