Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. While regional studies allow detailed insights into land use and ecosystem service interactions, information on a national scale can aid biodiversity assessments. However, for most European countries this information is not yet widely available. We used an analysis-ready-data cube that contains dense time series of co-registered Sentinel-2 and Landsat 8 data, covering the extent of Germany. We propose an algorithm that detects mowing events in the time series based on residuals from an assumed undisturbed phenology, as an indicator of grassland use intensity. A self-adaptive ruleset enabled to account for regional variations in land surface phenology and non-stationary time series on a pixel-basis. We mapped mowing events for the years from 2017 to 2020 for permanent grassland areas in Germany. The results were validated on a pixel level in four of the main natural regions in Germany based on reported mowing events for a total of 92 (2018) and 78 (2019) grassland parcels. Results for 2020 were evaluated with combined time series of Landsat, Sentinel-2 and PlanetScope data. The mean absolute percentage error between detected and reported mowing events was on average 40% (2018), 36% (2019) and 35% (2020). Mowing events were on average detected 11 days (2018), 7 days (2019) and 6 days (2020) after the reported mowing. Performance measures varied between the different regions of Germany, and lower accuracies were found in areas that are revisited less frequently by Sentinel-2. Thus, we assessed the influence of data availability and found that the detection of mowing events was less influenced by data availability when at least 16 cloud-free observations were available in the grassland season. Still, the distribution of available observations throughout the season appeared to be critical. On a national scale our results revealed overall higher shares of less intensively mown grasslands and smaller shares of highly intensively managed grasslands. Hotspots of the latter were identified in the alpine foreland in Southern Germany as well as in the lowlands in the Northwest of Germany. While these patterns were stable throughout the years, the results revealed a tendency to lower management intensity in the extremely dry year 2018. Our results emphasize the ability of the approach to map the intensity of grassland management throughout large areas despite variations in data availability and environmental conditions.

关于草地范围和管理的空间明确知识对于理解和监测草地利用强度对生态系统服务和生物多样性的影响至关重要。虽然区域研究可以详细了解土地利用和生态系统服务的相互作用，但全国范围的信息可以帮助生物多样性评估。然而，对于大多数欧洲国家来说，这一信息尚未广泛提供。我们使用了一个分析就绪数据立方体，其中包含共同注册的 Sentinel-2 和 Landsat 8 数据的密集时间序列，涵盖德国的范围。我们提出了一种算法，该算法基于来自假设的未受干扰物候的残差检测时间序列中的割草事件，作为草地利用强度的指标。自适应规则集能够以像素为基础解释地表物候和非平稳时间序列的区域变化。我们绘制了从 2017 年到 2020 年德国永久草原地区的割草事件。根据报告的总共 92 个（2018 年）和 78 个（2019 年）草原地块的割草事件，在德国四个主要自然区域的像素级别对结果进行了验证。2020 年的结果是通过 Landsat、Sentinel-2 和 PlanetScope 数据的组合时间序列评估的。检测到的和报告的割草事件之间的平均绝对百分比误差平均为 40%（2018 年）、36%（2019 年）和 35%（2020 年）。平均在报告割草后 11 天（2018 年）、7 天（2019 年）和 6 天（2020 年）检测到割草事件。德国不同地区的绩效衡量标准有所不同，在 Sentinel-2 不经常重新访问的区域中发现了较低的准确度。因此，我们评估了数据可用性的影响，发现当草地季节至少有 16 次无云观测时，割草事件的检测受数据可用性的影响较小。尽管如此，整个季节可用观测的分布似乎很关键。在全国范围内，我们的结果显示，总体而言，割草较少的草地所占的比例较高，而高度集约化管理的草地所占的份额较小。在德国南部的高山前陆以及德国西北部的低地发现了后者的热点。虽然这些模式多年来一直保持稳定，但结果显示，在极度干旱的 2018 年，管理强度有降低的趋势。