Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Fang, Junjun; Fang, Jingchun; Chen, Baozhang; Zhang, Huifang; Dilawar, Adil; Guo, Man; Liu, Shu’an

doi:10.1038/s41597-024-03291-3

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Journal Article · Mon Jun 03 00:00:00 EDT 2024 · Scientific Data (Online)

DOI:https://doi.org/10.1038/s41597-024-03291-3· OSTI ID:2471077

Fang, Junjun ^[1]; Fang, Jingchun ^[1]; ^[2]; Zhang, Huifang ^[3]; Dilawar, Adil ^[4]; Guo, Man ^[5]; Liu, Shu’an ^[6]

Chinese Academy of Sciences (CAS), Beijing (China); Univ. of Chinese Academy of Sciences (CAS), Beijing (China)
Chinese Academy of Sciences (CAS), Beijing (China); Univ. of Chinese Academy of Sciences (CAS), Beijing (China); Jiangsu Center for Collaborative Innovation of Geographical Information Resources Development and Application, Nanjing (China)
Chinese Academy of Sciences (CAS), Beijing (China); Jiangsu Center for Collaborative Innovation of Geographical Information Resources Development and Application, Nanjing (China)
Chinese Academy of Sciences (CAS), Beijing (China); Univ. of Chinese Academy of Sciences (CAS), Beijing (China); Beijing Normal Univ., Beijing (China)
Chinese Academy of Sciences (CAS), Beijing (China); Univ. of Nottingham, Ningbo (China)
Nanjing Univ. of Information Science and Technology (China)

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). AmeriFlux

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2471077

Journal Information:: Scientific Data (Online), Journal Name: Scientific Data (Online) Journal Issue: 1 Vol. 11; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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