Modeling synthetic aperture radar (SAR) scattering from a seasonally varying snow-covered sea ice volume at 5.3 and 9.25 GHz

Barber, David G.; LeDrew, E.

doi:10.3402/polar.v13i1.6679

Cited by 6 publications

(4 citation statements)

References 21 publications

(26 reference statements)

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“…Alternatively, backscatter models can be used to identify the likely response of backscatter to environmental changes. Surface scattering models typically parameterize the roughness of the surface relative to the radar wavelength, the imaging geometry and permittivity (Barber and LeDrew, 1994; Fung 1994). The complexity of such models is in part represented by the parameterization of the surface roughness.…”

Section: Backscatter Processes and Modellingmentioning

confidence: 99%

“…Solutions are normally based on either a Gaussian correlation function in simpler models or a Fourier transform in the more complex models. Here we use a simple geometric optics formulation of a Kirchhoff scattering model to estimate backscatter from a rough snow surface (Barber and LeDrew, 1994; Guneriussen, 1997), and the small-perturbation model (SPM) for smoother surfaces (Fung, 1994; Rees, 2001).…”

Section: Backscatter Processes and Modellingmentioning

confidence: 99%

“…This correction introduces an optically equivalent grain-size which is usually larger than the physical grain-size in snow but smaller than the grain-size in firn. The integration of the models is weighted by the transmissivity of the medium and the propagation loss to account for extinction within the volume (Barber and LeDrew, 1994; Guneriussen, 1997). Surface scattering between snow and firn layers is ignored (Forster and others, 1999).…”

Section: Backscatter Processes and Modellingmentioning

confidence: 99%

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Problems with the retrieval of glacier net surface balance from SAR imagery

Brown

Klingbjer

Dean³

2005

Ann. Glaciol.

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ABSTRACT. There are relatively few comparisons between synthetic aperture radar (SAR) observations and glacier mass-balance measurements. More typically, SAR has been deployed to identify changes in the end-of-summer snowline and other facies boundaries. In this paper, we analyze the geophysical processes affecting SAR amplitude data over two Arctic glacier systems in northern Scandinavia to assess the potential of SAR observations for the retrieval of surface balance parameters. Using a backscatter model and in situ data, we identify the controls on SAR imagery in terms of mass-balance measurement and discuss the glaciological parameters which can reasonably be derived from multi-temporal SAR data. Our results show that amplitude SAR imagery, in the absence of in situ measurements, is not capable of providing meaningful mass-balance data. We show that backscatter from temperate glaciers is affected primarily by snow grain-size and density, and therefore processes such as firnification or depth-hoar formation can complicate the analysis of imagery. We conclude that SAR imagery over temperate glaciers can provide valuable proxy information but not direct mass-balance terms.

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Section: Backscatter Processes and Modellingmentioning

confidence: 99%

Section: Backscatter Processes and Modellingmentioning

confidence: 99%

Section: Backscatter Processes and Modellingmentioning

confidence: 99%

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Problems with the retrieval of glacier net surface balance from SAR imagery

Brown

Klingbjer

Dean³

2005

Ann. Glaciol.

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“…The "original layer" consisted of a high-density, low-salinity, medium-size snow grain layer, and the "new layer" consisted of a medium-density, zero-salinity, small-size snow grain layer. Further details of the snow-sampling methods are available elsewhere [Barber and LeDrew, 1994b;Barber et al, 1994].…”

Section: -Min Averages On Campbell Scientific Instruments Data Loggmentioning

confidence: 99%

On the relationship between energy fluxes, dielectric properties, and microwave scattering over snow covered first‐year sea ice during the spring transition period

Barber¹,

Papakyriakou²,

LeDrew³

1994

J. Geophys. Res.

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In this research we investigate the seasonal nature of the co-variability in surface energy balance variables, volume dielectrics, and microwave scattering (ERS 1) of a snow-covered first-year sea ice surface during a spring transitional period. Variables required to derive the components of the energy balance and dielectric properties were measured during the Seasonal Sea Ice Monitoring and Modeling site in the Canadian Arctic Archipelago in 1992. We observed that both the energy terms and dielectric properties followed a pattern similar to the total relative scattering cross section (oø) over the seasonal transition from winter to spring. We explain this relationship through the impact of surface fluxes on dielectric and geophysical properties of the snow-covered first-year sea ice. We speculate that ice surface scattering dominated the total scattering cross section oø prior to Julian day 120 and that the snow volume contributed an increasing amount of scattering to •ø over the remainder of the season. From a multivariate statistical analysis we find that the surface temperature Ts and the net shortwave energy flux K* explained a statistically significant amount of the variation observed in the seasonal evolution of •o. An inverse relationship existed between both T s and K* relative to •,o and the influence of T s was approximately twice that of K* in explaining the observed variation in •ø. 22,401 22,402 BARBER ET AL.: ENERGY FLUXES AND MICROWAVE SCATTERING Methods Observed Variables Data used in this investigation were collected in 1992 during the Seasonal Sea Ice Monitoring and Modeling Site (SIMMS'92) experiment. SIMMS is a multiyear field experiment conducted annually in the areas adjacent to Cornwallis Island in the Canadian Arctic Archipelago [LeDrew and Barber, 1994]. Data used here were obtained from ET AL.' ENERGY FLUXES AND MICROWAVE SCATTERING Vant, M.R., R.O. Ramseier, and V. Makios, The complex-dielectric constant of sea ice at frequencies in the range 0.1 to 40 GHz, J. Appl. Phys., 49(3), 1264-1280, 1978. Wiscombe, W.J., and S.G. Warren, A model for the spectral albedo of snow, I, Pure snow, J. Atmos. Sci.

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