## 3 Results
### Machine Learning Interpolation  
#### 3.2.1 Correlation Analysis between NEE and Meteorological Factors  
The amount of a CO\({}_{2}\) flux is affected by the climate, the land type, the vegetation status, and other factors. In order to explore the sensitivity of the CO\({}_{2}\) flux to environmental factors, we selected meteorological factors for CO\({}_{2}\) flux interpolation, and the 30 min data of the input meteorological factors and the CO\({}_{2}\) flux were selected for the correlation analysis. The Pearson correlation coefficient in statistics was used to measure the linear correlation between the CO\({}_{2}\) flux and the meteorological factors in this paper. The results show that there was a significant correlation between the CO\({}_{2}\) flux and the selected meteorological factors in the study area, and the significant \(p\) values were less than 0.001, indicating that the level was significant. The correlation was as follows: shortwave downward radiation (RG) > saturated water vapor pressure (VPD) > soil heat flux (Soil_G) > air temperature (Tair) > relative humidity (RH) > soil temperature (Soil_T) > soil moisture content (Soil_VWC) > wind speed (WS). The CO\({}_{2}\) flux was negatively correlated with RG, VPD, Soil_G, Tair, Soil_T, Soil_VWC, and WS, and it was positively correlated with RH as shown in Figure 4.  
2.2 The Accuracy Comparison of Each Model Algorithm under Different Meteorological Factor Input Combinations  
According to the correlation analysis between the NEE and the meteorological factors in Figure 4, the first three meteorological factors of the average correlation coefficient were selected as the characteristic combination 1. Moreover, the range from combination 2 to combination 6 was added according to the order of the average correlation coefficients, and six input combinations based on the machine learning algorithm were built. A total of 80% of the observed values were used as the training set, and 20% were used as the test set (Table 3). Figure 3: Comparison of results before and after quality control of CO\({}_{2}\) flux data output by Eddypro (**a**) before elimination and (**b**) after removal. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Input Characteristics** & **Feature Number** \\
**Combination** & & \\ \hline combination 1 & RG, VPD, Soil\_G & 3 \\ combination 2 & RG, VPD, Soil\_G, Tair & 4 \\ combination 3 & RG, VPD, Soil\_G, Tair, RH & 5 \\ combination 4 & RG, VPD, Soil\_G, Tair, RH, Soil\_T & 6 \\ combination 5 & RG, VPD, Soil\_G, Tair, RH, Soil\_T, Soil\_VWC & 7 \\ combination 6 & RG, VPD, Soil\_G, Tair, RH, Soil\_T, Soil\_VWC, WS & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model parameter combinations of different meteorological factors. It can be seen from Table 4 that, in combination 1, MLR, CART, and Adaboost had poor prediction effects, and the accuracy R\({}^{2}\) was less than 0.5. RF and XGBoost had the highest prediction accuracies, and R\({}^{2}\) reached 0.58. In combination 2, CART had the lowest prediction accuracy (R\({}^{2}\) = 0.24), and RF and MLP had the highest prediction accuracies (R\({}^{2}\) = 0.60). In combination 3, CART had the lowest prediction accuracy (R\({}^{2}\) = 0.25), and MLP and XGBoost had the highest prediction accuracies (R\({}^{2}\) = 0.61). In combination 4, CART had the lowest prediction accuracy (R\({}^{2}\) = 0.22), and MLP and XGBoost had the highest prediction accuracies (R\({}^{2}\) = 0.61). In combination 5, CART had the lowest prediction accuracy (R\({}^{2}\) = 0.22), and MLP had the highest prediction accuracy (R\({}^{2}\) = 0.61). In combination 6, CART had the lowest prediction accuracy (R\({}^{2}\) = 0.25), and RF, MLP, and XGBoost had the highest prediction accuracies (R\({}^{2}\) = 0.62). The R\({}^{2}\) values of the eight types of machine learning algorithm models in combination 6 were the largest among the six feature combinations, which showed that the feature factor of combination 6 was the best input for the model training.