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Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning

文献类型：期刊论文

作者Ping, Bo5; Meng, Yunshan4; Su, Fenzhen3; Xue, Cunjin2; Li, Zhi1
刊名MARINE ENVIRONMENTAL RESEARCH
出版日期2024-07-01
卷号199页码:17
关键词Dissolved oxygen (DO) Machine learning (ML) Sea surface temperature (SST) Sea surface salinity (SSS) Chlorophyll -a concentration (chlor-a)
ISSN号0141-1136
DOI10.1016/j.marenvres.2024.106578
英文摘要Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 mu mol/ kg to 0.99 and 4.01 mu mol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.
WOS关键词TEMPERATURE ; INTERIOR ; HYPOXIA ; DECLINE
资助项目National Natural Science Foundation of China[42101338] ; National Natural Science Foundation of China[42101383] ; Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals[CBAS2022IRP05] ; Tianjin University ; National Marine Data and Information Service ; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; Aerospace Information Research Institute, Chinese Academy of Sciences
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Toxicology
语种英语
WOS记录号WOS:001263636600001
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China ; Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals ; Tianjin University ; National Marine Data and Information Service ; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; Aerospace Information Research Institute, Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/207706]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Xue, Cunjin
作者单位1.China Ctr Resources Satellite Data & Applicat, Beijing 100094, Peoples R China
2.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Natl Marine Data & Informat Serv, Tianjin 300171, Peoples R China
5.Tianjin Univ, Inst Surface Earth Syst Sci, Sch Earth Syst Sci, Tianjin 300072, Peoples R China
推荐引用方式
GB/T 7714		 Ping, Bo,Meng, Yunshan,Su, Fenzhen,et al. Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning[J]. MARINE ENVIRONMENTAL RESEARCH,2024,199:17.
APA Ping, Bo,Meng, Yunshan,Su, Fenzhen,Xue, Cunjin,&Li, Zhi.(2024).Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning.MARINE ENVIRONMENTAL RESEARCH,199,17.
MLA Ping, Bo,et al."Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning".MARINE ENVIRONMENTAL RESEARCH 199(2024):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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