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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. 43-51

Automatic identification of sea ice leads from Suomi NPP satellite images

E.G. Boikaya 1 , K.G. Kortikova 1 , L.N. Dyment 1 , A.A. Ershova 1 
1 Arctic and Antarctic Research Institute, Saint Petersburg, Russia
Accepted: 18.09.2025
DOI: 10.21046/2070-7401-2025-22-6-43-51
The impact of dynamic factors on drifting ice cover in the Arctic seas causes formation of sea ice leads that can elongate up to several thousand kilometers. Ship movement along the favorable lead improves significantly the economic efficiency and safety of navigation. In this regard, information on sea ice leads is included as part of hydrometeorological support of voyages along the Northern Sea Route. The study of spatial and temporal variability of such characteristics of sea ice leads as prevailing orientation, length and spatial density in the water area requires a large amount of initial data. At present, low-resolution satellite images serve as a source of information on sea ice leads. However, so far there has been no automatic interpretation method to calculate all principal sea ice lead characteristics. The paper presents a method for automatic identification of leads from Suomi NPP infrared images with a spatial resolution of 375 m. The method is developed on the basis of a U-net convolutional neural network. The model is trained on data of manual expert interpretation of sea ice leads in 187 images of ice cover of the Laptev and East Siberian seas of 2021–2024 ice seasons. Jaccard coefficient is 0.64, and Dice coefficient is 0.77. After processing the image, the model creates a georeferenced monochrome image of the identified leads. Then individual lead objects are detected using the developed algorithm. Each lead object is a sequence of segments that correspond to relatively straight segments of the lead. The geographic coordinates of the segments ends are entered into the results file and are used to calculate the length and orientation of each individual lead. The developed method is verified using 30 Suomi NPP images of ice cover of the Laptev and East Siberian seas, preliminary interpreted by expert and excluded from model training. Based on the data from automatic and manual interpretation, the modal orientation and normalized length of leads are calculated, averaged over 100×100 km squares. The mean difference is 7° for the values of modal orientation and 15 m/km2 for normalized length.
Keywords: leads in ice cover, Arctic seas, automatic interpretation, satellite images, infrared range, neural networks
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