 This study demonstrated that combining optical spectral vegetation indices, OSVIs, and radar polarimetric parameters, RPPs, could improve the accuracy of estimating leaf area index, LAI, and biomass of winter wheat. Specifically, the product of modified triangular vegetation index 2 and TVI2, and double-bounce eigenvalue relative difference, DRD, was found to be the most accurate predictor of LAI, while the product of enhanced vegetation index, EVI, and radar vegetation index, RVI, was the best predictor of biomass. Furthermore, the product of MTVI2 and DRD was more accurate than the product of EVI and RVI when estimating LAI, while the product of EVI and RVI was more accurate than the product of MTVI2 and DRD when estimating biomass. Additionally, the partial leaf squares regression, PLSR, model was found to be superior to the multiple stepwise regression, and the SAR, model in terms of accuracy of estimating LAI and biomass. This article was authored by Xiu Yang-jin, Gui Junyang, Xingyang-su, and others. We are article.tv. Links in the description below.