Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model

doi:10.3389/fmars.2024.1378488

IOCAS-IR > 海洋环流与波动重点实验室

	Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model
	Zhao, Xianzhi 1,2; Gong, Xiang 1,2; Gong, Xun 3,4,5; Liu, Jiyao 1,2; Wang, Guoju 1,2; Wang, Lixin 1,2,6; Guo, Xinyu7 ; Gao, Huiwang 8,9,10
	2024-05-21
发表期刊	FRONTIERS IN MARINE SCIENCE
卷号	11 页码:17
通讯作者	Gong, Xiang([email protected])
摘要	Insufficient studies in characterizing vertical structure of Chlorophyll-a (Chl-a) in the ocean critically limit better understanding about marine ecosystem based on global climate change. In this study, we developed a Gaussian-activation deep neural network (Gaussian-DNN) model to assess vertical Chl-a structure in the upper ocean at high spatial resolution. Our Gaussian-DNN model used the input variables including satellite data of sea surface Chl-a and in-situ vertical physics profiles (temperature and salinity) in the northwestern Pacific Ocean (NWPO). After validation test based on two independent datasets of BGC-Argo and ship measurement, we applied the Gaussian-DNN model to reconstruct temporal evolution of 3-D Chl-a structure in the NWPO. Our modelling results successfully explain over 80% of the Chl-a vertical profiles in the NWPO at a horizontal resolution of 1 degrees x 1 degrees and 1 m vertical resolution within upper 300 meters during 2004 to 2022. Moreover, according to our modelling results, the Subsurface Chlorophyll Maxima (SCMs) and total Chl-a within 0-300 m depths were extracted and presented seasonal variability overlapping longer-time trends of spatial discrepancies all over the NWPO. In addition, our sensitivity testing suggested that sea-water temperatures predominantly control 3-D structures of the Chl-a in the tropical NWPO, while salinity played a key role in the temperate gyre of the NWPO. Here, our development of the Gaussian-DNN model may also be applied to craft long term, 3-D Chl-a products in the global ocean.
关键词	deep neural network Gaussian activation 3-D chlorophyll structure subsurface chlorophyll maximum long-term trend
DOI	10.3389/fmars.2024.1378488
收录类别	SCI
语种	英语
资助项目	Ministry of Science and Technology of the People's Republic of China[2019YFE0125000]; National Nature Science Foundation of China-Shandong Joint Fund[U1906215]; National Natural Science Foundation of China[41406010]; Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Chinese Academy of Sciences Opening Fund[2020KFJJ04]
WOS研究方向	Environmental Sciences & Ecology ; Marine & Freshwater Biology
WOS类目	Environmental Sciences ; Marine & Freshwater Biology
WOS记录号	WOS:001237851500001
出版者	FRONTIERS MEDIA SA
WOS关键词	STEADY-STATE SOLUTIONS ; CENTRAL NORTH PACIFIC ; SUBSURFACE CHLOROPHYLL ; VERTICAL PROFILES ; EDDY VARIABILITY ; MAXIMUM LAYERS ; GLOBAL OCEAN ; ARGO DATA ; PHYTOPLANKTON ; SURFACE
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/186045
专题	海洋环流与波动重点实验室
通讯作者	Gong, Xiang
作者单位	1.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao, Peoples R China 2.Qingdao Univ Sci & Technol, Qingdao Innovat Ctr Artificial Intelligence Ocean, Qingdao, Peoples R China 3.China Univ Geosci, Inst Adv Marine Res, Guangzhou, Peoples R China 4.China Univ Geosci, State Key Lab Biogeol & Environm Geol, Hubei Key Lab Marine Geol Resources, Wuhan, Peoples R China 5.Qilu Univ Technol, Key Lab Comp Power Network & Informat Secur, Minist Educ, Shandong Comp Sci Ctr,Natl Supercomp Ctr Jinan,Sha, Jinan, Peoples R China 6.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 7.Ehime Univ, Ctr Marine Environm Study, Matsuyama, Japan 8.Ocean Univ China, Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Minist Educ, Qingdao, Peoples R China 9.Ocean Univ China, Key Lab Marine Environm & Ecol, Minist Educ, Qingdao, Peoples R China 10.Qingdao Marine Sci & Technol Ctr, Lab Marine Ecol & Environm Sci, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Xianzhi,Gong, Xiang,Gong, Xun,et al. Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model[J]. FRONTIERS IN MARINE SCIENCE,2024,11:17.
APA	Zhao, Xianzhi.,Gong, Xiang.,Gong, Xun.,Liu, Jiyao.,Wang, Guoju.,...&Gao, Huiwang.(2024).Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model.FRONTIERS IN MARINE SCIENCE,11,17.
MLA	Zhao, Xianzhi,et al."Evolution of 3-D chlorophyll in the northwestern Pacific Ocean using a Gaussian-activation deep neural network model".FRONTIERS IN MARINE SCIENCE 11(2024):17.