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

文献类型：期刊论文

作者Fang, Junjun5,6; Fang, Jingchun5,6; Chen, Baozhang4,5,6; Zhang, Huifang4,6; Dilawar, Adil3,5,6; Guo, Man2,6; Liu, Shu'an1
刊名SCIENTIFIC DATA
出版日期2024-06-03
卷号11期号:1页码:569
DOI10.1038/s41597-024-03291-3
产权排序1
文献子类Article
英文摘要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.
WOS关键词ECOSYSTEM PRODUCTIVITY ; FOOTPRINT ; VARIABILITY ; LANDSCAPE ; NETWORK ; IMAGES ; MODEL ; TIME
WOS研究方向Science & Technology - Other Topics
WOS记录号WOS:001237867200001
出版者NATURE PORTFOLIO
源URL[http://ir.igsnrr.ac.cn/handle/311030/205319]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Chen, Baozhang
作者单位1.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
2.Univ Nottingham, Fac Sci & Engn, Sch Geog Sci, Ningbo 315100, Peoples R China
3.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface & Ecol Resources, Beijing 100875, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resource & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Fang, Junjun,Fang, Jingchun,Chen, Baozhang,et al. Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015[J]. SCIENTIFIC DATA,2024,11(1):569.
APA Fang, Junjun.,Fang, Jingchun.,Chen, Baozhang.,Zhang, Huifang.,Dilawar, Adil.,...&Liu, Shu'an.(2024).Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015.SCIENTIFIC DATA,11(1),569.
MLA Fang, Junjun,et al."Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015".SCIENTIFIC DATA 11.1(2024):569.

入库方式： OAI收割

来源：地理科学与资源研究所

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