## 4 Discussion  
### Impact of Fractional Vegetation Coverage Estimation Errors on FAPAR\({}_{\text{MOD}}\)  
In this paper, we demonstrate that although the same set of MODIS LAI data is used to calculate FAPAR, the accuracy of FAPAR\({}_{\text{LAI}}\) is clearly better than FAPAR\({}_{\text{MOD}}\), and the accuracy of FAPAR\({}_{\text{FVC}}\) is clearly better than FAPAR\({}_{\text{LAI}}\). FAPAR\({}_{\text{MOD}}\) calculates FAPAR based on the stochastic radiative transfer (SRT) model [54; 55; 56]. The SRT-based model was particularly improved for heterogeneous vegetation canopies, considering a mechanism of streaming radiation through canopy gaps; the detailed radiative transfer process of light through the canopy was taken into account in calculating FAPAR; the differences in parameters of different vegetation types were taken into account. However, errors in the estimation of FVC may have led to errors in the estimation of FAPAR\({}_{\text{MOD}}\) due to the lack of an accurate model of the FVC [40].  
There is a large intra-annual variation in the FVC of the green part of the canopy in summer-green vegetation such as grasslands, shrubs, and forests, or in vegetation that is clearly influenced by the wet and dry seasons. The green FVC is low during the off-peak vegetation growth period and may be clearly lower than the range of FVC given in the MODIS algorithm, and it may be only during the peak growth period that the FVC is roughly in line with the range given in the MODIS algorithm [7; 57]. Thus, at times other than the peak growth period, the FVC may be clearly lower than the range given in the MODIS algorithm [58].  
The overestimation of FVC in the MODIS algorithm actually exaggerates the distribution area of the green vegetation canopy, that is, it is equivalent to spreading the green leaves of the vegetation more dispersed inside the pixel (sample site) (Figure 1). As the green leaves are more evenly spread within the pixel, the shading effect between the leaves is reduced, and thus may lead to an overestimation of the absorption of solar radiation from the vegetation by the green leaves, resulting in an overestimation of the FAPAR.  
The results of this paper showed that FAPAR\({}_{\text{MOD}}\) exhibited overestimation for all biome types except for tropical broadleaf evergreen forest and boreal forest during the growth period, and that the overestimation of FAPAR\({}_{\text{MOD}}\) was more pronounced during the off-peak growth period within the growth period (Figure 6), which may be related to the fact that MODIS algorithms for these vegetation types overestimated FVC during the off-peak growth period. Other studies have also shown that FAPAR\({}_{\text{MOD}}\) overestimation occurs during the off-peak vegetation growth period [20; 21; 22; 23; 24], suggesting that the overestimation of FVC by the MODIS algorithm during these periods may have contributed to the overestimation of FAPAR\({}_{\text{MOD}}\).  
In addition to the errors arising from FVC due to the MODIS FVC model, the misclassification of land cover type in MODIS pixels and the presence of heterogeneous land cover types with low LAI due to the presence of snow, water bodies, etc., can also affect the accuracy of the FVC estimation in calculating the estimation of the FAPAR. A study in the Mongo area of Zambia concluded that misclassifying the vegetation type at this observation site as savanna led to an incorrect estimation of the FVC, which ultimately resulted in an overestimation of FAPAR [7]. In the polar tundra of Alaska, the overestimation of FAPAR in the early and late growth period may be related to snow cover on the ground [59; 60].