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21 October 2019 Remotely sensed data to support insurance strategies in agriculture
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Abstract
Climate variability is one of the greatest risks for farmers. The ongoing increase of natural calamities suggests that insurance strategies have to be more dynamic than previously. In this work a remote sensing-based service prototype is presented aimed at supporting insurance companies by defining an operative tool to objectively calibrate insurance annual fares, tending to cost reduction able to attract more potential customers. Methodology was applied to an agriculture devoted area located in the Vercelli province (Piemonte - NW Italy). COPERNICUS Sentinel-2 Level 2A image time series were used for this purpose jointly with MODIS data. High resolution Sentinel-2 data (GSD = 10 m) were used to map local spatial differences of crop performance, aimed at locally tuning insurance risk and fares around the average one estimated with reference to MODIS data on a longer period. The agricultural seasons 2018 were considered for this purpose. Although the work with MODIS data was carried out by authors in previous works, their integration with S2 data proved to locally tune at single field and crop type level the agronomic performances of insured areas.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
F. Sarvia, S. De Petris, and E. Borgogno-Mondino "Remotely sensed data to support insurance strategies in agriculture", Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491H (21 October 2019); https://doi.org/10.1117/12.2533117
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