## 3 Results
### Relationships between Optical Spectral Vegetation Indices and LAI, Biomass
Estimation of LAI and Biomass Using Multiple Stepwise Regression (MSR) and Partial Least Squares Regression (PLSR) Methods  
The MSR and PLSR methods were used to estimate LAI and biomass for winter wheat based on the COSVI-RPPs, OSVIs, and RPPs. The LAI values were estimated from MSR and PLSR regression equations obtained based on all the COSVI-RPPs, which had R\({}^{2}\) values of 0.73 and 0.76, respectively. Similarly, the biomass values were estimated from MSR and PLSR regression equations and the R\({}^{2}\) values were 0.81 and 0.85, respectively (Table 9). All the COSVI-RPPs, OSVIs, and RPPs were selected as variables to estimate LAI using MSR and PLSR, and the R\({}^{2}\) values of the regression equation were 0.74 and 0.78, respectively. The R\({}^{2}\) of the MSR and PLSR biomass models based on all the COSVI-RPPs, OSVIs, and RPPs were 0.83 and 0.87, respectively. To validate the regression equations, the predicted values obtained from the MSR and PLSR regression equations were compared with the measured values acquired during the entire growth season of winter wheat (n = 40). The results showed that the predicted LAI values were in good agreement with the measured LAI values (Figure 4). RMSEs values of 0.64 and 0.61 and nRMSEs values of 19.22% and 18.13% for LAI were obtained ffrom the MSR and PLSR models based on all the COSVI-RPPs, respectively (Table 9). The RMSEs and nRMSE of the LAI model based on all the COSVI-RPPs, OSVIs, and RPPs using MSR and PLSR were 0.63 and 0.58 and 18.92% and 17.42%, respectively. Furthermore, the results revealed a good relationship between predicted and measured biomass (Figure 5). For the biomass values estimated based on all the COSVI-RPPs using the MSR and PLSR regression equations, the RMSEs were 142.63 g/m\({}^{2}\) and 137.21 g/m\({}^{2}\) and the nRMSEs were 19.71% and 18.96%, respectively (Table 9). RMSEs values of 140.34 g/m\({}^{2}\) and 134.68 g/m\({}^{2}\) and nRMSEs values of 19.39% and 18.61% from the MSR and PLSR biomass models were obtained based on all the COSVI-RPPs, OSVIs, and RPPs, respectively (Table 9). The results showed that the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**LAI**} & \multicolumn{3}{c}{**Biomass**} \\  & \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**RMSE**} & **nRMSE** & \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**R\({}^{2}\)**} & **RMSE** & **nRMSE** \\  & & & & & **(\%)** & & **(g/m\({}^{2}\))** & **(\%)** \\ \hline \multirow{2}{*}{**Multiple Stepwise regression**} & EVI \(\times\) RVI, & & & & & & \\  & MTVI2 \(\times\) DERD & 0.73\({}^{\ast}\) & 0.64 & 19.22 & \multirow{2}{*}{OSAVI \(\times\) DERD, } & \multirow{2}{*}{0.81\({}^{\ast}\)} & \multirow{2}{*}{142.63} & \multirow{2}{*}{19.71} \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{2}{*}{**Partial least squares regression**} & \multirow{2}{*}{12 COSVI-RPPs} & \multirow{2}{*}{0.76\({}^{\ast}\)} & \multirow{2}{*}{0.61} & \multirow{2}{*}{18.31} & \multirow{2}{*}{12 COSVI-RPPs} & \multirow{2}{*}{0.85\({}^{\ast}\)} & \multirow{2}{*}{137.21} & \multirow{2}{*}{18.96} \\  & & & & & & & \\ \hline \multirow{2}{*}{**Multiple stepwise regression**} & EVI \(\times\) RVI, & & & & & & \\  & EVI \(\times\) RVI, & 0.74 & 0.63 & 18.92 & \multirow{2}{*}{OSAVI \(\times\) RVI, } & \multirow{2}{*}{140.34} & \multirow{2}{*}{19.39} \\  & MTVI2 \(\times\) DERD & & & & & & \\ \hline \multirow{2}{*}{**Partial least squares regression**} & \multirow{2}{*}{12 COSVI-RPPs, 6 OSVIs, 15 RPPs} & \multirow{2}{*}{0.78} & \multirow{2}{*}{0.58} & \multirow{2}{*}{17.42} & \multirow{2}{*}{12 COSVI-RPPs, 6 OSVIs, 15 RPPs} & \multirow{2}{*}{0.87\({}^{\ast}\)} & \multirow{2}{*}{134.68} & \multirow{2}{*}{18.61} \\  & & & & & & & \\ \hline \hline \end{tabular}  
* **Note:** Model significant at the 0.01 probability level (\(p<0.01\)).  
\end{table}