Remote sensing methods for detecting and mapping hailstorm damage: a case study from the 20 July 2020 hailstorm, Baragan Plain, Romania

Claudiu-Valeriu Angearu (), Irina Ontel (), Anisoara Irimescu (), Burcea Sorin () and Emma Dodd ()
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Claudiu-Valeriu Angearu: National Meteorological Administration
Irina Ontel: National Meteorological Administration
Anisoara Irimescu: National Meteorological Administration
Burcea Sorin: National Meteorological Administration

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 114, issue 2, No 38, 2013-2040

Abstract: Abstract Hail is one of the dangerous meteorological phenomena facing society. The present study aims to analyse remote sensing methods for detecting and assessing hailstorm damage on agricultural crops. The event used as a case study occurred on 20 July 2020 within Traian commune/Administrative Territorial Units, north of Baragan Plain. The analysis was performed for agricultural areas using: optical satellite imagery from Sentinel-2A, Landsat-8 and Terra MODIS; Soil Water Index (SWI) derived from Sentinel-1 SAR satellite imagery; and weather radar data. The change detection method (difference between pre- and post-event data) was applied. Based on Sentinel-2A images and using a threshold of more than 0.05 in the Normalized Difference Vegetation Index (NDVI) difference between 14 and 21 July, it was found that 3,142.98 ha were affected. Results show that the intensity of hail damage was directly proportional to the Land Surface Temperature (LST) difference derived from Landsat − 8 between 15 and 31 July. LST difference values higher than 12 °C were observed in areas where NDVI decreased by 0.4–0.5. By comparing a hail mask extracted from NDVI with the SWI difference from 14 and 21 July, it was confirmed that the hail event occurred and caused the most damage in the west of the analysed area. This is supported by large values (greater than 55 dBZ) of weather radar reflectivity, indicating medium–large hail. This research also shows that satellite data is useful for cross-validation of surface-based weather reports and weather radar-derived products.

Keywords: Hail; NDVI; LST; SWI; Sentinel; Landsat; Weather radar (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05457-x

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