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 (ρs) 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.
- Snow depth
- Snow water equivalent (SWE)
- Synthetic aperture radar (SAR)