Assessment of water quality in natural river based on HJ1A/1B CCD multi-spectral remote sensing data

Xiao Xiao; Jian Xu; Chengfang Hu; Zhaohui Wang; Dengzhong Zhao; Xiongfei Wen; Bo Cao; Xuejun Cheng

doi:10.1117/12.2068402

18 November 2014 Assessment of water quality in natural river based on HJ1A/1B CCD multi-spectral remote sensing data

Xiao Xiao, Jian Xu, Chengfang Hu, Zhaohui Wang, Dengzhong Zhao, Xiongfei Wen, Bo Cao, Xuejun Cheng

Proceedings Volume 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V; 92631M (2014) https://doi.org/10.1117/12.2068402
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

This study selects the typical middle and lower reaches of Han River as the study area and focuses on water quality evaluation methods and water quality evaluation of the surface water of the river basin. On the basis of the field survey, the author conducted a water quality sampling survey in the study area in spring and summer in 2012. The main excessive factors in the study area are determined as TN and TP. Using HJ1A/1B CCD multi-spectral data, the multiple linear regression inversion model and neural network inversion model are established for content of TN and TP. In accordance with these inversion results, the single factor water quality identification indexes in the study area are obtained. The results show that, BP neural network model boasts the highest inversion accuracy and that the single factor water quality identification indexes resulting from its inversion results are highly accurate, reliable and applicable, which can really reflect the changes in water quality and better realize the evaluation of water quality in the study area. Water quality evaluation results show that the water pollution in the study area is organic pollution; the water quality of Han River experiences large differences in different regions and seasons; downstream indexes are superior to upstream indexes, and the indexes in summer are superior to those in spring; the TN index seriously exceeds the standard in spring and the TP index seriously exceeds the standard in some regions.

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Xiao Xiao, Jian Xu, Chengfang Hu, Zhaohui Wang, Dengzhong Zhao, Xiongfei Wen, Bo Cao, and Xuejun Cheng "Assessment of water quality in natural river based on HJ1A/1B CCD multi-spectral remote sensing data", Proc. SPIE 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 92631M (18 November 2014); https://doi.org/10.1117/12.2068402

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KEYWORDS

Neural networks

Data modeling

Remote sensing

Charge-coupled devices

Image quality

Neurons

Pollution

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