## 2 Results and Discussion
### Estimation of Green FPAR  
Presently, FPAR is mainly estimated based on empirical models (e.g., spectral vegetation indices, LAI) and radiative transfer model. Spectral vegetation indices play a significantly important role in FPAR inversion [32]. Similarly with the total FPAR, green FPAR also demonstrates strong linear correlations with four vegetation indices (NDVL, EVI, EVI2, and SAVI). The difference in correlation coefficients between green FPAR and four vegetation indices is small. But compared with other three vegetation indices, EVI has higher correlation coefficients with green FPAR. In an earlier study, EVI is founded that it is linearly correlated with the green leaf area index (LAI) in crop fields based on airborne multispectral data [33]. Additionally, the seasonal dynamics of EVI better mimic those of GPP in terms of phase and amplitude [32]. Therefore, if spectral vegetation indices are used to estimate the PAR absorbed by green leaves, EVI should be a good choice. Nevertheless, there is a problem solved, namely the canopy structure variations. Given an invariant proportion of green components, the empirical model between vegetation indices and FPAR can be constructed easily. Unfortunately, the ratio of green to non-green components in the deciduous broadleaf forest canopy is seasonal. And only in forests with a high vegetation index, the impacts of the variation of the proportion of canopy components on relationships between green FPAR and vegetation indices is negligible. It seems that to accurately estimate green FPAR in forests, the ratio of components must be known. However, it is a very difficult task to quantify green and non-green components at the canopy levels. For a mature deciduous forest, PAI values increase rapidly with leaf expansion and non-green components growth after the spring growing season begins. Compared with green leaves, the variations of non-green components are small, and the increase of PAI mainly comes from leaf development [34]. Therefore, in a year, non-green components can be regarded as a constant. In winter, there are only non-green components in the canopy, so the PAI in this period can represent NPV area index. According to some studies, PAI of deciduous broadleaf forests in winter is about 0.5 [35; 36]. While, mature evergreen coniferous forests are different from deciduous broadleaf forests. Evergreen forests can preserve a high PAI and a low proportion of non-green components all the year round. Consequently, the proportion of canopy components can be regarded a constant. Field campaigns found that the proportion of needle leaves in the canopy is generally 90% [23]. According to above analysis, we re-simulated the canopy FPAR and reflectance using SAIL model. In the new simulation, NPV area index of deciduous broadleaf forests and the proportion of green components of evergreen coniferous forests were set as 0.5, 90%, respectively and PAI varied from 1 to 7, while other parameters were preserved. The results are shown in Figure 9. The Among four vegetation indices, EVI shows the highest correlations with FPAR\({}_{\text{PAV}}\) (r = 0.99), while NDVI has the lowest correlations with green FPAR. It is well known that EVI, EVI2, and SAVI are modification or optimization versions of NDVI [16; 37; 38]. The fundamental objective of optimization is to reduce atmospheric or soil effects and to isolate the green photosynthetically-active signal from the mixed and contaminated pixels [39]. Consequently, the three vegetation indices show better relationships with green FPAR than NDVI. Through the linear regression method, the formulas between \(FPAR_{PAV}\) and \(EVI\) for deciduous broadleaf forests and evergreen coniferous forests were constructed. Their \(FPAR_{PAV}\) can be represented as respectively  
\[FPAR_{PAV,DBF}=2.31\times EVI-0.67\]  
\[FPAR_{PAV,ENF}=2.37\times EVI-0.34\]where, \(DBF\) is deciduous broadleaf forests, \(ENF\) is evergreen coniferous forests. The errors were small when \(FPAR_{PAV}\) was estimated through the model.