## 3 Results  
### Quantitative Assessment  
Table 4 displays the accuracy measures scored per dataset and per input configuration. The random forest trained on the four most important VIs and textural features combined with the Sentinel-2 bands achieved the highest OA (mean = 85.1%, standard deviation = \(\pm\)2.0%), PA (81.9 \(\pm\) 3.9%) and F1 (84.6 \(\pm\) 2.4%) using the refined dataset. The highest PA suggested that more actual cocoa data were correctly classified as cocoa compared with the other models. However, the PA achieved a lower performance compared to UA (87.5 \(\pm\) 0.8%), suggesting that the random forest missed classifying more true cocoa pixels. Fusing the 25 features with the Sentinel-2 bands resulted in the highest UA (88.9 \(\pm\) 0.9%) and the second-highest OA (84.7 \(\pm\) 1.9%) and F1 (83.8 \(\pm\) 2.3%). After removing the four most important features and using the 12 Sentinel-2 bands alone, the mean OA and F1 dropped by approximately 1% and the PA dropped by 2%, while the mean UA increased by 0.3%. Similarly, removing the 25 features from the Sentinel-2 bands resulted in a small drop in accuracy for OA, PA and F1. The handcrafted features therefore improved the classification accuracy when they were combined with the reflectance bands, especially when adding the most important features. Yet, their added value was not big. For the raw dataset, fusion worked in the opposite way; combining the four most important features with the Sentinel-2 bands caused a small accuracy reduction. Nevertheless, the sole use of handcrafted features resulted in the lowest accuracy scoresfor both the refined and raw datasets, suggesting that feature engineering alone was not adequate for training the random forest. Employing the same number of inputs for the respective configuration sets of the refined and raw datasets allowed for a wall-to-wall comparison. Also, using the identical test dataset for evaluating the random forest models enabled us to assess the accuracy of the refined and raw datasets under the same conditions. The cocoa quality refinement resulted in an accuracy gain of 1.7% for OA, 2.5% for UA, 0.7% for PA and 1.6% for F1 on average when fusing the four most important features with the Sentinel-2 bands. The refinement approach resulted in an accuracy gain for all input configurations, except when using solely the four most important features. The accuracy gain was greater in some configurations, e.g., the mean F1 increased by 1.9% when using 25 features, while the gain was smaller in other cases, e.g., the mean F1 improved by 0.4% when using solely the Sentinel-2 bands. The accuracy gain induced by the data refinement confirmed its beneficial impact on model performance. The two-times-bigger raw dataset did not enable the random forest to perform better, which could be attributed to the lower data quality. Most of the accuracy measures obtained from the raw dataset also exhibited wider one standard deviation intervals compared with the refined dataset, indicating greater uncertainty. The corresponding UA, PA and F1 measures for the non-cocoa class are presented in Table 10 in Appendix A to provide a complete overview of both classes. The random forest trained on the refined dataset using the four most important features and the 12 Sentinel-2 bands was employed to classify the study area. The classified map is presented in Figure 3 depicting full-sun-cocoa-growing areas for 2021 at a 10 m spatial resolution.