## 3 Results
### Simulating GPP with MOD09 GA VIs  
The comparison between GPP from MODIS NDVI and GPP from eddy covariance in 2017 and 2018 at the AR station are shown in Figure 9a,b. In the growing season of 2017, the R\({}^{2}\), RMSE, and MAD between GPP_NDVI and GPP from eddy covariance were 0.36, 3.30 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\), and 2.46 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\) respectively. In 2017 growing season, the GPP_NDVI had the highest accuracy in autumn, followed by spring and summer (Table 3). The R\({}^{2}\), RMSE, and MAD between GPP_NDVI and GPP from eddy covariance were 0.46, 3.29 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\), and 2.45 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\) in the growing season of 2018, respectively. In 2018, the GPP_NDVI had the highest accuracy in autumn, followed by summer and spring (Table 3). The comparison between GPP from MODIS EVI and GPP from eddy covariance in 2017 and 2018 at the AR station are shown in Figure 9c,d. In the 2017 growing season, the R\({}^{2}\), RMSE, and MAD of GPP_EVI and GPP from eddy covariance were 0.53, 2.84 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\), and 2.19 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\), and 2.19 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\), and 2.19 g C\(\cdot\)m\({}^{-2}\)-day\({}^{-1}\), respectively. In 2018, the GPP_NDVI had the highest accuracy in autumn, followed by summer and spring (Table 3). \begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **GPP** & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAD**} \\ \hline  & & 2017 & 2018 & 2017 & 2018 & 2017 & 2018 \\ GPP\_CInorm & Spring & 0.53 & 0.38 & 1.56 & 1.16 & 1.24 & 0.95 \\  & Summer & 0.55 & 0.75 & 2.77 & 2.07 & 2.44 & 1.80 \\  & Autumn & 0.70 & 0.71 & 2.55 & 2.16 & 2.20 & 1.88 \\  & Spring & 0.32 & 0.24 & 1.12 & 1.34 & 0.85 & 1.08 \\ GPP\_NDVI & Summer & 0.29 & 0.34 & 3.80 & 4.07 & 2.94 & 3.12 \\  & Autumn & 0.36 & 0.49 & 3.52 & 3.10 & 2.84 & 2.46 \\  & Spring & 0.51 & 0.38 & 0.88 & 1.25 & 0.61 & 0.99 \\ GPP\_EVI & Summer & 0.42 & 0.49 & 3.18 & 3.46 & 2.57 & 2.82 \\  & Autumn & 0.49 & 0.57 & 3.14 & 2.76 & 2.61 & 2.23 \\ \hline \end{tabular} GPP refers to the GPP from eddy covariance. The seasons on the Tibetan Plateau, with spring as May, summer as June to July, and autumn as August to September.  
\end{table} 
Table 3: Statistical indicators of the GPP from eddy covariance with the GPP simulation values of remote sensing GPP model in spring, summer, and autumn. Figure 9: The daily dynamics of the GPP_NDVI, GPP_EVI and the GPP from eddy covariance during the growing seasons (1 May to 1 October), (**a**) 2017, (**b**) 2018, (**c**) 2017, and (**d**) 2018.  
respectively. The LUE model with EVI as input (GPP_EVI) had the highest accuracy in spring, followed by autumn and summer (Table 3). During growing season in 2018, the R\({}^{2}\), RMSE, and MAD of GPP_EVI and GPP from eddy covariance were 0.59, 2.85 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), and 2.22 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), respectively. The LUE model with EVI as input (GPP_EVI) performed best in autumn, followed by summer and spring (Table 3). GPPs estimated from MODIS NDVI (GPP_NDVI) and EVI (GPP_EVI) were able to successfully capture the season dynamics of GPP from eddy covariance (Figure 9a-d). However, throughout the growing season, daily GPP_NDVI/GPP_EVI was dramatically underestimated at some dates comparing with GPP from eddy covariance, this was mainly caused by cloud or snow contamination of the satellite remote sensing data. The number of cloud contaminated data during the growing season in 2017 was 56, and 49 in 2018. The cumulative MAD caused by GPP_NDVI and GPP_EVI were 3.794 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)-3.849 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\) and 3.151 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)-3.193 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), respectively. More information about the changes of GPP from eddy covariance and GPP_NDVI and GPP_EVI in two years (2017 and 2018) can be found in the appendix (Appendix A, Figure A2).