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Proceedings Paper

A new remote sensing model for retrieving snow depth within 30 centimeters using MODIS data
Author(s): Sanmei Li; Yujie Liu; Zhen Huang; Hua Fu
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Paper Abstract

Snow depth, a very significant factor in agriculture and climate research, is one of the most important parameters for snow amount calculation. It is proved there is a good linear relationship between snow depth and snow surface reflectance in visible to short-infrared window channels when snow has a depth within 30cm, which makes it possible to retrieve snow depth using AVHRR or MODIS data and station-measured snow-depth data. This paper mainly introduces the principle theory and process to establish a snow-depth retrieval model within 30cm using EOS/MODIS visible to short-infrared window channels' data and station-measured data, considering snow characteristics in different physical states and various complex underneath conditions including DEM, land cover such as grassland, forest, cropland and so on. Based on snow characteristics and underneath conditions, snow is devided into many types: old dry snow in flat grassland, new dry snow in flat grassland, old dry snow in mountainous grassland, old dry snow in flat cropland and so on. Fourteen kinds of snow have been modeled respectively in this retrieval model. Through 4 years validation in XinJiang Province of China since 2002, the precision of snow-depth retrieval model using MODIS visible to short-infrared channels' data can reach more than 80%. In flat area with single underneath condition, where wind power can be ignored, the model can always get a better precision. On the contrary, in mountainous forests, the precision of the model is not that good.

Paper Details

Date Published: 22 December 2006
PDF: 12 pages
Proc. SPIE 6405, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications, 64050Q (22 December 2006); doi: 10.1117/12.694048
Show Author Affiliations
Sanmei Li, China Meteorological Administration (China)
Yujie Liu, China Meteorological Administration (China)
Zhen Huang, Xinjiang Province Meteorological Bureau (China)
Hua Fu, Xinjiang Province Meteorological Bureau (China)

Published in SPIE Proceedings Vol. 6405:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications
William L. Smith Sr.; Allen M. Larar; Tadao Aoki; Ram Rattan, Editor(s)

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