The intensity of land use and management in permanent grasslands affects both biodiversity and important ecosystem services. Comprehensive knowledge about these intensities is a crucial factor for sustainable decision-making in landscape policy. For meadows, the management intensity can be described by proxies such as the mowing frequency, usually, a higher number of cuts indicate higher intensities. Dense time series of medium resolution (10–30 m) remote sensing data are suitable for the detection of mowing events. However, existing studies revealed a general lack of consensus about the most appropriate input data set for a consistent and reliable mowing detection.

We systematically evaluated the synergistic use of acquisitions from Sentinel-1, Sentinel-2, and Landsat 8 to detect the occurrence, frequency, and date of mowing events as an indicator of grassland management intensity. Dense time series of NDVI (Sentinel-2 and Landsat 8), γ⁰ backscatter, backscatter cross-ratio, backscatter second-order texture metrics as well as 6-day interferometric coherence (Sentinel-1) were used as input features. All possible combinations of input features were tested to train a one-dimensional convolutional neural network, which enables enhanced exploitation of the temporal domain of the data. The evaluation was conducted on 64 meadows for an overall of 257 mowing events from 2017 to 2019 in Germany.

Our results revealed that the combination of input features improves the detection performance. The highest overall accuracy was reached by a combination of NDVI, backscatter cross-ratio, and interferometric coherence with an F1-Score of 0.84. The mowing frequency was predicted with a mean absolute error of 0.38 events per year, while the date of the events was missed by 3.79 days on average. NDVI time series alone mostly underperformed in comparison to optical/SAR combinations but clearly outperformed input-sets that were solely based on SAR features. The proposed model performed well for meadows with low to medium management intensities but further testing is recommended for highly intensive managed parcels.

The results clearly demonstrate the additional value of fusing time series of the three present Earth observation systems that deliver a freely available global coverage of the land surface at medium resolution.

永久性草原的土地利用和管理强度影响生物多样性和重要的生态系统服务。关于这些强度的综合知识是景观政策可持续决策的关键因素。对于草地，管理强度可以通过诸如割草频率之类的代理来描述，通常，切割次数越多表明强度越高。中等分辨率（10-30 m）遥感数据的密集时间序列适用于检测割草事件。然而，现有的研究表明，对于一致和可靠的割草检测最合适的输入数据集普遍缺乏共识。

我们系统地评估了 Sentinel-1、Sentinel-2 和 Landsat 8 采集的协同使用，以检测割草事件的发生、频率和日期，作为草地管理强度的指标。NDVI 的密集时间序列（Sentinel-2 和 Landsat 8）、γ ⁰后向散射、后向散射交叉比、后向散射二阶纹理度量以及 6 天干涉相干性（Sentinel-1）用作输入特征。测试了所有可能的输入特征组合以训练一维卷积神经网络，从而增强对数据时域的利用。评估是在 2017 年至 2019 年在德国的 64 块草地上进行的，共计 257 场割草活动。

我们的结果表明，输入特征的组合提高了检测性能。NDVI、反向散射交叉比和干涉相干性的组合达到了最高的整体精度，F1-Score 为 0.84。预测割草频率的平均绝对误差为每年 0.38 个事件，而事件发生的日期平均错失了 3.79 天。与光学/SAR 组合相比，单独的 NDVI 时间序列大多表现不佳，但明显优于仅基于 SAR 特征的输入集。所提出的模型对于中低管理强度的草地表现良好，但建议对高度密集的管理地块进行进一步测试。

结果清楚地证明了融合三个现有地球观测系统的时间序列的附加价值，这些系统以中等分辨率提供免费可用的全球陆地表面覆盖。