Mapping human settlements with remote sensing is particularly challenging. The heterogeneity of urban areas and the complex interactions with their surrounding environment require both an understanding of urban processes and a solid methodology to accurately quantify urban land cover. Sentinel-2 offers free and globally available optical remote sensing data and provides improved spatial, temporal and spectral resolution compared to similar previous and existing sensors. Its use is, thus, considered to have a huge potential to contribute to urban monitoring. Even if artificial materials are of most interest in urban surface mapping, urban spaces are also largely characterized by vegetation. Vegetation is present in urban green areas and street green as well as surrounding natural green areas, urban forests or even agriculture in close distance to cities. The quality of urban land cover mapping is, thus, dependent on temporal image availability and selection, because vegetation phenology has an impact on land cover patterns throughout the year. This study shows that information from Sentinel-2 image time series is potentially able to enhance the quality of mapping urban land surfaces in the context of vegetation/imperviousness/soil detection. Our land cover mapping methodology is based on a machine learning support vector regression approach trained with synthetic spectral mixtures generated from image spectral libraries containing pure surface cover types. We compared model performance using four seasonal Sentinel-2 images (Spring, Summer, Fall, Winter) as well as derived time series metrics (e.g. spectral percentiles or vegetation indices) for the area of Berlin, Germany, as input data. We systematically evaluated all model results in ten selected regions of interest with different neighborhood characteristics within the city of Berlin and the urban fringe. Reference data for validation was composed of publicly available cadastral information and manually digitized urban features. We find that spring and summer imagery as model input perform better than fall and winter imagery. In the latter case, there is a particular conflict between urban vegetated areas and soils as well as impervious surfaces. Spring and summer imagery work well within the urban core, but performance decreases at the urban fringe, where uncultivated agricultural fields show high fractions of imperviousness. This issue is resolved with time series information. When all cloud-free data available for each pixel within one year is accounted for, model performance in comparison with spring and summer models is stable in the urban center and further increases in the outskirt areas, where surface distinction is clearer with time series metrics. The study shows that using Sentinel-2 imagery, different time series metrics computed from a one-year-period can resolve the need to find a best observation for urban mapping without requiring complex image compositing. Time series information might be particularly useful to regionally generalize regression models and apply urban mapping methods to different world regions and larger areas.