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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 C\u00f4te d\u2019Ivoire 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 \u00b1 2.0% and an F1 score of 84.6 \u00b1 2.4% (mean \u00b1 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.<\/jats:p>","DOI":"10.3390\/rs16030598","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T09:31:58Z","timestamp":1707125518000},"page":"598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Critical Assessment of Cocoa Classification with Limited Reference Data: A Study in C\u00f4te d\u2019Ivoire and Ghana Using Sentinel-2 and Random Forest Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8536-8587","authenticated-orcid":false,"given":"Nikoletta","family":"Moraiti","sequence":"first","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 4, 6708 PB Wageningen, The Netherlands"}]},{"given":"Adugna","family":"Mullissa","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 4, 6708 PB Wageningen, The Netherlands"},{"name":"Ctrees.org, 12 S Raymond Avenue, Pasadena, CA 91105, USA"},{"name":"Institute of the Environment and Sustainability, University of California Los Angeles, 619 Charles E. Young Drive East, Los Angeles, CA 90095, USA"}]},{"given":"Eric","family":"Rahn","sequence":"additional","affiliation":[{"name":"International Center for Tropical Agriculture (CIAT), Km 17 Recta Cali-Palmira, Cali 763537, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8844-7437","authenticated-orcid":false,"given":"Marieke","family":"Sassen","sequence":"additional","affiliation":[{"name":"Plant Production Systems Group, Wageningen University, Bornsesteeg 48, 6708 PE Wageningen, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4327-4349","authenticated-orcid":false,"given":"Johannes","family":"Reiche","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 4, 6708 PB Wageningen, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M.R., Kuemmerle, T., Meyfroidt, P., and Mitchard, E.T.A. 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