Research on Chlorophyll Concentration Retrieval Models of Keqin Lake based on Genetic Neural Networks
Received date: 2011-08-27
Revised date: 2011-10-16
Online published: 2012-02-10
The concentration of Chlorophyll-a could reflect the water quality of inland lakes to some extent. In order to monitor the concentration of chlorophyll-a, hyperspectral reflectance was measured from July to August in 2010 with ASD FieldSpec HH in Keqin Lake. Concurrently, water samples were collected. Three models including spectral ratio, artificial neural networks, and genetic neural networks were developed by analyzing the correlations between concentration and hyperspectral reflectance data. The results show that spectral ratio gives determination coefficient R2=0.67. Artificial neural networks gives better results with higher determination coefficient R2=0.882 which was further improved to 0.956 after introducing genetic algorithm to neural networks. All of the three models with significance level P<0.01, and are applied to estimate chlorophyll-a concentration. Finally, the author used the predicted results of the GANN model and the Kriging analysis technique to obtain the quantitative estimation of the spatial distribution of Chlorophyll-a in the study area. These algorithms provided a research basis of future large area remote sensing inversion with hyperspectral data in Keqin Lake.
Zhu Zixian, Zang Shuying . Research on Chlorophyll Concentration Retrieval Models of Keqin Lake based on Genetic Neural Networks[J]. Advances in Earth Science, 2012 , 27(2) : 202 -208 . DOI: 10.11867/j.issn.1001-8166.2012.02.0202
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