Articles | Volume 16, issue 1
The Cryosphere, 16, 87–101, 2022
https://doi.org/10.5194/tc-16-87-2022
The Cryosphere, 16, 87–101, 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 et al.

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.