Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-87-2022
https://doi.org/10.5194/tc-16-87-2022
Research article
 | 
06 Jan 2022
Research article |  | 06 Jan 2022

Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox

Viewed

Total article views: 2,821 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,981 756 84 2,821 65 61
  • HTML: 1,981
  • PDF: 756
  • XML: 84
  • Total: 2,821
  • BibTeX: 65
  • EndNote: 61
Views and downloads (calculated since 28 May 2021)
Cumulative views and downloads (calculated since 28 May 2021)

Viewed (geographical distribution)

Total article views: 2,821 (including HTML, PDF, and XML) Thereof 2,719 with geography defined and 102 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Jun 2024
Download
Short summary
To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.