## 3 Results
### GPP Estimation by AI Models
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Year** & **Method** & **Min (\({}^{\text{(1)}}\) (g m\({}^{-2}\) d\({}^{-1}\))** & **Max (\({}^{\text{(2)}}\) (g m\({}^{-2}\) d\({}^{-1}\))** & **Mean (g m\({}^{-2}\) d\({}^{-1}\))** \\ \hline
2006 & EC & 0.42 & 9.68 & 3.10 \\  & SVM & 0.64 & 7.69 & 3.27 \\  & RF & 0.73 & 7.70 & 3.34 \\  & ANN & 0.90 & 7.60 & 3.37 \\  & DNN & 0.74 & 8.15 & 3.27 \\ \hline
2007 & EC & 0.44 & 8.47 & 3.19 \\  & SVM & 0.62 & 7.97 & 3.15 \\  & RF & 0.76 & 7.82 & 3.28 \\  & ANN & 0.91 & 7.22 & 3.25 \\  & DNN & 0.55 & 7.44 & 2.82 \\ \hline
2008 & EC & 0.32 & 9.76 & 3.65 \\  & SVM & 0.65 & 7.55 & 3.33 \\  & RF & 0.76 & 7.71 & 3.40 \\  & ANN & 0.74 & 7.65 & 3.39 \\  & DNN & 1.04 & 8.85 & 3.77 \\ \hline
2009 & EC & 0.28 & 9.66 & 3.53 \\  & SVM & 0.59 & 7.69 & 3.25 \\  & RF & 0.77 & 7.81 & 3.36 \\  & ANN & 0.87 & 7.60 & 3.28 \\  & DNN & 0.34 & 8.95 & 3.45 \\ \hline
2010 & EC & 0.41 & 8.55 & 2.98 \\  & SVM & 0.56 & 7.74 & 3.11 \\  & RF & 0.71 & 7.73 & 3.16 \\  & ANN & 0.86 & 7.60 & 3.16 \\  & DNN & 0.59 & 8.30 & 2.95 \\ \hline
2011 & EC & 0.36 & 9.26 & 3.14 \\  & SVM & 0.47 & 7.85 & 2.94 \\  & RF & 0.70 & 7.62 & 3.10 \\  & ANN & 0.85 & 7.22 & 3.20 \\  & DNN & 1.38 & 8.57 & 3.27 \\ \hline
2012 & EC & 0.26 & 8.51 & 3.35 \\  & SVM & 0.60 & 7.70 & 3.24 \\  & RF & 0.77 & 7.83 & 3.39 \\  & ANN & 0.86 & 7.70 & 3.39 \\  & DNN & 0.32 & 8.50 & 3.32 \\ \hline
2013 & EC & \(-\)0.03 & 8.84 & 3.05 \\  & SVM & 0.58 & 7.72 & 3.30 \\  & RF & 0.73 & 7.73 & 3.29 \\  & ANN & 0.83 & 7.20 & 3.37 \\  & DNN & 0.32 & 8.49 & 3.07 \\ \hline
2014 & EC & 0.33 & 9.35 & 3.56 \\  & SVM & 0.57 & 7.70 & 3.43 \\  & RF & 0.77 & 7.89 & 3.53 \\  & ANN & 0.90 & 7.19 & 3.49 \\  & DNN & 0.65 & 8.04 & 3.51 \\ \hline
2015 & EC & 0.25 & 8.48 & 3.49 \\  & SVM & 0.61 & 7.84 & 3.56 \\  & RF & 0.73 & 8.10 & 3.66 \\  & ANN & 0.92 & 7.22 & 3.62 \\  & DNN & 0.72 & 7.72 & 3.45 \\ \hline \hline \end{tabular} \({}^{\text{(1)}}\) Minimum value except for lower 2% data; \({}^{\text{(2)}}\) maximum value except for upper 2% data.  
\end{table} 
Table 3: Statistics of daily gross primary productivity (GPP) at the Gwangneung site for each year between 2006 and 2015, measured by eddy covariance (EC) and predicted by support vector machine (SVM), random forest (RF), artificial neural network (ANN), and deep neural network (DNN). Figure 4: Daily gross primary productivity (GPP) observed by the eddy covariance (EC) method and predicted by a support vector machine (SVM) model at the Gwangneung site during 2006–2015 (**a**), and the predictions zoomed-in for 2011 (**b**) and 2015 (**c**). Figure 5: Observed vs. predicted daily gross primary productivity (GPP) by a random forest (RF) model at the Gwangneung site during 2006–2015 (**a**), and the predictions zoomed-in for 2011 (**b**) and 2015 (**c**). Figure 6: Observed vs. predicted daily gross primary productivity (GPP) by an artificial neural network (ANN) model at the Gwangneung site during 2006–2015 (**a**), and the predictions zoomed-in for 2011 (**b**) and 2015 (**c**). Figure 7: Observed vs. predicted daily gross primary productivity (GPP) by a deep neural network (DNN) model at the Gwangneung site during 2006–2015 (**a**), and the predictions zoomed-in for 2011 (**b**) and 2015 (**c**).