Articles | Volume 19, issue 16
https://doi.org/10.5194/bg-19-3739-2022
https://doi.org/10.5194/bg-19-3739-2022
Research article
 | 
16 Aug 2022
Research article |  | 16 Aug 2022

Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation

Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde

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Cited articles

Abbasian, H., Solgi, E., Mohsen Hosseini, S., and Hossein Kia, S.: Modeling terrestrial net ecosystem exchange using machine learning techniques based on flux tower measurements, Ecol. Model., 466, 109901, https://doi.org/10.1016/j.ecolmodel.2022.109901, 2022. 
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Baldocchi, D. D.: Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future, Global Change Biol., 9, 479–492, https://doi.org/10.1046/j.1365-2486.2003.00629.x, 2003. 
Berryman, E. M., Vanderhoof, M. K., Bradford, J. B., Hawbaker, T. J., Henne, P. D., Burns, S. P., Frank, J. M., Birdsey, R. A., and Ryan, M. G.: Estimating soil respiration in a subalpine landscape using point, terrain, climate, and greenness data, J. Geophys. Res.-Biogeo., 123, 3231–3249, 2018. 
Borenstein, M., Hedges, L. V., Higgins, J. P., and Rothstein, H. R.: Introduction to meta-analysis, John Wiley & Sons, https://doi.org/10.1002/9780470743386, 2011. 
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Short summary
A number of studies have been conducted by using machine learning approaches to simulate carbon fluxes. We performed a meta-analysis of these net ecosystem exchange (NEE) simulations. Random forests and support vector machines performed better than other algorithms. Models with larger timescales had a lower accuracy. For different plant functional types (PFTs), there were significant differences in the predictors used and their effects on model accuracy.
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