Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-2723-2021
https://doi.org/10.5194/essd-13-2723-2021
Data description paper
 | 
15 Jun 2021
Data description paper |  | 15 Jun 2021

Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning

Yi-Ran Wang and Xiao-Ming Li

Viewed

Total article views: 4,184 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,027 1,073 84 4,184 94 94
  • HTML: 3,027
  • PDF: 1,073
  • XML: 84
  • Total: 4,184
  • BibTeX: 94
  • EndNote: 94
Views and downloads (calculated since 10 Nov 2020)
Cumulative views and downloads (calculated since 10 Nov 2020)

Viewed (geographical distribution)

Total article views: 4,184 (including HTML, PDF, and XML) Thereof 3,811 with geography defined and 373 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Nov 2024
Download
Short summary
Sea ice cover is the most fundamental factor that indicates the underlying great changes in the Arctic. We propose novel sea ice cover data in high resolution of a few hundred meters by spaceborne synthetic aperture radar, which is more than 10 times that of the operational sea ice cover and concentration data. The method is based on a deep learning architecture of U-Net. We have been processing data acquired by Sentinel-1 since 2014 to obtain high-quality sea ice cover data in the Arctic.
Altmetrics
Final-revised paper
Preprint