Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 9
Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4189-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-4189-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 9
Data description paper
 | 
13 Sep 2024
Data description paper |  | 13 Sep 2024

Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery

Le Gao, Yuan Guo, and Xiaofeng Li

Viewed

Total article views: 2,718 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,890 760 68 2,718 163 86 118
  • HTML: 1,890
  • PDF: 760
  • XML: 68
  • Total: 2,718
  • Supplement: 163
  • BibTeX: 86
  • EndNote: 118
Views and downloads (calculated since 06 May 2024)
Cumulative views and downloads (calculated since 06 May 2024)

Viewed (geographical distribution)

Total article views: 2,718 (including HTML, PDF, and XML) Thereof 2,651 with geography defined and 67 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Dec 2025
Download
Short summary
Since 2008, the Yellow Sea has faced a significant ecological issue, the green tide, which has become one of the world's largest marine disasters. Satellite remote sensing plays a pivotal role in detecting this phenomenon. This study uses AI-based models to extract the daily green tide from MODIS and SAR images and integrates these daily data to introduce a continuous weekly dataset, which aids research in disaster simulation, forecasting, and prevention.
Read more
Share
Altmetrics
Final-revised paper
Preprint