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

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Cited articles

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Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.: Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review, Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011WR010745, 2011. 
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.