## 3 Results
### Qualitative Assessment with Visual Inspection  
Figures 4 and 5 illustrate the sites we selected to perform qualitative assessment through visual inspection. In Figure 4, there are examples of successful cocoa classification performed in our study. Figure 5 showcases cocoa misclassification examples, i.e., commission and omission errors. In both figures, each column corresponds to a single zoomed-in site, where the panels in the first row illustrate the 2021 Sentinel-2 median composite image at a 10 m spatial resolution. The panels in the second row include the classified full-sun cocoa by our study. In the third and fourth row, the cocoa classification performed by [PERSON] et al. [34] and [PERSON] et al. [45] are illustrated, respectively. Since these authors did not specify the cocoa cultivation type they studied, our legend refers to cocoa in general. All classifications were overlaid on top of very high-spatial-resolution Google Earth images for more reliable and detailed interpretation. The capture dates of the Google Earth images are provided on the respective panels. In Figure 4a, a primary forest area in Ghana is illustrated with some cocoa encroachment evident therein. Our random forest successfully detected the cocoa trees and delineated them from the forest trees. The CNN model by [PERSON] et al. [34] performed in the same manner. In contrast, the random forest by [PERSON] et al. [45] classified in a seemingly arbitrary and random fashion. Figure 4b exemplifies the typical heterogeneous and complex landscape in Ghana. In Figure 4b, patches of self-standing cocoa trees are mixed with other shade trees, smallholder oil palm trees, bare ground, grassland and a settlement. Our random forest correctly classified the full-sun cocoa, but some sparse shade trees and grassland in between were also classified as cocoa. The remaining land-use/cover was correctly classified as non-cocoa with minor discrepancies. [PERSON] et al. [34] classified this site in a similar way with small deviations by assigning shade trees and some bare ground to the cocoa class. However, [PERSON] et al. [45] omitted identifying full-sun cocoa and their model seems to have classified at random. Figure 3: The 2021 classified map of full-sun-cocoa-growing areas at a 10 m spatial resolution over the study area in Côte d’Ivoire and Ghana. White pixels correspond to masked-out pixels. The map is available via [[https://my-cocoa-project-102021.projects.eartheengine.app/view/cocoa-mapping-west-africa](https://my-cocoa-project-102021.projects.eartheengine.app/view/cocoa-mapping-west-africa)]([https://my-cocoa-project-102021.projects.eartheengine.app/view/cocoa-mapping-west-africa](https://my-cocoa-project-102021.projects.eartheengine.app/view/cocoa-mapping-west-africa)) (accessed on 2 February 2024). Figure 4: _Cont._  
Figure 4: Illustrative example sites in (**a–c**) Ghana and (**d–f**) Côte d’Ivoire, where our random forest performed well in classifying full-sun cocoa (second row). Cocoa maps classified by [PERSON] et al. [34] and [PERSON] et al. [45] were inspected for comparative analysis (third and fourth row, respectively). The 2021 Sentinel-2 median composite image is provided in true colour (first row). Classified maps were overlaid on top of Google Earth images for finer interpretation (capture dates are attached). Latin numbers refer to the geographic location of the sites relative to the study area, as presented in Figure A2. Legend and north arrow apply to all maps. Figure 5: _Cont._  
Figure 4c depicts another site in Ghana with full-sun cocoa mixed with shade trees, forming a dense canopy at certain points (i.e., having a darker green colour in the Sentinel-2 image). This landscape likely constitutes part of a cocoa agroforestry system. Our model successfully segregated the cocoa trees from the closed-canopy shade trees. Also, the bare  
Figure 5: Illustrative example sites in (**a,b,d–f**) Göte d’Ivoire and (**c**) Ghana where our random forest performed omission and commission errors in classifying full-sun cocoa (second row).