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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 3
The Cryosphere, 8, 997–1018, 2014
https://doi.org/10.5194/tc-8-997-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 8, 997–1018, 2014
https://doi.org/10.5194/tc-8-997-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 May 2014

Research article | 27 May 2014

SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification

X. Tian-Kunze1, L. Kaleschke1, N. Maaß1, M. Mäkynen2, N. Serra1, M. Drusch3, and T. Krumpen4 X. Tian-Kunze et al.
  • 1Institute of Oceanography, University of Hamburg, Bundesstraße 53, 20146 Hamburg, Germany
  • 2Finnish Meteorological Institute, Erik Palmenin aukio 1, 00560 Helsinki, Finland
  • 3European Space Agency, ESA-ESTEC, 2200 AG Noordwijk, the Netherlands
  • 4Alfred Wegener Institute for Polar and Marine Research, Bussestraße 24, 27570 Bremerhaven, Germany

Abstract. Following the launch of ESA's Soil Moisture and Ocean Salinity (SMOS) mission, it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In the first demonstration study, sea ice thickness up to 50 cm has been derived using a semi-empirical algorithm with constant tie-points. Here, we introduce a novel iterative retrieval algorithm that is based on a thermodynamic sea ice model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within the SMOS spatial resolution are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS-based sea ice thickness data set from 2010 on. The data set is compared to and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study (Kaleschke et al., 2012).

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