## 5 Discussion
The vertical LAI distribution in different vegetation covers follows its own rules (Figure 11), and therefore, remote sensing-based understory and overstory LAI estimations are important for separating the APAR of sub-ecosystems and adding unique LUE values when mapping the global GPP [86; 87; 88; 89]. For all of the cases in a natural environment, when the total LAI is four, the highest GPP appears when all of the leaves are grass species. As shown in Figure 7, the LAI of the grass layer would not be higher than \(2~{}\mathrm{m}^{2}/\mathrm{m}^{2}\), and the highest GPP would thus appear when the LAI of both the tree and grass layers is two. The highest GPP in a dense natural forest would have a ratio of 0.5:0.5 for the tree, shrub, and grass layers. Under such circumstances, GPPdif reaches its highest value, and increases in the LUE of both the tree and grass layers would produce a greater GPP. The greatest GPP difference is 25% higher than that estimated via the single-layer method, and if the canopy has a higher proportion of shrubs, the GPP difference is 20% lower. When the proportion of LAI is high in the shrub layer, the total GPP is lower than that estimated by the single-layer method because the shrub LUE is low. As discussed in Section 4.2.2, a high LAI in the understory produces a high GPP because the understory component absorbs diffuse light and provides a better growing environment than that of the overstory. Figure 10 shows that different ecosystems have different vertical LAI ratios. In forest sites such as EBF, DBF, ENF, and DNF, the tree LAI ratio can be 60% or more, and the understory LAI can be as a great as 20% during the growing season. With different LAI distributions in the forest ecosystem, the total APAR allocation of the canopy in a sub-ecosystem would lead to uncertainties in the GPP estimation. [PERSON]'s model exhibited the best performance of the three models. This model better considers the effect of temperature on GPP in overstory and understory layers. [PERSON]'s model and the VPM consider the temperature stresses when the temperature is high. Therefore, these models exhibit better variance when the canopy temperature is high in both the multilayer model and single-layer model. In the VPM, phenology was proven to be an important indicator. However, at the site level, the VPM cannot obtain accurate spatially matched enhanced vegetation index (EVI) values, and the LAI of the canopy changes only slightly during the growing season; thus, we omitted the effect of phenology. In future research on multilayer GPP models, the phenology variance between understory and overstory layers would be a potential way to improve the GPP estimation accuracy in the VPM and [PERSON]'s model. The continuous vertical acquisition of LAI is also challenging. On the one hand, many researchers have shown that remote sensing methods underestimate understory LAI, indicating that other GPP components are also underestimated in dense forests [39; 94]. On the other hand, most remote sensing-based LAI products currently do not separate the overstory from the understory in forest sites. Without the LAI of different layers, the GPP of distinct vertical layers cannot be modeled; thus, understory GPP is often underestimated from the high understory LUE. Future versions of remote sensing-based GPP products should add forest vertical stratification information to GPP models. For example, the use of remote sensing-based understory and overstory LAI [90; 91; 92; 93] to separate sub-ecosystems and the addition of prior LUE when combining the specific LUEmax from sub-ecosystem-based temperature and VPD response functions can improve GPP estimations compared to the performance of approaches that do not consider the vertical structure (\"adjusted\" method in Table 4). The 'adjusted' method in Table 4 showed lower systemic biases and a RMSE that was similar to that of GPPm, which indicated that this method has an estimation accuracy that is similar to that of the multilayer method.