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A Machine-Learning-Based Ocean-Current Velocity Inversion Model Using OCN From Sentinel-1 Observations

文献类型：期刊论文

作者Bai, Yang3; Zhang, Yubin3; Zhang, Xudong1,2; Li, Xiaofeng1,2
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2025
卷号18页码:9622-9635
关键词Oceans High frequency radar Sentinel-1 Training Spatial resolution Sea surface Synthetic aperture radar Accuracy Data models Directive antennas Machine learning ocean-current velocity inversion
ISSN号1939-1404
DOI10.1109/JSTARS.2025.3554229
通讯作者Zhang, Xudong(zhangxd@qdio.ac.cn) ; Li, Xiaofeng(xiaofeng.li@ieee.org)
英文摘要High-precision ocean-current velocity inversion is crucial for maritime activities. Synthetic aperture radar (SAR) has become a key data source for ocean-current velocity inversion. However, traditional methods, such as the Doppler centroid anomaly (DCA) and along-track interferometry methods, face challenges, such as low inversion accuracy, poor robustness, and limited data sources. This study developed OCN-CIM, a machine-learning-based model that directly derives the radial ocean-current velocity from Sentinel-1 observations. The model is trained using 186 scenes of Sentinel-1 Level 2 ocean data (OCN) collected between 14 July 2020 and 16 May 2024, in regions with strong currents along the East Coast of the United States. The ground truth is obtained from matched high-frequency radar data. Built on a fully connected neural network, the OCN-CIM features a custom loss function focused on high ocean-current velocities. The model achieved a mean absolute error (MAE) of 0.16 m/s, root-mean-square error (RMSE) of 0.20 m/s, and mean deviation (MD) of 0.005 m/s on the test dataset. When applying the OCN-CIM to ten independent cases, the average MAE, RMSE, and MD were 0.13 m/s, 0.16 m/s, and -0.03 m/s, compared with 0.26 m/s, 0.34 m/s, and 0.06 m/s for the traditional DCA method, demonstrating significant improvement in inversion accuracy. In addition, the OCN-CIM exhibits robustness, with reduced sensitivity to local wind and SAR data anomalies, and consistent results across various electromagnetic direction error-correction methods.
WOS关键词SURFACE CURRENTS ; NEURAL-NETWORKS
资助项目National Natural Science Foundation of China[62031005] ; National Natural Science Foundation of China[42306214] ; National Natural Science Foundation of China[U2006211] ; National Natural Science Foundation of China[41906157] ; National Natural Science Foundation of China[42076200] ; Qingdao National Laboratory for Marine Science and Technology, the Special Fund of Shandong Province[2022QNLM050301-2] ; Shandong Provincial Natural Science Foundation[ZR2024QD054]
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001469400300007
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.qdio.ac.cn/handle/337002/201662]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Zhang, Xudong; Li, Xiaofeng
作者单位1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266003, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Qingdao 266003, Peoples R China
3.Ocean Univ China, Fac Informat Sci & Engn, Qingdao 266005, Peoples R China
推荐引用方式
GB/T 7714		 Bai, Yang,Zhang, Yubin,Zhang, Xudong,et al. A Machine-Learning-Based Ocean-Current Velocity Inversion Model Using OCN From Sentinel-1 Observations[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:9622-9635.
APA Bai, Yang,Zhang, Yubin,Zhang, Xudong,&Li, Xiaofeng.(2025).A Machine-Learning-Based Ocean-Current Velocity Inversion Model Using OCN From Sentinel-1 Observations.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,9622-9635.
MLA Bai, Yang,et al."A Machine-Learning-Based Ocean-Current Velocity Inversion Model Using OCN From Sentinel-1 Observations".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):9622-9635.

入库方式： OAI收割

来源：海洋研究所

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