Snow Cover Characterization includes qualitative and quantitative characterization of snow. The qualitative characterization of snow aims to identify different types of snow based on the magnitude of reflectance and liquid water content. To achieve this, different methods based on machine learning using hyperspectral data and a combination of multispectral and polarimetric synthetic aperture radar data (SAR) data are investigated. The quantitative characterization of snow is carried out based on the estimates of snow geophysical parameters, which are derived using various frameworks.
Dry/wet snow modelled overlay over reference
Comparison of snow liquid water content derived from two models
Studies of glacier dynamics including estimation of glacier ice-volume thickness, surges, and mapping of glacier facies. Understanding the relationship between the glacier dynamics and the regional hydrology.
The hydrological potential of snow is investigated using the snow water equivalent which is the product of snow density and snow depth. In the previous work, different methods were developed for the estimation of snow geophysical parameters including density. The density estimates from these methods are used with the snow depth estimated from a novel method based on DInSAR processing of Sentinel-1 dual polarimetric SAR data for the determination of snow density.
The hydrological potential of snow is investigated using the snow water equivalent which is the product of snow density and snow depth. In the previous work, different methods were developed for the estimation of snow geophysical parameters including density. The density estimates from these methods are used with the snow depth estimated from a novel method based on DInSAR processing of Sentinel-1 dual polarimetric SAR data for the determination of snow density.
Figure shows the differences in the slope gradient conventional versus the directional filter used for orographic correction.