In recent years, Polarization SAR ( PolSAR) has been widely used in the filed of crop biomass estimation. However, high dimensional features extracted from PolSAR data will lead to information redundancy which will result in low accuracy and poor transfer ability of the estimation model. Aiming at this problem, we proposed a estimation method of crop biomass based on automatic feature selection method using genetic algorithm ( GA ) . Firstly, the backscattering coefficient, the polarization parameters and texture features were extracted from PolSAR data. Then, these features were automatically pre-selected by GA to obtain the optimal feature subset. Finally, based on this subset, a support vector regression machine ( SVR) model was applied to estimate crop biomass. The proposed method was validated using the GaoFen-3 ( GF-3) QPSΙ ( C-band, quad-polarization) SAR data. Based on wheat and rape biomass samples acquired from a synchronous field measurement campaign, the proposed method achieve relative high validation accuracy ( over 80％) in both crop types. For further analyzing the improvement of proposed method, validation accuracies of biomass estimation models based on several different feature selection methods were compared. Compared with feature selection based on linear correlation, GA method has increased by 5.77％ in wheat biomass estimation and 11.84％ in rape biomass estimation. Compared with the method of recursive feature elimination ( RFE) selection, the proposed method has improved crops biomass estimation accuracy by 3.90％ and 5.21％, respectively.