Articles | Volume 11, issue 4
The Cryosphere, 11, 1987–2002, 2017
https://doi.org/10.5194/tc-11-1987-2017
The Cryosphere, 11, 1987–2002, 2017
https://doi.org/10.5194/tc-11-1987-2017

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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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Svenja Lange on behalf of the Authors (08 Feb 2017)  Author's response
ED: Referee Nomination & Report Request started (22 Feb 2017) by Lars Kaleschke
RR by Mohammed Shokr (04 Mar 2017)
RR by Anonymous Referee #4 (08 Mar 2017)
RR by Anonymous Referee #2 (10 Mar 2017)
ED: Reconsider after major revisions (23 Mar 2017) by Lars Kaleschke
AR by Anna Mirena Feist-Polner on behalf of the Authors (01 Jun 2017)  Author's response
ED: Referee Nomination & Report Request started (03 Jun 2017) by Lars Kaleschke
RR by Anonymous Referee #4 (20 Jun 2017)
RR by Anonymous Referee #2 (25 Jun 2017)
ED: Publish subject to minor revisions (Editor review) (08 Jul 2017) by Lars Kaleschke
AR by Anna Wenzel on behalf of the Authors (18 Jul 2017)  Author's response
ED: Publish subject to technical corrections (18 Jul 2017) by Lars Kaleschke
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Short summary
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.