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首页> 外文期刊>Data in Brief >Scaling photosynthetic function and CO 2 dynamics from leaf to canopy level for maize – dataset combining diurnal and seasonal measurements of vegetation fluorescence, reflectance and vegetation indices with canopy gross ecosystem productivity
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Scaling photosynthetic function and CO 2 dynamics from leaf to canopy level for maize – dataset combining diurnal and seasonal measurements of vegetation fluorescence, reflectance and vegetation indices with canopy gross ecosystem productivity

机译：缩放光合作用函数和CO 2 从叶片到玉米冠层水平的动态 - 数据集与植被荧光的昼夜季节性测量相结合，具有冠层粗糙生态系统生产率的植被荧光，反射率和植被指数

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Recent advances in leaf fluorescence measurements and canopy proximal remote sensing currently enable the non-destructive collection of rich diurnal and seasonal time series, which are required for monitoring vegetation function at the temporal and spatial scales relevant to the natural dynamics of photosynthesis. Remote sensing assessments of vegetation function have traditionally used actively excited foliar chlorophyll fluorescence measurements, canopy optical reflectance data and vegetation indices (VIs), and only recently passive solar induced chlorophyll fluorescence (SIF) measurements. In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence observations more closely relate to the dynamic changes in plant photosynthetic function. With this dataset we link leaf level actively excited chlorophyll fluorescence, canopy proximal reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). The dataset was collected during the 2017 growing season on maize, using three automated systems (i.e., Monitoring Pulse-Amplitude-Modulation fluorimeter, Moni-PAM; Fluorescence Box, FloX; and from eddy covariance tower). The data were quality checked, filtered and collated to a common 30 minutes timestep. We derived vegetation indices related to canopy functioning (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) to investigate how SIF and VIs can be coupled for monitoring vegetation photosynthesis. The raw datasets and the filtered and collated data are provided to enable new processing and analyses.
机译：叶片荧光测量和冠层近端遥感的最新进展目前能够在与光合作用自然动态相关的时间和空间尺度处监测植被功能所需的无损收集。植被函数的遥感评估传统上使用了积极激发致命的叶绿素荧光测量，冠层光学反射数据和植被指数（VIS），并且只有最近被动的太阳能诱导叶绿素荧光（SIF）测量。通常，反射数据对冠覆叶绿素含量和叶状生物量的季节变异更敏感，而荧光观察与植物光合作用的动态变化更密切相关。通过该数据集，我们将叶子水平积极激发叶绿素荧光，冠层近端反射和SIF，具有涡流的生态系统生产力（GEP）的涡流调节。使用三个自动化系统（即监测脉冲幅度调制荧光计，Moni-PAM，荧光盒，闪光灯，在2017年在玉米生长季节期间收集了数据集。数据是质量检查，过滤并整理到Commentep 30分钟。我们衍生与冠层功能相关的植被指数（例如，光化学反射率指数，PRI;归一化差异植被指数，NDVI;叶绿素红边，CLRE）来研究SIF和VIS如何耦合以监测植被光合作用。提供原始数据集和过滤和整理的数据，以实现新的处理和分析。

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