## 4 Results and Analysis
### Cross-Validation
#### 4.2.3 Correlation Analysis  
In this paper, the correlation coefficient (R) and root mean square error (RMSE) were used as the correlation indicators between FY-FPAR and other FPAR products. The normal range of the correlation coefficient (R) is from 0 to 1. The closer it was to 1, the greater the correlation between the two sets of data. The RMSE is widely used as a way to measure the deviation between two sets of data. The closer the RMSE is to 0, the better the relationships between the data. The correlation statistics of FY-FPAR and MODIS-FPAR, GEOV1-FPAR, and GLASS-FPAR are exhibited in Table 7.  
Overall, the annual FY-FPAR of the grassland, cropland, shrub, broadleaf forest, and needleleaf forest areas had a high correlation with that of the other three FPAR products in 2014, and higher correlations were concentrated in the period when vegetation was lusher. Especially for grassland and cropland, the correlation coefficient (R) of the FY-FPAR and FPAR products in the lush period of the grassland (June-October) was as follows: with MODIS-FPAR: R = 0.5549/RMSE = 0.1103; with GEOV1-FPAR: R = 0.6102/RMSE = 0.1157; with GLASS-FPAR: R = 0.5827/RMSE = 0.1205. The correlation coefficient (R) of the FY-FPAR and FPAR products in the lush period of cropland (January, February, April, July, and November) was as follows: with MODIS-FPAR: R = 0.6012/RMSE = 0.1151; with GEOV1-FPAR: R = 0.5913/RMSE = 0.131; with GLASS-FPAR: R = 0.495/RMSE = 0.1377. This was much higher than the annual correlation coefficient (R) of the respective vegetation type. This occurred because  
Figure 7: FPAR time series change chart of the study area (2014); (**a–e**) are the grassland, cropland, shrub, broadleaf forest, and needleleaf forest study areas, respectively.  
the vegetation in the sparse period of vegetation had more exposed land, and the reflection spectrum of the vegetation was mainly affected by the soil's reflectivity, which affected the inversion accuracy.  
The correlation coefficient (R) of FY-FPAR and MODIS-FPAR was as follows: cropland (lush period) \(>\) cropland (Annual) \(>\) grassland (lush period) \(>\) needleleaf forest \(>\) broadleaf forest \(>\) grassland (Annual) \(>\) shrub. The correlation coefficient (R) of FY-FPAR and GEOV1-FPAR was grassland (lush period) \(>\) cropland (lush period) \(>\) grassland \(>\) needleleaf forest \(>\) shrub \(>\) broadleaf forest. The correlation coefficient (R) of FY-FPAR and GLASS-FPAR was grassland \(>\) needleleaf forest \(>\) cropland \(>\) broadleaf forest \(>\) shrub.  
In summary, the inversion accuracy of FY-FPAR for cropland and grassland areas during the lush vegetation period was relatively high; the inversion accuracy was good for broadleaf forest and needleleaf forest, and the inversion accuracy for shrubs was relatively low, as shown in Table 7. Thus, the annual FPAR of the shrubs was low, indicating that the inversion strategy based on FY-3C data was more suitable for the inversion of the FPAR of high-value areas. In addition, due to the difference in the surface reflectance data used by FY-FPAR and other FPAR products during inversion (as shown in Table 4), the FY-FPAR was relatively suitable for cropland and grassland during the lush period and in the broadleaf forest and needleleaf forest. The inversion of shrub FPAR also needed to consider the factors affecting bare soil.