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Abstract

Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.