收稿：2021-04-26，
纸质出版：2023-09-07
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合成孔径雷达（SAR）影像的后向散射系数与电磁波入射角以及地表特性密切相关，因此应用宽幅SAR影像对格陵兰冰盖开展研究分析时，应订正入射角对后向散射回波信号的影响。目前主流的针对冰盖表面入射角改正算法为余弦平方法，该方法假设冰雪表面为朗伯体对SAR影像后向散射系数进行改正，但将冰体假设为朗伯体存在明显不合理之处。本文提出了一种基于线性回归的后向散射系数改正算法，该方法假设近同时获取的格陵兰冰盖Sentinel-1双极化SAR影像后向散射特性保持不变，后向散射系数的差异仅与入射角差异相关。通过寻找后向散射系数与入射角之间的定量关系，获得归一化的双极化SAR影像后向散射系数。考虑到在不同季节和海拔的格陵兰冰盖冰雪表面后向散射特性不同，本文引入了海拔和季节两个参数，估算了不同季节与海拔条件下的归一化改正系数。将本文提出的后向散射系数改正方法应用于格陵兰Sentinel-1影像，结果表明本文提出的方法对于同极化影像的改正优于余弦平方法，而交叉极化影像改正效果与余弦平方法相近。本研究提出的改正方法可以更好地改正格陵兰冰盖的Sentinel-1宽幅SAR影像的后向散射系数，降低后续应用的不确定性。
The backscatter coefficient of Synthetic Aperture Radar (SAR) images is highly related to its incidence angle and surface characteristics. For analysis based on the backscatter coefficient
the influence of incidence angle on the backscattered signal must be corrected when analyzing wide-swath SAR images
for example
monitoring ice sheets and glaciers. The cosine square correction method is commonly used 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) because the dry-snow zone is transparent to the C-band signal
volume scattering dominates percolation zone
and specular scattering dominates the wet-snow zone and the bare-ice zone.
In this paper
a backscatter coefficient normalization algorithm is proposed 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. These two Sentinel-1 images share the same backscatter characteristics on the GIS
and only incidence angle differences induce backscatter coefficient differences. Considering the backscatter characteristics of the GIS surface vary with seasons and altitudes
which leads to variations of the regression coefficients
these two factors are introduced to evaluate the different regression coefficients. Then
the 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 paper
the regression coefficients are derived with Sentinel-1 images obtained in northwest Greenland
where the overlapping area between ascending and descending acquisitions is large enough to cover different glacier zones. In the testing part
our proposed backscatter coefficient correction method with the derived regression coefficients is applied to the Sentinel-1 images in IW and EW modes observing most areas of the GIS
and the backscatter coefficients at the overlapping area are compared.
Results show the proposed method performs better than the cosine-square method for correcting the co-polarization images and similarly for correcting the cross-polarization images. For IW mode imagery
RMSEs are lower than 0.7
1.0
2.0
and 1.0 dB for Jan
Apr
Jul
and Oct
respectively. For EW mode imagery
RMSEs are lower than 1.4
1.9
2.9
and 2.9 dB for Jan
Apr
Jul
and Oct
respectively. Our proposed method shows lower RMSE for cross-polarization SAR images than co-polarization SAR images. Our method is performed in the same data source of NSIDC-0723
Greenland Image Mosaic from Sentinel-1A and 1B v3
and yields SAR imagery mosaics without sharp changes of backscatter coefficient among adjacent orbits.
The proposed backscatter coefficient normalization method can benefit correcting the backscatter coefficient of wide-swath Sentinel-1 SAR images for the GIS and reduce the uncertainty of the subsequent applications including SAR image mosaicking and surface freeze–thaw monitoring.
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