Interpretation of snow properties from imaging spectrometry

a r t i c l e i n f o 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. What is there to say about the " color " of snow? In the visible part of the spectrum clean, deep snow is bright and white, irrespective of the size of the grains. Beyond the visible wavelengths in the near-infrared and shortwave-infrared, however, snow is one of the most colorful substances in nature. Newly fallen snow usually has a fine grain size, but metamorphism and sintering throughout the winter and spring increase the grain size and reduce reflectance in wavelengths beyond about 0.8 μm. This behavior of snow is important to the snowpack's energy balance, because the reduced albedo often occurs in the spring and summer as the incoming solar radiation becomes greater, and also to remote sensing of snow properties. Estimating properties for hydrologic and climate models from imaging spectrometer data is best done through an understanding of the relationship between snow's physical properties and the resulting electromagnetic signal. Initially, imaging spectrometers mainly focused on identification of surface materials from the wavelength position of known absorption bands. The heritage for the work on snow began with the use of the depth of absorption features, rather than their wavelength position, to infer some geophysical property of the absorbing substance. The primary example of such work is the retrieval of …