Articles | Volume 8, issue 8
https://doi.org/10.5194/gmd-8-2611-2015
https://doi.org/10.5194/gmd-8-2611-2015
Model description paper
 | 
24 Aug 2015
Model description paper |  | 24 Aug 2015

MEMLS3&a: Microwave Emission Model of Layered Snowpacks adapted to include backscattering

M. Proksch, C. Mätzler, A. Wiesmann, J. Lemmetyinen, M. Schwank, H. Löwe, and M. Schneebeli

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Cited articles

Arnaud, L., Picard, G., Champollion, N., Domine, F., Gallet, J. C., Lefebvre, E., Fily, M., and Barnola, J. M.: Measurement of vertical profiles of snow specific surface area with a 1 cm resolution using infrared reflectance: instrument description and validation, J. Glaciol., 57, 17–29, https://doi.org/10.3189/002214311795306664, 2011.
Chandrasekhar, S.: Radiative Transfer, Dover Publ., New York, NY, 1960.
Chang, W., Tan, S., Lemmetyinen, J., Tsang, L., Xu, X., and Yueh, S.: Dense media radiative transfer applied to SnowScat and SnowSAR, IEEE J. Sel. Top. Appl., 7, 3811–3825, https://doi.org/10.1109/JSTARS.2014.2343519, 2014.
Denoth, A., Foglar, A., Weiland, P., Mätzler, C., and Aebischer, H.: A comparative study of instruments for measuring the liquid water content of snow, J. Appl. Phys., 56, 2154–2160, https://doi.org/10.1063/1.334215, 1984.
Ding, K.-H., Xu, X., and Tsang, L.: Electromagnetic scattering by bicontinuous random microstructures with discrete permittivities, IEEE T. Geosci. Remote, 48, 3139–3151, 2010.
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Short summary
The measurement of snow properties on global scale relies on microwave remote sensing data. The interpretation of the data is however challenging. Here we introduce MEMLS3&a, an extension of the snow emission model MEMLS, to include a backscatter model for active microwave remote sensing. In MEMLS3&a, snow input parameters can be derived by objective measurement methods, which avoids fitting the scattering efficiency of snow. The model is validated with combined active and passive measurements.
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