The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Authors

Gilberto Pastorello, Lawrence Berkeley National Laboratory, Berkeley, CA
Timothy Arkebauer, University of Nebraska-LincolnFollow
Dave P. Billesbach, University of Nebraska - LincolnFollow
Anatoly Gitelson, University of Nebraska-LincolnFollow
Adam Liska, University of Nebraska - LincolnFollow
Andrew Suyker, University of Nebraska - LincolnFollow
Elizabeth Walter-Shea, University of Nebraska-LincolnFollow
More than 200 other, international researchers

ORCID IDs

Gilberto Pastorello http://orcid.org/0000-0002-9387-3702

Elizabeth Walter-Shea http://orcid.org/0000-0001-9235-3257

Date of this Version

2020

Citation

Scientific Data (2020) 7:225

https://doi.org/10.1038/s41597-020-0534-3

Comments

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License

This is a U.S. government work and not under copyright protection in the U.S.

An Author Correction is attached below: Scientific Data | (2021) 8:72 | https://doi.org/10.1038/s41597-021-00851-9

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.

Supplement file (88 pp) attached below.