Development of a snow wetness inversion algorithm using polarimetric scattering power decomposition model

Surendar, M.; Bhattacharya, Avik; Singh, Gulab; Yamaguchi, Yoshio; Venkataraman, G.

doi:10.1016/j.jag.2015.05.010

Cited by 24 publications

(14 citation statements)

References 35 publications

(32 reference statements)

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“…The principal properties that determine the three scattering components are surface roughness of the snow and soil, snow wetness and grain size [28]. The absorption loss is high for wet snow, so the scattering at the snow-soil interface can be neglected in those circumstances ( Figure 1; [28,29]). The absorption coefficient increases with LWC, and thus volume scattering varies inversely with snow wetness [30].…”

Section: Introductionmentioning

confidence: 99%

Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

Manickam

Barros

2020

Remote Sensing

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This study investigates the spatial signatures of seasonal snow in Synthetic Aperture Radar (SAR) observations at different spatial scales and for different physiographic regions. Sentinel-1 C-band (SAR) backscattering coefficients (BSC) were analyzed in the Swiss Alps (SA), in high elevation forest and grasslands in Grand Mesa (GM), Colorado, and in North Dakota (ND) croplands. GM BSC exhibit 10 dB sensitivity to wetness at small scales (~100 m) over homogeneous grassland. Sensitivity decreases to 5 dB in the presence of trees, and it is demonstrated that VH BSC sensitivity enables wet snow mapping below the tree-line. Area-variance scaling relationships show minima at ~100 m and 150–250 m, respectively, in barren and grasslands in SA and GM, increasing up to 1 km and longer in GM forests and ND agricultural fields. The spatial organization of BSC (as described by 1D-directional BSC wavelength spectra) exhibits multi-scaling behavior in the 100–1000 m range with a break at (180–360 m) that is also present in UAVSAR L-band measurements in GM. Spectral slopes in GM forested areas steepen during accumulation and flatten in the melting season with mirror behavior for grasslands reflecting changes in scattering mechanisms with snow depth and wetness, and vegetation mass and structure. Overall, this study reveals persistent patterns of SAR scattering variability spatially organized by land-cover, topography and regional winds with large inter-annual variability tied to precipitation. This dynamic scaling behavior emerges as an integral physical expression of snowpack variability that can be used to model sub-km scales and for downscaling applications.

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Section: Introductionmentioning

confidence: 99%

Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

Manickam

Barros

2020

Remote Sensing

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“…These variables are widely used in the snowmelt runoff model (SRM) for seasonal forecasts on runoff (Rango and Martinec, 1979). Active microwave remote sensing data based on fully polarimetric synthetic aperture radar (SAR) systems have been significantly incorporated into physical scattering models for estimating snow wetness at C-band (Shi and Dozier, 1995;Surendar et al, 2015). Singh and Venkataraman (2009) used the dual-polarimetric ASAR C-band SAR data for the determination of snow dielectric constant using a SAR based inversion model and estimated the snow density using the Looyenga's formula (Looyenga, 1965).…”

Section: Introductionmentioning

confidence: 99%

“…Singh and Venkataraman (2009) used the dual-polarimetric ASAR C-band SAR data for the determination of snow dielectric constant using a SAR based inversion model and estimated the snow density using the Looyenga's formula (Looyenga, 1965). Polarimetric decomposition techniques have also been efficiently utilized for estimation of snow wetness * Corresponding author using fully polarimetric Radarsat-2 SAR data by Surendar et al, (Surendar et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Surface Snow Wetness Using Sentinel-2 Multispectral Data

Varade

Dikshit

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.</p>

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“…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. The X-and C-band SAR measurements are sensitive to snow characteristics and have been widely used to monitor snowpack density and wetness [23,24]. It also has been demonstrated that the SD can be related to the co-polar phase difference (CPD) and anisotropy factors [25,26].…”

mentioning

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.…”

mentioning

confidence: 99%

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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Development of a snow wetness inversion algorithm using polarimetric scattering power decomposition model

Cited by 24 publications

References 35 publications

Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

Estimation of Surface Snow Wetness Using Sentinel-2 Multispectral Data

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

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