Comparison of methods of snow cover mapping by analysing the solar spectrum of satellite remote sensing data in China
Three methods, supervised classification (SC), digital number (DN) statistics and Normalized Difference Snow Index (NDSI), are used to map snow cover and then calculate snow cover area. Data sets from Landsat TM, Moderate Resolution Imaging Spectroradiometer (MODIS) and NOAA/AVHRR are selected because these sensors of different spatial resolution provide the most up to date remote sensing data for China. The results show that the best method for obtaining the snow index is different for each of these sensor products because of their different spatial and temporal resolutions and objectives of application. Reflectivity threshold statistics (RTs) should be used if the data series is incomplete; whereas SC needs a relatively accurate signature file for classification. A valid and rational method has been certified which selects NDSI for extracting snow pixels. Meanwhile, we introduce the brightness compensation method for decreasing the impact of topographic shading on distinguishing of snow pixels.
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Document Type: Research Article
Affiliations: Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Science 730000 Lanzhou PR China, Email: [email protected]
Publication date: 2003-11-01