## 5 Conclusions  
In our study, we aimed at a good trade-off between the limited reference data and an effective cocoa classification using a random forest. To achieve this, we undertook reference data refinement, a sampling strategy and feature selection. Applying quality refinement to the cocoa data contributed to the accuracy improvement. As a complementary tool to the quantitative accuracy measures, we performed a qualitative assessment by visually inspecting our classification outcome overlaid on very high-resolution images. The visual inspection revealed potentialities and limitations in random forest performance, insights that were not evident from the accuracy measures alone. Therefore, it is critical for studies with limited reference data and with large, complex areas to classify to optimise the preprocessing methodology steps and to critically interpret the classification outcome. This can help to clearly communicate the value of the classified map to the end users to leverage it accordingly. **Author Contributions:** Conceptualisation, A.M., J.R. and N.M.; methodology, A.M., J.R. and N.M.; data acquisition, E.R. and M.S. ; data curation, N.M.; software, N.M.; writing--original draft preparation, N.M.; writing--review and editing, A.M., E.R., M.S., J.R. and N.M.; visualisation, N.M.; supervision, A.M. and J.R.; funding acquisition, J.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Norway's Climate and Forest Initiative (NICFI), the US Government's SilvaCarbon program, and the Netherlands Science Funding PIPP project MINERVA No. KNW19001. Cocoa field data were contributed through the CocoaSoils project, which was funded by the Norwegian Agency for Development Cooperation (NORAD), grant number RF-17/0009-Cocoaosil. [PERSON] also acknowledges support from the UK Research and Innovation's Global Challenges Research Fund (UKRI GCRF) through the Trade, Development and Environment Hub project (project number ES/S008160/1). **Data Availability Statement:** The cocoa data used in this study are subject to a non-disclosure agreement. They are available from the corresponding author upon reasonable request. The generated maps, i.e., binary classification map, probability score map and Sentinel-2 composite image, are available for visualisation and download in the following Google Earth Engine application: [[https://my-cocoa-project-102021.projects.earthengine.app/view/cocoa-mapping-west-africa](https://my-cocoa-project-102021.projects.earthengine.app/view/cocoa-mapping-west-africa)]([https://my-cocoa-project-102021.projects.earthengine.app/view/cocoa-mapping-west-africa](https://my-cocoa-project-102021.projects.earthengine.app/view/cocoa-mapping-west-africa)) (accessed on 2 February 2024). **Acknowledgments:** The authors would like to thank the three anonymous reviewers for their constructive feedback. **Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**s2 cloudless Parameters ***} & \multicolumn{1}{c}{**Values**} \\ \hline Cloud filter & 100 \\ Cloud probability threshold & 20 \\ Near-infrared dark threshold & 0.2 \\ Cloud projection distance & 1.5 \\ Buffer & 50 \\ \hline \hline \end{tabular}  
* Parameter names follow the nomenclature defined in s2 cloudless algorithm [66].  
\end{table} 
Table 1: Parameter values set for cloud and cloud shadow masking with s2 cloudless algorithm. Figure 13: Generated GLCM-based textural features were grouped into clusters after performing hierarchical clustering. In the dendrograms derived from (**a**) the raw dataset and (**b**) the refined dataset, the small distances indicate higher correlation between features. We cut both dendrograms at 1.5 (dashed line) and 14 clusters were yielded in each case.