## Materials and Methods
### PROSAIL RTM and Data Simulation  
To investigate the optimal inversion method for estimating the FPAR based on the RTM, the crop canopy spectra were simulated using the PROSAIL model. The primary PROSAIL model was proposed by [PERSON] et al. [32], who coupled the PROSPECT leaf optical \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Growth Stages** & **N** & **Max** & **Min** & **Mean** & **SD** \\ \hline \multirow{3}{*}{2021} & Seeding & 62 & 0.780 & 0.465 & 0.621 & 0.095 \\  & Flowering & 40 & 0.741 & 0.409 & 0.605 & 0.082 \\  & Maturity & 40 & 0.706 & 0.404 & 0.564 & 0.075 \\ \hline \multirow{3}{*}{2022} & Seeding & 18 & 0.770 & 0.442 & 0.616 & 0.104 \\  & Flowering & 60 & 0.770 & 0.417 & 0.590 & 0.084 \\ \cline{1-1}  & Maturity & 16 & 0.705 & 0.530 & 0.602 & 0.057 \\ \hline \hline \end{tabular} Note: The table reports the sample number (N), maximum value (max), minimum (min), mean (mean) and standard deviation (SD) of the observations.  
\end{table} 
Table 1: Summary statistics of the canopy FPAR in three stages from 2020 to 2022. Figure 3: The UAV remote sensing platform and the multispectral camera bands properties. The arrows point to the multispectral camera on the DJI Inspire 2 UAV and the wavelengths measured by the multispectral camera. properties model [33] with the SAIL canopy bidirectional reflectance model [34] to describe the variations in the canopy spectral reflectance as a function of the biochemistry and architecture parameters. At the leaf level, the PROSPECT model parameters included leaf chlorophyll a + b content (C\({}_{\text{ab}}\)), carotenoid content (C\({}_{\text{ar}}\)), brown pigment content (C\({}_{\text{brown}}\)), equivalent water thickness (C\({}_{\text{w}}\)), dry matter content (C\({}_{\text{m}}\)) and leaf internal structure parameter (N). For the canopy level, the SAIL model parameters included the LAI, ALA, hot spot size parameters (hspot), SZA, observer zenith angle (OZA), relative azimuth angle (psi) and soil brightness parameters (posit). In general, the canopy structure parameters (e.g., LAI, SZA, ALA and posoil), as well as the leaf-level parameters (C\({}_{\text{ab}}\) and N), have been found to have an effect on the FPAR variation [12; 35]. According to the prior information, the LAI, C\({}_{\text{ab}}\), ALA and SZA were identified as particularly influential factors that affect the FPAR. To explore the effects of different parameters and ranges on the FPAR, two simulated datasets were generated. One was a general dataset parameter distribution according to the literature [36; 37; 38]. Based on prior information and the previous research results of [PERSON] et al. [39], the specific dataset for canola was determined by reducing the value range and step size of the canopy structure parameters (e.g., C\({}_{\text{ab}}\) and LAI) and illumination parameter (SZA). The parameter ranges and specific meanings within the PROSAIL model for both datasets are shown in Table 2.  
The FPAR could be derived by combining the law of conservation of energy with the PROSAIL model [40]. First, the leaf reflectance and transmittance were obtained using the PROSPECT part of the PROSAIL model. Second, the multiple scattering effect caused by the interaction between the canopy and background soil was partially accounted for by the SAIL component of the PROSAIL model. Finally, the canopy FPAR was solved by calculating both the direct and scattered PAR.