THE EFFECT OF GRAIN-SIZE ON SPECTRAL MIXTURE ANALYSIS OF SNOW-COVEREDAREA 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 endmem bers. Snow reflectance in near-infrared wavelengths is sensitive to sn ow grain size while in visible wavelengths it is relatively insensitiv e. Snow-covered alpine regions often exhibit large surface grain size gradients due to changes in aspect and elevation. The sensitivity of s now spectral reflectance to grain size translates these grain size gra dients into spectral nature of snow must be accounted for by use of mu ltiple snow endmembers of varying grain size. We performed numerical s imulations to demonstrate the sensitivity of mixture analysis to grain size for a range of sizes and snow fractions. From Airborne Visible/I nfrared Imaging Spectrometer (AVIRIS) data collected over Mammoth Moun tain 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 endmembe r. For each pixel, the snow fraction estimated by the model with least mixing error (RMS) was chosen to produce an optimal map of subpixel s now-covered area. Results were verified with a high spatial resolution aerial photograph demonstrating equivalent accuracy. Analysis of frac tion under/overflow and residuals confirmed mixture analysis sensitivi ty to grain size gradients. (C) Elsevier Science Inc., 1998