## 6 Conclusions  
In this article, we used three different LUE-based GPP models to analyze the effect of vertical vegetation stratification on GPP calculations. Additionally, in situ temperature, VPD, and PAR data were used to analyze the environmental factors that control GPP modeling. The results showed the following. (1) Vertical temperature and VPD stratification at the selected sites varied with the forest type, time of day, and season, but the difference was not significant. Moreover, incident PAR followed the Beer-Lambert law; there was a significant difference between the overstory and understory layers, and this difference was affected by the distributions of LAI in different sub-ecosystems. (2) The stratifications of temperature, VPD, PAR, LAI, and LUEmax have an interactive influence on the improvement of GPPm. Due to the importance of PAR in the estimation of GPP, the stratification of PAR becomes obvious when the incident PAR increases and the difference between GPPm and GPPs exceeds 50% of GPPs. GPPMif is approximately 30% when temperature stratification is considered, and this value is 20% when LAI stratification is considered. Multilayer LUEmax inputs are important for accurate GPP estimations, and different inputs may lead to contrary results concerning the difference between GPPm and GPPs. Therefore, quantitatively corrected understory LUEmax reduces the bias in GPP estimations. (3) The accuracy of the GPP estimation can be improved by using multilayer models, but the improvement is related to the forest type, the stratification of the canopy, and the growing conditions. Furthermore, the improvement is more obvious when the multilayer model is applied to canopies with obvious vertical stratification under optimal growing conditions. In this article, GPPm was the most accurate at the TMK site from July to September. Since a multilayer GPP estimation model considers the high photosynthetic ability and LAI of grass, GPPm is always larger than GPPs. However, when the conditions are not optimal for growth--for example, if the temperature becomes very low or high--the GPPm will be smaller than the GPPs.  
In future studies on GPP remote sensing products, vertical stratification should be considered in the models. Adding overstory and understory information such as LAI and LUEmax for diverse sub-ecosystems can improve the estimation accuracy of the GPP product.  
The following are available online at [[http://www.mdpi.com/2072-4292/10/9/1329/s1](http://www.mdpi.com/2072-4292/10/9/1329/s1)]([http://www.mdpi.com/2072-4292/10/9/1329/s1](http://www.mdpi.com/2072-4292/10/9/1329/s1)). Models and input parameters. Table S1: Parameter inputs in different models.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the original paper and completed the revisions.  
This work was supported by the National Natural Science Foundation of China (No. 41671374) and the National Key R&D Program of China (2017 YFA0603001).  
We would also like to acknowledge [PERSON] and [PERSON] from the South China Botanical Garden, CAS, for providing the experimental data for the DHS site. We thank [PERSON], [PERSON] and [PERSON] from the National Institute for Environmental Studies, who provided the in situ measurement data for the TMK site. We acknowledge [PERSON] from the USDA Agricultural Research Service, who offered us valuable suggestions concerning the modeling. And we thank the anonymous reviewers gave us valuable suggestions to improve this research.  
The authors declare no conflicts of interest.