title: [An artificial neural network model for lake color inversion using TM imagery].
abstract: The technology of artificial neural network was used for inversing water quality parameters from TM imagery data in the paper in order to study water quality and eutrophic status of lake. On the basis of satellite synchronous monitoring experiment, a BP neural network model was constructed, in which concentrations of SS, CODMn, DO, T-N, T-P and chlo-a were inversed from Landsat TM data and the accuracy of which was good, the relative error of which could be controlled below 25%. Moreover, the reasons of simulating error, ways of improving model and applications of the model were also analyzed in detail. The results of this research told that based on a small-scale of satellite synchronous experiment, the model could be applied successfully in investigation, analysis and estimation of lake water quality.
authors.affiliation: Department of Environmental Science and Engineering, Tsinghua University, Beijing 100084, China.
authors.forename: Jianping
authors.initials: J
authors.lastname: Wang
authors.forename: Shengtong
authors.initials: S
authors.lastname: Cheng
authors.forename: Haifeng
authors.initials: H
authors.lastname: Jia
authors.forename: Zhishi
authors.initials: Z
authors.lastname: Wang
authors.forename: U Wa
authors.initials: UW
authors.lastname: Tang
mesh_terms: D003116:Color; D004784:Environmental Monitoring / Q000379:methods*; D016571:Neural Networks, Computer; D011786:Quality Control; D017215:Satellite Communications; D014876:Water Pollution / Q000032:analysis*; D014881:Water Supply