## 5 Discussion
Among all vegetation types, ENF has the lowest CI, with 0.63 in the leaf-off season and 0.52 in the leaf-on season, which is consistent with a previous study that reported that needleleaf forests have an extremely gathered canopy (0.5 \(<\Omega<\) 0.7) [91]. Seasonal differences in CI can reflect changes in the canopy structure of deciduous vegetation types [42; 43]. An interesting finding is that the improvement of the estimated GPP depending on two \(\Omega\)s (8.97%) of the leaf-on season and the leaf-off season is nearly as high as the estimated GPP that relies on 3 \(\Omega\)s (9.76%) of the leaf-scattering, leaf-gathering, and leaf-off seasons, which may be explained by the small difference in the CI between the leaf-gathering and leaf-scattering seasons. The appearance of a canopy has already been formed in the early stage of vegetation growth due to the rapid leaf growth. Therefore, the CI only exhibits slight changes in most of the leaf-on season after the completion of leaf growth. The error of GPP estimation at a certain degree is decreased by two or three \(\Omega\)s from different seasons instead of by only \(\Omega\) in the TL-LUE model. The current MODIS CI values retrieved from the MODIS BRDF product (MCD43 V006) released in 2016 with a higher temporal resolution, such as the eight-day dataset, exhibits a large uncertainty, as measured by SD, in intra-annual variation (Figure 4), which likely contributes to uncertainties in GPP estimation. The accuracy of GPP estimation improved by the eight-day CI in the TL-LUE model is limited, and even less than that is improved by the \(\Omega\)s in different seasons, although this result is not presented in the study due to space constraints. Hence, to better understand the structure and temporal variation in global vegetation [43], further studies are required to reduce the retrieval uncertainties of the eight-day CI, especially from the trajectory of daily BRDF. In addition, further research is needed to explore the effects of other significant parameters, such as vegetation albedo and LUE, on the estimation of GPP. The LUE, including \(\epsilon_{\text{max}}\), \(\epsilon_{\text{msu}}\), and \(\epsilon_{\text{msh}}\), is variable with diffuse PAR rather than the constant used in the TL-LUE model [92]. The photochemical reflectance index (PRI), solar-induced chlorophyll fluorescence (SIF), and near-infrared reflectance of terrestrial vegetation (NIR\({}_{\
u}\)) that appeared in recent decades have great potential to advance LUE models and improve GPP estimation. For example, the combination of PRI and SIF has been able to enhance the accuracy of GPP estimation [93]. Additionally, considering other key environmental stress factors, especially plant available water, would decrease the uncertainty of GPP estimation for LUE models. Several recently published studies explored the influence of various environmental stress factors on GPP estimation in LUE models. For instance, a previous study reported that soil temperature and soil water are the main control factors for vegetation GPP in cold and dry regions, respectively [86]. Another study found that a complex adjustment mechanism results in a nonlinear response of vegetation to VPD [86]. A published study improved the accuracy of global GPP estimation by considering the fertilization of CO\({}_{2}\) in an LUE model [12]. Integrating multiple environmental stress factors is expected to improve the estimation of GPP. Although EC flux data are widely used as benchmark data, GPP is influenced by various systematic and random errors, such as instrument failure, the subpar filter method, and the split GPP approach lacking sufficient accuracy from NEE. Therefore, further improvement of the quality of EC flux data is necessary to more precisely estimate GPP.