## 4 Discussion  
In this study, six optical spectral vegetation indices (OSVIs) were used to analyze the relationships of OSVIs with LAI and biomass for estimating LAI and biomass in winter wheat. The results showed that OSVIs were correlated with LAI and biomass (Table 6). The effects of LAI and biomass on crop canopy spectral reflectance in the NIR and visible spectrum are known [10, 11, 12, 13, 14, 15, 16, 17]. Therefore, these OSVIs based on different combinations of visible and NIR reflectance was significantly related with LAI and biomass. The results of our study confirmed previous results [13, 14, 15, 16, 17]. The results showed that MTVI2 and EVI more accurately estimate LAI and biomass than other OSVIs. In addition to the red and NIR bands, EVI includes the blue band, which was used to correct for aerosol influences in the red band. Furthermore, the EVI is an optimized index designed to enhance the vegetation signal with improved sensitivity in high biomass regions through decoupling of the canopy background signal and a reduction in atmospheric and soil background noise influences [53]. Therefore, EVI improves the estimation accuracy of LAI and biomass. The MTVI2 includes the green, red, and NIR bands. The decrease or increase in these bands reflectance influences the total area of the triangle, which was highly related with LAI [18]. In order to reduce soil contamination effects, a soil adjustment factor is incorporated into MTVI2. The results of [PERSON] _et al._[18] indicated that MTVI2 was more sensitive to medium-high LAI. Therefore, the MTVI2 was used to boost the estimation accuracy of LAI and biomass. The results indicated that the EVI and MTVI2 could be used to estimate LAI and biomass in winter wheat. The results of OSVIs showed that the least-correlated with LAI and biomass was RVI1. Our results were consistent with the study of [PERSON] _et al._[24]. In contrast, OSAVI, SAVI, and NDVI were very sensitive to low LAI (LAI \(<\) 3) and were saturated at medium to high LAI values (LAI \(>\) 3) [16, 50, 51, 52]. In this study, most of the LAI values were higher than 3. Therefore, MTVI2 and EVI were better than OSAVI, SAVI, and NDVI for estimating LAI and biomass. These results suggested that the OSVIs could be used to estimate LAI and biomass in winter wheat. Fifteen radar polarimetric parameters (RPPs) were used to analyze the relationships between LAI, biomass and RPPs. The results showed that good correlations existed with the exception of the Dbl/Span and Odd/Span. The RVI and DERD indices exhibited the strongest correlations with LAI and biomass (Table 7). The results of [PERSON] _et al._[57] suggested that the increase in HH during the tillering to filling stages was the main reason for the increase in single-volume backscattering as rice canopy became much denser. However, the denser paddy plants canopy showed more vertically oriented scatter, which led to a gradual reduction in the VV from the tillering to filling stages. As for HV, the double-volume scattering is the dominant scattering source at four winter wheat growth stages. The RVI not only included HH, HV, VV, backscattering difference information and then was sensitive to crops structure, but also reduced the environmental and incidence angle effects [48, 58]. Therefore, the RVI showed higher correlations with LAI and biomass. The DERD are derived from the eigen-decomposition of the coherency matrix considering the reflection symmetry hypothesis. The results of [PERSON] _et al._[47] indicated that DERD provides a better inversion of crop parameters in their natural environment because it is easier to discriminate the different scattering mechanisms and eliminate the additive noise term for reducing the biases over the sample eigenvalues. Hence DERD was highly correlated with LAI and biomass. The HH, HV, VV, HH/VV, HH/HV, and VV/HV indices were strongly correlated with LAI and biomass.