Phenology is the study of reoccurring events during a year or season. It can be linked to the behavior of animals, such as phases of mating, breeding, or movement and to events such as green-up, bud burst, flowering, or senescence when referring to vegetation, as a response to changing environmental factors throughout a season. While these changes can be tracked on the level of individual species, their observation is usually restricted to small spatial extents. To broaden the extent of the observed area remote sensing data have been proven useful.As remote sensing data capture the seasonal change rather on a pixel than on a species level, they enable to analyze the phenology of the observed vegetation on a different scale, which is known as land surface phenology. Land surface phenological metrics that can, for example, be derived from time series of vegetation indices, allow to analyze the observed spatial and temporal patterns in relation to ecosystem processes (e.g., primary productivity). Subsequently, the derived metrics can be grouped based on their similarities into land surface phenological archetypes (LSP), defined as areas with comparable phenologies.However, the spatial resolution of the data used is crucial, which becomes even more critical when looking at heterogeneous ecosystems such as the Brazilian savanna, known as the Cerrado. The Cerrado covers an extent of approximately 2 mio. km², hosts many endemic species and is considered as a biodiversity hotspot that provides several ecosystem services of national and even global importance. However, due to a lack of extensive conservation regulations the Cerrado is prone to land cover changes for agricultural expansion, highlighting the need for detailed mapping and monitoring approaches.To reveal and analyze the spatial patterns of the remaining share of natural vegetation based on their land surface phenology, we analyzed a dense 8-day time series of combined enhanced vegetation data derived from Landsat 7 ETM+ and Landsat 8 images. Data gaps that were due to cloud contamination or sensor errors were filled using a radial basis convolution filter, enabling to subsequently derive phenological metrics for the season 2013/2014 using TIMESAT (Eklundh and Jönsson 2017). As these variables, such as start and end of season, amplitude or the base value, relate to the seasonality and primary productivity of the observed vegetation, we clustered them based on their similarities and defined 8 land surface phenological archetypes (LSP) of the Cerrado.The GeoTiff file contains the 8 LSPs that are explained in detail in Schwieder et al. in prep. For further questions please contact Marcel Schwieder.Class labels:0 = Unclassified1 = FORMBMS2 = SAFORMS3 = FORHBLS4 = GLSAVLB5 = GLSAVHB6 = FORHBHS7 = VEGINMS8 = VEGINLS