Time series of optical image allow to derive land surface phenology metrics. These metrics are only complete with a statement about their uncertainty. A source of uncertainty is the radiometry of the sensor. We focused on Sentinel-2 MSI data since quantitative estimates of radiometric uncertainties are available. Uncertainties were propagated within a Monte-Carlo framework into the Normalized Difference Vegetation Index (NDVI), three-band Enhanced Vegetation Index (EVI) and Green Leaf Area Index (GLAI) derived from radiative transfer model inversion. In addition, we studied the effect of propagated uncertainties on scene pre-classification. Propagation was carried out for 34 scenes acquired for a crop growing season over an agricultural region in Switzerland using the TIMESAT approach for crop phenology estimation. Propagated uncertainties had little impact on the classification except for spectrally mixed pixels. Effects on the spectral indices and GLAI were more pronounced. In detail, GLAI was more uncertain due to the ill-posedness of radiative transfer model inversion (median relative uncertainty for all crop pixels and Sentinel-2 scenes: 4.4\%) than EVI (2.7\%) and NDVI (1.1\%). In crop phenology, phenological metrics exhibited largest uncertainties in the case of GLAI. The magnitude of uncertainty in the metrics depends on the error correlation between the scenes, which we assumed to be either zero (uncorrelated) or one (fully correlated) since the actual degree of correlation is unknown. If uncertainties are fully correlated, uncertainties in phenological metrics are small (2 to 3 days) but can take values up to greater 10 days under the uncorrelated assumption.