Leveraging multidecadal satellite time series to estimate grassland age on national scale

Grasslands are biodiversity hotspots and provide a wide range of ecosystem services in the agricultural landscape. One important factor that drives ecosystem services is grassland age, as it influences the carbon sequestration potential but also supports biodiversity and the resilience of grassland to disturbances. Satellite image time series enable grassland age estimates, given the long-term coverage of virtual constellations and methodologies able to deal with variable satellite data availability. We present an approach to estimate grassland age in temperate regions of intensive agriculture using multidecadal time series of Landsat and Sentinel-2 data and bare soil occurrence to indicate agricultural land use periods. Every clear-sky satellite observation from 1986 to 2023 in grassland is classified as bare soil or non-bare soil and normalized to seasonal bare soil occurrence frequency. We then calibrate a rule set to derive grassland age based on the temporal bare soil frequency patterns. We demonstrate our approach at national scale for Germany. Grassland establishment was detected with an F-score of 78.92 ± 0.64, and differentiated from grassland persistent throughout all analyzed years with 99.21 ± 0.02% overall accuracy. The mean absolute error for grassland age is 1.3 years, and estimates remain reliable throughout low observation densities. Regional and temporal patterns align with political changes due to the collapse of the German Democratic Republic and adaptations in European agricultural policies since the 1990s. Our study demonstrates the potential of long-term image time series from satellite constellations to generate reliable data on grassland age for e.g., biodiversity or ecosystem service modelling.