Research Papers

Water quality monitoring using Landsat Themate Mapper data with empirical algorithms in Chagan Lake, China

[+] Author Affiliations
Kaishan Song

Chinese Academy of Sciences, Northeast Institute of Geography and Agricultural Ecology, Changchun 130012, China

Zongming Wang

Chinese Academy of Sciences, Northeast Institute of Geography and Agricultural Ecology, Changchun 130012, China

John Blackwell

Charles Sturt University, International Centre of Water for Food Security, Wagga Wagga NSW 2678, Australia

Bai Zhang

Chinese Academy of Sciences, Northeast Institute of Geography and Agricultural Ecology, Changchun 130012, China

Fang Li, Guangjia Jiang

Chinese Academy of Sciences, Northeast Institute of Geography and Agricultural Ecology, Changchun 130012, China

Yuanzhi Zhang

The Chinese University of Hong Kong, Institute of Space and Earth Information Science, Esther Lee Building, Shatin, Hong Kong

J. Appl. Remote Sens. 5(1), 053506 (March 14, 2011). doi:10.1117/1.3559497
History: Received October 20, 2009; Revised December 19, 2010; Accepted December 29, 2010; Published March 14, 2011; Online March 14, 2011
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Lake Chagan represents a complex situation of major optical constituents and emergent spectral signals for remote sensing analysis of water quality in the Songnen Plain. As such it provides a good test of the combined radiometric correction methods developed for optical remote sensing data to monitor water quality. Landsat thematic mapper (TM) data and in situ water samples collected concurrently with satellite overpass were used for the analysis, in which four important water quality parameters are considered: chlorophyll-a, turbidity, total dissolved organic matter, and total phosphorus in surface water. Both empirical regressions and neural networks were established to analyze the relationship between the concentrations of these four water parameters and the satellite radiance signals. It is found that the neural network model performed at better accuracy than empirical regressions with TM visible and near-infrared bands as spectral variables. The relative root mean square error (RMSE) for the neural network was < 10%, while the RMSE for the regressions was less than 25% in general. Future work is needed on establishing the dynamic characteristic of Chagan Lake water quality with TM or other optical remote sensing data. The algorithms developed in this study need to be further tested and refined with multidate imagery data

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© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Kaishan Song ; Zongming Wang ; John Blackwell ; Bai Zhang ; Fang Li, et al.
"Water quality monitoring using Landsat Themate Mapper data with empirical algorithms in Chagan Lake, China", J. Appl. Remote Sens. 5(1), 053506 (March 14, 2011). ; http://dx.doi.org/10.1117/1.3559497


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