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.

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Cited articles

Brodzik, M. J., Long, D. G., and Hardman, M. A.: Best practices in crafting the calibrated, Enhanced-Resolution passive-microwave EASE-Grid 2.0 brightness temperature Earth System Data Record, Remote Sens., 10, 1793, https://doi.org/10.3390/rs10111793, 2018. 
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Derksen, C., Sturm, M., Liston, G. E., Holmgren, J., Huntington, H., Silis, A., and Solie, D.: Northwest Territories and Nunavut snow characteristics from a subarctic traverse: Implications for passive microwave remote sensing, J. Hydrometeorol., 10, 448–463, https://doi.org/10.1175/2008JHM1074.1, 2009. 
Derksen, C., Toose, P., Rees, A., Wang, L., English, M., Walker, A., and Sturm, M.: Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data, Remote Sens. Environ., 114, 1699–1709, https://doi.org/10.1016/j.rse.2010.02.019, 2010. 
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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.