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The classification of agricultural crop types is an important application of remote sensing. With the improvement in spatial, temporal and spectral resolution of satellite data, a complete seasonal crop growth profile and separability between different crop classes can be studied by using ensemble-learning techniques. This study compares the performance of Random Forest (RF), which is a decision tree based ensemble learning method and Naïve Bayes ( a probabilistic learning technique) for crop classification of <i>Lekoda</i> gram panchayat, <i>Ujjain</i> district, using multi-temporal Sentinel 2 of Rabi 2017&ndash;18. The study area contains seven different classes of crop types, and in each class, we have used 65% of the ground data for training and 35% to test the classifier. The performance of RF classifier was found to be better than NB classifier. Kappa coefficient of RF classifier in mid of the crop season (December&ndash;January) was found to be 0.93. This result indicates that an accurate in-season crop map of the study area can be generated through integrated use of Sentinel 2 temporal data and RF classifier.