## 3 Results
### Relationship Between FAPAR Error and LAI Error  
There was a significant correlation between LAI error (difference between MODIS LAI and in situ measured LAI) and FAPAR error (difference between estimated FAPAR and in situ measured FAPAR). From all samples, LAI error was significantly positively associated with errors from FAPAR\({}_{\text{MOD}}\), FAPAR\({}_{\text{LAI}}\), and FAPAR\({}_{\text{FVC}}\), and the R\({}^{2}\) of the regression equations were 0.401, 0.612, and 0.321, respectively (N = 288, \(p\) < 0.001). For the samples at biome types with overestimated FAPAR\({}_{\text{MOD}}\), the R\({}^{2}\) of the regression equations of LAI errors with errors from FAPAR\({}_{\text{MOD}}\), FAPAR\({}_{\text{LAI}}\), and FAPAR\({}_{\text{FVC}}\) were 0.215, 0.534, and 0.168 (N = 224, \(p\) < 0.001), respectively. For the samples at biome types with underestimated FAPAR\({}_{\text{MOD}}\), the R\({}^{2}\) of the regression equations of LAI errors and errors from FAPAR\({}_{\text{MOD}}\), FAPAR\({}_{\text{LAI}}\), and FAPAR\({}_{\text{FVC}}\) were 0.278 (N = 64, \(p\) < 0.001), 0.746 (N = 64, \(p\) < 0.001), and 0.101 (N = 64, \(p\) = 0.011), respectively (Figure 8).  
Figure 7: Scatterplot of fractional vegetation coverage versus FAPAR reduction calculated by taking fractional vegetation coverage into account: **(a)** FAPAR reduction from different assumed LAI after considering fractional vegetation coverage; **(b)** FAPAR reduction from the data used in this study after considering fractional vegetation coverage. Change rate = (FAPAR\({}_{\text{LAI}}\) - FAPAR\({}_{\text{FVC}}\))/FAPAR\({}_{\text{LAI}}\), with the extinction coefficient set to 0.5 when calculating FAPAR using the Beer-Lambert’s law. FAPAR was the same in Figure 1. FAPAR\({}_{\text{LAI}}\), FAPAR\({}_{\text{FVC}}\), and FVC were the same in Figure 2. The proportion of FAPAR decrease gradually increased with decreasing FVC.  
Figure 8: Scatterplot of MODIS LAI error versus errors for FAPARMOD, FAPAR\({}_{\text{LAI}}\), and FAPAR\({}_{\text{FVC}}\): (**a**) All data; (**b**) Samples in biome types with FAPAR\({}_{\text{MOD}}\) overestimated; (**c**) Samples in biome types with FAPAR\({}_{\text{MOD}}\) underestimated. \(Y\)-axis represents the estimated FAPAR error, which is the difference between the estimated FAPAR and the measured FAPAR; \(X\)-axis represents the MODIS LAI error, which is the difference between the MODIS LAI and the measured LAI. FAPAR was the same in Figure 1. FAPAR\({}_{\text{MOD}}\), FAPAR\({}_{\text{LAI}}\) and FAPAR\({}_{\text{FVC}}\) and FVC were the same in Figure 2. There was a significantly positive correlation between LAI error and FAPAR error.  
From the full data, the LAI error explained 61.2% of the variation in FAPAR\({}_{\text{LAI}}\) error and a 32.1% for the FAPAR\({}_{\text{FVC}}\) error. For the samples at biome types with FAPAR\({}_{\text{MOD}}\) overestimated, the LAI error explained 53.4% of the variation in FAPAR\({}_{\text{LAI}}\) error and 16.8% for the FAPAR\({}_{\text{FVC}}\) error. For the samples at sites with FAPAR\({}_{\text{MOD}}\) underestimated, the LAI error explained 74.6% of the variation in the FAPAR\({}_{\text{LAI}}\) error and 10.1% for FAPAR\({}_{\text{FVC}}\) error. For the three groups of samples, after considering the FVC, the ability of LAI error to explain the variation in the FAPAR error was reduced by 47.5%, 68.5%, and 86.5%, respectively, basically more than 50% (Figure 8).