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 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 (R2), 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 R2 was selected from the models with a prediction accuracy of R2 > 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 R2 > 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.

Spatiotemporal Modeling of Carbon Fluxes over Complex Underlying Surfaces along the North Shore of Hangzhou Bay

“…The RF model is considered a reasonable and suitable method for simulating CO 2 fluxes from site to regional scales. Firstly, as a machine learning algorithm, the RF model selects the optimal output from multiple regression trees to capture the features of the data, effectively enhancing the accuracy of flux data [56,57]. Secondly, by extracting the multivariate functional relationships between observed data and explanatory variables, the RF model can integrate data from different sources and simplify complex processes, addressing nonlinear issues in ecosystems [58].…”

Universality of ecological memory for local and global net ecosystem exchange, atmospheric CO2, and sea surface temperature

“…Interestingly, in ecological dynamics of net ecosystem exchange (NEE), Kong et al (2022) expressed that NEE's interactions with other environmental variables remained a challenging task for the reasons that the latter varies across ecosystems and climate regimes. In Zhang et al (2023), the inclusion of NEE was observed to improve the terrestrial photosynthesis model and global carbon cycle prediction amidst climate changes. The same assertion was shown by Mahmud et al (2021) for dryland global carbon cycle contribution using the global terrestrial biosphere model including those predicted for urban forests 22,23 , peatland mosses 24 , tropical vegetation 25 , as well as environmental processes of soil respirations ?,27 and land-ocean-atmosphere carbon exchange 28,29,30 .…”