## 4 Conclusions  
Forest canopies are composed of green and non-green components with different optical properties and functions. Green FPAR determined by green component in the canopy represents canopy photosynthetic capacity, so it is more important than the total FPAR in carbon cycle research. In this study, we used a modified version of SAIL model to study light absorption by forests canopies (including evergreen coniferous forests and deciduous broadleaf forests). The new SAIL model can distinguish the PAR absorbed by the canopy green and non-green components. The canopy FPAR including the total FPAR, green and non-green FPAR (FPAR\({}_{\text{NPV}}\)) were simulated in the given scenarios.  
Green FPAR varies with canopy structure. Plant area index, the proportion of canopy components, optical properties of components have impacts on green FPAR. Plant area index is the most important factor influencing the magnitude of green FPAR among three variables. The proportion of canopy components is second. Green FPAR rises with the increase of plant area index and the proportion of green leaves. In the dense forests, green FPAR is close to the total FPAR, but in open canopies, non-green components have higher contribution to the total FPAR. There are significant relationships between green FPAR and four vegetation indices. Compared with NDVI, EVI2 and SAVI, EVI has a higher correlation coefficient with green FPAR. It may be a good indicator of green FPAR.  
There is currently a lack of in situ independent data for evaluating green FPAR, due to the difficulty of measurement in the field. This study represents our effort in usinga radiative transfer model to partition the canopy FPAR into green FPAR and FPAR\({}_{\rm NPV}\), and provides a method to estimate green FPAR. For two typical forests, two formulas to calculate green FPAR are constructed, respectively. It should be noted that the results of the model have not been validated due to the lack of field green FPAR. In this study, only three variables (plant area index, the optical properties and proportion of canopy components) are discussed in the simulation. It is not enough because factors influencing canopy absorption and optical properties are various. More variables need to be considered when simulating in future study. Moreover, forest background and air conditions are not considered and discussed in the study. In open canopies, there are more photons through canopy gap on the soil and these photons are reflected. Therefore, the effect of background may be significant. Solar photons reflected by canopies are attenuated by clouds, aerosols and molecules along its way to satellite sensors. Though remotely sensed data can be corrected through atmospheric correction algorithms, some residual effects still exist. Moreover, it is also difficult to obtain accurate atmospheric correction parameters. Consequently, to invert green FPAR using remotely sensed data using vegetation indices, atmospherically resistant vegetation indices is needed. These factors will be discussed in future studies.  
Conceptualization, S.L.; methodology, S.L. and X.S.; formal analysis, S.L. and M.W.; writing--original draft preparation, S.L., H.L. and X.S.; writing--review and editing, S.L. and W.M.; funding acquisition, S.L.; all the authors analyzed the results and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Shandong Provincial Natural Science Foundation (Nos. ZR2020 MD019, ZR2020 QD019) and National Natural Science Foundation of China (Nos. 42101337, 41401407).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors thank the anonymous reviewers in this article for their helpful suggestions. The authors are grateful to the Oak Ridge National Laboratory Distributed Active Archive Center for providing some spectral data.  
The authors declare no conflict of interest.