The Cryosphere
The Cryosphere
TC
Articles | Volume 15, issue 3
Articles | Volume 15, issue 3
https://doi.org/10.5194/tc-15-1551-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-1551-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 3
Research article
 | 
26 Mar 2021
Research article |  | 26 Mar 2021

Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

Stephan Paul and Marcus Huntemann

Viewed

Total article views: 3,299 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,881 1,322 96 3,299 83 86
  • HTML: 1,881
  • PDF: 1,322
  • XML: 96
  • Total: 3,299
  • BibTeX: 83
  • EndNote: 86
Views and downloads (calculated since 13 Jul 2020)
Cumulative views and downloads (calculated since 13 Jul 2020)

Viewed (geographical distribution)

Total article views: 3,299 (including HTML, PDF, and XML) Thereof 3,167 with geography defined and 132 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 Nov 2024
Download
Short summary
Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.
Read more