Tree phenology is a major driver of forest–atmosphere mass and energy exchanges. Yet, tree phenology has rarely been monitored in a consistent way throughout the life of a flux-tower site. Here, we used seasonal time series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (greenness chromatic coordinate), broadband NDVI, LAI (leaf area index), fAPAR (fraction of absorbed photosynthetic active radiation), CC (canopy closure), fR_vis (fraction of reflected radiation) and GPP (gross primary productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest using an asymmetric double sigmoid function (ADS) fitted to the time series. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured the interannual variability of spring phenology very well (R²>0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) of less than 4 d. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 d. The ADS method used to derive the phenological markers provides the most biased estimates for the GPP and GCC. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R²=0.63 and 0.80 for NDVI and CC, respectively), with an MAD of less than 7 d. Our results show that proximal-sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at eddy covariance (EC) flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

中文翻译：

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温带落叶林叶片物候监测近端方法研究

树木物候是森林-大气质量和能量交换的主要驱动力。然而，在通量塔站点的整个生命周期中，很少以一致的方式监测树木物候。在这里，我们使用了地面 NDVI（归一化差异植被指数）、RGB 相机 GCC（绿度色度坐标）、宽带 NDVI、LAI（叶面积指数）、f APAR（吸收光合有效辐射的分数）、CC 的季节性时间序列（天篷关闭）， f R _vis（反射辐射的分数）和 GPP（总初级生产力）来预测六个物候标记，使用适合时间序列的不对称双 sigmoid 函数 (ADS) 来检测温带落叶林中萌芽和叶片衰老的开始、中间和结束. 我们将它们与田间物候学家在 13 年间观察到的萌芽和叶片衰老进行了比较。GCC、NDVI 和 CC 很好地捕捉了春季物候的年际变化 ( R ² > 0.80) 并提供了观察到的萌芽日期的最佳估计，平均绝对偏差 (MAD) 小于 4 d。对于 CC 和 GCC 方法，春季物候过渡期间的中幅度 (50%) 阈值日期与观察到的物候日期非常吻合。对于基于 NDVI 的方法，平均观测日期与 NDVI 达到其年变化幅度的 25% 的日期重合。对于其他方法，MAD 的范围为 6 至 17 天。用于推导物候标记的 ADS 方法为 GPP 和 GCC 提供了最有偏差的估计。在叶片衰老阶段，NDVI 和 CC 衍生的日期与观测日期显着相关（R ² =0.63NDVI 和 CC 分别为 0.80），MAD 小于 7 d。我们的结果表明，近端传感方法可用于推导出稳健的物候指标。它们可用于在涡流协方差 (EC) 通量测量站点检索长期物候序列，并帮助解释质量和能量交换的年际变化和趋势。