Campbell Petya, Middleton Elizabeth, Huemmrich Karl, Ward Lauren, Julitta Tommaso, Yang Peiqi, van der Tol Christiaan, Daughtry Craig, Russ Andrew, Alfieri Joseph, Kustas William
University of Maryland Baltimore County, MD, USA.
NASA Goddard Space and Flight Center, Greenbelt, MD, USA.
Data Brief. 2021 Nov 20;39:107600. doi: 10.1016/j.dib.2021.107600. eCollection 2021 Dec.
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.
叶片荧光测量和冠层近程遥感技术的最新进展，目前能够实现对丰富的昼夜和季节时间序列进行无损采集，而这些时间序列是在与光合作用自然动态相关的时间和空间尺度上监测植被功能所必需的。传统上，植被功能的遥感评估使用主动激发的叶片叶绿素荧光测量、冠层光学反射率数据和植被指数（VIs），直到最近才开始使用被动太阳诱导叶绿素荧光（SIF）测量。一般来说，反射率数据对冠层叶绿素含量和叶片生物量的季节变化更为敏感，而荧光观测则与植物光合功能的动态变化更为密切相关。利用这个数据集，我们将叶片水平的主动激发叶绿素荧光、冠层近程反射率和SIF与生态系统总生产力（GEP）的涡度协方差测量联系起来。该数据集是在2017年玉米生长季节期间，使用三个自动化系统（即监测脉冲幅度调制荧光计，Moni-PAM；荧光箱，FloX；以及来自涡度协方差塔）收集的。对数据进行了质量检查、过滤，并整理为30分钟的通用时间步长。我们推导了与冠层功能相关的植被指数（例如，光化学反射指数，PRI；归一化植被指数，NDVI；叶绿素红边，Clre），以研究如何将SIF和VIs结合起来监测植被光合作用。提供了原始数据集以及经过过滤和整理的数据，以便进行新的处理和分析。
