Estimation of Hail Damage Using Crop Models and Remote Sensing

Insurance agents often provide crop hail damage estimates based on their personal experience and field samples, which are not always representative of the investigated field’s spatial variability. For these reasons, farmers insurance market ask for a reliable, objective, less labor-intensive method to determine losses. Integrating remote sensing modeling provides unique opportunity estimate damage. To this end, study was conducted eight distinct maize fields total 90 hectares. Five were damaged by hailstorm that occurred 13 July 2019 three damaged. Soil plant samples collected characterize experimental areas. The Surface Energy Balance Algorithm Land (SEBAL) deployed aboveground biomass obtainable yield at harvest, using Landsat 7 8 satellite images. Modeled damages (HDDSSAT1, coupling SEBAL DSSAT-based potential yield; HDDSSAT2, map harvest Decision Support System Agrotechnology Transfer (DSSAT)-based yield) calculated compared company (HDinsurance). SEBAL-based agreed with in-season measurements (−4% +0.5%, respectively). While some under overestimations observed, HDinsurance HDDSSAT1 averaged similar values (−4.9% +3.4%) reference approach (HDDSSAT2).