###### Abstract
For instance, inadequately trained field staff or farmers' self-reporting might result in unreliable measurements [24; 25], whilst photointerpreted datasets can be associated with uncertainty [22], and some degree of disagreement is anticipated among multiple experts [26]. The primary error sources that downgrade the quality of reference data can be thematic mis-labeling, position mis-registration and mis-synchronisation with the remote sensing imagery used [27; 28]. Allocating correct labels can be particularly confounded when data collection takes place in thematically heterogeneous areas (i.e., where mixed pixels dominate) [26], and when different land types look spectrally similar [29]. Either random or systematic errors in reference data can adversely influence an accuracy assessment [29; 30] and ultimately result in area mis-estimation [31]. However, correcting erroneous reference datasets was shown to improve map accuracy [32]. High-quality crop maps are currently the subject of focus for detecting the location and estimating the yields of commodity tree crops, like cocoa, coffee and oil palm [33; 34; 35]. This is because the cultivation of such tropical cash crops, which is integral to the global supply chain and a livelihood source for millions of farmers, has considerable environmental impacts [36]. In particular, cocoa farming has attracted the spotlight for being a major deforestation factor and driver in the degradation of protected areas in West Africa [37; 38]. In response to the high deforestation risks, intervention programmes have been put in place to help cocoa-producing countries and companies halt cocoa-driven deforestation and restore degraded areas. The Cocoa and Forests Initiative (CFI) is such a programme where Cote d'Ivoire and Ghana, which are the biggest cocoa producers worldwide, and industry stakeholders are committed to a deforestation-free and sustainable cocoa sector [39]. Mondelez International and Nestle, who are members of CFI, have set action plans for mapping and monitoring their supply chain at the farm level with regard to forest protection [40; 41]. Moreover, the European Union has adopted a new regulation to ensure that imported commodities, including cocoa, are not associated with deforestation and forest degradation [42]. Mapping cocoa expansion in an accurate and timely way can therefore be a valuable tool for assessing cocoa-driven deforestation and monitoring landscape restoration efforts through agroforestry [34; 43; 44]. Over the past decade, a few studies on cocoa mapping have been conducted using wall-to-wall remote sensing data in West and Central Africa [34; 43; 45; 46; 47; 48]. A commonality we observed between most of them is the use of limited reference data, where the total magnitude of the reference samples ranged from 1838 to 6450 pixels at the landscape scale [43,48], and 19,196 pixels over a larger area [45] at a 10 \(\times\) 10 m pixel resolution. Some of these studies called their mapping techniques \"effective\" or \"successful\" based solely on confusion matrix-derived accuracy measures and the general look of their thematic map [43,45,47,48]. It was often neglected to reflect on both the strengths and the shortcomings of the classification outcome or provide insight into the potential impacts of the limited reference dataset. Yet, it is of great importance to convey map accuracy in an informed way and to communicate its potential or likely errors and their implications for the intended purposes, particularly when classified maps constitute the basis for further analysis [28]. The objective of our study was to explore the potential of classifying cocoa growing areas with the use of limited reference data over a large and heterogeneous study area in Cote d'Ivoire and Ghana. We undertook quality refinement of our limited reference data, sampling design and feature selection for training a random forest model using Sentinel-2 imagery.