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Volume 11, issue 4
The Cryosphere, 11, 1987–2002, 2017
https://doi.org/10.5194/tc-11-1987-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 11, 1987–2002, 2017
https://doi.org/10.5194/tc-11-1987-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 Aug 2017

Research article | 30 Aug 2017

New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

Carolina Gabarro et al.
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Revised manuscript accepted for TC
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Cited articles  
AMAP: Arctic Climate Issues 2011: Changes in Arctic Snow, Water, Ice and Permafrost, SWIPA 2011 Overview Report, Arctic Monitoring and Assessment Programme (AMAP), Oslo, xi + 97 pp., 2012.
Becker, F. and Choudhury, B. J.: Relative Sensitivity of Normalized Difference Vegetation Index (NDVI) and Microwave Polarization Difference Index (MPDI) for Vegetation and Desertification Monitoring, Remote Sens. Environ., 24, 297–311, https://doi.org/10.1016/0034-4257(88)90031-4, 1988.
Brodzik, M. J. and Knowles, K. W.: EASE-Grid: A Versatile Set of Equal-Area Projections and Grids, in: Discrete Global Grids, edited by: Goodchild, M., National Center for Geographic Information & Analysis, Santa Barbara, California, USA, 2002.
Burke, W., Schmugge, T., and Paris, J.: Comparison of 2.8- and 21-cm Microwave Radiometer Observations Over Soils With Emission Model Calculations, J. Geophys. Res., 84, 287–294, https://doi.org/10.1029/JC084iC01p00287, 1979.
Camps, A., Vall-llossera, M., Duffo, N., Torres, F., and Corbella, I.: Performance of Sea Surface Salinity and Soil Moisture Retrieval Algorithms with Different Ancillary Data Sets in 2D L-band Aperture Synthesis Interferometic Radiometers, IEEE T. Geosci. Remote, 43, 1189–1200, https://doi.org/10.1109/TGRS.2004.842096, 2005.
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We present a new method to estimate sea ice concentration in the Arctic Ocean using different brightness temperature observations from the Soil Moisture Ocean Salinity (SMOS) satellite. The method employs a maximum-likelihood estimator. Observations at L-band frequencies such as those from SMOS (i.e. 1.4 GHz) are advantageous to remote sensing of sea ice because the atmosphere is virtually transparent at that frequency and little affected by physical temperature changes.
We present a new method to estimate sea ice concentration in the Arctic Ocean using different...
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