New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data

New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data

Abstract The eddy covariance (EC) flux stations have great limitations in the evaluation of the global net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed me...

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Bibliographic Details
Main Authors: Wenqiang Zhang, Geping Luo, Xiuliang Yuan, Chaofan Li, Mingjuan Xie, Yuangang Wang, Xiaofei Ma, Haiyang Shi, Rafiq Hamdi, Olaf Hellwich, Xiumei Ma, Piet Termonia, Philippe De Maeyer
Format: Article
Language:English
Published: Wiley 2023-09-01
Series:Methods in Ecology and Evolution
Subjects:
data‐driven
ecosystem carbon cycle
eddy covariance flux stations
machine learning
meteorological stations
net ecosystem carbon exchange
Online Access:https://doi.org/10.1111/2041-210X.14188

Internet

https://doi.org/10.1111/2041-210X.14188

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