This paper deals with the retrieval of snow depth (SD) and snow water equivalent (SWE) using dual-polarization (HH-VV) synthetic aperture radar (SAR) data. The effect of different snowpack conditions on the SD and SWE inversion accuracy was demonstrated by using three TerraSAR-X acquisitions. The algorithm is based on the relationship between the SD, the co-polar phase difference (CPD), and particle anisotropy. The Dhundi observatory in the Indian Himalaya was selected as a validation test site where a field campaign was conducted for ground truth measurements in January 2016. Using the field measured values of the snow parameters, the particle anisotropy has been optimized and provided as an input to the SD retrieval algorithm. A spatially variable snow density (ρ s) was used for the estimation of the SWE, and a temporal resolution of 90 m was achieved in the inversion process. When the retrieval accuracy was tested for different snowpack conditions, it was found that the proposed algorithm shows good accuracy for recrystallized dry snowpack without distinct layering and low wetness (w). The statistical indices, namely, the root mean square error (RMSE), the mean absolute difference (MAD), and percentage error (PE), were used for the accuracy assessment. The algorithm was able to retrieve SD with an average MAE and RMSE of 6.83 cm and 7.88 cm, respectively. The average MAE and RMSE values for SWE were 17.32 mm and 21.41 mm, respectively. The best case PE in the SD and the SWE retrieval were 8.22 cm and 18.85 mm, respectively.

中文翻译：

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利用双极化SAR数据反演雪深和雪水当量

本文使用双极化（HH-VV）合成孔径雷达（SAR）数据处理雪深（SD）和雪水当量（SWE）。通过使用三个TerraSAR-X采集，证明了不同积雪条件对SD和SWE反演精度的影响。该算法基于SD，同相相位差（CPD）和粒子各向异性之间的关系。印度喜马拉雅山的Dhundi天文台被选为验证测试​​场，于2016年1月在该场进行了实地测量，用于实地测量。利用雪参数的实测值，优化了颗粒各向异性并将其作为输入提供给SD检索算法。使用空间可变的雪密度（ρs）估算SWE，反演过程获得了90 m的时间分辨率。当针对不同的积雪条件测试取回精度时，发现所提出的算法对于重结晶的干燥积雪显示出良好的精度，而没有明显的分层和低湿度（w）。统计指标，即均方根误差（RMSE），平均绝对差（MAD）和百分比误差（PE），用于准确性评估。该算法能够分别以6.83 cm和7.88 cm的平均MAE和RMSE检索SD。SWE的平均MAE和RMSE值分别为17.32 mm和21.41 mm。SD和SWE检索中最好的PE分别为8.22 cm和18.85 mm。结果发现，所提出的算法对于重结晶的干燥积雪显示出良好的精度，而没有明显的分层和低湿度（w）。统计指标，即均方根误差（RMSE），平均绝对差（MAD）和百分比误差（PE），用于准确性评估。该算法能够分别以6.83 cm和7.88 cm的平均MAE和RMSE检索SD。SWE的平均MAE和RMSE值分别为17.32 mm和21.41 mm。SD和SWE检索中最好的PE分别为8.22 cm和18.85 mm。结果发现，所提出的算法对于重结晶的干燥积雪显示出良好的精度，而没有明显的分层和低湿度（w）。统计指标，即均方根误差（RMSE），平均绝对差（MAD）和百分比误差（PE），用于准确性评估。该算法能够分别以6.83 cm和7.88 cm的平均MAE和RMSE检索SD。SWE的平均MAE和RMSE值分别为17.32 mm和21.41 mm。SD和SWE检索中最好的PE分别为8.22 cm和18.85 mm。用于准确性评估。该算法能够分别以6.83 cm和7.88 cm的平均MAE和RMSE检索SD。SWE的平均MAE和RMSE值分别为17.32 mm和21.41 mm。SD和SWE检索中最好的PE分别为8.22 cm和18.85 mm。用于准确性评估。该算法能够分别以6.83 cm和7.88 cm的平均MAE和RMSE检索SD。SWE的平均MAE和RMSE值分别为17.32 mm和21.41 mm。SD和SWE检索中最好的PE分别为8.22 cm和18.85 mm。