Abstract Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types.

中文翻译：

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使用红色色度坐标表征森林冠层光合作用的物候

摘要 植被物候学在气候变化研究中越来越受到重视。使用数字重复摄影的近地表传感已被证明可用于植被物候的生态系统规模监测。然而，我们对来自数字重复摄影的物候指标与森林冠层光合作用物候之间联系的理解仍然不完整，尤其是对于常绿植物物种。使用来自北美八个常绿针叶林 (ENF) 和六个落叶阔叶林 (DBF) 站点的 PhenoCam 网络的 49 个站点年数字图像，我们探索了绿色色度 (GCC) 和红色色度坐标 (RCC) 的潜力) 通过比较相机衍生的生长季节开始和结束（SOS 和 EOS，分别）与从涡流协方差导出的每日总初级生产力（GPP）得出的相应估计值。我们发现对于 DBF 站点，GCC 和 RCC 在捕获 SOS 和 EOS 方面的表现相当。然而，与早期的研究类似，GCC 在为 ENF 站点捕获基于 GPP 的 SOS 或 EOS 方面的潜力有限。相比之下，我们发现 RCC 是 ENF 站点基于 GPP 的 SOS 和 EOS 的更好预测器。环境和生态解释均假设来自 RCC 的物候转变与春季和秋季的气象条件高度相关，并且与基于 LAI 的物候学具有整体更高的相关性，LAI 是描述冠层发育的关键变量。我们的结果表明 RCC 是一个被低估的用于跟踪植被物候的指标，特别是对于 GCC 无法为基于 GPP 的 SOS 或 EOS 提供可靠估计的 ENF 站点。我们的结果提高了使用数字重复摄影来表征不同森林类型冠层光合作用物候的信心。