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Optimal sampling strategy of water quality monitoring at high dynamic lakes: A remote sensing and spatial simulated annealing integrated approach
Li, Jian1; Tian, Liqiao2; Wang, Yihong3; Jin, Shuanggen1,5; Li, Tingting2; Hou, Xuejiao4
2021-07-10
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
Volume777Pages:14
AbstractAn efficient and precise spatial sampling design is critical to capture spatial and temporal water quality variations under cost and labor constraints. Therefore, it is practically essential to optimize the sampling locations using limited sampling numbers to obtain the most comprehensive water quality monitoring results considering both the spatial and temporal dynamics. Existing sampling methods were restricted due to lacking pre-information and specific sampling objective function. This paper proposed an optimal sampling strategy using remote sensing (RS) big data and spatial sampling annealing (SSA) integrated approach for sampling design. The proposed method involved spatial-temporal clustering of the total suspended sediment (TSS) using long-term remote sensing data (Terra/Aqua MODIS, 2000-2014), determining the required sampling numbers using geostatistical analysis, and SSA simulation following the objective function of minimization of the spatial-temporal mean estimation error using remote sensing data as references. Taking total suspended sediment (TSS) observations at Poyang Lake, China, as the case study and application region. Results showed that the RS + SSA sampling approach is superior to conventional sampling methods such as systematic, stratified, and expert sampling, concerning spatial and temporal sampling accuracy. TSS estimation errors of the whole lake were reduced by 18.11% and 29.34% on average when compared to systematic and stratified sampling under the same sample size. The annual TSS estimation errors were dropped by approximately 50%. The sampling accuracy was affected by the synthetic effects of sampling strategy (station numbers and spatial distributions) and water quality variations (coefficient of variation, CV). Sampling optimization is more efficient to improve the sampling accuracy than increasing sampling size, which requires more cost and human resources. Remote sensing showed great potential as ideal means to provide spatially contiguous and comprehensive data as prior-knowledge for efficient sampling design. This paper provides solutions and recommendations for evaluating existing monitoring stations in their representation of water quality or optimizing a new sampling network for future implications of more efficient and precise water quality sampling and routine monitoring. (C) 2021 Elsevier B.V. All rights reserved.
KeywordWater quality Sampling method Spatial sampling annealing Remote sensing Poyang Lake
Funding OrganizationStrategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research
DOI10.1016/j.scitotenv.2021.146113
WOS KeywordLARGE RIVER ; DESIGN ; OPTIMIZATION ; NETWORK ; MODEL ; PARAMETERS ; STATIONS ; SYSTEMS ; NUMBER ; SCALE
Language英语
Funding ProjectStrategic Priority Research Program Project of the Chinese Academy of Sciences[XDA23040100] ; National Key R&D Program of China[2018YFB0504904] ; National Key R&D Program of China[2016YFC0200900] ; National Natural Science Foundation of China[41571344] ; National Natural Science Foundation of China[41701379] ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research[IWHR-SKL-KF201809]
Funding OrganizationStrategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; Strategic Priority Research Program Project of the Chinese Academy of Sciences ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research ; Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000655617300009
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/77140
Collection中国科学院国家天文台
Corresponding AuthorJin, Shuanggen
Affiliation1.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
2.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
3.Jiangsu Hydraul Res Inst, Nanjing 210029, Peoples R China
4.Southern Univ Sci & Technol China, Sch Environm Sci & Engn, Shenzhen 5180055, Peoples R China
5.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
Recommended Citation
GB/T 7714
Li, Jian,Tian, Liqiao,Wang, Yihong,et al. Optimal sampling strategy of water quality monitoring at high dynamic lakes: A remote sensing and spatial simulated annealing integrated approach[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2021,777:14.
APA Li, Jian,Tian, Liqiao,Wang, Yihong,Jin, Shuanggen,Li, Tingting,&Hou, Xuejiao.(2021).Optimal sampling strategy of water quality monitoring at high dynamic lakes: A remote sensing and spatial simulated annealing integrated approach.SCIENCE OF THE TOTAL ENVIRONMENT,777,14.
MLA Li, Jian,et al."Optimal sampling strategy of water quality monitoring at high dynamic lakes: A remote sensing and spatial simulated annealing integrated approach".SCIENCE OF THE TOTAL ENVIRONMENT 777(2021):14.
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