The backscatter coefficient of synthetic aperture radar (SAR) images is highly related to its incidence angle and surface characteristics. For analysis basing on the backscatter coefficient, it is necessary to correct the influence of incidence angle on the backscattered signal when analyzing wide-swath SAR images, for example, monitoring ice sheets and glaciers. The cosine square correction method is a commonly used method for such purpose, which assumes the snow and ice surface are Lambertian when normalizing the SAR images backscatter coefficient scattered by the ice and snow surface to a reference incidence angle. However, presuming scattering radar signal equally to all directions lying in the half-space adjacent to the surface is unreasonable for the Greenland Ice Sheet (GIS), as the dry-snow zone is basically transparent to C-band signal, volume scattering dominates percolation zone, and specular scattering dominates wet-snow zone and bare-ice zone. In this research, we proposed a backscatter coefficient normalization algorithm based on linear regression to backscatter coefficient differences and incidence angle differences of two quasi-simultaneous observations, usually one obtained in ascending tracks and another in descending tracks. It assumes that these two Sentinel-1 images share the same backscatter characteristics on the GIS, and only incidence angle differences induce backscatter coefficient differences. Considering that the backscatter characteristics of the GIS surface vary with seasons and altitudes, which leads to variations of the regression coefficients, we introduced these two factors to evaluate the different regression coefficients. Then backscatter coefficient of Sentinel-1 dual-polarization SAR images can be normalized to a reference angle according to the regression coefficient at the given altitude and season. In the model training part in this study, the regression coefficients were derived with Sentinel-1 images obtained in northwest Greenland, where the overlapping area between ascending and descending acquisitions was large enough to cover different glacier zones. In the testing part, we applied our proposed backscatter coefficient correction method with the derived regression coefficients to the Sentinel-1 images in both IW and EW mode observing most areas of the GIS, and compared the backscatter coefficients at the overlapping area. The results showed that the proposed method had a better performance than the cosine-square method for correcting the co-polarization images, and similar for correcting the cross-polarization images. For IW mode imagery, RMSEs were lower than 0.7 dB, 1.0 dB, 2.0 dB, 1.0 dB for Jan, Apr, Jul, and Oct respectively; for EW mode imagery, RMSEs were lower than 1.4 dB, 1.9 dB, 2.9 dB, 2.9 dB for Jan, Apr, Jul, and Oct respectively. Our proposed method roughly showed lower RMSE for cross-polarization SAR images than co-polarization SAR images. We performed our method to the same data source of NSIDC-0723, Greenland Image Mosaic from Sentinel-1A and 1B v3, and yielded with SAR imagery mosaics without sharp changes of backscatter coefficient among adjacent orbits. The proposed backscatter coefficient normalization method can benefit in correcting the backscatter coefficient of wide-swath Sentinel-1 SAR images for the GIS and reduce the uncertainty of the subsequent applications including SAR images mosaicking and surface freeze-thaw monitoring.