D'Odorico Petra, Besik Ariana, Wong Christopher Y S, Isabel Nathalie, Ensminger Ingo
Department of Biology, University of Toronto, Mississauga, ON, L5L 1C6, Canada.
Graduate Program in Cell & Systems Biology, University of Toronto, Toronto, ON, M5S 1A1, Canada.
New Phytol. 2020 Jun;226(6):1667-1681. doi: 10.1111/nph.16488. Epub 2020 Mar 20.
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.
物候学是环境变化以及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一直不太可靠，因为针叶在一年中变化很小。这对于模拟生态系统碳吸收以及监测物候以进行强化育种（基因组选择）和森林健康状况来说是一项挑战。在此，我们证明基于无人机的类胡萝卜素敏感光谱指数，如叶绿素/类胡萝卜素指数（CCI），可以利用季节性变化的类胡萝卜素水平与光合活性变化之间的密切关系来追踪针叶树的物候。包括光合色素和叶绿素荧光最大量子产率在内的生理地面测量表明，对于在1年时间内测量的30株白云杉幼苗，CCI比其他植被指数能更好地追踪光合活性的变化。一种机器学习方法，利用基于无人机多光谱图像得出的CCI，被用于对整个谱系群体（6000株幼苗）的光合作用物候进行建模。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影响以及环境变化对数千株田间种植针叶树生理状态的影响。
