文献类型： 外文期刊

作者： Zhang, Fei ¹ ; Chan, Ngai Weng ³ ; Liu, Changjiang ¹ ; Wang, Xiaoping ¹ ; Shi, Jingchao ⁴ ; Kung, Hsiang-Te ⁴ ; Li, Xi ¹ ;

作者机构： 1.Xinjiang Univ, Key Lab Wisdom City & Environm Modeling Resources, Urumqi 830046, Peoples R China

2.Xinjiang Univ, Environm Sci Coll, Urumqi 830046, Peoples R China

3.Univ Sains Malaysia, Sch Humanities, George Town 11800, Malaysia

4.Univ Memphis, Dept Earth Sci, Memphis, TN 38152 USA

5.Xinjiang Normal Univ, Sch Geog & Tourism, Urumqi 830046, Peoples R China

6.Sichuan Acad Agr Sci, Inst Remote Sensing & Digital Agr, Chengdu 610066, Peoples R China

关键词： Water Quality Index (WQI); Ebinur Lake; remote sensing

期刊名称：WATER （ 影响因子：3.103； 五年影响因子：3.229 ）

ISSN：

年卷期： 2021 年 13 卷 22 期

页码：

收录情况： SCI

摘要： 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 R-2 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.