## 4 Discussion  
### Sensitivity Analysis of the FPAR and VIs  
The purpose of the sensitivity analysis was to reveal the influences of the different leaf and canopy parameters on the variation in the crop FPAR and filter VIs with high resistance capability. For the EFAST, the range of PROSAIL model parameters also changed the contribution to the final FPAR. Previous studies showed that PROSAIL model parameters exhibited varying sensitivities across different spectral bands. For example, the red and near-infrared bands were sensitive to changes in C\({}_{\text{ab}}\) and LAI while being relatively insensitive to the soil matrix and atmospheric conditions [72]. ALA was sensitive to the near-infrared band, whereas the visible light band was more sensitive to SZA [73; 74]. The results of two datasets with wide and narrow parameter ranges showed that the FPAR was mainly sensitive to the vegetation canopy structure parameters, including the LAI, ALA and SZA (Figure 5). Both LAI and ALA determined the canopy absorption efficiency by adjusting the direct and diffuse directional transmittance and reflectance. SZA ultimately affected the FPAR simulation value by altering the total incoming PAR above the canopy [75]. [PERSON] et al. [12] suggested that SZA, LAI, ALA, CA\({}_{\text{ab}}\) and the canopy background reflectance had significant effects on the FPAR, while other parameters (hspot, C\({}_{\text{ar}}\), C\({}_{\text{m}}\)) had lesser effects on the FPAR. The results of this study indicate that C\({}_{\text{ab}}\) and spoil also exerted certain effects on the FPAR.  
The VIs are formed using a mathematical combination of different bands' reflectances to enhance the target signatures and eliminate other interferences [76; 77]. Therefore, the relationships between the VIs and FPAR are comprehensively affected by various factors, such as atmospheric environment, vegetation type and remote sensing data quality [23; 78; 79]. For example, OSAVI eliminated effects from the soil background [80] and mSR might be less sensitive to canopy optical and geometric properties than NDVI [81]. WDRVI used NDVI near-infrared band reflectance for weighting calculations, reducing the weight of these bands under medium- and high-biomass conditions [82]. The sensitivity analysis of the VIs to the PROSAIL model parameters showed that the relationship between OSAVI and FPAR was less affected by spoil and C\({}_{\text{ab}}\) in comparison with mSR and WDRVI (Figure 6). The findings regarding spoil agreed well with the proposal of [PERSON] and [PERSON] [83], but the results for C\({}_{\text{ab}}\) contradicted those reported by [PERSON] et al. [84] and [PERSON] and [PERSON] [83]. This discrepancy can be attributed to the change in C\({}_{\text{ab}}\) content having a small effect on the FPAR at low LAI values. Moreover, this study did not observe the advantage of mSR being less sensitive to canopy optical and geometric properties (e.g., ALA and SZA). [PERSON] et al. [85] also showed that mSR was susceptible to environmental impacts. WDRVI exhibited more fluctuations with C\({}_{\text{ab}}\) compared with OSAVI. In contrast, the anti-interference capability of WDRVI to parameter changes was the worst compared with OSAVI and mSR (Figure 7).  
Figure 9: Inversion of the canola FPAR seedling, flowering and maturity periods based on ANN-OSAVI.