Mapping alpine snow using a spectral mixture modeling technique

Abstract

Remote sensing has provided a means of obtaining estimates of snow-covered area, yet traditional methods have had difficulty mapping snow in shaded and vegetated areas. Spectral mixture analysis is a linear mixture modeling technique that shows promise for mapping land surface covers, particularly when imaging spectrometer data are used. Applying this technique to AVIRIS data collected over the Sierra Nevada, California, we have estimated the fraction of snow cover in each pixel, even in areas that are shaded or forested. This modeling technique enables us to map snow cover at the sub-pixel level and provides a means of estimating the errors associated with the calculation.