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 article
 | 
04 Jul 2024
Data description article |  | 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

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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.
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