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Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas

Key Laboratory of Wisdom City and Environment Modeling of Resources and Environmental Science College, Xinjiang University, Urumqi 830046, China
School of Humanities, Universiti Sains Malaysia, George Town 11800, Malaysia
Departments of Earth Sciences, The University of Memphis, Memphis, TN 38152, USA
School of Geography and Tourism, Xinjiang Normal University, Urumqi 830046, China
Institute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China
Author to whom correspondence should be addressed.
Academic Editor: Dimitrios E. Alexakis
Water 2021, 13(22), 3250;
Received: 25 September 2021 / Revised: 12 November 2021 / Accepted: 14 November 2021 / Published: 17 November 2021
Water Resource Sustainability Management plays a vitally important role in ensuring sustainable development, especially in water-stressed arid regions throughout the world. In order to achieve sustainable development, it is necessary to study and monitor the water quality in the arid region of Central Asia, an area that is increasingly affected by climate change. In recent decades, the rapid deterioration of water quality in the Ebinur Lake basin in Xinjiang (China) has severely threatened sustainable economic development. This study selected the Ebinur Lake basin as the study target, with the purpose of revealing the response between the water quality index and water body reflectivity, and to describe the relationship between the water quality index and water reflectivity. The methodology employed remote sensing techniques that establish a water quality index monitoring model to monitor water quality. The results of our study include: (1) the Water Quality Index (WQI) that was used to evaluate the water environment in Ebinur Lake indicates a lower water quality of Ebinur Lake, with a WQI value as high as 4000; (2) an introduction of the spectral derivative method that realizes the extraction of spectral information from a water body to better mine the information of spectral data through remote sensing, and the results also prove that the spectral derivative method can improve the relationship between the water body spectral and WQI, whereby R2 is 0.6 at the most sensitive wavelengths; (3) the correlation between the spectral sensitivity index and WQI was greater than 0.6 at the significance level of 0.01 when multi-source spectral data were integrated with the spectral index (DI, RI and NDI) and fluorescence baseline; and (4) the distribution map of WQI in Ebinur Lake was obtained by the optimal model, which was constructed based on the third derivative data of Sentinel 2 data. We concluded that the water quality in the northwest of Ebinur Lake was the lowest in the region. In conclusion, we found that remote sensing techniques were highly effective and laid a foundation for water quality detection in arid areas. View Full-Text
Keywords: Water Quality Index (WQI); Ebinur Lake; remote sensing Water Quality Index (WQI); Ebinur Lake; remote sensing
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MDPI and ACS Style

Zhang, F.; Chan, N.W.; Liu, C.; Wang, X.; Shi, J.; Kung, H.-T.; Li, X.; Guo, T.; Wang, W.; Cao, N. Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas. Water 2021, 13, 3250.

AMA Style

Zhang F, Chan NW, Liu C, Wang X, Shi J, Kung H-T, Li X, Guo T, Wang W, Cao N. Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas. Water. 2021; 13(22):3250.

Chicago/Turabian Style

Zhang, Fei, Ngai W. Chan, Changjiang Liu, Xiaoping Wang, Jingchao Shi, Hsiang-Te Kung, Xinguo Li, Tao Guo, Weiwei Wang, and Naixin Cao. 2021. "Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas" Water 13, no. 22: 3250.

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