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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 6, pp. 241-259

SMOS MIRAS data for estimation of sea ice concentration

J.V. Sokolova 1 , V.V. Tikhonov 1, 2, 3 , D.R. Katamadze , T.A. Alekseeva 2, 1 , E.V. Afanasyeva 2, 1 , I.V. Khvostov 3 , A.N. Romanov 3 
1 Space Research Institute RAS, Moscow, Russia
2 Arctic and Antarctic Research Institute, Saint Petersburg, Russia
3 Institute for Water and Environmental Problems SB RAS, Barnaul, Russia
Accepted: 26.09.2025
DOI: 10.21046/2070-7401-2025-22-6-241-259
The paper explores the feasibility of using low-frequency data from the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) radiometer installed on the SMOS (Soil Moisture and Ocean Salinity) satellite to estimate sea ice concentration. The XGBoost (eXtreme Gradient Boosting) machine learning method was used to calculate sea ice concentration. Results obtained were compared with reference data from the Arctic and Antarctic Research Institute (AARI), as well as with outputs of the most common algorithms based on processing data from the AMSR-2 (Advanced Microwave Scanning Radiometer 2) and SSMIS (Special Sensor Microwave Imager/Sounder) microwave radiometers. The analysis showed that during the winter period, the SMOS-based algorithm provides accuracy comparable to or exceeding that of high-frequency algorithms, demonstrating resilience to variability in ice emissivity and meteorological conditions. In summer, all algorithms exhibit an increase in errors: for high-frequency algorithms, this is due to presence of meltwater on ice surface, while for the low-frequency algorithm, it is caused by the transparency of ice thinner than 50 cm at L-band. Additionally, false increases in ice concentration, associated with the lack of weather filters in the machine learning algorithm, were identified during the ice-free season. The obtained results confirm the potential of using low-frequency measurements from SMOS MIRAS in sea ice concentration estimation. The best efficiency is expected through their synergistic use with data from high-frequency radiometers AMSR-2 and SSMIS.
Keywords: sea ice concentration, satellite microwave radiometry, machine learning, Arctic, SMOS
Full text

References:

  1. Alekseeva T. A., Sokolova J. V., Afanasyeva E. V. et al., The contribution of sea-ice contamination to inaccuracies in definition of sea-ice concentration retrieval from satellite microwave radiometry data during the ice-melt period, Izvestiya, Atmospheric and Oceanic Physics, 2022, V. 58, No. 12, pp. 1470–1484, DOI: 10.1134/S0001433822120039.
  2. Afanasyeva E. V., Alekseeva T. A., Sokolova J. V. et al., AARI methodology for sea ice charts composition, Russian Arctic, 2019, No. 7, pp. 5–20 (in Russian), DOI: 10.24411/2658-4255-2019-10071.
  3. Zabolotskikh E. V., Khvorostovsky K. S., Zhivotovskaya M. A. et al., Satellite microwave remote sensing of the Arctic sea ice: Review, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, V. 20, No. 1, pp. 9–34 (in Russian), DOI: 10.21046/2070-7401-2023-20-1-9-34.
  4. Tikhonov V. V., Raev M. D., Sharkov E. A. et al., Satellite microwave radiometry of sea ice of polar regions: A review, Izvestiya, Atmospheric and Oceanic Physics, 2016, V. 52, No. 9, pp. 1012–1030, DOI: 10.1134/S0001433816090267.
  5. Tikhonov V. V., Alekseeva T. A., Afanasyeva E. V. et al., On the possibility to determine the concentration of Arctic sea ice using SMOS satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, V. 20, No. 6, pp. 329–335 (in Russian), DOI: 10.21046/2070-7401-2023-20-6-329-335.
  6. Tikhonov V. V., Katamadze D. R., Alekseeva T. A. et al. (2024a), Analysis of ice concentration in the Kara Sea based on SMOS MIRAS data using machine learning methods, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, V. 21, No. 6, pp. 344–355 (in Russian), DOI: 10.21046/2070-7401-2024-21-6-344-355.
  7. Tikhonov V. V., Khvostov I. V., Romanov A. N., Sharkov E. A. (2024b), A model of microwave emission from mouth regions of Arctic rivers providing for radiometer pixel land contamination, Izvestiya, Atmospheric and Oceanic Physics, 2024, V. 60, No. 9, pp. 1020–1030, DOI: 10.1134/S0001433824700981.
  8. Alekseeva T., Tikhonov V., Frolov S. et al., Comparison of Arctic sea ice concentrations from the NASA Team, ASI, and VASIA2 algorithms with summer and winter ship data, Remote Sensing, 2019, V. 11, No. 21, Article 2481, 31 p., DOI: 10.3390/rs11212481.
  9. Andersen S., Tonboe R., Kern S., Schyberg H., Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields: An intercomparison of nine algorithms, Remote Sensing of Environment, 2006, V. 104, No. 4, pp. 374–392, DOI: 10.1016/j.rse.2006.05.013.
  10. Comiso J. C., Characteristics of Arctic winter sea ice from satellite multispectral microwave observations, J. Geophysical Research: Oceans, 1986, V. 91, No. C1, pp. 975–994, DOI: 10.1029/JC091iC01p00975.
  11. Comiso J. C., SSM/I sea ice concentrations using the Bootstrap algorithm, Greenbelt, Maryland: Goddard Space Flight Center, NASA, 1995, 57 p.
  12. Comiso J. C., Cavalieri D. J., Markus T., Sea ice concentration, ice temperature, and snow depth using AMSR-E data, IEEE Trans. Geoscience and Remote Sensing, 2003, V. 41, No. 2, pp. 243–252, DOI: 10.1109/TGRS.2002.808317.
  13. Copernicus Imaging Microwave Radiometer (CIMR) Mission Requirements Document, Iss. 5, Earth and Mission Science Division, 2023, 269 p.
  14. Gabarró C., Gupta M., Inter-comparison of high and low microwave frequency sea ice concentration algorithms, OSI SAF Visiting Scientist Activity Report AVS_2017_05, EUMETSAT OSI SAF, 2018, 31 p.
  15. Gabarro C., Turiel A., Elosegui P. et al., New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator, The Cryosphere, 2017, V. 11, No. 4, pp. 1987–2002, DOI: 10.5194/tc-11-1987-2017.
  16. Global Sea Ice Concentration (AMSR2) (2017a), EUMETSAT OSI SAF, 2017, DOI: 10.15770/EUM_SAF_OSI_NRT_2023.
  17. Global Sea Ice Concentration (netCDF) — DMSP (2017b), EUMETSAT SAF on Ocean and Sea Ice, 2017, DOI: 10.15770/EUM_SAF_OSI_NRT_2004.
  18. Hersbach H., Bell B., Berrisford P. et al., ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2023, DOI: 10.24381/cds.adbb2d47.
  19. Hwang P. A., Foam and roughness effects on passive microwave remote sensing of the ocean, IEEE Trans. Geoscience and Remote Sensing, 2012, V. 50, No. 8, pp. 2978–2985, DOI: 10.1109/TGRS.2011.2177666.
  20. Ivanova N., Pedersen L. T., Tonboe R. T. et al., Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations, The Cryosphere, 2015, V. 9, No. 5, pp. 1797–1817, DOI: 10.5194/tc-9-1797-2015.
  21. Kaleschke L., Maaß N., Haas C. et al., A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice, The Cryosphere, 2010, V. 4, No. 4, pp. 583–592, DOI: 10.5194/tc-4-583-2010.
  22. Kaleschke L., Tian-Kunze X., Maaß N. et al., Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period, Geophysical Research Letters, 2012, V. 39, No. 5, Article L05501, 5 p., DOI: 10.1029/2012GL050916.
  23. Kern S., Lavergne T., Notz D. et al., Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations, The Cryosphere, 2019, V. 13, No. 12, pp. 3261–3307, DOI: 10.5194/tc-13-3261-2019.
  24. Kilic L., Prigent C., Aires F. et al., Ice concentration retrieval from the analysis of microwaves: A new methodology designed for the Copernicus Imaging Microwave Radiometer, Remote Sensing, 2020, V. 12, No. 7, Article 1060, 14 p., DOI: 10.3390/rs12071060.
  25. Kwok R., Comiso J. C., Martin S., Drucker R., Ross Sea polynyas: Response of ice concentration retrievals to large areas of thin ice, J. Geophysical Research: Oceans, 2007, V. 112, No. C12, Article C12012, 13 p., DOI: 10.1029/2006JC003967.
  26. Lu J., Heygster G., Spreen G., Atmospheric correction of sea ice concentration retrieval for 89 GHz AMSR-E observations, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2018, V. 11, No. 5, pp. 1442–1457, DOI: 10.1109/JSTARS.2018.2805193.
  27. Lu J., Scarlat R., Heygster G., Spreen G., Reducing weather influences on an 89 GHz sea ice concentration algorithm in the Arctic using retrievals from an optimal estimation method, J. Geophysical Research: Oceans, 2022, V. 127, No. 9, Article e2019JC015912, 31 p., DOI: 10.1029/2019JC015912.
  28. Lubin D., Massom R., Polar remote sensing. V. I: Atmosphere and oceans, Berlin, Heidelberg: Springer, 2006, 756 p., DOI: 10.1007/3-540-30785-0.
  29. Meier W. N., Fetterer F., Windnagel A. K., Stewart J. S., NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration. Version 4, Boulder, Colorado, USA: National Snow and Ice Data Center, 2021, DOI: 10.7265/EFMZ-2T65.
  30. Oliva R., Daganzo E., Richaume P. et al., Status of Radio Frequency Interference (RFI) in the 1400–1427 MHz passive band based on six years of SMOS mission, Remote Sensing of Environment, 2016, V. 180, pp. 64–75, DOI: 10.1016/j.rse.2016.01.013.
  31. Paţilea C., Heygster G., Huntemann M., Spreen G., Combined SMAP – SMOS thin sea ice thickness retrieval, The Cryosphere, 2019, V. 13, No. 2, pp. 675–691, DOI: 10.5194/tc-13-675-2019.
  32. Pedersen L. T., Retrieval of sea ice concentration by means of microwave radiometry: PhD thesis, Denmark: Electromagnetics Institute, 1991, 148 p.
  33. Ricker R., Hendricks S., Kaleschke L. et al., A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 2017, V. 11, No. 4, pp. 1607–1623, DOI: 10.5194/tc-11-1607-2017.
  34. Rückert J. E., Rostosky P., Huntemann M. et al., Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition, Elementa: Science of the Anthropocene, 2023, V. 11, No. 1, 22 p., DOI: 10.1525/elementa.2023.00039.
  35. Schmitt A. U., Kaleschke L., Consistent combination of brightness temperatures from SMOS and SMAP over polar oceans for sea ice applications, Remote Sensing, 2018, V. 10, No. 4, Article 553, 20 p., DOI: 10.3390/rs10040553.
  36. Shokr M., Sinha N., Sea ice: Physics and remote sensing. Hoboken, New Jersey: American Geophysical Union, John Wiley and Sons, Inc., 2015, 579 p., DOI: 10.1002/9781119028000.
  37. Spreen G., Kaleschke L., Heygster G., Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophysical Research: Oceans, 2008, V. 113, No. C2, Article C02S03, 14 p., DOI: 10.1029/2005JC003384.
  38. Tian-Kunze X., Kaleschke L., Maaß N. et al., SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification, The Cryosphere, 2014, V. 8, No. 3, pp. 997–1018, DOI: 10.5194/tc-8-997-2014.
  39. Tonboe R. T., Nandan V., Mäkynen M. et al., Simulated geophysical noise in sea ice concentration estimates of open water and snow-covered sea ice, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2022, V. 15, pp. 1309–1326, DOI: 10.1109/JSTARS.2021.3134021.