Snowpack Density Retrieval Using Fully Polarimetric TerraSAR-X Data in the Himalayas

Singh, Gulab; Verma, Ashutosh Kumar; Kumar, Sanjeev; Snehmani,; Ganju, Ashwagosha; Yamaguchi, Yoshio; Kulkarni, Anil V.

doi:10.1109/tgrs.2017.2725979

Cited by 29 publications

(17 citation statements)

References 24 publications

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“…On the other hand, the volume scattering in snowpack occurs due to multiple reflections of EM waves. However, double-bounce scattering is small as compared to the surface and volume scattering in case of snow [13,14], but in A glaciated region, the crevasse and extremely rough interfaces may lead to significant double-bounce scattering contribution, because microwave radar signals passes through dry snowpack and interacts with the glacier surface [27,37].…”

Section: Cmentioning

confidence: 99%

“…The information on snowpack parameters is also decisive for forecasting of snowmelt runoff [1] and avalanche activity [2] in snow-covered mountainous areas. Furthermore, local meteorological [3] and topographical [4] conditions control distribution of snow accumulation and spatial variability in snowpack parameters, for example and hence considers the local topography variation as required in the mountainous regions [13]. However, POLSAR data decomposition methods are independent of the frequency and incident angle, and they are then more suitable for snow-cover studies in mountainous terrains [14,17].…”

Section: Introductionmentioning

confidence: 99%

“…A frequent GPR survey (ground or air platform based) will not only be costly but also limited in spatial and temporal coverage. Conversely, remote sensing techniques using satellites is A cost-effective monitoring system that can be used frequently to retrieve snowpack accumulation (depth) variability in time and space [13][14][15][16][17][18][19].Passive microwave radiometers are used to generate global snow water equivalent (SWE) and snow depth (SD) at coarse resolutions [20][21][22], but fails to capture spatial variability at the catchment and glacier-system levels. Retrieving the SD is one of the critical problems faced by the global scientific community, and many efforts have been taken in the past to develop several SD retrieval algorithms using high-resolution spaceborne active microwave synthetic aperture radar (SAR) sensor measurements.…”

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confidence: 99%

“…The extinction coefficient calculated from the imaginary part of the dielectric constant enables the SD estimation from the X-band full polarization SAR data. However, selection of the correct model to relate the dielectric constant to wetness to retrieve the imaginary part of dielectric constant is difficult.The retrieval of snow wetness and density based on physical approaches using fully polarimetric SAR (POLSAR) data are well established [13][14][15][16][17][18][19]23,24]. However, there is no operational SD estimation algorithm available based on POLSAR data.…”

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confidence: 99%

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confidence: 99%

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Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements

Singh

Lavrentiev

Glazovsky

et al. 2019

Water

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The highly dynamic nature of snow requires frequent observations to study its various properties. Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of A snow depth (SD) inversion model for SD retrieval. Snow depth retrieved using ground penetrating radar (GPR) at 500 MHz over Austre Grønfjordbreen in the Svalbard region was used to understand the behaviour of certain polarimetric parameters. A significant correlation was found between field-measured SD and POLSAR parameters, namely coherence and normalized volume scattering power (R 2 = 0.84 and R 2 = 0.73, respectively.) Using the POLSAR scattering powers obtained from the six-component model-based decomposition (6SD), the heterogeneity and anisotropic behaviour in the firn areas are also explained. Further, based on the analyses shown in this work, A polarimetric parameter-based SD inversion algorithm have been proposed and validated. The univariate model with co-polarization coherence has the highest correlation (R 2 = 0.84, Root Mean Square Error (RMSE) = 0.18). We have even tested several multivariate models for the same, to conclude that A combination of coherence, normalized volume and double-bounce scattering have A high correlation with SD (R 2 = 0.84, RMSE = 0.18). Additionally, temporal and spatial variability in SD was also observed from three polarimetric SAR images acquired between 4 April 2015 and 15 May 2015 over the Western Nordenskiöld Land region. Increase in snow depth corresponding to snow precipitation events were also detected using the POLSAR data.Water 2020, 12, 21 2 of 23 depth and density. However, field-based single-point measurements of snow depth may not be A representative of the entire distribution of snowpack accumulation over A large area. Previous investigations [5-7] over Svalbard have indicated that deposition of snow on the glaciers in the region is highly diverse in space due to varying topoclimatological conditions. Snow deposition increases with elevation, but it is also observed that this altitudinal trend is highly variable due to the drifting wind and other local topoclimatological factors in Svalbard. Recent investigations [8][9][10][11][12] suggest that the altitudinal gradient of snow accumulation has decreased, and the average snow thickness on the Austre Grønfjordbreen has increased by 17 cm compared to average snow thickness in 1979. Spatial variability has also increased over the region. Hence, continuous monitoring of the spatial and temporal variability of snow cover depth is the need of the hour, which can be used to understand the state of the glacier and the changes and processes taking place in it.An alternative method to field-based manual measurements of snow depth is A ground penetrating radar (GPR) survey. This survey is an optimal technique to provide A spatial profile over large areas for understanding snow depth variability [8,9]. Apart from the spatial variability, snow accumulatio...

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