Scaling photosynthetic function and CO2 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

Petya Campbell; Elizabeth Middleton; Karl Huemmrich; Lauren Ward; Tommaso Julitta; Peiqi Yang; Christiaan van der Tol; Craig Daughtry; Andrew Russ; Joseph Alfieri; William Kustas

Data in Brief (Dec 2021)

Scaling photosynthetic function and CO2 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

Petya Campbell,
Elizabeth Middleton,
Karl Huemmrich,
Lauren Ward,
Tommaso Julitta,
Peiqi Yang,
Christiaan van der Tol,
Craig Daughtry,
Andrew Russ,
Joseph Alfieri,
William Kustas

Affiliations

Petya Campbell: University of Maryland Baltimore County, MD, USA; NASA Goddard Space and Flight Center, Greenbelt, MD, USA; Corresponding author at: University of Maryland Baltimore County, MD, USA.
Elizabeth Middleton: NASA Goddard Space and Flight Center, Greenbelt, MD, USA
Karl Huemmrich: University of Maryland Baltimore County, MD, USA; NASA Goddard Space and Flight Center, Greenbelt, MD, USA
Lauren Ward: NASA Goddard Space and Flight Center, Greenbelt, MD, USA; University of Hawai'i at Mañoa, Hawai'i, USA
Tommaso Julitta: J-B Hyperspectral Devices GmbH, Dusseldorf, Germany
Peiqi Yang: University of Twente, Twente, the Netherland
Christiaan van der Tol: University of Twente, Twente, the Netherland
Craig Daughtry: USDA Agricultural Research Center, Beltsville, MD, USA
Andrew Russ: USDA Agricultural Research Center, Beltsville, MD, USA
Joseph Alfieri: USDA Agricultural Research Center, Beltsville, MD, USA
William Kustas: USDA Agricultural Research Center, Beltsville, MD, USA

Journal volume & issue: Vol. 39
p. 107600

Abstract

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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.

Published in Data in Brief

ISSN: 2352-3409 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics; Science: Science (General)
Website: http://www.journals.elsevier.com/data-in-brief/

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