14 November 2007 Estimating sub pixel snow cover area by a new spectral unmixing method using MODIS data and ASTER data
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Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 67900Z (2007); doi: 10.1117/12.748101
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
Snow cover area is a very critical parameter for hydrologic cycle of the Earth. Furthermore, it will be a key factor for the effect of the climate change. Most research on estimating snow cover area is binary: pixels are verified either "snow" or "not snow". Most pixels, however, are mixed with snow, vegetation, soil, rock or water. This paper presents a spectral unmixing to estimate sub pixel snow cover. Firstly, a manmade selection for endmember was set up based on PCA method. Then an automatic selection of snow endmember and nonsnow endmember based on NDSI and NDVI can be achieved. The algorithm was tested on several different MODIS scenes in Tibetan Plateau. The efficiency and precision of classification equals that obtainable from the PCA method but is faster, cheaper. Lastly, Two sub pixel snow cover mapping means (regression method based on NDSI and spectral unmixing method based on the endmember automatic selection) was compared and analysised. And it takes the ASTER 15m data as ground true data to calculate the percentage of snow cover for 500m cells. It shows that the spectral unmixing can map fractional snow cover more precision and the automatic selection mean is stable and robost.
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Lina Xu, Ruiqing Niu, Xueqiang Zhang, "Estimating sub pixel snow cover area by a new spectral unmixing method using MODIS data and ASTER data", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67900Z (14 November 2007); doi: 10.1117/12.748101; https://doi.org/10.1117/12.748101
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KEYWORDS
Snow cover

MODIS

Reflectivity

Principal component analysis

Visible radiation

Vegetation

Binary data

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