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Eddy covariance fluxes over managed ecosystems extrapolated to field scales at fine spatial resolutions

Zhu, S. ORCID:, Olde, L. ORCID:, Lewis, K., Quaife, T. ORCID:, Cardenas, L., Loick, N. ORCID:, Xu, J. ORCID: and Hill, T. (2023) Eddy covariance fluxes over managed ecosystems extrapolated to field scales at fine spatial resolutions. Agricultural and Forest Meteorology, 342. 109675. ISSN 0168-1923

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To link to this item DOI: 10.1016/j.agrformet.2023.109675


To enable an evidence-based management of ecosystems to adapt to the climate crisis, we require fine spatiotemporal resolution estimates of carbon, water, and energy fluxes at the field scale. To overcome the limitations resulting from the coarse spatial resolution of existing flux products, e.g. 500 m (Running et al., 2015), and the challenges in matching eddy covariance (EC) footprints with land use field scales, we for the first time investigate the influence of satellite resolution on flux estimation, which is to support the fine-scale extrapolation of EC fluxes from the tower footprint to the field scale. We validate the extrapolation at 206 FLUXNET2015 tower sites to pave the way for estimating field-scale fluxes extrapolated from three towers in a managed European grazing pasture on a fine-scale, 30 m spatial resolution. The findings suggest that (a) tower-level flux estimates from 30 m satellites were in agreement with fluxes estimated from moderate-resolution satellites, which are extensively employed in literature (R2 difference ≪ 0.1); (b) flux estimates were in reasonable agreement with EC measurements (R2: 0.7 and annual bias < 2 Mg ha−1 yr−1 for carbon fluxes); (c) Sentinel-2 was advantageous in capturing land-use variability over other satellites in European pastures; (d) the machine-learning extrapolation algorithm was resistant to livestock grazing.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:114841


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