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

作者：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 · 发表于：Methods in Ecology and Evolution · 年份：2023 · DOI：10.1111/2041-210x.14188 · 被引用次数：29 · 研究领域：Plant Water Relations and Carbon Dynamics、Climate variability and models、Hydrology and Watershed Management Studies

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 > 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 > 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a ...