Abstract: In order to monitor and evaluate the growth information and environment of winter wheat, a multilayer non-homogeneous hybrid electromagnetic scattering model was constructed for winter wheat overlaying farmland surface by introducing a three-phase mixed-media model to characterize the vegetation layer and a Gaussian random roughness surface to characterize the rough surface of the farmland. Firstly, the validity of the model was analyzed and verified by comparing the backward scattering coefficient prediction results of the proposed multilayer non-homogeneous hybrid model with those of the water cloud model and the Oh model in the nodulation and tasseling stages of winter wheat. Subsequently, the model equivalent dielectric constant was analyzed and the electromagnetic scattering and radiative transfer equations were solved to obtain the effects of the vegetation growth information, the water content of vegetation and the soil roughness on the surface of the covered farmland. The effect of factors such as vegetation growth information, vegetation water content and soil roughness on the surface equivalent dielectric constant and radar backscattering coefficient of the covered farmland was obtained. The results showed that the multilayer non-homogeneous hybrid model proposed was in good agreement with the prediction results of the water cloud model and the Oh model, and in good agreement with the equivalent dielectric constant R~2 of the wheat layer obtained by the double dispersion model with the values of 0.981 7, 0.992 2, 0.986 3, 0.971 1, respectively. Moreover, the R~2 of the model proposed for the prediction results of the water content of the wheat at the stage of pulling out and the stage of spiking were the same as the actual measurement values, and the R~2 of the predicted results were the same as the predicted results of the model. The RSME of the prediction results and the actual measured values were 0.88% and 4.65%, respectively, and the model can better simulate the electromagnetic scattering characteristics of the surface of the overlying farmland, which provided a solid theoretical basis for the subsequent UAV microwave inversion of winter wheat growth and soil moisture information.