Semi‐natural grasslands represent ecosystems with high biodiversity. Their conservation depends on the removal of biomass, for example, through grazing by livestock or wildlife. For this, spatially explicit information about grassland forage quantity and quality is a prerequisite for efficient management. The recent advancements of the Sentinel satellite mission offer new possibilities to support the conservation of semi‐natural grasslands. In this study, the combined use of radar (Sentinel‐1) and multispectral (Sentinel‐2) data to predict forage quantity and quality indicators of semi‐natural grassland in Germany was investigated. Field data for organic acid detergent fibre concentration (oADF), crude protein concentration (CP), compressed sward height (CSH) and standing biomass dry weight (DM) collected between 2015 and 2017 were related to remote sensing data using the random forest regression algorithm. In total, 102 optical‐ and radar‐based predictor variables were used to derive an optimized dataset, maximizing the predictive power of the respective model. High R² values were obtained for the grassland quality indicators oADF (R² = 0.79, RMSE = 2.29%) and CP (R² = 0.72, RMSE = 1.70%) using 15 and 8 predictor variables respectively. Lower R² values were achieved for the quantity indicators CSH (R² = 0.60, RMSE = 2.77 cm) and DM (R² = 0.45, RMSE = 90.84 g/m²). A permutation‐based variable importance measure indicated a strong contribution of simple ratio‐based optical indices to the model performance. In particular, the ratios between the narrow near‐infrared and red‐edge region were among the most important variables. The model performance for oADF, CP and CSH was only marginally increased by adding Sentinel‐1 data. For DM, no positive effect on the model performance was observed by combining Sentinel‐1 and Sentinel‐2 data. Thus, optical Sentinel‐2 data might be sufficient to accurately predict forage quality, and to some extent also quantity indicators of semi‐natural grassland.

Background: Increased soil phosphorus (P) caused by agricultural intensification has been associated with decreased plant species richness (SR) in central Europe. How plant communities and soil P gradients are related in unimproved open habitats remains unclear. Aims: The aim of this article was to characterise the relationship between soil chemical parameters and plant species composition and richness in unimproved open habitats. Methods: The influence of soil chemical parameters (pH, P, K, Mg) on species composition was assessed, using data from 40 heathland and 54 grassland plots, by non-metric multidimensional scaling and permutational multivariate analysis of variance. The relationship between soil chemical parameters and SR was tested by linear mixed effects models. Results: A direct relationship between heathland community composition and pH was observed, explaining 10% of variation in species composition, while P, Mg and pH together explained 17% of variation in grassland composition. In heathlands, SR increased with increasing pH, whereas in grasslands, SR decreased with increasing soil P. Conclusions: Soil chemical parameters were substantially related to plant community composition and richness. In an area spared from a century of agricultural intensification, reduced pH appeared to constrain SR in heathlands, while even slight P increases (<10 mg kg⁻¹) depressed plant SR in semi-natural grasslands.