1
|
Tian D, Zhao X, Gao L, Liang Z, Yang Z, Zhang P, Wu Q, Ren K, Li R, Yang C, Li S, Wang M, He Z, Zhang Z, Chen J. Estimation of water quality variables based on machine learning model and cluster analysis-based empirical model using multi-source remote sensing data in inland reservoirs, South China. Environ Pollut 2024; 342:123104. [PMID: 38070645 DOI: 10.1016/j.envpol.2023.123104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/08/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
Reservoirs play important roles in the drinking water supply for urban residents, agricultural water provision, and the maintenance of ecosystem health. Satellite optical remote sensing of water quality variables in medium and micro-sized inland waters under oligotrophic and mesotrophic status is challenging in terms of the spatio-temporal resolution, weather conditions and frequent nutrient status changes in reservoirs, etc., especially when quantifying non-optically active components (non-OACs). This study was based on the surface reflectance products of unmanned aerial vehicle (UAV) multispectral images, Sentinel-2B Multispectral instrument (MSI) images and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) by utilizing fuzzy C-means (FCM) clustering algorithm was combined with band combination (BC) model to construct the FCM-BC empirical model, and used mixed density network (MDN), extreme gradient boosting (XGBoost), deep neural network (DNN) and support vector regression (SVR) machine learning (ML) models to invert 12 kinds of optically active components (OACs) and non-OACs. Compared with the unclustered BC (UC) model, the mean coefficient of determination (MR) of the FCM-BC models was improved by at least 46.9%. MDN model showed best accuracy (R2 in the range of 0.60-0.98) and stability (R2 decreased by up to 13.2%). The accuracy of UAV was relatively higher in both empirical methods and machine learning methods. Additionally, the spatio-temporal distribution maps of four water quality variables were mapped based on the MDN model and UAV images, all platforms showed good consistency. An inversion strategy of water quality variables in various monitoring frequencies and weather conditions were proposed finally. The purpose of introducing the UAV platform was to cooperate with the satellite to improve the monitoring response ability of OACs and non-OACs in small and micro-sized oligotrophic and mesotrophic water bodies.
Collapse
Affiliation(s)
- Di Tian
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xinfeng Zhao
- Zhuhai Ecological Environment Monitoring Station of Guangdong Province, Zhuhai, 519070, China
| | - Lei Gao
- Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China
| | - Zuobing Liang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Zaizhi Yang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Pengcheng Zhang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Qirui Wu
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Kun Ren
- Key Laboratory of Karst Dynamics, Ministry of Natural Resources & Guangxi, Institute of Karst Geology, Chinese Academy of Geological Sciences, No. 50, Qixing Road, Guangxi, Guilin, 541004, China
| | - Rui Li
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Chenchen Yang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Shaoheng Li
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Meng Wang
- Zhuhai Ecological Environment Monitoring Station of Guangdong Province, Zhuhai, 519070, China
| | - Zhidong He
- Zhuhai Ecological Environment Monitoring Station of Guangdong Province, Zhuhai, 519070, China
| | - Zebin Zhang
- Zhuhai Ecological Environment Monitoring Station of Guangdong Province, Zhuhai, 519070, China
| | - Jianyao Chen
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China.
| |
Collapse
|