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Proceedings Paper

Improving alpine region spectral mixture analysis estimates of snow-covered area
Author(s): Thomas H. Painter; Dar A. Roberts; Robert O. Green; Jeff Dozier
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Paper Abstract

A technique has been developed to improve alpine-region spectral mixture analysis estimates of snow-covered area. Snow reflectance in near infrared wavelengths is sensitive to snow grain size while insensitive in visible wavelengths. Alpine regions often exhibit significant snow grain size gradients due to changes in aspect and elevation. A suite of snow image endmembers corresponding to the region's snow grain size range were extracted. Mixture models with fixed vegetation, rock, and shade were applied with each snow endmember to AVIRIS data collected over Mammoth Mountain, Calif., April 5, 1994. For each pixel, the snow-fraction estimated by the model with least mixing error (rms) was chosen to produce an optimal map of snow-covered area. Fraction under/overflow analysis and limited residuals analysis were performed on the test results.

Paper Details

Date Published: 24 November 1995
PDF: 11 pages
Proc. SPIE 2585, Remote Sensing for Agriculture, Forestry, and Natural Resources, (24 November 1995); doi: 10.1117/12.227196
Show Author Affiliations
Thomas H. Painter, Univ. of California/Santa Barbara (United States)
Dar A. Roberts, Univ. of California/Santa Barbara (United States)
Robert O. Green, Univ. of California/Santa Barbara and Jet Propulsion Lab. (United States)
Jeff Dozier, Univ. of California/Santa Barbara (United States)


Published in SPIE Proceedings Vol. 2585:
Remote Sensing for Agriculture, Forestry, and Natural Resources
Edwin T. Engman; Gerard Guyot; Carlo M. Marino, Editor(s)

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