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

作者：Junjun Fang, Jingchun Fang, Baozhang Chen, Huifang Zhang, Adil Dilawar, Man Guo, Shu’an Liu · 发表于：Scientific Data · 年份：2024 · DOI：10.1038/s41597-024-03291-3 · 被引用次数：25 · 研究领域：Atmospheric and Environmental Gas Dynamics、Climate variability and models、Plant Water Relations and Carbon Dynamics

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...