Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4189-2024
https://doi.org/10.5194/essd-16-4189-2024
Data description article
 | 
13 Sep 2024
Data description article |  | 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: 3,076 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,115 864 97 3,076 186 97 125
  • HTML: 2,115
  • PDF: 864
  • XML: 97
  • Total: 3,076
  • Supplement: 186
  • BibTeX: 97
  • EndNote: 125
Views and downloads (calculated since 06 May 2024)
Cumulative views and downloads (calculated since 06 May 2024)

Viewed (geographical distribution)

Total article views: 3,076 (including HTML, PDF, and XML) Thereof 3,032 with geography defined and 44 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Feb 2026
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.
Share
Altmetrics
Final-revised paper
Preprint