Phenology is an important indicator of environmental variation and climate change impacts on tree responses. In conifers, monitoring phenology of photosynthesis through remote sensing has been unreliable, because needle foliage varies little throughout the year. This is challenging for modelling ecosystem carbon uptake and monitoring phenology for enhanced breeding (genomic selection) and forest health. Here, we demonstrate that drone-based carotenoid-sensitive spectral indices, such as the Chl/carotenoid index (CCI), can be used to track phenology in conifers by taking advantage of the close relationship between seasonally changing carotenoid levels and the variation of photosynthetic activity. Physiological ground measurements, including photosynthetic pigments and maximum quantum yield of Chl fluorescence, indicated that CCI tracked the variation of photosynthetic activity better than other vegetation indices for 30 white spruce seedlings measured over 1 yr. A machine-learning approach, using CCI derived from drone-based multispectral imagery, was used to model phenology of photosynthesis for the entire pedigree population (6000 seedlings). This high-throughput drone-based phenotyping approach is suitable for studying climate change impacts and environmental variation on the physiological status of thousands of field-grown conifers at unprecedented speed and scale.

中文翻译：

---

基于高通量无人机的遥感技术可以可靠地跟踪数千个针叶树幼苗的物候。

物候学是环境变化和气候变化对树木反应影响的重要指标。在针叶树中，通过遥感监测光合作用的物候状态一直是不可靠的，因为全年针叶变化不大。这对于建模生态系统碳吸收和监测物候以增强育种（基因组选择）和森林健康具有挑战性。在这里，我们证明了基于无人机的类胡萝卜素敏感光谱指数（例如Chl /类胡萝卜素指数（CCI））可通过利用季节性变化的类胡萝卜素水平与光合作用变化之间的密切关系来跟踪针叶树的物候活动。生理基础测量，包括光合色素和Chl荧光的最大量子产率，指出，在超过1年的时间里，CCI跟踪了30株白云杉幼苗的光合作用变化优于其他植被指数。使用从基于无人机的多光谱图像获得的CCI的机器学习方法，为整个谱系种群（6000棵幼苗）的光合作用物候模型。这种基于无人机的高通量表型分析方法适合以前所未有的速度和规模研究气候变化对环境的影响和环境变化对成千上万针叶树的生理状况的影响。用来模拟整个谱系种群（6000棵幼苗）的光合作用物候。这种基于无人机的高通量表型分析方法适合以前所未有的速度和规模研究气候变化对环境的影响和环境变化对成千上万针叶树的生理状况的影响。用来模拟整个谱系种群（6000棵幼苗）的光合作用物候。这种基于无人机的高通量表型分析方法适合以前所未有的速度和规模研究气候变化对环境的影响和环境变化对成千上万针叶树的生理状况的影响。