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

文献类型：期刊论文

作者Qi, Jifeng1,3; Sun, Guimin1,3; Xie, Bowen1,4; Li, Delei1; Yin, Baoshu1,2,3
刊名JOURNAL OF OCEANOLOGY AND LIMNOLOGY
出版日期2024-01-11
页码13
ISSN号2096-5508
关键词machine learning convolutional neural network (CNN) ocean subsurface salinity structure (OSSS) Indian Ocean satellite observations
DOI10.1007/s00343-023-3063-z
通讯作者Qi, Jifeng(jfqi@qdio.ac.cn)
英文摘要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.
WOS关键词SEA-SURFACE SALINITY ; THERMAL STRUCTURE ; IN-SITU ; TEMPERATURE ; AQUARIUS
资助项目Chinese Academy of Sciences
WOS研究方向Marine & Freshwater Biology ; Oceanography
语种英语
出版者SCIENCE PRESS
WOS记录号WOS:001141710300004
源URL[http://ir.qdio.ac.cn/handle/337002/184368]  
专题海洋研究所_海洋环流与波动重点实验室
海洋研究所_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.Chinese Acad Sci, Inst Oceanol, CAS Engn Lab Marine Ranching, Qingdao 266071, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Peoples R China
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.

入库方式： OAI收割

来源：海洋研究所

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