## 3 Results
### Environmental Conditions  
The feature rankings obtained by impurity-based and permutation-based were generally consistent across biomes (Figure 3 and Figure S1). For the impurity-based result (Figure 3), there were two biomes in the two models with the top three consistent importance of variables (DBF, in order of soil temperature (TS), net radiation(NETRAD), and atmospheric pressure (PA); WSA, in order of PA, NETRAD, and incident longwave radiation (LW_IN)), and six biomes with two identical variables in the top three of variable importance (EBF with PA and LW_IN; ENF with NETRAD and wind speed (WS); MF with NETRAD and TS; GRA with NETRAD and TS; CRO with NETRAD and vapor pressure deficit (VPD); CSH with NETRAD and LW_IN; SAV with WS and VPD; and WSA with PA and LW_IN). The biome of OSH has one identical variable in the top three of variable importance soil water content (SWC). Among all the biomes, the number of occurrences for variables in the top three variable importance was: eight occurrences of NETRAD; five occurrences of PA; three occurrences of LW_IN, SW_IN, and TS for RF model; and seven occurrences of NETRAD, five occurrences of PA, and two occurrences of LW_IN and SWC for XGBoost model. The variables with the highest number of occurrences in the last three of variable importance was precipitation (P) (nine occurrences), WS (five occurrences), LW_IN (four occurrences) for RF model; and P (eight occurrences), WS (six occurrences), LW_IN (four occurrences) for XGBoost model. In general, NETRAD, PA, and TS are the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Biome**} & \multirow{2}{*}{**Training Samples**} & \multicolumn{2}{c}{**Training Duration (Minute)**} & \multirow{2}{*}{**Efficiency Ratio 1**} \\ \cline{3-3} \cline{5-5}  & & **RF** & & **XGBoost** \\ \hline DBF & 16197 & 200.0 & 5.3 & 38 \\ EBF & 3658 & 10.1 & 1.1 & 9 \\ ENF & 22603 & 445.6 & 8.1 & 55 \\ MF & 4502 & 15.9 & 1.4 & 11 \\ GRA & 20860 & 439.0 & 7.1 & 62 \\ CRO & 7926 & 48.9 & 2.7 & 18 \\ OSH & 2324 & 6.4 & 0.6 & 11 \\ CSH & 1806 & 2.9 & 0.5 & 6 \\ SAV & 3759 & 10.5 & 1.1 & 10 \\ WSA & 5118 & 27.9 & 1.5 & 19 \\ \hline \hline \end{tabular}  
* Efficiency ratio = training duration of RF/training duration of XGBoost.  
\end{table} 
Table 3: Training duration for different biomes and models. most important variables, P and WS are the least important variables in each biome for both two models. Figure 2: Comparison of predicted and actual values using RF and XGBoost models in each biome. The x-axis represents actual values of NEE by eddy-covariance and y-axis denotes the predicted values by the two models above. The red and green scatters indicate the prediction of RF and XGBoost for each biome, respectively. The dash line represents the equality of predicted and actual values. R\({}^{2}\) refers to the determination, RMSE refers to root mean square error, N refers to the number of test data points. * _p_-value \(\leq\) 0.05, ** _p_-value \(\leq\) 0.01, and *** _p_-value \(\leq\) 0.001. (DBF, deciduous broadleaf forest; EBF, evergreen broadleaf forest; ENF, was evergreen needleleaf forest; MF, mixed forest; GRA, grassland; CRO, cropland; OSH, open shrublands; CSH, closed shrublands; SAV, savannas; WSA, woody savannas). Similar results appear in the permutation-based method (Figure S1). Among all the biomes, the number of occurrences for variables in the top three variable importance was: NETRAD (eight occurrences), PA (seven occurrences), SWC (four occurrences) for RF model; NETRAD (eight occurrences), PA (five occurrences), and SWC (four occurrences) for XGBoost model. The variables with the highest number of occurrences in the last three of variable importance were P (ten occurrences), WS (five occurrences), LW_IN (four occurrences) for RF model; and P (ten occurrences), WS (six occurrences), LW_IN (five occurrences) for XGBoost model. Note that because of the different calculation principles, the values reported in Figure 3 are not directly comparable with that in Figure S1.