Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a ~ 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based phenology dataset, we design and evaluate a framework based on a comprehensive set of ground observations, including eddy covariance gross primary production (GPP), PhenoCam green chromatic coordinate (GCC), and phenology phases from the Pan-European Phenological database (PEP725). We test three vegetation indices (VI) — the normalized difference vegetation index (NDVI), the two-band enhanced vegetation index (EVI2), and the plant phenology index (PPI) — regarding their capability to track the seasonal trajectories of GPP and GCC and their performance in reflecting spatial variabilities of the corresponding GPP and GCC phenometrics, i.e., start of season (SOS) and end of season (EOS). We find that for GPP phenology, PPI performs the best, in particular for evergreen coniferous forest areas where the seasonal variations in leaf area are small and snow is prevalent during wintertime. Results are inconclusive for GCC phenology, for which no index is consistently better than the others. When comparing to PEP725 phenology phases, PPI and EVI2 perform better than NDVI regarding the spatial correlation and consistency (i.e., lower standard deviation). We also link VI phenometrics at various amplitude thresholds to the PEP725 phenophases and find that PPI SOS at 25% and PPI EOS at 15% provide the best matches with the ground-observed phenological stages. Finally, we demonstrate that applying bidirectional reflectance distribution function correction to Sentinel-2 reflectance is a step that can be excluded for phenology mapping in Europe.

从遥感数据的时间序列获得的植被物候与一系列生态应用有关。免费提供的Sentinel-2图像具有10 m的空间分辨率，重复周期约为5天，从而提供了以前所未有的精细空间比例绘制植被物候图的机会。为了促进基于欧洲范围内哥白尼土地监测前哨2的物候数据集的产生，我们设计和评估了基于一组全面的地面观测结果的框架，包括涡旋协方差总一次生产量（GPP），PhenoCam绿色色坐标（GCC） ），以及来自泛欧洲物候数据库（PEP725）的物候期。我们测试了三个植被指数（VI）-归一化差异植被指数（NDVI），两波段增强植被指数（EVI2），和植物物候指数（PPI）-关于它们跟踪GPP和GCC的季节性轨迹的能力以及它们在反映相应GPP和GCC物候特征的空间变异性方面的性能，即季节开始（SOS）和季节结束（EOS） ）。我们发现，对于GPP物候而言，PPI表现最佳，特别是对于常绿的针叶林地区，该地区的叶面积季节性变化较小，并且冬季雪很盛行。对于GCC物候学而言，结果尚无定论，因为没有任何一项指标始终比其他指标更好。与PEP725物候期相比，就空间相关性和一致性（即较低的标准偏差）而言，PPI和EVI2的性能优于NDVI。我们还将各种幅度阈值下的VI物候计量学与PEP725物候相联系，发现25％的PPI SOS和15％的PPI EOS提供了与地面观测的物候阶段的最佳匹配。最后，我们证明了将双向反射率分布函数校正应用于Sentinel-2反射率是可以在欧洲进行物候映射的步骤。