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Quantitative monitoring of inland water using remote sensing of the upper reaches of the Huangpu River, China

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In this paper, a quantitative framework using common and readily available remote sensing data, including ground hyperspectral data, multispectral remote sensing images and a regular in situ water quality monitoring programme, is proposed to monitor inland water quality. The entire framework has three steps: (1) collecting and processing basic data, including remote sensing data and water quality data; (2) examining the relationships between water quality parameters and water reflectance from both remote sensing images and in situ measurement data. According to their relationships with ground hyperspectral reflectance, the water quality parameters are classified into three categories, and the corresponding monitoring models using remote sensing data are presented for these three categories; and (3) analysing the spatial distribution by using water quality concentration maps generated with the monitoring models. The upper reaches of the Huangpu River were chosen as our study area to test this framework. The results show that the concentration maps inverted by the proposed models are in accordance with the actual situation. Therefore, we can conclude that the proposed framework for quantifying water quality based on multisource remote sensing data and regular in situ measurement data is an effective and economic tool for the rapid detection of changes in inland water quality and subsequent management.
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Document Type: Research Article

Affiliations: 1: Department of Surveying and Geoinformatics, Tongji University, Shanghai, P. R. China 2: Key Laboratory of Yangtze Aquatic Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai, P. R. China 3: State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai, P. R. China

Publication date: 2010-03-01

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