Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

doi:10.1007/s00343-023-3063-z

IOCAS-IR > 海洋环流与波动重点实验室

	Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations
	Qi, Jifeng 1,2; Sun, Guimin 1,2; Xie, Bowen 1,3; Li, Delei 1; Yin, Baoshu 1,2,4
	2024-01-11
发表期刊	JOURNAL OF OCEANOLOGY AND LIMNOLOGY
ISSN	2096-5508
页码	13
通讯作者	Qi, Jifeng([email protected])
摘要	Accurately estimating the ocean subsurface salinity structure (OSSS) is crucial for understanding ocean dynamics and predicting climate variations. We present a convolutional neural network (CNN) model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations. We evaluated the performance of the CNN model in terms of its vertical and spatial distribution, as well as seasonal variation of OSSS estimation. Results demonstrate that the CNN model accurately estimates most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS. However, the estimation accuracy of the CNN model varies with depth, with the most challenging depth being approximately 70 m, corresponding to the halocline layer. Validations of the CNN model's accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes. The results show that the CNN model effectively captures the seasonal variability of salinity, demonstrating its high performance in salinity estimation using sea surface data. Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers, while sea surface height anomaly plays a more significant role in deeper layers. These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.
关键词	machine learning convolutional neural network (CNN) ocean subsurface salinity structure (OSSS) Indian Ocean satellite observations
DOI	10.1007/s00343-023-3063-z
收录类别	SCI
语种	英语
资助项目	Chinese Academy of Sciences
WOS研究方向	Marine & Freshwater Biology ; Oceanography
WOS类目	Limnology ; Oceanography
WOS记录号	WOS:001141710300004
出版者	SCIENCE PRESS
WOS关键词	SEA-SURFACE SALINITY ; THERMAL STRUCTURE ; IN-SITU ; TEMPERATURE ; AQUARIUS
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/184369
专题	海洋环流与波动重点实验室海洋生态与环境科学重点实验室
通讯作者	Qi, Jifeng
作者单位	1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, CAS Engn Lab Marine Ranching, Qingdao 266071, Peoples R China
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Qi, Jifeng,Sun, Guimin,Xie, Bowen,et al. Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations[J]. JOURNAL OF OCEANOLOGY AND LIMNOLOGY,2024:13.
APA	Qi, Jifeng,Sun, Guimin,Xie, Bowen,Li, Delei,&Yin, Baoshu.(2024).Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations.JOURNAL OF OCEANOLOGY AND LIMNOLOGY,13.
MLA	Qi, Jifeng,et al."Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations".JOURNAL OF OCEANOLOGY AND LIMNOLOGY (2024):13.