AI in Satellite Remote Sensing of the Ocean
文献类型：期刊论文
| 作者 | Li, Xiaofeng4,5; Xu, Qing1,6; Wang, Haoyu4,5; Jiang, Haoyu2; Yin, Xiaobin1,6; Mu, Shanshan4,5; Li, Xiaolong4,5; Su, Hua3; Wang, An3; Yang, Yi4,5 |
| 刊名 | PROCEEDINGS OF THE IEEE
 |
| 出版日期 | 2026-02-27 |
| 页码 | 34 |
| 关键词 | Oceans Microwave radiometry Artificial intelligence Satellite broadcasting Sea surface Remote sensing Radar measurements Ocean temperature Microwave imaging Spaceborne radar Artificial intelligence (AI) ocean remote sensing |
| ISSN号 | 0018-9219 |
| DOI | 10.1109/JPROC.2026.3664121 |
| 通讯作者 | Li, Xiaofeng(Xiaofeng.Li@ieee.org) |
| 英文摘要 | Satellite remote sensing plays a fundamental role in observing oceanic processes by providing large-scale, long-term, and continuous measurements. With the increasing availability of multisource satellite data, challenges such as data gaps, complex environmental conditions, and the limitations of conventional retrieval methods have become more evident. In recent years, artificial intelligence (AI) has emerged as a practical and effective approach to address these issues. This article reviews the development of AI techniques in satellite ocean remote sensing, focusing on three main application areas: parameter retrieval, data reconstruction, and image-based ocean phenomenon detection. For geophysical variable retrieval, AI models such as convolutional neural networks (CNNs) and Transformer architectures have improved the accuracy of ocean waves, sea surface, salinity, wind, and ocean color estimates, especially under extreme or noisy conditions. In the field of data reconstruction, AI methods enable the completion of missing data in both surface and subsurface ocean layers, offering finer spatial-temporal resolution and better consistency than traditional interpolation approaches. For image interpretation, deep learning (DL) models have been applied to detect and segment dynamic ocean features such as mesoscale eddies, internal waves, sea ice, and tropical cyclones (TCs), achieving high efficiency and precision. This article also highlights the integration of AI with physical knowledge, the use of multisource fusion, and the trend toward near real-time (NRT) applications. These developments indicate that AI will play an increasingly important role in future satellite-based ocean observation and environmental monitoring. |
| WOS关键词 | TROPICAL CYCLONE INTENSITY ; APERTURE RADAR IMAGE ; NEURAL-NETWORK ; SURFACE ; MESOSCALE ; SALINITY ; SAR ; RECONSTRUCTION ; PERFORMANCE ; ALGORITHM |
| 资助项目 | Innovative Research Group Program of the National Natural Science Foundation of China[42221005] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB42000000] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001703040200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/204886]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Ocean Univ China, Sanya Oceanog Inst, Fac Informat Sci & Engn, Qingdao 266100, Peoples R China 2.Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518000, Peoples R China 3.Fuzhou Univ, Natl & Local Joint Engn Res Ctr Satellite Geospati, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou 350108, Peoples R China 4.Chinese Acad Sci, Key Lab Ocean Observat & Forecasting, Qingdao 266000, Peoples R China 5.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266000, Peoples R China 6.Sanya Oceanog Lab, Sanya 572019, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xiaofeng,Xu, Qing,Wang, Haoyu,et al. AI in Satellite Remote Sensing of the Ocean[J]. PROCEEDINGS OF THE IEEE,2026:34. |
| APA | Li, Xiaofeng.,Xu, Qing.,Wang, Haoyu.,Jiang, Haoyu.,Yin, Xiaobin.,...&Wang, Chong.(2026).AI in Satellite Remote Sensing of the Ocean.PROCEEDINGS OF THE IEEE,34. |
| MLA | Li, Xiaofeng,et al."AI in Satellite Remote Sensing of the Ocean".PROCEEDINGS OF THE IEEE (2026):34. |
入库方式： OAI收割
来源：海洋研究所
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