Wide mode SAR images have an apparent incidence angle effect. The existing incident angle normalization methods assume that the relationship between the incident angle (theta) and the backscattering coefficient (sigma(PQ)) does not change with the growth stage of crops, which is in conflict with the real-life situation. Therefore, the normalization results of sigma(PQ) based on these existing methods will affect the accuracy of object classification, target recognition, and land surface parameter inversion. Here, the change in theta-sigma(PQ) relationship was investigated based on time-series (April to October) sigma(PQ) of maize canopies in northeast China, and a dynamic method based on normalized difference vegetation index (NDVI) was developed to normalize the effect of theta on sigma(PQ). Through the accuracy evaluation, the following conclusions are obtained: (1) the dependence (referring to N) of Sentinel 1 C-band sigma(PQ) on theta varies with maize NDVI. In addition, the value of N changed from 9.35 to 0.66 at VV polarization from bare soil to biomass peak, and from 6.26 to 0.99 at VH polarization; (2) a dynamic method was proposed to quantify the change of N based on its strong correlation with NDVI, indicated by R-2 of 0.82 and 0.80 for VV and VH polarization, respectively; and (3) the overall root mean square error of normalized sigma(PQ) based on the newly-developed dynamic method is 0.51 dB, and this accuracy outperforms the original first-order cosine method (1.37 dB) and cosine square law method (1.08 dB) by about 63% and 53% on the whole. This study provides a dynamic framework for normalizing radar backscatter coefficient, improving the retrieval accuracy of land surface parameters from radar remote sensing.