Hail Damage Detection: Integrating Sentinel-2 Images with Weather Radar Hail Kinetic Energy

Hailstorms represent one of the most damaging convective hazards for agriculture, yet quantifying their impacts at a landscape scale remains challenging due to their localized and short-lived nature. In this study, we combine weather radar parameters and Sentinel-2 multispectral imagery to assess vegetation damage caused by two major hail events in northeastern Romania: Rădăuți (17 July 2016) and Dolhasca (30 July 2020). Radar-derived hail kinetic energy (HKE) was used as a rapid temporal indicator of hail occurrence, with a threshold of 300 J m⁻² applied to delineate potentially affected areas. Sentinel-2 Level-1C imagery, selected under strict temporal and cloud cover criteria, was processed to generate pre- and post-event Normalized Difference Vegetation Index (NDVI) maps, from which NDVI differences (ΔNDVI) were computed. Thresholds of 0.10 and 0.20 were applied to identify moderate and severe vegetation stress, respectively. The results demonstrate strong spatial correspondence between radar-derived HKE cores and Sentinel-2 ΔNDVI reductions. In Rădăuți, where only one post-event image was available, ΔNDVI thresholds identified between 2236 and 5856 ha of affected vegetation within the HKE > 300 J m⁻² zone. In Dolhasca, where three post-event images were available (5, 8, and 15 days), the analysis revealed 6200–9100 ha affected at 5 days, decreasing to 4800–7200 ha at 8 days, and further to 3100–5600 ha at 15 days post-event. This temporal gradient highlights both the recovery of vegetation and the diminishing sensitivity of the ΔNDVI signal with increasing time elapsed since the event. Analysis by land use classes showed arable fields to be the most sensitive, followed by orchards and pastures, while forests exhibited smaller but persistent declines. This study demonstrates the robustness of integrating radar-derived hail kinetic energy with Sentinel-2 NDVI differencing for the spatiotemporal assessment of hail damage. The approach provides both rapid detection and temporally resolved mapping of hail damage, underlining the critical role of time as a determining factor in impact assessments. These findings have strong implications for operational crop monitoring, disaster response, and risk management in hail-prone regions.