| Summary: | The backscatter coefficients of Synthetic Aperture Radar (SAR) images that observe the Greenland Ice Sheet (GrIS) are incidence angle dependent, which impedes subsequent applications, such as monitoring its surface melting. Therefore, backscatter intensities with varying incidence angles should be normalized. This study proposes an incidence angle normalization method for dual-polarized Sentinel-1 images for GrIS. A multiple linear regression model is trained using the ratio between the backscatter coefficient differences and the incidence angle differences of quasi-simultaneously observed ascending and descending image pairs. Regression factors include the geographical position and elevation. The precision evaluation to the ascending and descending images suggests better normalization results than the widely used cosine-square correction method for horizontal transmit and horizontal receive (HH) images and a slight improvement for horizontal transmit and vertical receive (HV) images. Another dataset of GrIS Sentinel-1 mosaics in four 6-day repeating periods in 2020 is also tested to evaluate the proposed method and yields similar results. For HH images, the proposed method performs better than the cosine-square method, reducing 0.34 dB RMSE on average. The overall accuracy of our proposed method is 0.77 and 0.75 dB for HH and HV images, respectively. The proposed incidence angle normalization method can benefit the application of wide-swath SAR images to the study of large-scale and long-period observation on GrIS.
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