title: [Research on hyperspectral remote sensing in monitoring snow contamination concentration].
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.
authors.affiliation: Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun 130012, China. tang11100@163.com
authors.forename: Xu-guang
authors.initials: XG
authors.lastname: Tang
authors.forename: Dian-wei
authors.initials: DW
authors.lastname: Liu
authors.forename: Bai
authors.initials: B
authors.lastname: Zhang
authors.forename: Jia
authors.initials: J
authors.lastname: Du
authors.forename: Xiao-chun
authors.initials: XC
authors.lastname: Lei
authors.forename: Li-hong
authors.initials: LH
authors.lastname: Zeng
authors.forename: Yuan-dong
authors.initials: YD
authors.lastname: Wang
authors.forename: Kai-shan
authors.initials: KS
authors.lastname: Song