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Mapping grassland locations and area is of paramount importance for the management of these areas, particularly when the primary use is for traditional livestock production over common lands. Often the institutions responsible for drafting land use policies lack the tools to support their decisions. This is the case in the mountains of Sardegna. The availability of satellite imagery with fine temporal resolution and the variety of classification techniques, offer several methods to obtain an efficient and accurate land cover map. The objective of this study is to offer an accurate, easy-to-use method for mapping grasslands. Using Sentinel-2 data, an analysis of NDVI is conducted through the whole year. For known grasslands, these analyses provide contrasting evidence for different months. Using the difference between the month with the NDVImax and NDVImin the Intra-Annual delta NDVI layer is constructed. To this layer three classification algorithms are applied to discriminate grasslands from other land covers: random forests (RF), clustering and thresholding. The accuracies of these maps are evaluated in terms of OA, PA, UA and area estimates. All three methods produced similar results. Clustering showed the greatest OA, 89% with area estimate of 613.67 ha; RF produced OA of 87% with area estimate of 546.56 ha and tresholding produced OA of 88% with area estimate of 579.69 ha. Because the results are quite similar, the operator has the opportunity to use a classification method of choice. The results are auspicious and replicable each year. Further work is recommended to determine whether these results apply in other regions.