New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data. Zhang, W., Luo, G., Yuan, X., Li, C., Xie, M., Wang, Y., Ma, X., Shi, H., Hamdi, R., Hellwich, O., Ma, X., Termonia, P., & De Maeyer, P. Methods in Ecology and Evolution, 14(9):2449–2463, September, 2023.
New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data [link]Paper  doi  abstract   bibtex   
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 meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty. In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the R square ( R 2 ), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest R 2 was selected from the models with a prediction accuracy of R 2  \textgreater 0.5 for the specific meteorological stations to estimate its NEE. 4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of R 2  \textgreater 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9%) screened meteorological stations around the world are negative (carbon sink) and most (65.3%) of those showed an increasing trend in the mean annual NEE (carbon sink). The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with https://doi.org/10.6084/m9.figshare.20485563.v1 .
@article{zhang_new_2023,
	title = {New data‐driven method for estimation of net ecosystem carbon exchange at meteorological stations effectively increases the global carbon flux data},
	volume = {14},
	issn = {2041-210X, 2041-210X},
	url = {https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14188},
	doi = {10.1111/2041-210X.14188},
	abstract = {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 meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing uncertainty.
                
                
                  
                    In this study, we developed a framework for estimating NEE at meteorological stations. We first optimized the hyperparameters and input variables of the ML model based on the optimization method called an adaptive genetic algorithm. Then, we developed 566 random forest (RF)‐based NEE estimation models by the strategy of spatial leave‐out‐one cross‐validation. We innovatively established the Euclidean distance‐based accuracy projection algorithm of the
                    R
                    square (
                    R
                    2
                    ), which could test the accuracy of each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest
                    R
                    2
                    was selected from the models with a prediction accuracy of
                    R
                    2
                     {\textgreater} 0.5 for the specific meteorological stations to estimate its NEE.
                  
                
                
                  
                    4674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of
                    R
                    2
                     {\textgreater} 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than the former studies. The NEE values of the most (96.9\%) screened meteorological stations around the world are negative (carbon sink) and most (65.3\%) of those showed an increasing trend in the mean annual NEE (carbon sink).
                  
                
                
                  
                    The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi‐observation data to assess the NEE products of the global grid. The NEE dataset is publicly available via the figshare with
                    https://doi.org/10.6084/m9.figshare.20485563.v1
                    .},
	language = {en},
	number = {9},
	urldate = {2024-11-15},
	journal = {Methods in Ecology and Evolution},
	author = {Zhang, Wenqiang and Luo, Geping and Yuan, Xiuliang and Li, Chaofan and Xie, Mingjuan and Wang, Yuangang and Ma, Xiaofei and Shi, Haiyang and Hamdi, Rafiq and Hellwich, Olaf and Ma, Xiumei and Termonia, Piet and De Maeyer, Philippe},
	month = sep,
	year = {2023},
	pages = {2449--2463},
}
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