Quantification of grassland land-use intensity and its components livestock density, number of mowing events and fertiliser application using machine learning algorithms and time series of Copernicus Sentinel-2 optical satellite data with 20 m spatial resolution. Land-use intensity was inferred using the index proposed by [Blüthgen 2012]. Input variables (livestock, mowing and fertilisation) were derived from Sentinel-2 optical satellite data using Convolutional Neural Networks trained on data from the DFG Biodiversity Exploratories. More details will be made available soon in [Lange 2021]. Grassland pixels were chosen according to the digital landscape model (DLM) of the official topographic-cartographic information system ATKIS [BKG 2015]. Livestock densities are livestock units (calculated from animal number, species and age) per ha and day and were aggregated into four livestock classes (see [Lange 2021]). Mowing number represents the number of cuts in the respective year. Fertiliser application is given as boolean, fertilised or not fertilised. [Blüthgen 2012]: Blüthgen N, Dormann C, Prati D, Klaus V, Kleinebecker T, Hölzel N, Alt F, Boch S, Gockel S, Andreas H, Müller J, Nieschulze J, Renner S, Schöning I, Schumacher U, Socher S, Wells K, Birkhofer K, Buscot F, Weisser W (2012). A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Basic and Applied Ecology. 13. 207-220. 10.1016/j.baae.2012.04.001. [Lange 2021]: Lange M, Feilhauer H, Kühn I, Doktor D (2021): Mapping land-use intensities of grasslands in Germany with machine learning and Sentinel-2 time series. Manuscript submitted to Remote Sensing of Environment. [BKG 2015]: BKG (2015): Digitales Basis-Landschaftsmodell (AAA-Modellierung). GeoBasis-DE. Geodaten der deutschen Landesvermessung. Bundesamt für Kartographie und Geodäsie.