The rapid expansion of cities globally leads to new challenges related to quality of life and health. The presence and fractional distribution of vegetation within urban cities directly impact the life and health of urban dwellers. This paper presents an approach to map urban vegetation from Sentinel-2 data. The twin Sentinel-2 satellites offer a 5-day revisit time global coverage at unprecedented spatial and temporal resolution. The temporal resolution allows for seasonal aggregation of the input data, thus providing phenological information. By considering seasonally aggregated Normalized Difference Spectral Vector (NDSV), a classification was performed using Random Forest (RF) and compared with Classification and Regression Trees (CART) and Support Vector Machines (SVM).