The Cryosphere
The Cryosphere
TC
Articles | Volume 11, issue 4
Articles | Volume 11, issue 4
https://doi.org/10.5194/tc-11-1987-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-11-1987-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 11, issue 4
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, Antonio Turiel, Pedro Elosegui, Joaquim A. Pla-Resina, and Marcos Portabella

Viewed

Total article views: 3,488 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,951 1,322 215 3,488 182 257
  • HTML: 1,951
  • PDF: 1,322
  • XML: 215
  • Total: 3,488
  • BibTeX: 182
  • EndNote: 257
Views and downloads (calculated since 03 Nov 2016)
Cumulative views and downloads (calculated since 03 Nov 2016)

Viewed (geographical distribution)

Total article views: 3,488 (including HTML, PDF, and XML) Thereof 3,201 with geography defined and 287 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 11 Jul 2025
Download
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.
Read more
Share