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This study analyzed, for the first time, the potential of combining the large European-wide land survey LUCAS (Land Use/Cover Area frame Survey) and Landsat-8 data for mapping pan-European land cover and land use. We used annual and seasonal spectral-temporal metrics and environmental features to map 12 land cover and land use classes across Europe. The spectral-temporal metrics provided an efficient means to capture seasonal variations of land surface spectra and to reduce the impact of clouds and cloud-shadows by relaxing the otherwise strong cloud cover limitations imposed by image-based classification methods. The best classification model was based on Landsat-8 data from three years (2014-2016) and achieved an accuracy of 75.1%, nearly 2 percentage points higher than the classification model based on a single year of Landsat data (2015). Our results indicate that annual pan-European land cover maps are feasible, but that temporally dynamic classes like artificial land, cropland, and grassland still benefit from more frequent satellite observations. The produced pan-European land cover map compared favorably to the existing CORINE (Coordination of Information on the Environment) 2012 land cover dataset. The mapped country-wide area proportions strongly correlated with LUCAS-estimated area proportions (r = 0.98). Differences between mapped and LUCAS sample-based area estimates were highest for broadleaved forest (map area was 9% higher). Grassland and seasonal cropland areas were 7% higher than the LUCAS estimate, respectively. In comparison, the correlation between LUCAS and CORINE area proportions was weaker (r = 0.84) and varied strongly by country. CORINE substantially overestimated seasonal croplands by 63% and underestimated grassland proportions by 37%. Our study shows that combining current state-of-the-art remote sensing methods with the large LUCAS database improves pan-European land cover mapping. Although this study focuses on European land cover, the unique combination of large survey data and machine learning of spectral-temporal metrics, may also serve as a reference case for other regions. The pan-European land cover map for 2015 developed in this study is available under https://doi.pangaea.de/10.1594/PANGAEA.896282.