Articles | Volume 16, issue 12
https://doi.org/10.5194/essd-16-5737-2024
https://doi.org/10.5194/essd-16-5737-2024
Data description article
 | 
18 Dec 2024
Data description article |  | 18 Dec 2024

A submesoscale eddy identification dataset in the northwest Pacific Ocean derived from GOCI I chlorophyll a data based on deep learning

Yan Wang, Ge Chen, Jie Yang, Zhipeng Gui, and Dehua Peng

Viewed

Total article views: 3,081 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,934 647 500 3,081 214 81 146
  • HTML: 1,934
  • PDF: 647
  • XML: 500
  • Total: 3,081
  • Supplement: 214
  • BibTeX: 81
  • EndNote: 146
Views and downloads (calculated since 30 May 2024)
Cumulative views and downloads (calculated since 30 May 2024)

Viewed (geographical distribution)

Total article views: 3,081 (including HTML, PDF, and XML) Thereof 3,020 with geography defined and 61 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 11 Feb 2026
Download
Short summary
Mesoscale eddies are ubiquitous in the ocean and account for 90 % of its kinetic energy, but their generation and dissipation are difficult to observe using current remote sensing technology. Our submesoscale eddy dataset, formed by suppressing large-scale circulation signals and enhancing small-scale chlorophyll structures, has important implications for understanding marine environments and ecosystems, as well as improving climate model predictions.
Share
Altmetrics
Final-revised paper
Preprint