Improving alpine region spectral mixture analysis estimates of snow-covered area

Thomas H. Painter; Dar A. Roberts; Robert O. Green; Jeff Dozier

doi:10.1117/12.227196

24 November 1995 Improving alpine region spectral mixture analysis estimates of snow-covered area

Thomas H. Painter, Dar A. Roberts, Robert O. Green, Jeff Dozier

Proceedings Volume 2585, Remote Sensing for Agriculture, Forestry, and Natural Resources; (1995) https://doi.org/10.1117/12.227196
Event: Satellite Remote Sensing II, 1995, Paris, France

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.

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Thomas H. Painter, Dar A. Roberts, Robert O. Green, and Jeff Dozier "Improving alpine region spectral mixture analysis estimates of snow-covered area", Proc. SPIE 2585, Remote Sensing for Agriculture, Forestry, and Natural Resources, (24 November 1995); https://doi.org/10.1117/12.227196

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KEYWORDS

Reflectivity

Error analysis

Data modeling

Near infrared

Statistical analysis

Absorption

Vegetation

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