## 3 Results
### Evaluation of the Optimal Inversion Model for Estimating Canada  
Although the ANN-OSAVI-based hybrid model exhibited the optimal accuracy compared with WDRVI and mSR, the difference between the three hybrid models was minimal, from which it is challenging to determine the capability in estimating the canola FPAR due to potential discrepancies between the RTM model and real field conditions. Therefore, hybrid models of the ANN algorithm combined with three VIs were evaluated using the canola FPAR of each growing stage and the whole growing stages (Figure 8).  
In general, the inversion accuracies of the hybrid model in the seedling stages were significantly higher than those in other stages. Similar trends were observed for the flowering and maturity stages. Specifically, for the seedling stage, ANN-OSAVI exhibited the highest accuracy, with R\({}^{2}\) and RMSE values of 0.78 and 0.043, respectively. ANN-WDRVI and ANN-mSR also performed well but had slightly lower R\({}^{2}\) values around 0.7 due to the discrete samples. Additionally, these models presented underestimated tendencies when the FPAR > 0.6 (Figure 7a). With respect to the flowering stage, the ranges of FPAR were narrower and mainly concentrated between 0.5 and 0.7. The underestimation trend was greatly aggravated for all hybrid models, inducing increased estimated errors. ANN-OSAVI produced the closest fit to the 1:1 reference, followed by ANN-mSR and ANN-WDRVI. Overall, the ANN-OSAVI hybrid model presented the optimal capability for retrieving FPAR in canola growth periods, with the highest estimation accuracy of R\({}^{2}\) of 0.65 and an RMSE of 0.051. Based on the optimal inversion method, the estimation and inversion of the FPAR in different growth stages of winter canola were mapped, as shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Inversion Method**} & \multirow{2}{*}{**VIs**} & \multicolumn{2}{c}{**Model Performance**} \\ \cline{3-5}  & & & \(\mathbf{R^{2}}\) & **RMSE** \\ \hline \multirow{6}{*}{General dataset 1} & \multirow{2}{*}{Curve fitting} & OSAVI & 0.801 & 0.077 \\  & & mSR & 0.703 & 0.122 \\  & & WDRVI & 0.750 & 0.080 \\ \cline{2-5}  & \multirow{2}{*}{ANN} & OSAVI & 0.822 & 0.055 \\  & & mSR & 0.772 & 0.069 \\  & & WDRVI & 0.769 & 0.062 \\ \cline{2-5}  & \multirow{2}{*}{SVR} & OSAVI & 0.817 & 0.067 \\  & & mSR & 0.741 & 0.088 \\  & & WDRVI & 0.768 & 0.072 \\ \hline \multirow{6}{*}{Specific dataset 2} & \multirow{2}{*}{Curve fitting} & OSAVI & 0.740 & 0.073 \\  & & mSR & 0.680 & 0.120 \\ \cline{1-1}  & & WDRVI & 0.735 & 0.080 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{ANN} & OSAVI & 0.775 & 0.059 \\  & & mSR & 0.750 & 0.079 \\  & & WDRVI & 0.752 & 0.066 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{SVR} & OSAVI & 0.749 & 0.066 \\  & & mSR & 0.722 & 0.083 \\ \cline{1-1}  & & WDRVI & 0.737 & 0.069 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the hybrid inversion models for estimating FPAR.  
Figure 8: Comparison of the measured FPAR and retrieved FPAR from ANN-OSAVI, ANN-WDRVI and ANN-mSR in the canola growing stages: (**a**) seedling stage; (**b**) flowering stage; (**c**) maturity stage; (**d**) whole growing stages. The dash line represents the 1:1 reference and the solid line represents the fitting regression.