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
Viewed
Total article views: 3,403 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
2,541
764
98
3,403
112
152
HTML: 2,541
PDF: 764
XML: 98
Total: 3,403
BibTeX: 112
EndNote: 152
Views and downloads (calculated since 02 Feb 2024)
Cumulative views and downloads
(calculated since 02 Feb 2024)
Total article views: 2,480 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,960
465
55
2,480
71
114
HTML: 1,960
PDF: 465
XML: 55
Total: 2,480
BibTeX: 71
EndNote: 114
Views and downloads (calculated since 04 Jul 2024)
Cumulative views and downloads
(calculated since 04 Jul 2024)
Total article views: 923 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
581
299
43
923
41
38
HTML: 581
PDF: 299
XML: 43
Total: 923
BibTeX: 41
EndNote: 38
Views and downloads (calculated since 02 Feb 2024)
Cumulative views and downloads
(calculated since 02 Feb 2024)
Viewed (geographical distribution)
Total article views: 3,403 (including HTML, PDF, and XML)
Thereof 3,293 with geography defined
and 110 with unknown origin.
Total article views: 2,480 (including HTML, PDF, and XML)
Thereof 2,406 with geography defined
and 74 with unknown origin.
Total article views: 923 (including HTML, PDF, and XML)
Thereof 887 with geography defined
and 36 with unknown origin.
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...
