## 4 Results and Analysis
### Influence of Vertical Stratification on GPP Estimation  
Two main factors determine the estimation of GPP in LUE models: (1) the influences of temperature, VPD, and LUEmax on LUE, and (2) the effects of PAR and LAI on the absorbed shortwave energy in different components of the whole ecosystem. In this study, the major difference between considering and not considering the vertical structure during the GPP estimation is the difference between the overstory and understory LUE and APAR. 2.1 LUE in Different Sub-Ecosystems Is Influenced by the Vertical Stratification of Temperature, VPD, and LUEmax  
Vertical variations in temperature, VPD, and the LUEmax of sub-ecosystems led to differences in LUE at various forest canopy heights. Some studies have shown that the vegetation in the understory layer such as grass has a lower LUEmax. In addition, the vertical differences in temperature and VPD led to variations during different periods of the year. Figure 4 shows the modeled LUE, which combined the vertical variations in temperature and VPD in the sub-ecosystems. At the DHS site, high layers in the canopy, such as the tree and shrub layers, had LUEs that were 0.3 gC/MJ to 0.6 gC/MJ lower than those of understory layers, such as grass layers, throughout the study period. Prior to DOY 320, the LUE of the shrub layer was 0.1 gC/MJ lower than that of the tree layer. After DOY 330, the LUEs of the shrub layer and the tree layer were almost the same. Similarly, the understory LUE at TMK was approximately 0.4 gC/MJ higher than that of the overstory layer throughout the growing season (DOY 210 to 300). Figure 4: Modeled LUE values in the different sub-ecosystems. Figure 3: Bar chart for the daily average incident PAR in sub-ecosystems at the selected sites; each bar represents the daily mean incident PAR in each layer (**A**) TMK; (**B**) DHS. The mean canopy air temperature influenced the estimation of GPP. The trend in GPPdif in Figure 5 shows that with decreasing canopy mean temperature, the estimation based on GPPm becomes much lower than that based on GPPs, and with increasing mean temperature, the estimation based on GPPm becomes larger than that based on GPPs. The estimates of the three models are similar: GPPdif reaches 0 at approximately 16 \({}^{\circ}\)C. The optimal growing temperatures of different species in one sub-ecosystem, such as the tree layer, would not exhibit much variance. When the mean canopy temperature reaches approximately 16 \({}^{\circ}\)C, the grass ecosystem reaches its optimal growing temperature, and the LUE of grass would be 10% higher than that at 10 \({}^{\circ}\)C or lower. At the same time, the LAI of the understory increases with increasing temperature, and the grass understory has a higher photosynthetic ability. This situation causes the value of GPPm to be higher than that of GPPs. The canopy vertical VPD stratification has a pattern that is similar to that of the canopy vertical temperature stratification: there are variations in photosynthesis in the vertical layers that lead to GPP estimation bias. Figure 6 shows that the GPPdif values are completely different at the DHS and TMK sites. The trend of the change in GPPdif in Figure 6B shows that the estimation from GPPm becomes much lower than that from GPPs as the mean canopy VPD decreases, and with an increase in the mean VPD, the estimation from GPPm becomes larger than that from GPPs. At the DHS site, the GPPdif varied nonsignificantly from approximately \(-5\%\) to \(5\%\). GPPdif declines with increasing canopy mean VPD, which is opposite the pattern observed at the TMK site, where GPPdif increases with VPD, because the VPD increases from 0 to a higher value at the DHS site. When the VPD is greater than 0, the photosynthetic capacities of different vegetation components, including the tree, shrub, and grass layers decrease, and the total GPP declines. GPPdif changes from \(-50\%\) to \(30\%\) when the mean canopy VPD increases from approximately 0 Pa to 500 Pa at the TMK site.