干旱区生态环境知识资源中心(Arid): Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study

Arid

DOI	10.2166/ws.2020.381
	Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study
	Li, Xiaohang; Ding, Jianli; Ilyas, Nurmemet
通讯作者	Ding, JL (corresponding author), Xinjiang Univ, Key Lab Smart City & Environm Modelling Higher Ed, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China.
来源期刊	WATER SUPPLY
ISSN	1606-9749
EISSN	1607-0798
出版年	2021
卷号	21 期号:3 页码:1291-1312
英文摘要	Surface water quality is an important factor affecting the ecological environment and human living environment. The monitoring of surface water quality by remote sensing monitoring technology can provide important research significance for water resources protection and water quality evaluation. Finding the optimal spectral index sensitive to water quality for remote sensing monitoring of water quality is extremely important for surface water quality analysis and treatment in the Ebinur Lake Basin in arid areas. This study used Sentinel-2MSI data at 10 m resolution to quickly monitor the water quality of the watershed. Through laboratory experiments and measurement data from the Ebinur Lake Basin, 22 water quality parameters (WQPs) were obtained. Through Z-score and redundancy analysis, 9 WQPs with significant contributions were extracted. Based on the remote sensing spectral band, four water indexes (NDWI, NWI, EWI, AWEI-nsh) and 2D modeling spectral index (DI, RI, NDI), the correlation analysis between WQPs and two kinds of spectral band indexes is carried out, and it is concluded that the overall correlation between WQP and 2D spectral modeling is more relevant. This paper calculates the evaluation and models the 2D spectrum of the Water Quality Index (WQI). The WQI is predicted and modeled through four machine learning algorithms (RF, SVM, PLSR, PLSR-SVM). The results show that the inversion effect of the two-dimensional spectral modeling index on water quality parameters (WQPs) is superior to that of the water index, and the correlation coefficient of the DI (R12-R1) SWIR-2 and BLUE band interpolation index reaches 0.787. On this basis, three kinds of two-dimensional spectral modeling indexes are used to inversely synthesize the WQI, and the correlation coefficient of the ratio index of the RI (R11/R8) SWIR-1 and near-infrared (NIR) bands is preferably 0.69. In the WQI prediction, the partial least squares regression support vector machine (PLSR-SVM) model in machine learning algorithms has good modeling and prediction effects (R(2)c = 0.873, R(2)v = 0.87), which can provide a good basis. The research results provide references for remote monitoring of surface water in arid areas, and provide a basis for water quality prediction and safety evaluation.
英文关键词	machine learning remote sensing reflectance Sentinel-2 MSI water quality index (WQI) water quality parameter (WQP)
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000651427700030
WOS关键词	LEAST-SQUARES REGRESSION ; SURFACE-WATER ; ORGANIC-MATTER ; EXTRACTION ; CLASSIFICATION ; CALIBRATION ; SPECTROSCOPY ; PREDICTION ; ALGORITHM ; LEVEL
WOS类目	Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351995
作者单位	[Li, Xiaohang; Ding, Jianli; Ilyas, Nurmemet] Xinjiang Univ, Key Lab Smart City & Environm Modelling Higher Ed, Coll Resources & Environm Sci, Urumqi 830046, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaohang,Ding, Jianli,Ilyas, Nurmemet. Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study[J]. 新疆大学,2021,21(3):1291-1312.
APA	Li, Xiaohang,Ding, Jianli,&Ilyas, Nurmemet.(2021).Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study.WATER SUPPLY,21(3),1291-1312.
MLA	Li, Xiaohang,et al."Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study".WATER SUPPLY 21.3(2021):1291-1312.