State Key Laboratory of Tropical Oceanography & Guangdong Key Laboratory of Ocean Remote Sensing, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
Chaoyu Yang
South China Sea Marine Forecast and Hazard Mitigation Center, Ministry of Natural Resource, Guangzhou, China
Key Laboratory of Marine Environment Survey Technology and Application, Ministry of Natural Resource, Guangzhou, China
Yuan Dong
State Key Laboratory of Tropical Oceanography & Guangdong Key Laboratory of Ocean Remote Sensing, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
State Key Laboratory of Tropical Oceanography & Guangdong Key Laboratory of Ocean Remote Sensing, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
Chuqun Chen
State Key Laboratory of Tropical Oceanography & Guangdong Key Laboratory of Ocean Remote Sensing, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
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A deep-learning model for gap-filling based on expected variance was developed. OI-SwinUnet achieves good performance reconstructing chlorophyll-a concentration data on the South China Sea. The reconstructed dataset depicts both the spatiotemporal patterns at the seasonal scale and a fast-change process at the weather scale. Reconstructed data show chlorophyll perturbations of individual eddies at different life stages, giving academics a unique and complete perspective on eddy studies.
A deep-learning model for gap-filling based on expected variance was developed. OI-SwinUnet...
