Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data

The ongoing war in Ukraine has significantly disrupted agricultural land use, leading to reduced cropland areas, increased land abandonment, and heightened uncertainty in food production. This study presents a multi-year assessment of war-induced agricultural land use changes in Ukraine using machine learning-based classification applied to Sentinel-1 and Sentinel-2 satellite imagery. By leveraging cloud computing platforms, including Google Earth Engine (GEE) and the Copernicus Data Space Ecosystem (CDSE), we develop high-resolution KPI-Ukraine (Igor Sikorsky Kyiv Polytechnic Institute (KPI) in Ukraine) land use maps spanning from 2016 to 2024. The study integrates Random Forest and Multi-Layer Perceptron classification techniques to improve accuracy, addressing spectral ambiguities and classification noise. Additionally, a novel transfer learning approach enables reliable classification in conflict-affected areas with limited ground-truth data. We achieved high classification accuracy across the 14 major crop types in Ukraine and abandoned land, validated through F1-scores exceeding 90% for most classes. The fusion of the results generated on the GEE and CDSE platforms enhanced the classification accuracy for minor classes. Our analysis reveals significant reductions in cultivated land in 2022-2024, particularly in conflict zones, where agricultural activity has been heavily disrupted. Overall, Ukraine’s arable land area shrunk by 10% nationwide. The consistently high accuracy of our classification methodology across the nine-year study period demonstrates its robustness and suitability for long-term monitoring of agricultural dynamics in conflict-affected regions and provides a valuable tool for guiding post-war recovery efforts. Our findings underscore the importance of leveraging satellite data for timely and accurate land use monitoring, supporting policymakers in addressing food security challenges and promoting sustainable agricultural practices. This framework also holds potential for broader applications in monitoring land use changes in conflict zones and regions undergoing rapid environmental shifts.

Ключові слова

Agricultural land use change, War impact, Cropland, Uncultivated lands, Ukraine, Google Earth Engine, Copernicus Data Space Ecosystem, Sentinels, Machine learning

Бібліографічний опис

Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data / Nataliia Kussul, Andrii Shelestov, Bohdan Yailymov, Hanna Yailymova, Guido Lemoine, Klaus Deininger // International Journal of Applied Earth Observation and Geoinformation. - Volume 140. - June 2025. - 104551.