## 3 Results
### Comparing the Performance of Different GPP Estimation Methods  
The statistical indicators of GPPs estimated by MOD09 GA and GCC according to the LUE model and BNNA are shown in Table 5. The seasonal patterns between the GPP from eddy covariance and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multirow{2}{*}{**Season**} & \multicolumn{2}{c}{**R2**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAD**} \\ \cline{3-8}  & & **Training** & **Validation** & **Training** & **Validation** & **Training** & **Validation** \\ \hline \multirow{3}{*}{1} & Spring & 0.17 & 0.15 & 1.90 & 2.22 & 1.64 & 1.90 \\  & Summer & 0.64 & 0.58 & 1.50 & 1.61 & 1.19 & 1.27 \\  & Autumn & 0.66 & 0.78 & 1.48 & 1.31 & 1.12 & 1.03 \\ \hline \multirow{3}{*}{2} & Spring & 0.53 & 0.29 & 2.62 & 2.18 & 2.38 & 1.83 \\  & Summer & 0.42 & 0.35 & 1.80 & 1.90 & 1.45 & 1.59 \\  & Autumn & 0.43 & 0.69 & 1.66 & 1.48 & 1.35 & 1.16 \\ \hline \multirow{3}{*}{3} & Spring & 0.60 & 0.37 & 2.10 & 1.67 & 1.87 & 1.35 \\  & Summer & 0.48 & 0.41 & 1.69 & 1.86 & 1.39 & 1.56 \\ \cline{1-1}  & Autumn & 0.47 & 0.65 & 1.63 & 1.63 & 1.26 & 1.26 \\ \hline \multirow{3}{*}{4} & Spring & 0.36 & 0.30 & 1.37 & 1.58 & 1.20 & 1.33 \\  & Summer & 0.66 & 0.58 & 1.51 & 1.62 & 1.23 & 1.30 \\ \cline{1-1}  & Autumn & 0.66 & 0.70 & 1.43 & 1.41 & 1.00 & 1.09 \\ \hline \hline \end{tabular}  
* 1, 2, 3, and 4 refer to Exp. 1, Exp. 2, Exp. 3, and Exp. 4, respectively. The seasons on the Tibetan Plateau, with spring as May, summer as June to July, and autumn as August to September. Training refers to the training period (from 26 April to 30 September 2017), Validation refers to the validation period (from 26 April to 30 September 2018).  
\end{table}
Table 4: Statistical indicators of the GPP from eddy covariance with the GPP simulation of the remote sensing GPP model in spring, summer, and autumn.  
Figure 11: Validation of the four experiments for BNNA method. (**a**), (**b**), (**c**), and (**d**) represent the year-round changes of GPP_valid and the GPP from eddy covariance in GPP_valid1, GPP_valid2, GPP_valid3, and GPP_valid4, respectively.  
GPP from machine learning method were generally consistent, except for some large fluctuations between May and June. We compared the R\({}^{2}\), RMSE, and MAD of the training results of GPP and found that the training accuracy of the GPP_train4 was the highest followed by GPP_train1, GPP_train3, and GPP_train2. The training accuracy of the GPP_train4 indicated that the training results can be improved by combining multisource data compared with the GPP_train1, GPP_train2, and GPP_train3 in machine learning. In addition, we found that the validation accuracy of GPP_valid4 was the highest followed by GPP_valid1, GPP_valid3, and GPP_valid2. Therefore, the effect of GPP_train4 and GPP_valid4 was the best of machine learning. Among the LUE models forced by different vegetation indices, the model accuracy from high to low were GPP_CInorm, GPP_EVI, and GPP_NDVI. When compared with the LUE models, the R\({}^{2}\), MAD, and RMSE of machine learning were clearly better than those of the LUE models. Therefore, the machine learning method was an effective approach and can significantly improve the simulation accuracy of daily GPP as compared to the LUE model.