Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

REMOTE SENSING OF ENVIRONMENT

卷:113

Article

Painter, Thomas H.¹  Rittger, Karl²  McKenzie, Ceretha³  Slaughter, Peter²  Davis, Robert E.³  Dozier, Jeff² 

[1] Univ Utah, Dept Geog, Salt Lake City, UT 84112 USA

[2] Univ Calif Santa Barbara, Donald Bren Sch Environm Sci & Management, Santa Barbara, CA 93106 USA

[3] USA, Cold Reg Res & Engn Lab, Hanover, NH 03755 USA

关键词: Snow;    Grain size;    Albedo;    Spectral mixture analysis;    MODIS;   

DOI  :  10.1016/j.rse.2009.01.001

来源: Elsevier

【 摘 要 】

We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA's Moderate Resolution Imaging Spectro radiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene's illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixel-weighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 mu m and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode. (C) 2009 Elsevier Inc. All rights reserved.

【 授权许可】

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