In vegetation monitoring from earth observation data, band reflectance is often transformed to vegetation indices (VIs) to enhance the spectral contribution of green vegetation while minimizing those from soil background, senescent vegetation, atmosphere, and variations in viewing geometry . VIs are dimensionless variables that mostly rely on the strong reflectance difference between visible and near-infrared reflectance and that are hence linked to relative abundance and activity of green vegetation . Due to this relationship, VIs constitute a convenient tool to monitor spatial and temporal patterns of vegetation canopies. The Normalized difference vegetation index (NDVI) is probably the most widely used VI. The NDVI is the difference between the near-infrared and red reflectance, related to their sum . The NDVI is adopted in many studies as well as for operational monitoring.
The Sentinel-2 NDVI maps are calculated based on the level 2A Sentinel-2A maps. The level 1C Sentinel-2A data are calibrated and atmospherically corrected by using the sen2cor processor. Snow areas and clouds are masked based on the quality snow/cloud confidence layers. This product covers all vegetated land surfaces as defined by the CORINE 2012 land cover classification (classes 211, 213, 221, 222, 223, 231, 242, 243, 311, 312, 313, 321, 322, 323, 324, 333), while all other surfaces are masked.
 Huete, A. (1989): Soil influences in remotely sensed vegetation canopy spectra. In G. Asrar(Ed.), Theory and applications of optical remote sensing (pp. 107–141). New York. Wiley.
 Glenn et al. (2008): Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape. Sensors 8 (4), 2136–2160.
 Rouse et al. (1974): Monitoring vegetation systems in the Great Plains with ERTS. In S.C. Freden, E.P. Mercanti, & M.A. Becker (Eds.), NASA SP-351: Proceedings of the Third Symposium on Significant Results Obtained with ERTS-1 (pp. 309–317).
For visualizing the time series, you can use the Time Series Visualizer Tool.