330 ha. The median overall accuracy of 1000 iterations was >95% (95.5%–96.0%) against independent test data Many tropical forest landscapes are now complex mosaics of intact forests agroforestry and betel nut plantations in Southern Myanmar and crops. The small patch size of each land cover type contributes to making them difficult to separate using satellite remote sensing data. We used Sentinel-2 data to conduct supervised classifications covering seven classes are able to differentiate these similar tree crop types. We suspect that this is due to the large number of spectral bands in Sentinel-2 data as well as vegetation and texture indices based on an extensive training dataset derived from expert interpretation of WorldView-3 and UAV data. We used a Random Forest classifier with all 13 Sentinel-2 bands classification; UAV; WorldView; Sentinel-2; palm oil; Random Forest; Myanmar; Google Earth Engine; rubber; betel nut even though the tree crop classes appear visually very similar at a 20 m resolution. We conclude that the Sentinel-2 data including oil palm indicating great potential for the wider application of Sentinel-2 data for the classification of small land parcels without needing to resort to object-based classification of higher resolution data. over an area of 13 pasture recovering forests rubber tree crops which are freely available with very frequent (five day) revisits