The Cryosphere
The Cryosphere
TC
Articles | Volume 16, issue 1
Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-87-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-87-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 1
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

Data sets

RPAS snow depth map Branden Walker, Evan Wilcox and Phil Marsh https://doi.org/10.5683/SP2/PWSKKG

JulienMeloche/Gaussian_process_smrt_simulation J. Meloche https://doi.org/10.5281/zenodo.5806672

Model code and software

JulienMeloche/Gaussian_process_smrt_simulation J. Meloche https://doi.org/10.5281/zenodo.5806672

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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.
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