Evidence map›Paper›PMID 42037601›Full record

ReviewGlobal change biology2026

Flux Footprints: A Critical Link to Bridge Eddy-Covariance Measurements With Models, Remote Sensing, and Other Observations.

Housen Chu, Stefan Metzger, Zutao Ouyang, Anne Griebel, Koong Yi, David Durden, Sebastian Wolf, Kuno Kasak, Camilo Rey-Sanchez, Leila Constanza Hernandez Rodriguez and 5 more

Abstract readReview
In one paragraph

Review in Global change biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Housen ChuClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Stefan MetzgerAtmoFacts, Longmont, Colorado, USA.
Zutao OuyangCollege of Forestry, Wildlife and Environment, Auburn University, Auburn, Alabama, USA.
Anne GriebelSchool of Life Sciences, University of Technology Sydney, Sydney, New South Wales, Australia.
Koong YiCenter for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Champaign, Illinois, USA.
David DurdenNational Ecological Observatory Network, Battelle, Boulder, Colorado, USA.
Sebastian WolfDepartment of Environmental Systems Science, ETH Zurich, Zurich, Switzerland.
Kuno KasakDepartment of Geography, University of Tartu, Tartu, Estonia.
Camilo Rey-SanchezDepartment of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina, USA.
Leila Constanza Hernandez RodriguezEnergy Geosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Patty OikawaDepartment of Earth and Environmental Sciences, California State University, East Bay, Hayward, California, USA.
Nicola FalcoClimate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Benjamin R K RunkleDepartment of Biological & Agricultural Engineering, University of Arkansas, Fayetteville, Arkansas, USA.
Arman AhmadiDepartment of Environmental Sciences, Policy, and Management, University of California, Berkeley, California, USA.
Jaclyn MatthesHarvard Forest, Harvard University, Cambridge, Massachusetts, USA.

Funding

Biological and Environmental Research DE-AC02-05CH11231
6 · The paper itself

Abstract

Global networks of eddy-covariance flux towers play a pivotal role in enhancing our predictive understanding of carbon and water cycling of biological systems in response to regional and global environmental change. Despite their broad application in numerous studies, the spatial aspects of the flux measurements have often been ignored or treated ambiguously, thereby remaining a primary source of uncertainty. The area contributing to the flux-referred to as the flux footprint-varies over time depending on wind direction, atmospheric turbulence, effective measurement heights, surface characteristics, and mesoscale forcings. The footprint dynamics, along with underlying source-sink heterogeneity, lead to spatial and temporal variability in the sensed fluxes, complicating the interpretation of flux data and their integration with a range of observations and models. This article addresses this critical link by reviewing the most up-to-date research, identifying knowledge gaps and challenges, and pointing out future research needs and opportunities. We begin with an overview of the current state of footprint modeling and its applications, from single-site studies to large-scale syntheses, summarizing how flux footprints have been used to interpret spatial flux variability and to integrate flux data with models, remote sensing, and other observations. We highlight how this critical spatial aspect could complicate the processing of eddy-covariance fluxes, the definition of mass and energy continuity, and the interpretation of flux response functions and parameters, all of which have significant implications for numerous applications and research. We then point out potential opportunities and future research needs to bridge this knowledge gap, highlighting both readily available and prominent emerging ones. We conclude by urging the scientific community to (re)consider the spatiotemporal dynamics of eddy-covariance flux measurements, and to investigate and evaluate potential approaches across scales, applications, and disciplines.

Indexed as

Carbon CycleClimate ChangeEnvironmental MonitoringModels, TheoreticalRemote Sensing TechnologyWater CycleAmeriFluxcarbon cycledata‐model integrationEddy covarianceflux footprintFLUXNETspatial heterogeneityupscalingwater cycle

Identifiers

PMID42037601
PMCPMC13112517

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.