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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #387089
Research Project: Enhancing Agricultural Management and Conservation Practices by Advancing Measurement Techniques and Improving Modeling Across Scales

Location: Hydrology and Remote Sensing Laboratory

Title: Dataset combining diurnal and seasonal measurements of vegetation fluorescence, reflectance and vegetation indices with photosynthetic function and CO2 dynamics for maize

Author
item CAMPBELL, P.K.E - Collaborator
item MIDDLETON, E.M. - National Aeronautics And Space Administration (NASA)
item HUMMERICH, K.F. - Collaborator
item WARD, L.A. - University Of Hawaii
item JULITTA, T. - Collaborator
item YANG, P. - University Of Twente
item VAN DER TOL, C. - University Of Twente
item Daughtry, Craig
item Russ, Andrew - Andy
item Alfieri, Joseph
item Kustas, William - Bill

Submitted to: Data in Brief
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2021
Publication Date: 11/20/2021
Citation: Campbell, P., Middleton, E., Hummerich, K., Ward, L., Julitta, T., Yang, P., Van Der Tol, C., Daughtry, C.S., Russ, A.L., Alfieri, J.G., Kustas, W.P. 2021. Dataset combining diurnal and seasonal measurements of vegetation fluorescence, reflectance and vegetation indices with photosynthetic function and CO2 dynamics for maize. Data in Brief. 39:107600. https://doi.org/10.17632/b84jk376c3.1.
DOI: https://doi.org/10.17632/b84jk376c3.1

Interpretive Summary: This data paper describes a dataset created jointly by NASA scientists and their ARS and university partners using measurements collected during the 2017 growing season at the OPE3 experimental watershed near Beltsville, MD. The dataset combines remotely sensed measurements of plant processes with field measurements of meteorological conditions and surface fluxes. Thus, this dataset links leaf level plant functions with the larger (field-scale) environment. This database will be used by ARS and other researchers both to understand and develop remote sensing approaches that can monitor water loss from agricultural systems. Ultimately, this will lead to improved irrigation and water management strategies for natural resource managers and producers.

Technical Abstract: In situ remote sensing enables the non-destructive collection of rich datasets for monitoring vegetation function at the temporal and spatial scales relevant to the dynamics of plant photosynthesis. Measurements assessing vegetation function are typically based on the use of actively exited chlorophyll fluorescence, reflectance, vegetation indices (VIs), and only recently on solar induced chlorophyll fluorescence (SIF). In general, reflectance data are more sensitive to the seasonal variations in canopy chlorophyll content and foliar biomass, while fluorescence data are more sensitive to the dynamic changes in photosynthetic function. This dataset links leaf level chlorophyll fluorescence, canopy reflectance and SIF, with eddy covariance measurements of gross ecosystem productivity (GEP). Vegetation indices indicative of canopy function (e.g., Photochemical Reflectance Index, PRI; Normalized Difference Vegetation Index, NDVI; Chlorophyll Red-edge, Clre) were derived to investigate how SIF and VIs can be used together for monitoring vegetation photosynthesis. The data were 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 fluxes from eddy covariance tower), filtered and collated to a common 30 minutes timestep. The raw data are provided to enable new processing and analyses.