The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
- Tuscia University
- Euro-Mediterranean Center on Climate Change
- California State University Sacramento
- Michigan State University
- University of Virginia
- TERN Ecosystrem Processes
- CAS - Institute of Geographical Sciences and Natural Resources Research
- University of Manitoba
- Agroscope Research Institute
- McMaster University
- Lund University
- University of Nebraska-Lincoln
- University of Melbourne
- University of Antwerp
- University of Liege
- University of California at Berkeley
- University of Saskatchewan
- People's Friendship University of Russia
- University of Western Australia
- AgroParisTech
- University of British Columbia
- University of Colorado Boulder
- Ohio State University
- Humboldt University of Berlin
- University of Minnesota System
- Université de Lorraine
- University of Utah
- University of Florida
- ETH Zurich
- INRAE UMR ECOFOG
- Université Paris-Saclay
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.
Details
Original language | English |
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Article number | 225 |
Journal | Scientific data |
Volume | 7 |
Issue number | 1 |
Publication status | Published - Dec 2020 |
Peer-reviewed | Yes |
External IDs
PubMed | 32647314 |
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ORCID | /0000-0003-2263-0073/work/163765944 |