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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, 2026. Т. 23. № 3. С. 279-290

Analysis of errors in determining the phenology of lake ice cover using MODIS satellite data and neural networks

G.A. Kochergin 1 , M.A. Kupriyanov 1 , O.I. Sokolkov 1 , O.A. Baysalyamova 1 , A.M. Zolotareva 1 , M.A. Rusanov 1 , A.V. Melnikov 1 , Yu.M. Polishchuk 1 
1 Ugra Research Institute of Information Technologies, Khanty-Mansiysk, Russia
Accepted: 13.03.2026
DOI: 10.21046/2070-7401-2026-23-3-279-290
The paper examines error assessment of remote sensing measurements of seasonally frozen lake ice conditions using MODIS (Moderate Resolution Imaging Spectroradiometer) images on the basis of a neural network approach. The primary indicators used are the onset and end dates of ice freezing and melting, and the duration of ice cover on lakes. The state of the art of accurate lake ice condition determination using satellite images is analyzed on the basis of research conducted in various regions. Eight lakes of varying sizes with hydrological stations in Siberia and Southern Urals were selected for the study. The paper briefly describes the methodological aspects of visual determining ice condition indicators using MODIS images. The neural network approach to image classification used in the study is described. The paper discusses the results of MODIS measurements from 2008 to 2023 presented as time series graphs of five ice condition indicators obtained using neural network and visual methods. A comparison of the time series revealed that the trends in all parameters obtained from images using the neural network method are similar (sometimes almost identical) to those determined visually, and the cross-correlation coefficients of the studied parameters obtained by different methods are high. An assessment of the errors in determining lake ice regime parameters was made by comparison with data from hydrological stations. It was shown that both the mean absolute and root-mean-square errors of all studied parameters do not exceed the corresponding values obtained by other researchers in different regions of Eurasia and America, which can serve as a basis for concluding that the neural network approach to determining lake ice regime phenology parameters from MODIS images is promising.
Keywords: seasonally freezing lake, lake ice cover, freezing and thawing dates, ice cover duration, climate change, satellite image, neural network, remote sensing errors
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