Grassland plays an important role in German agriculture. The interplay of ecological processes in grasslands secures important ecosystem functions and, thus, ultimately contributes to essential ecosystem services. To sustain, e.g., the provision of fodder or the filter function of soils, agricultural management needs to adapt to site-specific grassland characteristics. Spatially explicit information derived from remote sensing data has been proven instrumental for achieving this. In this study, we analyze the potential of Sentinel-2 data for deriving grassland-relevant parameters. We compare two well-established methods to calculate the aboveground biomass and leaf area index (LAI), first using a random forest regression and second using the soil–leaf-canopy (SLC) radiative transfer model. Field data were recorded on a grassland area in Brandenburg in August 2019, and were used to train the empirical model and to validate both models. Results confirm that both methods are suitable for mapping the spatial distribution of LAI and for quantifying aboveground biomass. Uncertainties generally increased with higher biomass and LAI values in the empirical model and varied on average by a relative RMSE of 11% for modeling of dry biomass and a relative RMSE of 23% for LAI. Similar estimates were achieved using SLC with a relative RMSE of 30% for LAI retrieval, and a relative RMSE of 47% for the estimation of dry biomass. Resulting maps from both approaches showed comprehensible spatial patterns of LAI and dry biomass distributions. Despite variations in the value ranges of both maps, the average estimates and spatial patterns of LAI and dry biomass were very similar. Based on the results of the two compared modeling approaches and the comparison to the validation data, we conclude that the relationship between Sentinel-2 spectra and grassland-relevant variables can be quantified to map their spatial distributions from space. Future research needs to investigate how similar approaches perform across different grassland types, seasons and grassland management regimes.

Forests, savannas, and grasslands are prevalent across the landscapes of South America. Land uses associated with these ecosystems have influenced economies from household to country scales, shaping social-ecological organization across the region since pre-Hispanic societies. Recent studies suggest that tropical and subtropical grasslands, savannas, and forests represent alternative ecosystem states. Transitions between these ecosystem states can be promoted by changes in disturbance regimes and by land uses determined by the organization of societies and their activities. We analyzed how changes in agriculture, fire, and livestock management influenced forest cover over a 45-year span (1966-2011) in the Campos region, an extensive subtropical ecotone between rain forests and grasslands of South America. We found that forests contracted in areas with high crop agriculture, whereas forests increased in those grasslands where livestock densities had been reduced. These patterns were strongly associated with soil and topographic conditions because they broadly determine the potential land productivity and use. Our results show that current land use and disturbance regimes explain the large extent of grasslands across the South American Campos and suggest that changes in land use and disturbance regimes could facilitate or prevent transitions between subtropical forests, savannas, and grasslands altering the provision of ecosystem services linked to them.Ecology and Society 24(2): 19 https://www.ecologyandsociety.org/vol24/iss2/art19/ analyzed changes in native forest cover over a 45-year period in Uruguay in relation to fire occurrence, livestock density, agricultural land cover, climate, soils, topography, and road density.