###### Abstract  
Cocoa is the economic backbone of Cote d'Ivoire and Ghana, making them the leading cocoa-producing countries in the world. However, cocoa farming has been a major driver of deforestation and landscape degradation in West Africa. Various stakeholders are striving for a zero-deforestation cocoa sector by implementing sustainable farming strategies and a more transparent supply chain. In the context of tracking cocoa sources and contributing to cocoa-driven deforestation monitoring, the demand for accurate and up-to-date maps of cocoa plantations is increasing. Yet, access to limited reference data and imperfect data quality can impose challenges in producing reliable maps. This study classified full-sun-cocoa-growing areas using limited reference data relative to the large and heterogeneous study areas in Cote d'Ivoire and Ghana. A Sentinel-2 composite image of 2021 was generated to train a random forest model. We undertook reference data refinement, selection of the most important handcrafted features and data sampling to ensure spatial independence. After refining the quality of the reference data and despite their size reduction, the random forest performance was improved, achieving an overall accuracy of 85.1 \(\pm\) 2.0% and an F1 score of 84.6 \(\pm\) 2.4% (mean \(\pm\) one standard deviation from ten bootstrapping iterations). Emphasis was given to the qualitative visual assessment of the map using very high-resolution images, which revealed cases of strong and weak generalisation capacity of the random forest. Further insight was gained from the comparative analysis of our map with two previous cocoa classification studies. Implications of the use of cocoa maps for reporting were discussed. cocoa classification; crop mapping; limited reference data; qualitative assessment; random forest; feature engineering; Sentinel-2; Ghana; Cote d'Ivoire 2024  
these satellite images and their high spatial and temporal resolution, researchers have been enabled to carry out challenging global mapping studies [10; 11], as opposed to the use of proprietary images, which are characterised by scale limitations and high cost. Good-quality land-use/cover maps are required to deliver reliable outputs to support policy decisions and prioritise actions, especially when the maps are linked to other datasets or models [12; 13]. Ensuring the good quality of a classification product is, however, a complex task and depends on various factors that can have a considerable impact on map accuracy. These factors involve, among others, the reference data size and their quality [14; 15], the sampling design [16], and the classification model and its parameterisation [17; 18]. Supervised machine learning models require sufficient and representative training and test data to perform effectively and prevent overfitting [19; 20]. However, acquiring adequately labeled data of good quality remains a big challenge in the field of remote sensing [5]. Reference datasets are typically collected with costly and labour-intensive field surveys and/or by interpreting greater resolution images than those used in classification. Practical reasons, such as a limited budget, time constraints and inaccessible areas, may limit field campaigns at the spatial and temporal levels. Photointerpretation, on the other hand, has considerably lower cost and can sometimes be equally effective to field visits [21], but it requires expertise in land-type interpretation [22]. Visual interpretation can be additionally constrained in chronically cloudy areas, e.g., in the tropics during the wet season. Nevertheless, the gradual outdating of the reference data, as well as the high demand for releasing frequent classification maps, renders data collection a highly demanding task [5]. Although reference data are expected to be more accurate than the classification outcome [23], they can be subject to errors regardless of the collection method used.