## 2 Data and Methods
### Simulation Experiment
#### 2.2.4 Metrics for Accuracy Assessment  
Several standard metrics were used to assess the level of accuracy for the trained NEE estimation model: mean absolute error (_MAE_), root mean squared error (_RMSE_), coefficient of determination (\(R^{2}\)), and relative error (_RE_).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Land Cover** & **List of Features** \\ \hline \multirow{4}{*}{DBF} & _TCG_, _Cum_Temperature_over_5_3 week_, _blue/green_, _DevPoint_, _Solar_, \\  & _\(NIR/SWIR1\)_, _SWIR2/green_, _Cum_precipitation_3 days_, \\  & _\(Cum\_Temperature_over_5_3 week_, _Cum_Temperature_over_2\_1 week_, \\  & _\(Cum\_Temperature_over_3\_3 week_\) \\ \hline \multirow{4}{*}{GRA} & _SWIR1/NIR_, _Cum_Temperature_1 week_, _green_/_red_, \\  & _\(Cum\_precipitation\_2 days_, _Mean_Solar_2 week_, _Delta_T_, _red_/_NIR_, _LST_, \\  & _\(NDV1\)_, _red_/_green_, _green_/_NIR_, _red_/_blue_, _SWIR_/_SWIR_, \\  & _\(Cum\_Solar\_days_, _blue_/_NIR_, _Mean_Solar_4 days_ \\ \hline \multirow{4}{*}{CRO} & _NIR/green_, _Mean_Solar_4 week_, _Cum_Temperature_over_5_3 week_, \\  & _\(NIR/SWIR2\), _DewPoint_, _blue_/_green_, _Cum_Solar_2 week_, \\  & _\(Cum\_Temperature\_over_2\_3 week_, _SWIR2\)/_NIR_, \\  & _\(Mean\_Temperature\_1 week_\) \\ \hline \multirow{4}{*}{ENF} & _Solar_, _SWIR1/NIR_- _Cum_Temperature_over_5_4 week_, \\  & _\(Cum\_Temperature\_over_3\_3 week_, _TemperatureAir_, \\  & _\(NIR/SWIR2\)_- _Mean_Solar_2 week_, _Delta_T_, _blue_/_SWIR2_, \\  & _\(Cum\_Temperature\_over_4\_3 week_, _NIR_/_SWIR2\)_- _Solar_, _NIR_ _SWIR1_, \\  & _\(Cum\_Temperature\_days_, _red_/_SWIR1_\) \\ \hline \multirow{4}{*}{MF} & _TCG_- _Solar_, _Delta_T_, _DevPoint_, _SWIR2/_blue_, _blue_/_red_, \\  & _\(Cum\_precipitation\_5 days_, _TCG_- _DevPoint_, _NIR_/_LST_, \\ \hline \multirow{4}{*}{BEF} & _LST/SWIR2_- _Solar_, _TemperatureAir_, _NIR_/_LST_, \\  & _\(Cum\_precipitation\_2 days_, _LST_/_SWIR2_- _Mean_Solar_1 week_, _Delta_T_, \\  & _\(red/SWIR2\)_- _Mean_Solar_2 week_ \\ \hline \multirow{4}{*}{WET} & _NIR Mean_Solar_3 week_, _Delta_T_, _SWIR1/NIR_, _SWIR1/green_, \\  & _\(Cum\_precipitation\_3 days_, _green_/_NIR_, _NDVI_- _Mean_Solar_4 week_, _TCG_- _Cum_Temperature\_2 week_, \\  & _\(TCG\)_- _Mean_Temperature_days_, \\  & _\(NIR/SWIR2\), _DewPoint_, _TCG_- _Cum_Temperature_over_4_2 week_, \\  & _\(TCG\)_- _Temperature_over_3_2 week_, _green_- _SWIR1_, \\  & _\(TCG\)_- _Cum_Temperature_over_2_2 week_, _SWIR2/_NIR_, \\ \hline \multirow{4}{*}{SAV} & _NDV1_- _Solar_, _NIR_/_SWIR2_- _Solar_, _TemperatureAir_, _green_/_red_, _red_/_green_, \\  & _\(Cum\_Temperature\_over_5\_2 week_, _SWIR1_/_red_, _SWIR2_/_SWIR1_, \\  & _\(EV1\)_- _DewPoint_, _SWIR1/_green_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Feature sets for predicting NEE for each IGBP class (detailed description in Appendix B).  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}^{t}-y_{i})^{2}} \tag{11}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(y_{i}^{t}-y_{i})^{2}}{\sum_{i=1}^{N}(y_{i}^{t}-\bar{ y})^{2}} \tag{12}\]  
\[RE=\frac{\frac{\frac{1}{N}\sum_{i=1}^{N}|y_{i}^{t}-y_{i}|}}{\frac{1}{N}\sum_{i=1 }^{N}|y_{i}^{t}-\bar{y}|} \tag{13}\]  
where \(N\) is the number of observation points to estimate; \(y_{i}^{t}\) and \(y_{i}\) are the \(i\)-th true and modeled NEE value respectively; and \(\bar{y}\) is the mean NEE value.