Sergii Skakun

Timely and reliable crop area estimates are essential for food security assessments. Remote sensing provides scalable, cost-effective solutions, contingent on rigorous uncertainty quantification. When in situ data collection is required, two-stage sampling designs—where primary sampling units (PSUs) partition the study region and secondary sampling units (SSUs) are sampled within selected PSUs—are widely adopted. Although in-season crop type maps (ISCTMs) are increasingly used for stratification in area estimation, their integration into SSU stratification within two-stage frameworks remains limited.This study evaluates the use of ISCTMs to inform SSU stratification in two-stage sampling for multi-crop area estimation. Monte Carlo simulations were run over Ukrainian winter croplands, varying map accuracy (user’s and producer’s accuracy from 0.5 to 0.9), SSU sampling strategy (simple vs. stratified random sampling), estimator type (stratified vs. regression), and design parameters (PSU size and number, total sample size). Performance was assessed for a prevalent crop (winter cereals, 15.8%) and a rare crop (rapeseed, 2.2%). Across 12,600 parameter combinations with 500 repetitions each, stratified SSU sampling combined with a regression estimator consistently minimized estimation variance. User’s accuracy (UA) had a stronger effect than producer’s accuracy in reducing uncertainties, particularly for rare classes. A 5% coefficient of variation was achieved for both crops with UA ≥ 0.8 and PSU sizes ≥10 km using crop-specific stratification. These results demonstrate that ISCTMs enable effective stratification in two-stage designs, supporting simultaneous multi-crop estimation, including rare classes, and substantially improving sampling efficiency and cost-effectiveness of ground surveys.

The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On June 6, 2023, the Kakhovka Dam was destroyed. The loss of this critical agricultural and energy infrastructure has left most farmers on the East bank of the Dnipro River without irrigation in the historically arid Kherson and Zaporizhzhia oblasts.Based on satellite analysis, pre-war irrigated areas between 2019 to 2021 were on average 187.94 ± 35.80 kHa, approximately 4% of all cropland and 8% of summer season cropland in Kherson and Zaporizhzhia oblasts. These numbers decreased in 2022 to 140.04 ± 17.55 kHa with the expanded occupation of these territories by Russia, seeing an additional decline in 2023 to 74.97 ± 17.84 kHa. Fields in 2023 also exhibited variability in irrigation, ranging interrupted irrigation owing to the destruction of the Kakhovka reservoir, to sufficiently irrigated. Irrigated areas diminished further in 2024 to 15.95± 9.97 kHa, a 90% decline from pre-war averages. Formerly irrigated fields before 2023 were either abandoned or converted to winter cropland by 2024–a pattern that continued into 2025 (total irrigated area 5.76± 0.87 kHa). These results of irrigation loss demonstrate a significant change in cropping patterns in response to the loss of irrigation as a result of conflict.

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.