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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. 4, pp. 118-132

Analysis of satellite data on ice conditions on rivers in Murmansk Region using machine learning: The case of the Kola River

I.M. Lazareva 1 , E.V. Zabolotskikh 2 , O.I. Lyash 1 , G.S. Shelegov 3 
1 Murmansk Arctic University, Murmansk, Russia
2 Russian State Hydrometeorological University, Saint Petersburg, Russia
3 EMERCOM Main Office for Murmansk Region, Murmansk, Russia
Accepted: 20.06.2025
DOI: 10.21046/2070-7401-2025-22-4-118-132
Complex climatic conditions in the Arctic region influence the process of ice cover formation in northern rivers. Monitoring ice conditions is an important component of systems designed to prevent dangerous phenomena on inland water bodies, such as floods, high water levels and early ice formation. Synthetic aperture radar images from the Sentinel-1 satellite platform were used to assess ice conditions on rivers in Murmansk Region. The section of the Kola River near the gauging station at 1429 km of the Oktyabrskaya Railway, for which field observation data are available, was analyzed. A logistic regression model built using backscatter data from VV and VH polarizations made it possible to determine the probability of ice appearance in each pixel of the image of the analyzed river section, as well as to visualize the patterns of ice formation. The task of classifying ice phenomena was addressed using a fully connected multilayer perceptron (MLP) neural network. The choice of this architecture was due to the relatively small size of the training sample and the flexibility it offers in terms of adaptation and extension, for instance, by adding new layers or adjusting the number of neurons in existing layers to improve performance. The model was trained to classify three categories: ice drift, open water, and off-season ice events. As part of the research, software modules were developed to analyze Sentinel-1A, -1B satellite images for detecting ice phenomena on rivers in Murmansk Region. Future work will focus on adapting the toolkit to utilize data from Russian satellites.
Keywords: satellite data analysis, Sentinel-1, river ice phenomena, machine learning methods, Arctic region
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References:

  1. Agafonova S. A., Frolova N. L., Vasilenko A. N., Shirocova V. A., Ice regime and dangerous hydrological phenomena on rivers of the arctic zone of European Russia, Vestnik Moskovskogo universiteta. Ser. 5. Geografiya, 2016, No. 6, pp. 41–49 (in Russian).
  2. Kozlov D. V., Buzin V. A., Frolova N. L., Agafonova S. A., Baburin V. L., Banshchikova L. S., Goroshkova N. I., Zavadskij A. S., Krylenko I. N., Savel’ev K. L., Kozlov K. D., Buzina L. F., Opasnye ledovye yavleniya na rekakh i vodokhranilishchakh Rossii: Monografiya (Dangerous ice phenomena on rivers and reservoirs of Russia: Monograph), D. V. Kozlov (ed.), Moscow: Izd. RGAU-MSKHA imeni K. A. Timiryazeva, 2015, 348 p. (in Russian).
  3. Mikhaylyukova P. G., Agafonova S. A., Frolova N. L., Analysis of ice situation on the Sukhona River (near the town of Veliky Ustyug) based on SAR imagery, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, V. 17, No. 2, pp. 162–175 (in Russian), DOI: 10.21046/2070-7401-2020-17-2-162-175.
  4. Shilin B. V., Kuznetsov A. Y., Place of video spectral imaging among remote sensing methods, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, V. 19, No. 1, pp. 9–24 (in Russian), DOI: 10.21046/2070-7401-2022-19-1-9-24.
  5. Abdelkader M., Bravo Mendez J. H., Temimi M. et al., Google Earth Engine platform to integrate multi-satellite and citizen science data for the monitoring of river ice dynamics, Remote Sensing, 2024, V. 16, No. 8, Article 1368, DOI: 10.3390/rs16081368.
  6. Beaton A., Whaley R., Corston K., Kenny F., Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario, Remote Sensing of Environment, 2019, V. 224, pp. 352–364, DOI: 10.1016/j.rse.2019.02.011.
  7. Chaouch N., Temimi M., Romanov P. et al., An automated algorithm for river ice monitoring over the Susquehanna River using the MODIS data, Hydrological Processes, 2014, V. 28, Iss. 1, pp. 62–73, DOI: 10.1002/hyp.9548.
  8. Chu T., Lindenschmidt K.-E., Integration of space-borne and air-borne data in monitoring river ice processes in the Slave River, Canada, Remote Sensing of Environment, 2016, V. 181, pp. 65–81, DOI: 10.1016/j.rse.2016.03.041.
  9. Filipponi F., Sentinel-1 GRD preprocessing workflow, Proceedings, 2019, V. 18, No. 1, Article 11, 4 p., DOI: 10.3390/ECRS-3-06201.
  10. Fukś M., Assessment of the impact of dam reservoirs on river ice cover — an example from the Carpathians (central Europe), The Cryosphere, 2024, V. 18, Iss. 5, pp. 2509–2529, DOI: 10.5194/tc-18-2509-2024.
  11. Gatto L. W., Monitoring river ice with Landsat images, Remote Sensing of Environment, 1990, V. 32, No. 1, pp. 1–16, DOI: 10.1016/0034-4257(90)90094-3.
  12. Geudtner D., Torres R., Sentinel-1 system overview and performance, IEEE Intern. Geoscience and Remote Sensing Symp., 2012, pp. 1719–1721, DOI: 10.1109/IGARSS.2012.6351191.
  13. Heinilä K., Mattila O.-P., Metsämäki S. et al., A novel method for detecting lake ice cover using optical satellite data, Intern. J. Applied Earth Observation and Geoinformation, 2021, V. 104, Article 102566, DOI: 10.1016/j.jag.2021.102566.
  14. Hicks F., An overview of river ice problems: CRIPE07 guest editorial, Cold Regions Science and Technology, 2009, V. 55, No. 2, pp. 175–185, DOI: 10.1016/j.coldregions.2008.09.006.
  15. Li Z., Lindenschmidt K.-E., Coherence of Radarsat-2, Sentinel-1, and ALOS-1 PALSAR for monitoring spatiotemporal variations of river ice covers, Canadian J. Remote Sensing, 2018, V. 44, No. 1, pp. 11–25, DOI: 10.1080/07038992.2018.1419424.
  16. Liu B., Ji H., Zhai Y., Luo H., Estimation of river ice thickness in the Shisifenzi reach of the Yellow River with remote sensing and air temperature data, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2023, V. 16, pp. 5645–5659, DOI: 10.1109/JSTARS.2023.3285229.
  17. Łoś H., Pawłowski B., The use of Sentinel-1 imagery in the analysis of river ice phenomena on the lower Vistula in the 2015–2016 winter season, 2017 Signal Processing Symp. (SPSympo), 2017, pp. 1–5, DOI: 10.1109/SPS.2017.8053663.
  18. Moalemi I., Kheyrollah Pour H., Scott K. A., Assessing ice break-up trends in Slave River Delta through satellite observations and random forest modeling, Remote Sensing, 2024, V. 16, No. 12, Article 2244, DOI: 10.3390/rs16122244.
  19. Newton B., Beltaos S., Burrell B. C., Ice regimes, ice jams, and a changing hydroclimate, Saint John (Wolastoq) River, New Brunswick, Canada, Natural Hazards, 2024, V. 120, pp. 12613–12642, DOI: 10.1007/s11069-024-06736-5.
  20. Purnell D., Dabboor M., Matte P. et al., Observations of river ice breakup using GNSS-IR, SAR, and machine learning, IEEE Trans. Geoscience and Remote Sensing, 2024, V. 62, pp. 1–13, DOI: 10.1109/TGRS.2024.3380554.
  21. Richards E., Stuefer S., Correa Rangel R. et al., An evaluation of GPR monitoring methods on varying river ice conditions: A case study in Alaska, Cold Regions Science and Technology, 2023, V. 210, Article 103819, DOI: 10.1016/j.coldregions.2023.103819.
  22. Singh A., Ray N., Loewen M., Ray N., River ice segmentation with deep learning, IEEE Trans. Geoscience and Remote Sensing, 2020, V. 58, No. 11, pp. 7570–7579, DOI: 10.1109/TGRS.2020.2981082.
  23. Sobiech J., Dierking W., Observing lake- and river-ice decay with SAR: advantages and limitations of the unsupervised k-means classification approach, Annals of Glaciology, 2013, V. 54, No. 62, pp. 65–72, DOI: 10.3189/2013AoG62A037.
  24. Sola D., Scott K. A., Efficient shallow network for river ice segmentation, Remote Sensing, 2022, V. 14, No. 10, Article 2378, DOI: 10.3390/rs14102378.
  25. Stonevicius E., Uselis G., Grendaite D., Ice detection with Sentinel-1 SAR backscatter threshold in long sections of temperate climate rivers, Remote Sensing, 2022, V. 14, No. 7, Article 1627, DOI: 10.3390/rs14071627.
  26. Surdu C. M., Duguay C. R., Kheyrollah Pour H., Brown L. C., Ice freeze-up and break-up detection of shallow lakes in Northern Alaska with spaceborne SAR, Remote Sensing, 2015, V. 7, No. 5, pp. 6133–6159, DOI: 10.3390/rs70506133.
  27. Temimi M., Abdelkader M., Tounsi A. et al., An automated system to monitor river ice conditions using Visible Infrared Imaging Radiometer Suite imagery, Remote Sensing, 2023, V. 15, No. 20, Article 4896, DOI: 10.3390/rs15204896.
  28. Vuyovich C. M., Daly S. F., Gagnon J. J. et al., Monitoring river ice conditions using web-based cameras, J. Cold Regions Engineering, 2009, V. 23, No. 1, pp. 1–17, DOI: 10.1061/(ASCE)0887-381X(2009)23:1(1).
  29. Yang X., Pavelsky T. M., Allen G. H., The past and future of global river ice, Nature, 2020, V. 577, No. 7788, pp. 69–73, DOI: 10.1038/s41586-019-1848-1.
  30. Zhang X., Zhao Z., Ran L. et al., FastICENet: A real-time and accurate semantic segmentation model for aerial remote sensing river ice image, Signal Processing, 2023, V. 212, Article 109150, DOI: 10.1016/j.sigpro.2023.109150.
  31. Zhang Y., Qiu Y., Li Y. et al., Spatial-temporal variation of river ice coverage in the Yenisei river from 2002 to 2021, J. Hydrology, 2024, V. 637, Article 131440, DOI: 10.1016/j.jhydrol.2024.131440.
  32. Zhao W., Xue Y., Han F. et al., Research on semantic segmentation algorithm of high latitude urban river ice based on deep transfer learning, Intern. J. Remote Sensing, 2024, V. 45, No. 13, pp. 4278–4299, DOI: 10.1080/01431161.2024.2360695.