## 4 Discussion
Previous studies have found that polarization ratios(HH/VV, HH/HV, and VV/HV) and backscattering coefficients (HH, HV, and VV) are suitable for LAI and biomass estimations in some crops and forests [9, 24, 36]. Our results were in agreement with these studies. As alpha, anisotropy, and entropy were used to identify the scattering type and its relevance [46], these indices were well correlated with LAI and biomass. The reason Vol/Span was correlated with LAI and biomass was because LAI and biomass largely influenced the range of the Vol change. [PERSON] _et al._[24] also indicated that Vol had a strong relationship with LAI and biomass. Our results further confirmed theirs results. The values of Odd and Dbl changed little and irregularly in the study of [PERSON] _et al._[24] and in our results. Therefore, Odd/Span and Dbl/Span were not correlated with LAI or biomass. The results indicated that the most of the RPPs were suitable for estimating LAI and biomass in winter wheat. Because the NIR reflectance was not sensitive to the LAI or biomass of winter wheat at medium to high LAI, most of the OSVIs demonstrated the saturation phenomenon. However, SAR has some advantages for estimating LAI and biomass at medium to high LAI and biomass [58], and therefore, RPPs were introduced in our study in combination with multispectral data. Previous studies have combined OSVIs and RPPs to estimate biomass and LAI in crops or forests by simply multiplying them [12, 24, 35, 36]. Their results indicated that this method can be used to improve the estimation accuracy of biomass and LAI. Therefore, we combined the optical spectral vegetation indices and radar polarimetric parameters to estimate biomass and LAI in winter wheat by simply multiplying them. The combined indices RVI \(\times\) OSVIs and DERD \(\times\) OSVIs were created based on the good relationships between LAI, biomass and OSVIs, RPPs. Compared with the OSVIs and RPPs alone, the results showed that the COSVI-RPPs were more suitable to estimate biomass and LAI at medium to high vegetation coverage. The values of R\({}^{2}\) were 0.68 for LAI and 0.80 for biomass, respectively (Table 8). The better performances of the COSVI-RPPs were attributed to the stronger penetration ability of SAR. The good consistency between the predicted values and measured values was due to the facts that OSVIs can provide an accurate interpretation of crop LAI and biomass and RPPs are more sensitive to crop canopy structure. Both of these factors contributed to the improved estimations of LAI and biomass. The results indicated that the advantages of optical and radar data were integrated and then could be used to enhance their application value. It had great significance to promote the development and integration of optical and radar technology. The results revealed that EVI \(\times\) RVI and MTVI2 \(\times\) RVI could be used for robust estimates of LAI and biomass in winter wheat, and the other combined indices were also valuable (Table 8). The result of [PERSON] _et al._[27] was also confirmed by our study. In this study, the COSVI-RPPs were acquired according to the spectral reflectance and SAR backscattering mechanism information. These new combined indices were used to estimate canopy structural information (LAI and biomass). The new combined indices were better than the OSVIs and RPPs alone, but more investigations and validations are needed before their regional applications. In this study, the acquisition time had affected the HJ and radar data. The LAI and biomass changed little during the tillering (4 March and 5 March) and filling stages (16 May and 20 May). The difference in the HJ and radar data acquisition time was ignored. However, because LAI and biomass changed at the jointing and antihesis stages, the difference in the HJ and radar data acquisition time may have led to some estimation errors. In particular, winter wheat grows quickly during the jointing stage.