Articles | Volume 18, issue 2
https://doi.org/10.5194/tc-18-747-2024
https://doi.org/10.5194/tc-18-747-2024
Research article
 | 
20 Feb 2024
Research article |  | 20 Feb 2024

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17

Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros

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Cited articles

Baghdadi, N., Saba, E., Aubert, M., Zribi, M., and Baup, F.: Evaluation of Radar Backscattering Models IEM, Oh, and Dubois for SAR Data in X-Band Over Bare Soils, IEEE Geosci. Remote Sens. Lett., 8, 1160–1164, https://doi.org/10.1109/LGRS.2011.2158982, 2011. 
Bateni, S. M., Margulis, S. A., Podest, E., and McDonald, K. C.: Characterizing Snowpack and the Freeze–Thaw State of Underlying Soil via Assimilation of Multifrequency Passive/Active Microwave Data: A Case Study (NASA CLPX 2003), IEEE T. Geosci. Remote, 53, 173–189, https://doi.org/10.1109/TGRS.2014.2320264, 2015. 
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Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
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