Flooded and Irrigation Area Monitoring After the Kakhovka Dam Disaster Based on Machine Learning and Satellite Data

This study aims to assess the impact of the Kakhovka Dam destruction in Ukraine that occurred on June 6, 2023, on cropland irrigation using satellite remote sensing data. The main goal of this study is to assess flooded areas and the impact on irrigated area before and after Kakhovka Dam destruction. In particular, we analyzed flooded areas in 2023 and the changes in irrigated areas before and after the dam destruction (in 2019 and 2024) was also assessed. Maps of water bodies were generated before and after the flood using Sentinel-1, Sentinel-2, and Landsat-9 images. The random forest (RF) classifier was used for flooded areas mapping, while the multi-layer perceptron (MLP) and U-shaped Network (U-Net) classifier were used for irrigated land identification. The main findings are as follows: (1) As of June 9, 2023, the total area of flooding under the Kakhovka Dam was 47.33 th. ha (473 km²), affecting 1.67 th. ha of cropland, 0.97 th. ha of forests, 12.3 th. ha of grasslands, 1.85 th. ha of settlements, and 29.4 th. ha of wetlands; (2) The analysis of irrigated area shows a decrease in irrigated cropland - from 351 th. ha in 2019 to 38 th. ha. in 2024. (3) The classification accuracy for 2019 irrigation mapping achieved 90.4% overall accuracy with F1-scores of 90.4% for both irrigated and non-irrigated classes based on ground truth data. (4) Complete disappearance of water in irrigation canals was documented, indicating systematic destruction of agricultural infrastructure with far-reaching consequences for regional food security. The flood also affected areas along the Ingulets River, which led to the flooding of agricultural land located near the river banks and affected water quality. The disappearance of water in canals used to irrigate cropland is also analyzed, indicating disruption of irrigation systems and possible far-reaching consequences for agriculture. Thus, this study shows the utility of satellite remote sensing and machine learning approaches for rapid monitoring and quantification of flood-related natural disaster impacts and analysis of irrigated areas in conflict-affected regions.