Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 7
Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3125-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3125-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 7
Data description paper
 | 
04 Jul 2024
Data description paper |  | 04 Jul 2024

A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet

Haibin Ye, Chaoyu Yang, Yuan Dong, Shilin Tang, and Chuqun Chen

Viewed

Total article views: 1,595 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,246 279 70 1,595 50 54
  • HTML: 1,246
  • PDF: 279
  • XML: 70
  • Total: 1,595
  • BibTeX: 50
  • EndNote: 54
Views and downloads (calculated since 02 Feb 2024)
Cumulative views and downloads (calculated since 02 Feb 2024)

Viewed (geographical distribution)

Total article views: 1,595 (including HTML, PDF, and XML) Thereof 1,542 with geography defined and 53 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Nov 2024
Download
Short summary
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.
Read more
Altmetrics
Final-revised paper
Preprint