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The studies on snow depth comprise a crucial area of research in the Indian Himalayas, where the seasonal snow cover primarily drives the rivers and significant water resources. In this paper, the initial estimates of the line of sight displacement obtained using differential interferometric phase in VV and VH polarizations using Sentinel-1 bi-temporal dual polarimetric SAR data corresponding to snow covered and snow free land cover, are improved by applying bias corrections for the snow phase and for residual errors in displacement derived from the corrected snow phase. The bias for the snow phase is computed from the observed phase in VV and VH polarizations for the snow free area and the bias for the residual errors is computed by observing the stationary pixels identified in the snow free area using a digital elevation model. The snow depth is computed as a weighted sum of the corrected displacements in the VV and VH polarization, with the weights derived using the local incidence angle. The snow depth results based on the proposed approach was evaluated with respect to field measurements and a coefficient of determination of 0.628 was observed with an improvement of ~0.4 as compared to the displacement observed in the VV and VH channel using the conventional method. [ABSTRACT FROM AUTHOR]

The physical properties of a snowpack strongly influence the emissions from the substratum, making snow property retrievals feasible by means of the surface brightness temperature observed by passive microwave sensors. Depending on the spatial resolution observed, time series records of daily snow coverage and critical snowpack properties such as snow depth (SD) and snow water equivalent (SWE) could be helpful in applications ranging from modelling snow variations for water resources management in a catchment to global climatologic studies. However, the challenge of including spaceborne SWE products in operational hydrological and hydroclimate modelling applications is very demanding with limited uptake by these systems, mostly attributed to insufficient SWE estimation accuracy. The root causes of this challenge include the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process caused by uncertainties with the forward emission modelling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of developed and adopted retrieval methodologies can provide the clarity needed to enhance our knowledge in this field. In this paper, we review snow depth and SWE retrieval methods using PM observations, taking only dry snow retrieval processes into consideration. Snow properties using PM observations can be modelled by purely empirical relations based on underlying physical processes, and SD and SWE can be estimated by regression-based approaches. Snow property retrievals have been refined gradually throughout four decades use of PM observations in tandem with better understanding of physical processes, inclusion of better snowpack parameterizations, improved uncertainty analysis frameworks, and applying better inversion algorithms. Studying available methods, we conclude that snowpack parameterization is key to accurate retrieval. By improving retrieval algorithm architectures to better capture dynamic snowpack evolution processes, SWE estimates are likely to improve. We conclude that this challenge can be addressed by coupling emission models and land surface models or integrating weather-driven snowpack evolution into emission models and performing inversion in a Bayesian framework. [ABSTRACT FROM AUTHOR]