## 2 Materials and Methods  
### Site Data  
Seven rice paddy sites were selected from Version 1 of the FLUXNET-CH4 database (Table 1, Figure 1) [17]. In this study, two tree-based machine learning models (XGBoost and RF) were used to predict ET, NEE, and FCH4 in rice paddies. The input factors are shown in Tables 2-4 respectively. When soil water content (SWC) and soil temperature (TS) have more than one observation depth, the mean value of SWC is taken into the model, and TS selects the depth with the highest statistical correlation with the predictors. The most used input factors in predicting FCH4 include 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), SWC, friction velocity (USTAR), net radiation (NETRAD), ecosystem respiration (RECO), sensible heat turbulent flux (H), gross primary productivity (GPP), soil heat flux (G), TS, normalized difference vegetation index (NDVI), latent heat turbulent flux (LE), NEE, and the temperature difference between the previous day and the current day (DeltaTA).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sites**} & **Latitude** & **Longitude** & **Start Year** & **End Year** & **Mean ET** & **Mean NEE** & **Mean FCH4** \\  & (°) & (°) & (Year) & (Year) & (Year) & (mm hr\({}^{-1}\)) & (gC m\({}^{-2}\) d\({}^{-1}\)) & (nmol m\({}^{-2}\) s\({}^{-1}\)) \\ \hline IT-Cas & 45.07 & 8.72 & 2009 & 2010 & 0.06 & \(-\)10.50 & 94.66 \\ JP-Mse & 36.05 & 140.03 & 2012 & 2012 & 0.11 & \(-\)15.39 & 61.17 \\ KR-CRK & 38.20 & 127.25 & 2015 & 2018 & 0.06 & \(-\)7.72 & 119.32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of seven rice paddy sites.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Factors/Sites** & **IT-Cas** & **JP-Mse** & **KR-CRK** & **PH-RiF** & **US-HRA** & **US-HRC** & **US-Twt** \\ \hline TA & 15.9 & 21.1 & 13.3 & 27.0 & 24.0 & 23.9 & 18.9 \\ SW\_IN & 265.0 & 342.2 & 282.7 & 219.5 & 211.2 & 283.5 & 358.4 \\ VPD & 14.0 & 8.4 & 6.2 & 9.7 & 5.4 & 7.6 & 12.3 \\ PA & 100.4 & 101.0 & 99.2 & 100.5 & 100.9 & 100.8 & 101.3 \\ WS & 1.1 & 2.4 & 2.2 & 1.9 & 1.5 & 2.0 & 4.3 \\ NDVI & 0.5 & 0.5 & 0.4 & 0.6 & 0.7 & 0.6 & 0.5 \\ SWC & 58.9 & 42.5 & 38.7 & 63.5 & 42.3 & 47.7 & \\ DeltaTA & \(-\)0.5 & \(-\)0.6 & \(-\)0.4 & \(-\)0.1 & 0.1 & 0.0 & \(-\)0.5 \\ Number & 15,497 & 4952 & 31,067 & 21,010 & 2149 & 2144 & 66,455 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mean values and number of samples of the input factors for ET prediction at seven rice paddy sites.  
Figure 1: Location of eddy covariance sites, with sites colored by wetland type. Cyan represents the seven rice paddy sites studied.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Factors/Sites** & **IT-Cas** & **JP-Mse** & **KR-CRK** & **PH-RiF** & **US-HRA** & **US-HRC** & **US-Twt** \\ \hline TA & 13.5 & 21.2 & 14.1 & 27.3 & 26.5 & 26.6 & 18.9 \\ SW\_IN & 221.1 & 364.1 & 239.5 & 267.7 & 353.7 & 370.4 & 348.9 \\ LW\_OUT & & 431.6 & 391.6 & 468.8 & 464.1 & 463.5 & \\ VPD & 12.9 & 8.5 & 5.7 & 10.7 & 7.7 & 9.7 & 12.4 \\ PA & 100.3 & 101.0 & 99.2 & 100.6 & 100.8 & 100.8 & 101.3 \\ WS & 1.1 & 2.4 & 2.2 & 2.1 & 2.5 & 2.5 & 4.4 \\ WD & 173.6 & 167.9 & 212.4 & 165.8 & 179.3 & 190.4 & 268.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The mean values and number of samples of the input factors for NEE prediction at seven rice paddy sites.