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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. 5, pp. 114-125

Satellite assessment of actual sowing date for spring crops based on neural network model and multispectral KMSS sensor data series of high temporal resolution

D.E. Plotnikov 1 , P.A. Kolbudaev 1 , E.S. Elkina 1 , Ie.A. Dunaieva 2 
1 Space Research Institute RAS, Moscow, Russia
2 Research Institute of Agriculture of Crimea, Simferopol, Russia
Accepted: 17.10.2025
DOI: 10.21046/2070-7401-2025-22-5-114-125
An assessment of the informativeness of daily-resolution KMSS (multispectral satellite survey complex) multispectral satellite data series and NCEP (National Centers for Environmental Prediction) reanalysis data for the remote determination of the actual sowing date of spring crops using a Bi-LSTM (Bi-Directional Long Short-Term Memory) neural network model is presented. The study includes a series of experiments to compare the effectiveness of a number of multi-seasonal and multimodal neural network models using multi-year satellite, agro-climatic, and field data for the 2018–2022 growing seasons in Kursk Oblast and the Republic of Crimea. The best-performing model was a multi-seasonal multimodal model trained on five growing seasons, achieving a MAE (mean absolute error) of 4.2 days regardless of the crop type, indicating the model’s generalizing power. Furthermore, the model was tested on yet unseen area (Orenburg Oblast) and growing season (2023), for which a sowing date information product with a spatial resolution of 60 meters was generated, resulting in MAE of 5.8 days. Further evaluation of the multi-seasonal model’s generalization capability with the inclusion of additional growing seasons is required.
Keywords: LOWESS, KMSS, LSTM, multiseasonal model, spring crops, sowing date
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