Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes.

监测常绿阔叶植被的遥感能力受到绿色信号的低时间变异性的限制。通过数字重复摄影 (PhenoCam) 计算的冠层绿度，我们研究了冠层绿度与叶龄和性状的季节性变化以及树木水通量的变化（以液流和冠层电导率为特征）之间的关系。结果表明，发芽的叶子是春季冠层绿色色坐标（GCC）快速增加的主要原因。我们发现不同叶龄的叶片在叶片性状和光谱特性上存在显着差异。具体来说，幼叶的平均 GCC 为 0.385 ± 0.010（平均值 ± SD），而成熟叶和老叶的 GCC 分别为 0.369 ± 0.003 和 0.376 ± 0.004。因此，冠层 GCC 的时间动态可以通过叶片光谱特性和叶片年龄的变化来解释。环境驱动因素和绿色度的结合很好地解释了液流和树冠导度（分别解释了方差的 96% 和 87%）。特别是，气温和蒸汽压不足（VPD）分别解释了大部分的液流和冠层导流变化。此外，GCC 是冠层电导变化的重要解释变量，因为 GCC 可以表示叶片个体发育信息。我们得出结论，PhenoCam GCC 可用于识别常绿阔叶树的叶片潮红，它携带有关叶片个体发育和性状的重要信息。因此，它有助于更​​好地估计限制水通量的冠层电导。