## 4 Results and Analysis
### Performance of GPP Estimation Results Based on Flux Tower-Based GPP  
Estimating GPP with stratification information in the multilayer GPP model results in higher estimation precision than with the single-layer method (Table 4). Table 4 illustrates that the GPPm estimates at the DHS and TMK sites had higher accuracies than the GPPs estimations, and generally had a lower RMSE (GPPm is 0.2 gC/m\({}^{2}\)/day lower than GPPs). At the DHS site, the modeled GPP that considered vertical stratification showed a similar correlation when vertical layers were considered, but with the higher LAI of the understory, the R\({}^{2}\) value was 2% to 5% higher with GPPm than with GPPs at the TMK site. The low mean bias in the multilayer model (DHS_m and TMK_m) indicates that the multilayer model performs better than the single-layer model. These selected GPP estimation models, including VPM, [PERSON]'s model, and MOD, showed high correlations with the flux tower-based GPP. [PERSON]'s model and the VPM showed higher R\({}^{2}\) values than the MOD model at DHS, because they consider scalar temperature at high temperatures. The MOD17 model showed lower RMSE and higher R\({}^{2}\) values than both [PERSON]'s model and the VPM at the TMK site, indicating that the MOD model exhibits better performance at that site. The estimation results for the two sites were combined, which indicated that [PERSON]'s model exhibited the best model performance with the highest mean R\({}^{2}\) (R\({}^{2}\) = 0.685) and the lowest mean RMSE at these two sites (RMSE = 1.66 gC/m\({}^{2}\)/day) when the vertical stratification information was included in the GPP estimation. Figure 9 indicates that [PERSON]'s model can track the variations in growing season GPP when the vertical stratification information in the canopy is incorporated. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **DHS\_s** & **DHS\_m** & **DHS\_adjust** & **TMK\_s** & **TMK\_m** & **TMK\_adjust** \\ \hline \multirow{4}{*}{VPM} & R\({}^{2}\) & 0.587 & 0.588 & 0.589 & 0.738 & 0.769 & 0.753 \\  & RMSE & 0.99 & 0.99 & 0.99 & 2.51 & 2.36 & 2.43 \\  & mean bias & \(-1.15\) & \(-1.08\) & \(-0.55\) & 4.16 & 1.85 & 0.59 \\ \hline \multirow{4}{*}{Wu} & R\({}^{2}\) & 0.595 & 0.599 & 0.595 & 0.726 & 0.771 & 0.754 \\  & RMSE & 0.98 & 0.97 & 0.96 & 2.57 & 2.35 & 2.44 \\  & mean bias & 0.53 & 0.49 & 0.09 & 3.84 & 1.5 & 0.21 \\ \hline \multirow{4}{*}{MOD} & R\({}^{2}\) & 0.491 & 0.489 & 0.491 & 0.777 & 0.801 & 0.786 \\  & RMSE & \(1.1\) & \(1.1\) & \(1.1\) & \(2.32\) & 2.19 & 2.27 \\  & mean bias & \(-1.67\) & \(-1.57\) & \(-1.05\) & 3.96 & 1.68 & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of flux tower-based GPP and different model output results when considering and not considering the vertical layer. Here, sitename_s is the single-layer GPP, sitename_m is the multilayer GPP, and sitename_adjust uses the same temperature and VPD inputs (root mean square error (RMSE) and mean bias unit is gC/m\({}^{2}\)/day). Simplifying the vertical stratification parameters in the GPP model will also yield high estimation accuracies. To simplify the parameters when considering vertical stratification during GPP estimation, the adjusted method in Table 4 used the same temperature and VPD for every layer as inputs, the LAI values in different sub-ecosystems were separated from that for the total ecosystem, and LUEmax was used for different sub-ecosystems. The results showed that among all the methods, the adjusted method had the smallest mean bias at the TMK and DHS sites. Additionally, the R\({}^{2}\) value differed depending on whether or not multilayer models were considered. This method requires the canopy mean temperature and the VPD and LAI proportions in the understory and overstory, the LUEmax set for different sub-ecosystems, and the incident PAR at the top of the canopy.