The Effect of Grain Size on Spectral Mixture Analysis of Snow-Covered Area from AVIRIS Data

We developed a technique to improve spectral mixture analysis of snow-covered area in alpine regions through the use of multiple snow endmembers. Snow reflectance in near-infrared wavelengths is sensitive to snow grain size while in visible wavelengths it is relatively insensitive. Snow-covered alpine regions often exhibit large surface grain size gradients due to changes in aspect and elevation. The sensitivity of snow spectral reflectance to grain size translates these grain size gradients into spectral gradients. To spectrally characterize a snow-covered image domain with mixture analysis, the variable spectral nature of snow must be accounted for by use of multiple snow endmembers of varying grain size. We performed numerical simulations to demonstrate the sensitivity of mixture analysis to grain size for a range of sizes and snow fractions. From Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data collected over Mammoth Mountain, CA on 5 April 1994, a suite of snow image endmembers spanning the imaged region’s grain size range were extracted. Mixture models with fixed vegetation, rock, and shade were applied with each snow endmember. For each pixel, the snow fraction estimated by the model with least mixing error (RMS) was chosen to produce an optimal map of subpixel snow-covered area. Results were verified with a high spatial resolution aerial photograph demonstrating equivalent accuracy. Analysis of fraction under/overflow and residuals confirmed mixture analysis sensitivity to grain size gradients.