## 6 Information Fusion Strategy  
The spectrally homogeneous areas extracted from satellite imagery (method described in Section 4) and the crop potential areas (method described in Section 5) are individually insufficient to identify the location of the selected crops for purposes of risk assessment. Fusion of both approaches leads to establishing a relationship between the agro-climatic requirements of potential crops with their spectral responses in situ, thus opening the doors for mapping at a very large scale. The main aim of the fusion strategy consists of assigning to each identified spectrally homogeneous region the most likely one among all the locally possible crops. This fusion process was carried out using all the data information in raster format, so the analysis of the homogeneous areas were performed through the analysis of the pixels inside the agro-potential areas of each entire country. In order to identify more clearly the membership of an area/pixel to one type of crop or another, the identification process was firstly performed in the areas without agro-potential uncertainty (just one type of crop with agro-potential in that area), and then the identified homogeneous classes were applied to the areas/pixels with agro-potential uncertainty (two or more crops sharing the same potential area). The first criteria to classify a homogeneous class was the potential crop type with the higher percentage of suitable sub-area for the areas without uncertainties. After obtaining the homogeneous classes related to each type of crop, we merged the areas/pixels with uncertainties, and then we summed the areas covered by the different selected homogeneous classes to estimate the amount of covered area of each crop, making sure at the same time that they were close to the reported statistics of harvested area (in this study, the data from the Food and Agriculture Organization), and that the identified areas covered the checkpoints. In cases where the obtained total area was far below the reported statistics, we returned to the first step and included the following homogeneous class with higher area percentage to the analyzed crop, provided it was not selected for another crop, and repeat the process until we were close to the target. In this case, the group of homogeneous classes selected were labeled as the analyzed crop. In the contrary case, when the total labeled area was far above the statistic, we discarded the selected homogeneous class and chose another class with smaller coverage area and repeated the process. Crop areas are mutually exclusive, so if an homogeneous class or a group of homogeneous classes were labeled as a certain type of crop, they can not be labeled as another type. Once the assignation was completed and the conditions had been verified, the next step was to extract from the homogeneous areas the assigned classes and create the final map of crop areas. A test was carried out on the region of San Miguel, El Salvador, which was first analyzed using high-resolution satellite imagery processing through fusing two different sources of multispectral spaceborne remote sensing images (i.e., Landsat-8 and Sentinel-2) and crowdsourcing data (e.g., in situ georeferenced photos).