## 3 Results  
### Relationships between Optical Spectral Vegetation Indices and LAI, Biomass  
Linear and nonlinear regression analysis was investigated using optical spectral vegetation indices (OSVIs) as the independent variable, and the LAI and biomass of winter wheat as the dependent variable. Six OSVIs were selected based on previous literature. Good relationships between the OSVIs and LAI, biomass were measured and the regression equations of LAI and biomass were built for winter wheat (Table 6). To find the more sensitive OSVIs, we analyzed the OSVIs behavior with respect to the R\({}^{2}\) values. Table 6 showed that the regression relationships between the VIs and LAI, biomasses were significant. RVI1 and MTVI2 had the lowest and highest R\({}^{2}\) values (0.38 and 0.58, respectively) for winter wheat LAI. The order of the indices from highest to lowest with respect to the R\({}^{2}\) values for the LAI regression was MTVI2, EVI, OSAVI, SAVI, NDVI, and RVI1. The R\({}^{2}\) values of MTVI2, EVI, OSAVI, SAVI, NDVI, and RVI1 were 0.58, 0.50, 0.43, 0.43, 0.39, and 0.38, respectively. Of the R\({}^{2}\) values, one was above 0.50, three were above 0.4, and two were below 0.4. The relationship between biomass and VIs behaved similarly to the relationship between LAI and OSVIs (Table 6). However, the R\({}^{2}\) of these relationships were different. The highest and lowest regression relationships were EVI and RVI1 for biomass. For the LAI regression, the MTVI2, EVI, SAVI, and RVI1 were fitted to power equations, whereas OSAVI was fitted to the linear equation. In contrast, all of the OSVIs were fitted to power equations for biomass. To validate the estimation accuracy of LAI and biomass, the predicted values and the measured values were compared based on their RMSE and nRMSE values. The results showed that the RMSE values ranged from 0.70 to 0.89 and the nRMSE values from 21.02% to 26.69% for LAI; the RMSE values ranged from 198.65 g/m\({}^{2}\) to 337.35 g/m\({}^{2}\) and the nRMSE values from 27.44% to 46.46% for biomass (Table 6). To validate the estimation accuracy of the regression model, the experimental data (n = 40) was used to compare the predicted LAI and biomass with the measured LAI and biomass. Correlations between the predicted and measured LAI were observed for the following parameters: RVI, DERD, HH/VV, HH, HH/HV, Anisotropy, Entropy, Alpha, VV/HV, Vol/Span, SERD, VV, HV, Dbl/Span, and Odd/Span (Table 7). Similarly, correlations between the predicted and measured biomass were revealed for the following parameters: DERD, RVI, HH, HH/VV, HH/HV, SERD, Alpha, Entropy, Anisotropy, VV/HV, VV, Vol/Span, HV, Dbl/Span, and Odd/Span. Based on the correlations (R\({}^{2}\)) between the RPPs and the LAI and biomass from the field experiment, the RPPs that were best correlated with LAI and biomass were RVI (R\({}^{2}\) = 0.63) and DERD (R\({}^{2}\) = 0.71). Thus, these indices were used to establish the combined optical spectral vegetation indices and radar polarimetric parameters (COSVI-RPPs) by multiplying each with optical spectral vegetation indices (RVI \(\times\) OSVIs and DERD \(\times\) OSVIs). In this study, six OSVIs were individually combined with RVI and with DERD to analyze the relationships of these COSVI-RPPs with LAI and biomass (Table 8). Further, we used these correlations to establish LAI and biomass regression equations for winter wheat. The results revealed that of the R\({}^{2}\) values for the LAI and COSVI-RPPs regressions, one was above 0.65, five were above 0.60, five were below 0.60, and two were equal to 0.60. Of the R\({}^{2}\) values for the biomass and COSVI-RPPs regressions, one was equal 0.80, twelve were above 0.7, and eight were above 0.75. The highest R\({}^{2}\) value was the MTVI2 \(\times\) RVI (R\({}^{2}\) = 0.68) and the lowest was the RVI1 \(\times\) RVI (R\({}^{2}\) = 0.56) for winter wheat LAI. In contrast, the highest R\({}^{2}\) was the EVI \(\times\) RVI (R\({}^{2}\) = 0.80) and the lowest was the RVI1 \(\times\) DERD (R\({}^{2}\) = 0.71) for winter wheat biomass.