Mapping cropland extent using sentinel-2 datasets and machine learning algorithms for an agriculture watershed

•10-m high-resolution cropland extent map is obtained for an agriculture watershed in the r environment.•Two machine learning algorithms are employed in mapping cropland extents.•Performance of machine learning algorithms is evaluated based on accuracy assessment.•Support vector machine algorithm outperformed random forest for the present study. Mapping and monitoring cropland extents are among the most appreciated element for resource management, monitoring, and assessment. India, being the most densely inhabited and agronomical country, mapping high-resolution cropland extent provides appreciated statistics and data for various resource management practices from an individual farm unit to larger administrative units. However, currently available cropland extent maps at global, continental, and regional extents are derived from coarser resolution satellite imageries. For smaller administrative unit with areas less than 10,000 Ha, these coarse resolution maps, often misclassify, resulting in uncertainties in accurate cropland accuracy. To overcome these limitations, in this study an effort is made to develop 10-m fine-resolution cropland extent maps using multi-temporal Sentinel-2 datasets for a watershed in Tadepalligudem, India. The selected study area is an agriculture dominant watershed covering an area of 5375 Ha. The satellite datasets used for the present study are selected with minimum cloud cover and are time-composited over three crop seasons (Kharif, Rabi, and Zaid). With the successful advent of machine learning methods in remote sensing applications, cropland extent maps are derived using Random Forest (RF) and Support Vector Machine (SVM) supervised machine learning algorithms in R-Studio, an R-programming interface. RF and SVM are CART-based machine learning algorithms, where the performance of RF depends on the number of trees (“ntrees”) and the number of sampled features (“mtry”), while for SVM it depends on Kernel functions used. To rejoice the need to develop a fine resolution, a comparative analysis is performed between 10-m resolution Sentinel-2 derived cropland extent maps and commonly used 30-m resolution Landsat-8 OLI-derived products. Training and testing samples collected from the field observations and high-resolution Google Earth Pro-is divided in a ratio of 75:25. In the present study, the accuracy of the classified cropland extent maps are validated with ground truth for both satellite sources and machine learning methods. The