## 3 Results
### Models Performance and Driving Factors of NEE Prediction  
The statistical result of the two machine learning models for predicting half-hour NEE at different sites is provided in Table 6. As seen in the table, except for KR-CRK and US-HRC sites, the XGBoost models showed higher \(R^{2}\) and lower _RMSE_ and _MAE_ as compared to the RF models. Only at the KR-CRK site was the _GRI_ value of XGBoost larger than that of RF. Figure 4 shows the scatter plots of the observed and predicted NEE values based on the XGBoost and RF models. Both models made good predictions at most points, with \(R^{2}\) ranging from 0.7525-0.9668 for XGBoost, and 0.7548-0.9581 for RF. When the predicted value was small, there was an overestimation, and when the predicted value was large, there was an underestimation. Figure 2: Scatter plots of the observed and predicted ET at seven rice paddy sites using the XGBoost and RF models. Red points represent the observed and predicted ET using the XGBoost model. Blue points represent the observed and predicted ET using the RF model. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{1}{c}{_RMSE_} & \(R^{2}\) & _MAE_ & _MBE_ & _GRI_ \\ \cline{2-5}  & **(gC m\({}^{-2}\) d\({}^{-1}\))** & & **(gC m\({}^{-2}\) d\({}^{-1}\))** & **(gC m\({}^{-2}\) d\({}^{-1}\))** & \\ \hline IT-Cas & & & & & \\ RF & 9.2419 & 0.9341 & 4.6274 & 0.0623 & 2.0000 \\ XGBoost & 9.1589 & 0.9355 & 4.6318 & 0.0429 & 0.0000 \\ \hline JP-Mse & & & & & \\ RF & 6.5770 & 0.9581 & 4.2104 & \(-\)0.1047 & \(-\)0.3422 \\ XGBoost & 6.4059 & 0.9603 & 4.1446 & \(-\)0.0420 & \(-\)1.0000 \\ \hline KR-CRK & & & & & \\ RF & 17.4811 & 0.7548 & 8.9706 & 0.0292 & 0.0000 \\ XGBoost & 17.5608 & 0.7525 & 9.1083 & 0.0103 & 2.0000 \\ \hline PH-RiF & & & & & \\ RF & 10.7097 & 0.9096 & 7.1376 & \(-\)0.1542 & 0.4636 \\ XGBoost & 9.6703 & 0.9254 & 6.6271 & \(-\)0.0326 & \(-\)1.0000 \\ \hline US-HRA & & & & & \\ RF & 17.6100 & 0.9173 & 8.8268 & 0.3322 & 3.0000 \\ XGBoost & 16.7583 & 0.9250 & 8.7333 & 0.2344 & \(-\)1.0000 \\ \hline US-HRC & & & & & \\ RF & 24.3017 & 0.8196 & 8.3080 & 0.0480 & 1.0000 \\ XGBoost & 23.4708 & 0.8286 & 8.8489 & \(-\)0.1704 & 1.0000 \\ \hline US-Twt & & & & & \\ RF & 7.7431 & 0.9579 & 4.9390 & \(-\)0.0023 & \(-\)1.0596 \\ XGBoost & 6.8748 & 0.9668 & 4.4939 & \(-\)0.0012 & \(-\)3.0596 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: NEE prediction statistical values of the two machine learning models at seven rice paddy sites. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{1}{c}{_RMSE_} & \(R^{2}\) & _MAE_ & _MBE_ & _GRI_ \\ \cline{2-5}  & **(mm hr\({}^{-1}\))** & & **(mm hr\({}^{-1}\))** & **(mm hr\({}^{-1}\))** & \\ \hline IT-Cas & & & & & \\ RF & 0.0207 & 0.9240 & 0.0121 & 0.0002 & 3.0000 \\ XGBoost & 0.0187 & 0.9377 & 0.0110 & 0.0000 & \(-\)1.0000 \\ \hline JP-Mse & & & & & \\ RF & 0.0262 & 0.9128 & 0.0163 & 0.0002 & 3.0000 \\ XGBoost & 0.0255 & 0.9175 & 0.0157 & 0.0000 & \(-\)1.0000 \\ \hline KR-CRK & & & & & \\ RF & 0.0250 & 0.8816 & 0.0147 & 0.0004 & 3.0000 \\ XGBoost & 0.0248 & 0.8841 & 0.0145 & 0.0000 & \(-\)1.0000 \\ \hline PH-RiF & & & & & \\ RF & 0.0268 & 0.8906 & 0.0171 & 0.0006 & 3.0000 \\ XGBoost & 0.0243 & 0.9098 & 0.0156 & 0.0000 & \(-\)1.0000 \\ \hline US-HRA & & & & & \\ RF & 0.0269 & 0.9025 & 0.0151 & 0.0003 & 3.0000 \\ XGBoost & 0.0255 & 0.9127 & 0.0142 & 0.0000 & \(-\)1.0000 \\ \hline US-HRC & & & & & \\ RF & 0.0325 & 0.8583 & 0.0174 & \(-\)0.0001 & 1.5783 \\ XGBoost & 0.0314 & 0.8671 & 0.0169 & \(-\)0.0003 & 0.0000 \\ \hline US-Twt & & & & & \\ RF & 0.0297 & 0.8980 & 0.0198 & 0.0005 & 3.0000 \\ XGBoost & 0.0246 & 0.9301 & 0.0160 & 0.0001 & \(-\)1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ET prediction statistical values of the two machine learning models at seven rice paddy sites. Figure 3: Characteristic importance plots for the input factors of the XGBoost model for predicting ET. The colors are used to identify input factors.