## 3 Results
### Qualitative Assessment with Visual Inspection
Cocoa maps classified by [PERSON] et al. (2018) and [PERSON] et al. (2018) were inspected for comparative analysis (third and fourth row, respectively). The 2021 Sentinel-2 median composite image is presented 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 presented in Figure 2. Legend and north arrow apply to all maps. ground and the unpaved road were successfully classified as non-cocoa. However, the CNN model by [PERSON] et al. [34] did not separate the bare ground and road from cocoa; instead, everything was classified in the cocoa class. The classification by [PERSON] et al. [45] showed inconsistency. In the sites presented in Figure 4d,e located in Cote d'Ivoire, we identified distinct full-sun cocoa plantations, rubber plantations and bare ground. It is apparent that the West African landscape consists of land-use patches of varying shapes and sizes. In both Figure 4d,e, our random forest delineated the areas of full-sun cocoa accurately, while rubber trees were classified as non-cocoa. A similar classification with small deviations was performed with the CNN model by [PERSON] et al. [34]. The random forest applied by [PERSON] et al. [45] seems to have omitted classifying cocoa correctly. Figure 4f displays another site in Cote d'Ivoire with extensive full-sun cocoa areas mixed with sparse shade trees. A line of remnant forest trees is also visible, along with a nearby settlement. Our random forest successfully classified most of the full-sun cocoa trees located in this site, and most of the forest trees were classified as non-cocoa. Some misclassification occurred for grassland. [PERSON] et al. [34] classified both cocoa and shade trees in the cocoa class; apparently their model was capable of detecting cocoa agroforestry. The classification by [PERSON] et al. [45] seems to have generated a random output. Figure 5a,b show two different Ivorian sites consisting of oil palm fields. In Figure 5a, there are notable commission errors performed by our random forest that confused young oil palm with cocoa, whereas mature oil palm fields were classified correctly as non-cocoa. We confirmed the presence of young oil palm trees from their different reflectance responses in the Sentinel-2 image (i.e., having a lighter green colour than mature oil palm). Additionally, our random forest confused grassland with cocoa. Conversely, the CNN model by [PERSON] et al. [34] had a more robust performance and generalised well. In Figure 5b, a typical industrial oil palm area with a predominantly closed canopy is presented. The industrial plots had a more structured shape where oil palm trees were usually planted in regular rows. Areas with scarce open-canopy oil palm trees were mistakenly detected as cocoa by our random forest. In addition to this, our model misclassified pixels along the oil palm field boundaries in the cocoa class. This issue was more noticeable when examining the same location from a broader perspective in Figure A4a. We observed similar commission errors occurring in other landscapes, where boundary non-cocoa pixels were misclassified as cocoa. These errors were more visually distinct in vegetated areas with straight-line boundaries, e.g., in rubber plantations crossed by paths (see Figure A4b) and in forest areas with logging roads (see Figure A4c). In contrast, the CNN model by [PERSON] et al. [34] showed robustness against such commission errors (Figure 5b). Figure 5c illustrates another site in Ghana with oil palm plantations mixed with grassland. Our random forest successfully detected the closed-canopy oil palm but some sparsely vegetated oil palm trees and grassland were misclassified as cocoa. The models by [PERSON] et al. [34] and [PERSON] et al. [45] also confused open-canopy oil palm with cocoa but to a smaller extent.