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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, 2024, Vol. 21, No. 6, pp. 344-355

Analysis of ice concentration in the Kara Sea based on SMOS MIRAS data using machine learning methods

V.V. Tikhonov 1, 2, 3 , D.R. Katamadze 4 , T.A. Alekseeva 3, 1 , E.V. Afanasyeva 3, 1 , J.V. Sokolova 3, 1 , I.V. Khvostov 2 , A.N. Romanov 2 
1 Space Research Institute RAS, Moscow, Russia
2 Institute for Water and Environmental Problems SB RAS, Barnaul, Russia
3 Arctic and Antarctic Research Institute, Saint Petersburg, Russia
4 Lomonosov Moscow State University, Moscow, Russia
Accepted: 29.11.2024
DOI: 10.21046/2070-7401-2024-21-6-344-355
The paper presents the first results of a study on the use of data from Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) of Soil Moisture and Ocean Salinity (SMOS) satellite to determine ice concentration in the Kara Sea. The SMOS L1C brightness temperature dynamics in 2022–2023 has been studied for eight regions of the Kara Sea. Machine learning methods were used to determine ice concentration in the selected areas from the SMOS L1C data. The study involved eight machine learning models employing a supervised learning approach. The training was performed based on data for 2022. The quality of predictions by the machine learning models was verified using data for 2023. By analyzing the three quality metrics, the optimal machine learning model (XGBoost) was selected as capable of obtaining good results in determining ice concentration from SMOS MIRAS data. The conducted studies show the prospect of employing machine learning to assess the state of the Arctic sea ice cover using data from the MIRAS radiometer of the SMOS satellite.
Keywords: satellite microwave radiometry, brightness temperature, sea ice concentration, visible range, infrared range, machine learning methods
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