## 3 Results
### Models Performance and Driving Factors of \(Fch_{4}\) Prediction  
The statistical result of the two machine learning models for predicting half-hour FCH\({}_{4}\) at different sites is provided in Table 7. Comparing the \(GRI\) values, the XGBoost models had better performance than RF models except for the IT-Cas and US-HRA sites. In the US-HRA site, the XGBoost model had lower \(RMSE\) and higher \(R^{2}\), but \(MAE\) and \(MBE\) were further away from 0 compared to the RF model. However, the RF model showed higher \(R^{2}\) and lower \(RMSE\) and \(MAE\) as compared to the XGBoost model in the IT-Cas site; only \(MBE\) performance was not good. Figure 6 shows the scatter plots of observed and predicted FCH\({}_{4}\) values based on XGBoost and RF models. The accuracy of both models was within the acceptable range, and the minimum \(R^{2}\) value was 0.6421. As seen in the figure, there were many sites with relatively serious underestimation, such as when the FCH\({}_{4}\) observed at the US-Twt site exceeded 200 nmol m\({}^{-2}\) s\({}^{-1}\); the underestimation was obvious. However, there were still many points distributed on both sides of the 1:1 line. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{\(R^{2}\)} & \multicolumn{1}{c}{\(MAE\)} & \multicolumn{1}{c}{\(MBE\)} & \multicolumn{1}{c}{\(GRI\)} \\ \cline{2-6}  & **(nmol m\({}^{-2}\) s\({}^{-1}\))** & & **(nmol m\({}^{-2}\) s\({}^{-1}\))** & **(nmol m\({}^{-2}\) s\({}^{-1}\))** & \\ \hline IT-Cas & & & & & \\ RF & 63.3657 & 0.7641 & 29.4863 & 0.7413 & 0.0000 \\ XGBoost & 63.5845 & 0.7623 & 30.3872 & \(-0.4892\) & 2.0000 \\ \hline JP-Mse & & & & & \\ RF & 36.8322 & 0.8979 & 14.2688 & 0.2430 & 2.0000 \\ XGBoost & 30.1109 & 0.9293 & 13.4638 & \(-0.2704\) & 0.0000 \\ \hline KR-CRK & & & & & \\ RF & 84.6257 & 0.7677 & 40.3356 & 0.9057 & 3.0000 \\ XGBoost & 80.4056 & 0.7893 & 37.5336 & 0.1151 & \(-1.0000\) \\ \hline PH-RiF & & & & & \\ RF & 43.9581 & 0.6421 & 18.6736 & 0.6135 & 2.0000 \\ XGBoost & 42.2099 & 0.6641 & 18.9505 & \(-0.0848\) & 0.0000 \\ \hline US-HRA & & & & & \\ RF & 37.0595 & 0.8292 & 15.0479 & 0.1860 & 1.0000 \\ XGBoost & 35.8628 & 0.8408 & 15.0570 & \(-0.5114\) & 1.0000 \\ \hline US-HRC & & & & & \\ RF & 28.7857 & 0.9444 & 13.7692 & 0.3039 & 2.0000 \\ XGBoost & 28.0140 & 0.9475 & 15.4187 & 0.1520 & 0.0000 \\ \hline US-Twt & & & & & \\ RF & 29.5718 & 0.8293 & 14.4098 & 0.2981 & 2.0000 \\ XGBoost & 28.9092 & 0.8355 & 14.8292 & 0.0876 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: FCH\({}_{4}\) prediction statistical values of the two machine learning models at seven rice paddy sites. Figure 5: Characteristic importance plots for the input factors of the XGBoost model for predicting NEE. The colors are used to identify input factors. Gain measures the improvement in the predictive power of the model after splitting the node; cover refers to the number of samples covered by a node; frequency represents the number of samples for different categories within a node. Input factors are air temperature (TA), incoming shortwave radiation (SW_IN), outgoing longwave radiation (LW_OUT), vapor pressure deficit (VPD), atmospheric pressure (PA), wind speed (WS), wind direction (WD), soil water content (SWC), friction velocity (USTAR), net radiation (NETRAD), soil temperature (TS), normalized difference vegetation index (NDVI), and the temperature difference between the previous day and the current day (DeltaTA). Figure 7 showed the importance of different factors at different sites to FCH\({}_{4}\) through the XGBoost model. The most important factors of the XGBoost model across all seven sites were SWC, NDVI, RECO, and TS; the average gain value exceeds 0.10. The gain of SWC exceeds 0.76 at two sites (US-HRA and US-HRC). It made a great contribution to the prediction of FCH\({}_{4}\) at these sites. LW, NEE, and PA were useful for some, but not all, sites.