Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers

Xi Zhu, Yansha Wen, Xiang Li, Feng Yan () and Shuhe Zhao ()
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Xi Zhu: School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
Yansha Wen: Nanjing University 5D Technology Co., Ltd., Nanjing 210019, China
Xiang Li: Nanjing University 5D Technology Co., Ltd., Nanjing 210019, China
Feng Yan: School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
Shuhe Zhao: Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

Sustainability, 2023, vol. 15, issue 8, 1-18

Abstract: The remote sensing inversion of the water quality parameters of a complex river network in the absence of historical ground data is a difficult problem in the field of remote sensing. In this paper, a sub-regional inversion method for typical water quality parameters is presented for a complex river network using Gaofen-1 satellite data. Qidong’s rivers were selected as the survey region, and different band combination models and datasets on different river sub-regions were used to perform the remote sensing inversion, which realized the inversion of the permanganate index (CODMn), ammonia nitrogen (NH3-N), total phosphorus (TP), and total nitrogen (TN) in the rivers. The results show that all the coefficients of determination (R^2) of the inversion models are larger than 0.5, indicating an increase of about 0.4 when compared with the inversion method of the whole region, indicating good relevance. Water quality data and satellite data collected at different times were used for validation, which showed good results. On the basis of the water quality inversion, the key polluted areas were extracted in combination with on-site surveys to find the pollution source in order to verify the results of the inversion. The sub-region inversion method proposed in this paper can be used for the remote sensing inversion of the water quality parameters of complex river networks in the absence of historical ground data.

Keywords: water quality parameters; remote sensing; water quality inversion; Gaofen-1 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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