## 8 Conclusions  
In this work, we present a methodology to map cash crops on a large geographical scale, for purposes of weather-related risk assessment. Cash crops are agricultural crops grown to be sold for profit, so losses on cash crops due to weather-related events such as flood, drought, and hail, can produce a large financial impact. Such impact may be especially dangerous for countries that strongly depend on their production like the Caribbean islands and Central American countries, so risk assessment and consequent insurance is necessary to keep those countries somehow safer from the financial effects of extreme weather events. A founding part of risk assessment is exposure mapping, i.e., mapping the spatial distribution of resources that are at stake. Mapping agricultural resources on a large area such the one addressed in this study may be very costly and may weigh excessively on the overall balance of cash crop businesses. Confronted with this problem, we developed a novel technique for large-scale crop mapping, largely relying on open data and algorithms offering low computational burdens. Specifically, two mapping techniques were merged, unsupervised classification on time-series of multispectral data, and climate-based zoning. Each of these techniques individually would be insufficient for cash crop mapping, but a suitable combination leads to our goal while curbing the cost of data procurement and processing. The space-based mapping technique clusters' homogeneous crop areas, i.e., areas showing an evolution in time of vegetative parameters that is \"sufficiently homogeneous\" within each region and \"sufficiently different\" across regions to conclude that areas of homogeneous crop species were identified. The agro-climatic technique can instead identify crops potentially grown at each different environmental condition. Each technique individually provides results with a certain degree of ambiguity, but merging information from both results in sensible crop maps, through cross-resolution of conveyed ambiguities. Figure 12: Comparison between estimated area vs. FAO’s statistics in the considered countries. We tested the developed methodology over the Caribbean and Central America countries. The resulting cash crop maps have been validated both visually and by comparison with FAO's data. On 294 different checkpoints and reference areas, the comparison of the mapped crops with geo-referenced pictures available via Google Panoramio showed very good agreement. In order to provide an additional assessment of the quality of results, FAO's data of the harvested area by countries and crops were compared with mapping results, showing mismatch figures below 10%. This mismatch level is considered sufficient for risk assessment purposes because weather threats to crop yield (droughs, excess of rain, tropical cyclones, etc.) tend to affect medium- to large-sized areas, thus averaging out local classification errors. With respect to other possible approaches to large-scale crop mapping, the main advantages of the proposed method are its lower cost (i.e., less manpower required while still maintaining high standards of accuracy) lower computational burden and its flexibility (geo-spatial evidence from other sources than satellite data, with special reference to crowdsourced pictures which may help to correctly label each homogeneous area). In the context of risk assessment, the importance of exposure data is fundamental. However, the accuracy and resolution of these data should be coherent with the purposes and the requirements of the risk model applied. Often, the approximations involved in the vulnerability or hazard component of the model would make overcast the accuracy and the resolution of exposure crop data. The developed methodology allows for a direct application on other territories since it only makes use of globally accessible and available data and of unsupervised automatic techniques.