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.

2018 年和 2019 年，严重干旱对中欧草原造成了前所未有的影响。 然而，干旱对草原的空间变化影响仍然知之甚少，因为它们是由环境条件和土地管理的复杂相互作用驱动的。Sentinel-2 时间序列为改善干旱期间的草地监测提供了未开发的潜力，具有所需的空间和时间细节。在这项研究中，我们使用基于回归的解混合框架量化了 2017 年至 2020 年中欧主要草原地区的干旱影响。基于 Sentinel-2 的光合植被 (PV)、非光合植被 (NPV) 和土壤覆盖率的年内时间序列提供了与理解干旱对草原的影响相关的易于解释的数量。来自 Sentinel-2 的部分覆盖估计匹配实地考察期间观察到的原位条件。与多时态参考数据集的比较显示 PV 覆盖的最佳一致性 (MAE = 7.2%)。土壤和 NPV 的一致性较低，但我们观察到 Sentinel-2 的覆盖率与参考数据之间的正相关关系，土壤和 NPV 的 MAE = 10.1% 和 MAE = 15.4%。基于部分覆盖估计，我们得出了一个归一化差异分数指数 (NDFI) 时间序列，对比 NPV 和土壤覆盖相对于 PV。与气象和土壤水分干旱指数以及归一化差异植被指数 (NDVI) 一致，NDFI 时间序列显示 2018 年干旱影响最严重，其次是 2019 年不太严重但持续的影响。来自 NDFI 时间序列的干旱特定指标揭示了干旱对草原影响的发生、持续时间、影响和结束的高度空间变异性。评估不同土壤类型的干旱指标，我们发现 2018 年和 2019 年，生产力较低的沙质 Cambisols 上的草原受到干旱的强烈影响。 相比之下，Gleysols 和 Histosols 上的草原受到的影响较小，表明这些草原具有更高的抗旱性。我们的研究强调，Sentinel-2 时间序列的高时空细节对于捕捉干旱下中欧低地草原的相关植被动态是必不可少的。我们发现，2018 年和 2019 年，生产力较低的沙质 Cambisols 上的草原受到干旱的强烈影响。 相比之下，Gleysols 和 Histosols 上的草原受到的影响较小，表明这些草原具有更高的抗旱性。我们的研究强调，Sentinel-2 时间序列的高时空细节对于捕捉干旱下中欧低地草原的相关植被动态是必不可少的。我们发现，2018 年和 2019 年，生产力较低的沙质 Cambisols 上的草原受到干旱的强烈影响。 相比之下，Gleysols 和 Histosols 上的草原受到的影响较小，表明这些草原具有更高的抗旱性。我们的研究强调，Sentinel-2 时间序列的高时空细节对于捕捉干旱下中欧低地草原的相关植被动态是必不可少的。