The Cryosphere
The Cryosphere
TC
Articles | Volume 18, issue 11
Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-5347-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 11
Research article
 | 
21 Nov 2024
Research article |  | 21 Nov 2024

Using deep learning and multi-source remote sensing images to map landlocked lakes in Antarctica

Anyao Jiang, Xin Meng, Yan Huang, and Guitao Shi

Viewed

Total article views: 2,157 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,763 331 63 2,157 102 63 95
  • HTML: 1,763
  • PDF: 331
  • XML: 63
  • Total: 2,157
  • Supplement: 102
  • BibTeX: 63
  • EndNote: 95
Views and downloads (calculated since 28 Sep 2023)
Cumulative views and downloads (calculated since 28 Sep 2023)

Viewed (geographical distribution)

Total article views: 2,157 (including HTML, PDF, and XML) Thereof 2,095 with geography defined and 62 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Oct 2025
Download
Short summary
Landlocked lakes are crucial to the Antarctic ecosystem and sensitive to climate change. Limited research on their distribution prompted us to develop an automated detection process using deep learning and multi-source satellite imagery. This allowed us to accurately determine the landlocked lake open water (LLOW) area in Antarctica, generating high-resolution time series data. We find that the changes in positive and negative degree days predominantly drive variations in the LLOW area.
Read more
Share