# Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & & **A-DMLP** & **RF** & **LSTM** & **DNN** & **CNN** \\ \hline YCA & R2 & 0.841 & 0.829 & 0.756 & 0.756 & 0.8018 \\  & MAE & 1.184 & 1.175 & 1.519 & 1.523 & 1.201 \\  & MSE & 3.083 & 3.304 & 3.991 & 4.907 & 3.455 \\  & RMSE & 1.755 & 1.817 & 1.997 & 2.215 & 1.858 \\ CBF1 & R2 & 0.824 & 0.793 & 0.676 & 0.728 & 0.781 \\  & MAE & 0.561 & 0.632 & 0.752 & 0.695 & 0.639 \\  & MSE & 0.735 & 0.761 & 1.443 & 1.158 & 0.825 \\  & RMSE & 0.857 & 0.872 & 1.201 & 1.076 & 0.908 \\ DHF & R2 & 0.762 & 0.713 & 0.524 & 0.531 & 0.750 \\  & MAE & 0.524 & 0.570 & 0.770 & 0.690 & 0.551 \\  & MSE & 0.447 & 0.530 & 0.910 & 0.790 & 0.505 \\  & RMSE & 0.669 & 0.730 & 0.950 & 0.890 & 0.710 \\ QYF & R2 & 0.776 & 0.758 & 0.680 & 0.640 & 0.719 \\  & MAE & 0.525 & 0.596 & 0.760 & 0.760 & 0.682 \\  & MSE & 0.539 & 0.586 & 0.840 & 0.873 & 0.679 \\  & RMSE & 0.699 & 0.765 & 0.910 & 0.934 & 0.824 \\ BNF & R2 & 0.656 & 0.682 & 0.363 & 0.455 & 0.483 \\  & MAE & 0.638 & 0.629 & 0.771 & 0.731 & 0.688 \\  & MSE & 0.815 & 0.759 & 1.618 & 1.089 & 0.842 \\  & RMSE & 0.902 & 0.871 & 1.272 & 1.043 & 0.917 \\ HBG & R2 & 0.869 & 0.835 & 0.786 & 0.826 & 0.837 \\  & MAE & 0.283 & 0.314 & 0.462 & 0.377 & 0.389 \\  & MSE & 0.203 & 0.203 & 0.393 & 0.224 & 0.220 \\  & RMSE & 0.441 & 0.450 & 0.627 & 0.866 & 0.469 \\ DXG & R2 & 0.773 & 0.749 & 0.583 & 0.684 & 0.719 \\  & MAE & 0.146 & 0.158 & 0.211 & 0.179 & 0.137 \\  & MSE & 0.051 & 0.043 & 0.101 & 0.061 & 0.056 \\  & RMSE & 0.226 & 0.207 & 0.317 & 0.248 & 0.236 \\ NMG & R2 & 0.658 & 0.619 & 0.317 & 0.415 & 0.595 \\  & MAE & 0.284 & 0.295 & 0.372 & 0.397 & 0.325 \\  & MSE & 0.246 & 0.268 & 0.349 & 0.397 & 0.289 \\  & RMSE & 0.485 & 0.517 & 0.590 & 0.630 & 0.537 \\ HBW & R2 & 0.909 & 0.871 & 0.532 & 0.806 & 0.819 \\  & MAE & 0.246 & 0.273 & 0.477 & 0.399 & 0.251 \\  & MSE & 0.215 & 0.152 & 0.683 & 0.351 & 0.311 \\  & RMSE & 0.463 & 0.389 & 0.884 & 0.588 & 0.531 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of nine sites with various data-driven methods. dices and ecological parameters such as NDVI, EVI, FPAR, and LAI in NEE simulation is strongly demonstrated. These vegetation indices and ecological parameters effectively signal changes in vegetation and growth status during the process of vegetation growth. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Site** & & \(\mathbf{R^{2}}\) & **MAE** & **MSE** & **RMSE** \\ \hline YCA & In & 0.852 & 1.1 & 2.86 & 1.69 \\  & out & 0.807 & 1.28 & 3.73 & 1.931 \\ CBF1 & In & 0.835 & 0.571 & 0.720 & 0.843 \\  & out & 0.791 & 0.661 & 0.96 & 0.979 \\ DHF & In & 0.771 & 0.514 & 0.447 & 0.669 \\  & out & 0.686 & 0.586 & 0.611 & 0.782 \\ QYF & In & 0.776 & 0.515 & 0.539 & 0.699 \\  & out & 0.724 & 0.639 & 0.668 & 0.517 \\ BNF & In & 0.427 & 0.698 & 0.926 & 0.962 \\  & out & 0.305 & 0.766 & 1.124 & 1.06 \\ HBG & In & 0.889 & 0.263 & 0.203 & 0.441 \\  & out & 0.822 & 0.36 & 0.316 & 0.526 \\ DXG & In & 0.775 & 0.125 & 0.051 & 0.226 \\  & out & 0.726 & 0.137 & 0.062 & 0.249 \\ NMG & In & 0.668 & 0.274 & 0.223 & 0.472 \\  & out & 0.529 & 0.327 & 0.316 & 0.562 \\ HBW & In & 0.919 & 0.226 & 0.135 & 0.367 \\  & out & 0.84 & 0.289 & 0.267 & 0.517 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of the A-DMLP model with and without the inclusion of remote sensing data. In indicates the inclusion of remote sensing data and out indicates the absence of remote sensing data. Figure 4: Boxplot results with normal curves. Comparison of four statistical metrics for A-DMLP, random forest, LSTM, DNN, and CNN (1D) models on nine sites (\(X\)-axis represents different models, \(Y\)-axis represents scale values for different metrics). (**A**) The statistical results of R2 of different models (**B**) The statistical results of RMSE of different models (**C**) The statistical results of MAE of different models (**D**) The statistical results of MSE of different models.