## 4 Discussion
### Research Uncertainties and Future Studies  
Uncertainty was first brought by NDVI data. In this paper, the FVC is introduced to improve the simulation of FAPAR, and the FVC calculation is obtained based on the NDVI calculated from remote sensing data. As the remote sensing data are affected by clouds and rain, it may lead to the underestimation of NDVI, and the underestimation of NDVI will lead to the underestimation of FVC, which in turn will lead to the underestimation of FAPAR\({}_{\text{FVC}}\). From the NDVI extracted from the tropical rainforest, the distribution of NDVI is about 0.4 from January to April, and 0.7 to 0.8 in other months in 2004. In the tropical rainforest area, the vegetation grows luxuirantly all year-round, and the lower NDVI may be the result of more rainfall during the corresponding period of this site [61; 62; 63]. The lower NDVI induced by clouds and rain may lead to the underestimation of FVC, which further leads to the underestimation of FAPAR\({}_{\text{FVC}}\). Therefore, the increased underestimation of FAPAR in the tropical rainforest region may also be related to the lower NDVI induced by clouds. The NDVI is used to calculate FVC; however, NDVI may reach saturation, particularly in tropical regions with high FVC, where the NDVI typically saturates at an average value of approximately 0.8 [64]. Under this condition, both LAI and FAPAR are also at relatively high levels, and FVC is also at a high level, nearly approaching 100%. This means that NDVI saturation should not affect the FVC estimation greatly. In addition, as illustrated in Figure 7, when FVC is high, its influence on the calculation of the FAPAR\({}_{\text{FVC}}\) model is relatively minor. When NDVI approaches saturation, FVC itself is already high. Thus, even if NDVI saturation induces some uncertainties in FVC estimation, its effect on the FAPAR\({}_{\text{FVC}}\) should be limited. However, under conditions of low FVC, NDVI may be influenced by soil background effects. Vegetation indices that are less affected by soil background, such as EVI and SAVI, could be considered for estimating FVC. Another uncertainty in FAPAR accuracy evaluation arises from the reliability of ground-based FAPAR measurements. The in situ measurements of FAPAR from the BigFoot project and three grassland in situ measured sites in Senegal, West Africa, were converted based on LAI data using Beer-Lambert's law [43], which assumes that leaves are opaque and does not take into account reflections from the soil and canopy. Therefore, the [PERSON]'s law calculation should yield the interception PAR [7; 65; 66]. Considering that leaves are opaque and that there is absorption of solar radiation by non-green parts, the in situ measured FAPAR absorbed by green leaves should be overestimated. Reducing the interception of solar radiation by excluding soil and canopy reflectance would underestimate the FAPAR absorbed by green leaves, but the underestimation would only be 1% on average [7]. The fraction of interception PAR absorbed by leaves varies widely among plants, generally from 0.75 to 0.90 [67, 68, 69, 70, 71], implying that soil and canopy reflectance have a relatively small effect on the measurement of FAPAR absorbed by green leaves. Thus, the overestimation of FAPAR absorbed by green leaves due to leaf opacity and the presence of non-green portions is likely to be more pronounced. Because of the possible overestimation of FAPAR as validation data, the degree of overestimation of FAPARMOD, FAPAR\({}_{\text{LAI}}\), and FAPAR\({}_{\text{FVC}}\) may be slightly higher than those in this study. The non-homogeneous turbid canopy of forests or shrubs may also influence the results. Although introducing FVC can partially solve the problem of non-green vegetation canopy, this method essentially assumes that the distribution of leaves of each individual tree or shrub can be approximated as satisfying the homogeneous turbid medium assumption.