Assessment of hail damages in maize using remote sensing and comparison with an insurance assessment: case study in Lombardy

The aim of this work is the detection of medium-low damages (i.e. between 10 and 30% of the Gross Salable Production) using the much-used Normalized Difference Vegetative Index (NDVI) in comparison with alternative vegetation indices (i.e., NDVI, ARVI, MCARI, SAVI, MSAVI, MSAVI2) and their change from pre-event to post-event in five hailstorms in Lombardy in 2020. Seventy-four overlapping scenes (<10 cloud cover) were collected from the Sentinel-2 in the spring-summer period of 2018 in Brescia district (Lombardy). . A database of 125 fields (average size 4 Ha) surveys after the hailstorm collected from the insurance allowed for the selection of the dates on which the event occurred and provided a proxy of the extent of the damage (in % of the decrease of the yield). Hail and strong wind damages registered ranged from 5 to 70% was used for the comparison with the satellite image change detection.The differences in the vegetative indices obtained by Sentinel 2 before and after the hailstorm and the insurance assessments of damage after the events were compared to assess the degree of concordance. he modified soil-adjusted vegetation index (MSAVI) index outperformed other vegetation indices to detect the hail-related damages, with the highest accuracy (73.3%). Future research will evaluate how much of the uncertainty can be found in the method's limitations with vegetative indices derived from satellites, how much is due to errors in estimating damage to the ground, and how much is due to other causes. ; JRC.D.3 - Land Resources and Supply Chain Assessments