GDCM: Generalized data completion model for satellite observations
文献类型：期刊论文
| 作者 | Wang, Haoyu1,2 ; Zhou, Yinfei1,2; Li, Xiaofeng2
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| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2025-07-01 |
| 卷号 | 324页码:18 |
| 关键词 | Satellite remote sensing Data completion CNNs Attention mechanisms |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2025.114760 |
| 通讯作者 | Li, Xiaofeng(lixf@qdio.ac.cn) |
| 英文摘要 | Ocean remote sensing data is crucial in understanding the global climate system. Due to satellite orbital coverage gaps and cloud cover, satellite ocean remote sensing products have significant data gaps. This paper introduces a Generalized Data Completion Model (GDCM) based on deep learning to reconstruct gap-free and cloud-free key oceanic variables such as sea surface temperature (SST), wind speed, water vapor, cloud liquid water, and precipitation rate derived from polar-orbiting satellite sensors including Advanced Microwave Scanning Radiometer 2 (AMSR2), the Special Sensor Microwave Imager (SSMI), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Utilizing Convolutional Neural Networks (CNNs) and attention mechanisms, the GDCM model effectively leverages spatio-temporal information within remote sensing data to fill in missing regions accurately. We use reanalysis data to simulate various data missing scenarios during model training for model development. We tested the model with the US East Coast region's global-coverage AMSR2/SSMI and localcoverage MODIS datasets. The experiments demonstrate that the GDCM model successfully and precisely completes the data for different satellites and types of missing data. To enable the model to capture enough data for the dynamical change patterns, we used seven consecutive days of observation data as inputs to improve the model's data-completion ability, significantly enhancing the handling of MODIS SST missing data due to cloud cover. When the input data's duration increased from one day to seven days, the model's R2 value improved from 0.062 to 0.93, and the Root Mean Square Difference (RMSD) decreased from 6.58 to 0.92. Besides the model framework design, we implemented the incremental learning training strategy to enhance the model's data completion capability for different types of missing data, especially for SST data from AMSR2 satellites. The model's completed SST data R2 value improved from 0.93 to 0.99, and the RMSD decreased from 2.64 degrees C to 0.50 degrees C. The Mean Absolute Difference (MAD) of water vapor data decreased from 0.88 kg/m2 to 0.76 kg/m2, and the RMSD decreased from 1.39 kg/m2 to 1.27 kg/m2. This study provides a generalized new solution to the problem of missing ocean data at different resolutions, contributing to a more comprehensive and supporting ocean science research and related applications. |
| WOS关键词 | SEA-SURFACE TEMPERATURE ; SST ; INSTRUMENT ; ALGORITHMS ; MODIS |
| 资助项目 | Science and Technology Innovation Project for Laoshan Laboratory[LSKJ202204303] ; Science and Technology Innovation Project for Laoshan Laboratory[LSKJ202204302] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000] ; Innovative Research Group Program of the National Natural Science Foundation of China[42221005] |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001476970700001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/201671]  |
| 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.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Haoyu,Zhou, Yinfei,Li, Xiaofeng. GDCM: Generalized data completion model for satellite observations[J]. REMOTE SENSING OF ENVIRONMENT,2025,324:18. |
| APA | Wang, Haoyu,Zhou, Yinfei,&Li, Xiaofeng.(2025).GDCM: Generalized data completion model for satellite observations.REMOTE SENSING OF ENVIRONMENT,324,18. |
| MLA | Wang, Haoyu,et al."GDCM: Generalized data completion model for satellite observations".REMOTE SENSING OF ENVIRONMENT 324(2025):18. |
入库方式： OAI收割
来源：海洋研究所
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