Caracterización del daño causado por granizo en la región agrícola argentina utilizando datos de sensores remotos satelitales ópticos y radares

Abstract

Weather hazards are becoming more frequent and intense as a result of climate change. Hailstorms usually cause the total loss of the harvest, exceeding the financial capacity of agricultural producers. For this reason, in Argentina, this risk is transferred to specialized agricultural insurance companies. After a hailstorm, the affected field is inspected by an insurance claims adjuster to assess yield loss.Assessment accuracy depends largely on detection of Homogeneous Damage Zones (HDZ). Currently, these areas are quantified in situ and their Identification becomes complex in large plots with tall crops and heterogeneous damage. This Thesis presents an algorithm for automatic detection of homogeneous hail damage. Unsupervised machine learning techniques are applied to vegetation indices calculated from satellite data provided by the Sentinel -1 and -2 missions of the EU's Copernicus Programme. The first step of the algorithm is the pre-processing