| 光谱学与光谱分析 |
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| Research on Hyperspectral Remote Sensing in Monitoring Snow Contamination Concentration |
| TANG Xu-guang1, 2, LIU Dian-wei1, ZHANG Bai1, DU Jia1, LEI Xiao-chun1, 2, ZENG Li-hong1, 2, WANG Yuan-dong1, 2, SONG Kai-shan1* |
1. Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun 130012, China
2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well.
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Received: 2010-06-23
Accepted: 2010-09-20
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Corresponding Authors:
SONG Kai-shan
E-mail: songks@neigae.ac.cn
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