The Cryosphere
The Cryosphere
TC
Articles | Volume 15, issue 6
Articles | Volume 15, issue 6
https://doi.org/10.5194/tc-15-2835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-2835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 6
Research article
 | 
24 Jun 2021
Research article |  | 24 Jun 2021

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling

Viewed

Total article views: 2,193 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,437 686 70 2,193 55 67
  • HTML: 1,437
  • PDF: 686
  • XML: 70
  • Total: 2,193
  • BibTeX: 55
  • EndNote: 67
Views and downloads (calculated since 11 Jan 2021)
Cumulative views and downloads (calculated since 11 Jan 2021)

Viewed (geographical distribution)

Total article views: 2,193 (including HTML, PDF, and XML) Thereof 2,166 with geography defined and 27 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.
Read more