<title>Estimating snow cover and grain size from AVIRIS data with spectral mixture analysis and modeled snow spectra</title>

Abstract

Models of hydrology and climate in alpine and other seasonally snow-covered regions require input of snow- covered area (SCA) and snow surface grain size. The spectral signature of snow depends on the snow grain size. We have shown in earlier work that to map either SCA or grain size with optical data, one must know the distribution of the other variable. Hence, we solve for SCA or grain size simultaneously with spectral mixture analysis. Alpine regions frequently exhibit large snow grain size gradients due to rugged terrain. Because the spectral signature of snow is dependent on grain size, the grain size gradients translate into spectral gradients. Snow must then be represented by a range of endmembers. To provide the range and resolution of grain size, we use modeled snow spectra of varying brain size as snow endmembers. To complete the spectral mixture library, we incorporate reference endmembers of vegetation, rock, and soil. On airborne visible/IR imaging spectrometer data of the Sierra Nevada, CA, we ran multiple mixture models. We established constraints on mixture RMS, residual, and fractions to select a subset of physically realistic models. The optimal mixture was then selected from this subset by means of the least RMS. The snow endmember fraction and grain size of the optimal mixture provide the estimates of sub-pixel SCA and surface grain size, respectively.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.