Novel computational algorithms along with cloud computing services present a great potential to revolutionize the processing of Earth Observation (EO) data for topsoil mapping. This work presents the first insights of the WORLDSOILS project, where open-access Copernicus Sentinel-2 data and auxiliary terrain attributes were synergistically utilized to derive regression models. Building on key results from well-studied data mining approaches, the current study compares various approaches of temporal mosaicking to generate a multi-year median composite dataset of exposed bare soil pixels, over cropland areas in European Union. Finally, we utilized a Random Forest model based on soil samples (calibration = 80% and validation 20%) of the recently released LUCAS 2015 database to predict soil clay content and organic carbon. Following a masking approach to generate a composite of multi-date bare soil pixels, a promising prediction performance (R2 = 0.52, n = 7605) was achieved for clay content, while the predictive performance for soil organic carbon was significantly lower (R2 = 0.30).