ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 2, pp. 32-42

Sea ice cover detection in the Russian Far Eastern seas using NOAA 20 VIIRS measurements and a neural network

M.O. Kuchma 1 , Z.N. Lotareva 1 , L.A. Korneva 1 , Yu.A. Shamilova 1 
1 Far Eastern Center of SRC Planeta, Khabarovsk, Russia
Accepted: 25.03.2022
DOI: 10.21046/2070-7401-2022-19-2-32-42
In this paper, we consider the technology for calculating the ice cover mask using a convolutional neural network on the data of VIIRS measurements from the NOAA-20 satellite. Specialists of the Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta” (FEC SRC Planeta) collected a training dataset using data from October 2020 to June 2021 that amounted to about 22 thousand textures. The optimal neural network architecture for solving the problem was obtained by the empirical method. During the experiments, the optimal size of input textures was obtained, which was 21×21 pixels. In the same way, the input parameters were obtained, which were the solar zenith angle and infrared channels with central wavelengths of 0.6, 1.6, 10.7, and 12.0 µm. As reference data, ice cover masks were used, manually created by experienced decoders of FEC SRC Planeta. When compared with the data of the Community Satellite Processing Package VIIRS Aerosols, Cryosphere, Clouds and Volcanic Ash Environmental Data Record Products, the validation results of the developed algorithm showed high accuracy and probability of correct event identification — 94 and 98 %, respectively.
Keywords: remote sensing, VIIRS, NOAA-20, convolutional neural network, texture, ice, ice cover mask
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