Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery
文献类型：期刊论文
| 作者 | Gao, Le1,2; Guo, Yuan1,2; Li, Xiaofeng1,2 |
| 刊名 | EARTH SYSTEM SCIENCE DATA
 |
| 出版日期 | 2024-09-13 |
| 卷号 | 16期号:9页码:4189-4207 |
| ISSN号 | 1866-3508 |
| DOI | 10.5194/essd-16-4189-2024 |
| 通讯作者 | Li, Xiaofeng(lixf@qdio.ac.cn) |
| 英文摘要 | Since 2008, the Yellow Sea has experienced the world's largest-scale marine disaster, the green tide, marked by the rapid proliferation and accumulation of large floating algae. Leveraging advanced artificial intelligence (AI) models, namely AlgaeNet and GANet, this study comprehensively extracted and analyzed green tide occurrences using optical Moderate Resolution Imaging Spectroradiometer (MODIS) images and microwave Sentinel-1 synthetic aperture radar (SAR) images. However, due to cloud and rain interference and the varying observation frequencies of the two types of satellites, the daily green tide coverage time series throughout the entire life cycle often contain large gaps and missing frames, resulting in discontinuity and limiting their use. Therefore, this study presents a continuous and seamless weekly average green tide coverage dataset with a resolution of 500 m, by integrating highly precise daily optical and SAR data for each week during the green tide breakout. The uncertainty assessment shows that this weekly product conforms to the life pattern of green tide outbreaks and exhibits parabolic-curve-like characteristics, with a low uncertainty (R-2=0.89 and RMSE=275 km(2)). This weekly dataset offers reliable long-term data spanning 15 years, facilitating research in forecasting, climate change analysis, numerical simulation, and disaster prevention planning in the Yellow Sea. The dataset is accessible through the Oceanographic Data Center, Chinese Academy of Sciences (CASODC), along with comprehensive reuse instructions provided at 10.12157/IOCAS.20240410.002 (Gao et al., 2024). |
| WOS关键词 | FLOATING-MACROALGAE ; COVERAGE ; GROWTH ; BLOOM ; ALGAE |
| 资助项目 | National Natural Science Foundation of China[42376175] ; National Natural Science Foundation of China[U2006211] ; National Natural Science Foundation of China[42090044] ; National Natural Science Foundation of China[42076200] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42040401] |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001312724500001 |
| 出版者 | COPERNICUS GESELLSCHAFT MBH |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/198701]  |
| 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.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Observat & Forecasting, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Le,Guo, Yuan,Li, Xiaofeng. Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery[J]. EARTH SYSTEM SCIENCE DATA,2024,16(9):4189-4207. |
| APA | Gao, Le,Guo, Yuan,&Li, Xiaofeng.(2024).Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery.EARTH SYSTEM SCIENCE DATA,16(9),4189-4207. |
| MLA | Gao, Le,et al."Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery".EARTH SYSTEM SCIENCE DATA 16.9(2024):4189-4207. |
入库方式： OAI收割
来源：海洋研究所
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