Interpretation of snow properties from imaging spectrometry

Abstract

Snow is among the most “colorful” materials in nature, but most of the variability in snow reflectance occurs beyond 0.8 µm rather than in the visible spectrum. In these wavelengths, reflectance decreases dramatically as the snow grains evolve and grow, whereas in the visible spectrum snow reflectance is degraded by contaminants such as dust, algae, and soot. From imaging spectrometer data, we can estimate the grain size of the snow in the surface layer, and thereby derive spectral and broadband albedo. We can also estimate the fraction of each pixel that is covered by snow, the liquid water content in the surface layer, and the amount of radiative forcing caused by absorbing impurities. Estimates of fractional snow-covered area and albedo dramatically improve the performance of spatially distributed snowmelt models that include net solar radiation as an input value, most significantly in locations and at times where incident solar radiation is high and temperatures low. Experience with imaging spectrometer data has allowed extension of the fractional snow-cover and albedo estimates to multispectral sensors, particularly MODIS, the Moderate-Resolution Imaging Spectroradiometer.