## 3 Results  
### Comparisons of Products  
Comparisons were conducted using data from each flux tower site and the corresponding grid data to obtain the results presented in Figure 1. The findings indicate that the GCFD demonstrated the most outstanding performance, whereas the predictions of the other datasets exhibited notable underestimations or overestimations. Evaluating the \(R^{2}\) and \(RMSE\) values, FLUXCOM and [PERSON] et al. showcased very similar performance, while the GCFD exhibited higher \(R^{2}\) values and lower \(RMSE\) values for all three predicted fluxes. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset Name** & **Spatial Resolution** & **Temporal Resolution and Coverage** & **Estimating Method** \\ \hline GCFD & 1 km & Every 10 days from January 1999 to June 2020 & \\ FLUXCOM & 0.5\({}^{\circ}\) & Monthly, from 2001 to 2010 & Machine learning \\ NIES & 0.1\({}^{\circ}\) & Every 10 days from 1999 to 2019 & \\ \hline TRENDY & 1\({}^{\circ}\) & Monthly, from 1999 to 2018 & Ecosystem model \\ EC-LUE & 0.05\({}^{\circ}\) & Every 8 days from 1999 to 2018 & Remote sensing \\ \hline GLASS & 0.05\({}^{\circ}\) & Every 8 days from 1999 to 2018 & Remote sensing \\ \hline \end{tabular}
\end{table} 
Table 1: Detailed information on global carbon flux datasets. Notably, when compared to FLUXCOM, the GCFD exhibited a remarkable enhancement in \(R^{2}\), with improvements of 49%, 49%, and 126% for GPP, RECO, and NEE, respectively. Among all the products, GCFD predictions exhibited the lowest biases, with GPP, RECO, and NEE showing prediction biases of \(-\)0.044, 0.018, and 0.053, respectively. Conversely, FLUXCOM displayed the most pronounced bias. The discrepancy between the predicted carbon flux values of the ecosystem model and the in situ observations is more pronounced in comparison to the machine learning products. This disparity could arise from a mismatch in the scales of the model and the site, possibly due to the low spatial resolution of the model data. Additionally, it could be attributed to the uncertainties generated by the model during the parameterization and simplification of the ecosystem structure. Compared to ecosystem model products, the remote sensing product produced GPP data that are closer to site data and exhibited significantly smaller bias. The validation accuracy stands at 0.56, though it remains lower than the site validation accuracy of machine learning. Remote sensing products, derived primarily from satellite-observed data, offer the advantage of reducing complex calculation processes and minimizing data generation uncertainties. However, satellite observations are subject to environmental and equipment conditions, leading to greater uncertainty compared to machine learning methods, which rely solely on data. These factors suggest that the accuracy of carbon flux products estimated through remote sensing methods falls between that of ecosystem model products and machine learning products. A further comparison was conducted by subtracting the 2010 annual averages of the three carbon fluxes predicted by the GCFD from the predicted values of other products. This process generated a distribution of differences, which is illustrated in Figure 2. Among the three carbon fluxes, NEE exhibited the smallest disparity across the data products due to its relatively narrow range of values. The GCFD only exhibited a slight overestimate or underestimate in tropical regions when compared to the two machine learning products. The distinction between GCFD and FLUXCOM predictions for RECO was more prominent in the Amazon region of South America. On the other hand, the gap between the GCFD and FLUXCOM was slightly larger than that between the GCFD and [PERSON] et al. In the case of GPP, the GCFD tended to slightly overestimate FLUXCOM in various regions, such as Siberia, North America, and South America, where the difference between the GCFD and [PERSON] et al. was smaller.