Deep Learning for Enhanced Ocean Color Remote Sensing: A Foundation Model Approach
文献类型：期刊论文
| 作者 | Yang, Yi1,2,3; Wang, Haoyu1,3; Li, Xiaofeng1,3 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:18 |
| 关键词 | Oceans Image color analysis Satellites Training Sea measurements Foundation models Adaptation models Deep learning Biological system modeling Remote sensing foundation model multitask learning ocean color remote sensing |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3600411 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | Ocean color remote sensing has advanced over the past 40 years, with algorithms primarily rooted in radiative transfer theory to estimate various ocean color properties. To address the diversity and region-specific distribution of these properties, numerous algorithms have been developed; however, each is tailored to specific variables. In recent years, deep learning has demonstrated remarkable progress in this field, but a general foundational model for robust ocean color product generation remains lacking. To address this limitation, this study introduces the ocean color deep-learning foundation model (OCFM), a comprehensive framework designed to extract multiple global ocean color properties from satellite observations. The development of OCFM follows three sequential phases: pretraining with operational satellite products to learn fundamental theoretical relationships, fine-tuning with in situ measurements to align with the real ocean state, and deploying the trained OCFM for flexible retrieval of ocean color properties. Quantitative evaluation shows that, in end-user application tests, the model achieved a coefficient of determination (R-2) of 0.90 for primary productivity and 0.71 for water clarity, with corresponding mean absolute percentage differences of 44.08% and 17.28%, respectively. OCFM provides a more efficient solution for few-shot and unevenly distributed samples compared with traditional retrieval algorithms. Even with limited user resources and small sample sizes, additional downstream training with the trained OCFM can achieve state-of-the-art performance in retrieving ocean color properties. This study highlights the potential of a deep-learning foundation model for generalizable and few-shot ocean color retrieval. |
| WOS关键词 | PHYTOPLANKTON BLOOMS ; OPTICAL-PROPERTIES ; DATA PRODUCT ; TIME-SERIES ; COASTAL ; ALGORITHM ; CHLOROPHYLL ; WATERS ; REFLECTANCE ; RETRIEVAL |
| 资助项目 | National Natural Science Foundation of China[42376175] ; National Natural Science Foundation of China[42090044] ; National Natural Science Foundation of China[42221005] ; National Natural Science Foundation of China[42076200] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB42040401] ; China-Portugal Xinghai Belt and Road Joint Laboratory and Joint Research on new air and sea Technologies[2022YFE0204600] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001565162400031 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/203326]  |
| 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.Qingdao Key Lab Artificial Intelligence Oceanog, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Coll Marine Sci, Beijing 10049, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Yi,Wang, Haoyu,Li, Xiaofeng. Deep Learning for Enhanced Ocean Color Remote Sensing: A Foundation Model Approach[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:18. |
| APA | Yang, Yi,Wang, Haoyu,&Li, Xiaofeng.(2025).Deep Learning for Enhanced Ocean Color Remote Sensing: A Foundation Model Approach.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,18. |
| MLA | Yang, Yi,et al."Deep Learning for Enhanced Ocean Color Remote Sensing: A Foundation Model Approach".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):18. |
入库方式： OAI收割
来源：海洋研究所
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