ИСТИНА

Near surface snow density affects energy and surface mass budget. The near surface density is influenced by snowfall, snowmelt, wind drift and snow metamorphism. Snow density is also an important parameter for GPR based snow depth retrieval. However, it is poorly studied, snow density should be estimated. Satellite remote sensing promises great potential in the snow study due to its repetitive monitoring capability and synoptic coverage. Microwave radar sensors are ideally suited for space imaging because almost all the sensors are weather independent, and microwaves propagate through the atmosphere with little deteriorating effects due to clouds, storms, rain, fog, aerosol saturation and haze. Radar remote sensing with its sensitivity to the dielectric and geometric characteristics of objects, and potential to acquire subsurface information, is one of the most promising approaches for predicting the snowpack density. Moreover, remote sensing with polarimetric radar offers an efficient and reliable means of collecting information required to extract snowpack layers which is not possible with optical methods. The density retrieval algorithm utilizes fully polarimetric ALOS-2/PALSAR-2 data sets acquired on April 04, 2015, field observations, and other ancillary information. The algorithm also involves the utilization of generalized volume scattering parameter from the generalised Singh-Cloude decomposition model. The estimation of snow density using fully polarimetric SAR data are based on physical modelling of the permittivity. The volume permittivity of the snowpack is derived from the Fresnel transmissivity coefficients. The retrieved permittivity is used to determine the snow density using empirical model. Nine snow pits (field data) measurements were carried out within near-real time of satellite passing over the area of Austre Grønfjordbreen, Western Nordenskiöld Land region, for collecting density. These field observations are further used for validation of the results obtained from the inversion algorithm. It is also found that the model-estimated snowpack density is highly congruent with the field-measured snowpack density. The mean absolute error of snowpack density is found to be 23.8 kg/m3, which are well within the range of acceptable value. Acknowledgments: this study was supported by the BRICS Science, Technology and Innovation Framework Programme (BRICS STI FP) for project ‘Col-GaSS’ from the Department of Science and Technology, India (project no. DST/IMRCD/BRICS/PilotCall1/Col-GaSS/2017 (G)) and the Russian Foundation for Basic Research, Russia (Project no. 17-55-80107).