## 1 Introduction  
Whereas subsistence crops are meant to support the producers or their livestock, cash crops are agricultural crops grown with the intended purpose of selling them for profit. Since cash crops are destined for future sale, they hold a relevant financial potential during their growth season, which is constantly in danger due to various types of threats, including weather-related threats. Among these latter threats, we should surely account extreme weather events, including:  
* excess rainfall, and consequent floods and damage to crops;
* hail, destroying growing plants;
* extreme drought, leading to death of plants.  
Events like those outlined above may have a significant impact on those nations whose Gross Domestic Product (GDP) is largely supported by agricultural activities. Mapping cash crops at a national, or even super-national level when regional strategies are concerned, is a necessary action to assess exposure. Thus, cash crop mapping is indeed a crucial part of any large-scale risk assessment actions intended to define a risk-mitigation--or at least an insurance--strategy to be put in place.  
The work described in this paper was carried out on the Caribbean area, but designed with a global perspective in mind, in order to make it reusable anywhere else in the world. This produced the following implications:  
* _avoidance of local datasets_; local datasets, such as municipality-level records of crop seeding, or surveying results from local authorities, are obviously not reusable out of their own geographical scope, but especially they may be severely inhomogeneous across different countries or regions. Global datasets were to be used, albeit at the cost of lower precision, accuracy, or resolution.
* _leveling of expected quality_; highly-refined components of the risk equation are of little use where they have to be forcibly combined with much coarser ones in the same risk computation.  
Typically, when large-scale mapping of vegetation is required, a popular approach to the problem is based on spaceborne remote sensing [1], for which different land cover classification strategies are available in literature. Hyperspectral remote sensing is a good option when classification of vegetation is concerned [2].  
Spaceborne remote sensing complies with the requirements set out above, as it is inherently global, and the widest range of possible classification approaches, from the coarsest to the finest, from the leanest to the most data-hungry, ensures a suitable approach can be defined for each expected quality level.  
In our case, we complemented the selected classification approaches through smart fusion of unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy believed to be reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task. Section 2 presents the context and explains the general terms of the problem from a risk assessment perspective. The following Section 3 users into the specific mapping problem. Section 4 presents some possible, standard strategies for large-scale crop mapping based on multispectral spaceborne data. Section 5 illustrates the system of constraints that originate from ranges of environmental conditions suitable for the growth of each type of cash crops. Section 6 reports on how the space-based data, environmental constraints and ground-based data can be jointly exploited to map cash crops even with limited resources. Section 7 presents results and Section 8 draws some conclusions.