## 3 Results
### Sensitivity Analysis
#### 3.2.2 Local Sensitivity Analysis of the FPAR  
In addition to the global sensitivity analysis, the variabilities of the VIs and FPAR as functions of each parameter were investigated as well. Theoretically, a robust VI should not only have a significant relationship with the target variable (FPAR) but also display resistance to changes in other variables. For the ALA parameter, the variations in ALA had a great influence on three VIs. The amount of canopy light interception decreased as ALA increased, resulting in a decline in the values of the VIs and FPAR (Figure 6a). A good linear regression between the VIs and FPAR could be observed when the ALA was less than 70\({}^{\circ}\). Similar variabilities of OSAVI and mSR were achieved with increasing and decreasing ALA, but the WDRVI presented remarkable changes beyond \(-\)100\(\%\) and 100\(\%\) (Figure 7a).  
The leaf chlorophyll content was another important indicator for considering the sensitivity of the VIs. Figures 6b and 7b depict the variations in the VIs and FPAR with the C\({}_{\text{ab}}\) changes. In general, the VIs decreased with a drop in C\({}_{\text{ab}}\) due to an increase in the spectral reflectance of visible light. Compared with the mSR and WDRVI, the OSAVI presented excellent robustness for C\({}_{\text{ab}}\) changes. The relationships between OSAVI and FPAR were fully consistent in most cases, with variations only observed at a C\({}_{\text{ab}}\) of 10 \(\upmu\)g cm\({}^{-2}\). The VIs of mSR and WDRVI showed good robustness with higher C\({}_{\text{ab}}\). Meanwhile, the sensitivities of mSR and WDRVI to C\({}_{\text{ab}}\) variations were significantly increased when C\({}_{\text{ab}}\) was lower than 30 \(\upmu\)g cm\({}^{-2}\). Substantial variations for mSR and WDRVI were noticed with a C\({}_{\text{ab}}\) of 10 \(\upmu\)g cm\({}^{-2}\), implying that the estimation accuracy was exacerbated during the crop maturity period when the leaves turned yellow.  
The effects of SZA changes on three VIs were illustrated in Figures 6c and 7c. The SZA had a minimal effect on the three VIs, except for slight changes at the lowest SZA (0\({}^{\circ}\)). Although the VIs presented a strong resistance to changes in SZA, the regressions between the VIs and FPAR were slightly different, with a high linear correlation with mSR  
Figure 5: The total-order sensitivity index for the FPAR derived from the PROSAIL simulation.  
and a nonlinear correlation with both OSAVI and WDRVI. These results indicated that the estimated FPAR suppressed the impact from SZA, which broke through the common time for capturing images at noon. The posoil was the important parameter in the PROSAIL, which could significantly influence the canopy spectral reflectance by controlling the soil brightness from dark (1) to bright (0). The variabilities in the VIs and FPAR in posoil are shown in Figures 6d and 7d. The VIs would be decreased with the increase in posoil. A smaller amplitude of regression for the variation in posoil was observed in OSAVI and the sensitivity of the posoil parameter was decreased under a higher canopy cover (FPAR > 0.8). Compared with OSAVI, WDRVI and mSR exhibited more variability and weaker resistance to changes in posoil, particularly when the FPAR was between 0.4 to 0.6.  
Figure 6: Sensitivity of OSAVI, WDRVI and mSR to the variation in PROSAIL model parameters across various FPAR values: (**a**) ALA; (**b**) Cab; (**c**) SZA; (**d**) posil.