The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Affiliation
Univ Arizona, Dept Ecol & Evolutionary BiolIssue Date
2020-07Metadata
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NATURE PUBLISHING GROUPCitation
Pastorello, G., Trotta, C., Canfora, E. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225 (2020).Journal
SCIENTIFIC DATARights
This article is licensed under a Creative Commons Attribution 4.0 International License. Copyright is held by the author(s) or the publisher. If your intended use exceeds the permitted uses specified by the license, contact the publisher for more information.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Note
Open access journalISSN
2052-4463EISSN
2052-4463PubMed ID
32647314Version
Final published versionScopus Count
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Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License. Copyright is held by the author(s) or the publisher. If your intended use exceeds the permitted uses specified by the license, contact the publisher for more information.
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