Diffuse component of lunar radar echoes

Burns, Alan A.

doi:10.1029/jb074i027p06553

Cited by 16 publications

(7 citation statements)

References 19 publications

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“…Hagfors [1967] pointed out that rocks were responsible for the diffuse scattering component, and that their nonspherical shape led to the observed partial polarization. Burns [1969] suggested that most of the scattering resulted from single scattering by rocks that were either on the lunar surface or buried in the regolith. Thompson et al [1970] modeled the single scattering behavior of rocks using Mie theory and suggested that models with single scattering by surface rocks or multiple scattering between buried rocks were capable of accounting for several observed properties of the diffuse component.…”

Section: Introductionmentioning

confidence: 99%

Modeling polarimetric radar scattering from the lunar surface: Study on the effect of physical properties of the regolith layer

Wieczorek

Heggy

2011

J. Geophys. Res.

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[1] A theoretical model for radar scattering from the lunar regolith using the vector radiative transfer theory for random media has been developed in order to aid in the interpretation of Mini-SAR data from the Chandrayaan-1 and Lunar Reconnaissance Orbiter missions. The lunar regolith is represented as a homogeneous fine-grained layer with rough upper and lower parallel interfaces that possesses embedded inclusions with a different dielectric constant. Our model considers five scattering mechanisms in the regolith layer: diffuse scattering from both the surface and subsurface, volume scattering from buried inclusions, and the interactions of scattering between buried inclusions and the rough interfaces (both the lunar surface and subsurface). Multiple scattering between buried inclusions and coherent backscatter opposite effect are not considered in the current model. The modeled radar scattering coefficients are validated using numerical finite difference time domain simulations and are compared with incident angle-averaged Earth-based radar observations of the Moon. Both polarized and depolarized radar backscattering coefficients and the circular polarization ratio (CPR) are calculated as a function of incidence angle, regolith thickness, surface and subsurface roughness, surface slope, abundance and shape of buried rocks, and the FeO+TiO 2 content of the regolith. Simulation results show that the polarized (opposite sense) radar echo strength at S and X bands is mostly dominated by scattering from the rough surface and buried rocks, while the depolarized (same sense) radar echo strength is dominated by scattering from buried rocks or ice inclusions. Finally, to explore the expected polarimetric signature of ice in the polar permanently shadowed areas, four parametric regolith models are considered and the possibility of detecting diffuse ice inclusions by the CPR is addressed. Our study suggests that detection of ice inclusions at the lunar poles using solely the CPR will be difficult given the small dielectric contrast between the regolith and ice.Citation: Fa, W., M. A. Wieczorek, and E. Heggy (2011), Modeling polarimetric radar scattering from the lunar surface: Study on the effect of physical properties of the regolith layer,

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Section: Introductionmentioning

confidence: 99%

Modeling polarimetric radar scattering from the lunar surface: Study on the effect of physical properties of the regolith layer

Wieczorek

Heggy

2011

J. Geophys. Res.

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“…CT HV/ CT VV = 1/3) [39], [47], Such scattering features might correspond to rock edges, ground surface cracks, or other topographic discontinuities. The HV-polarized returns emerge as a second-order component in most theoretical treatments of scattering and are sometimes interpreted as solely arising from multiple scattering (e.g., [9]).…”

Section: B Empirical Models Based On Scale-dependent Roughness Parammentioning

confidence: 99%

Scale-Dependent Surface Roughness Behavior and Its Impact on Empirical Models for Radar Backscatter

Campbell

2009

IEEE Trans. Geosci. Remote Sensing

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Abstract-One goal of radar remote sensing is the extraction of terrain statistics and surface dielectric properties from backscatter data for some range of wavelengths, incidence angles, and polarizations. This paper addresses empirical approaches used to estimate terrain properties from radar data over a wider range of roughness than permitted by analytical models. Many empirical models assume, at least implicitly, that roughness parameters like rms height or correlation length are independent of the horizontal length scale over which they are measured, in contrast to recent surveys of natural terrain, which show that self-affine, or powerlaw scaling, between horizontal scale and roughness statistics is very common. The rms slope at the horizontal scale of the illuminating wavelength s(A) is directly related to the variogram or structure function of a self-affine surface, can be readily obtained from field-measured topography, and, when used in an empirical model, avoids the need for arbitrary wavelength-dependent terms. To facilitate comparison with earlier approaches, an expression that links the rms height at some profile length with the rms (Allan) deviation at an equivalent horizontal sampling interval is obtained from numerical simulations. An empirical model for polarimetric scattering as a function of 6'(A) at 35°-60° incidence from smooth to rugged lava surfaces is derived and compared with earlier models for backscatter from modestly rough soil surfaces. The asymptotic behavior of polarization ratios for the lava flows suggests that the depolarization of linear-polarized illuminating signals occurs as a first-order process, likely through single scattering by rock edges or other discontinuities, rather than as the solely multiple-scattering effect predicted by some analytical models. Efforts to fully understand radar scattering from geological surfaces need to incorporate wavelength-scale roughness, perhaps through computational simulations.

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“…Much of the early work in this field focused on the Moon, for which Surveyor panoramic photos provide ground-truth measurements of the surface rock population. Burns (1969) made one of the earliest efforts to relate radar backscatter to regolith subsurface properties. Thompson et al (Î970) and Pollack and Whitehill (1972) used a Mie scattering model for the regolith to determine the likely roles of surface and subsurface scattering in 70-cm radar echoes ;Campbell etal.…”

Section: Introductionmentioning

confidence: 99%

Radar Backscatter from Mars: Properties of Rock-Strewn Surfaces

Campbell¹

2001

Icarus

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Diffuse component of lunar radar echoes

Cited by 16 publications

References 19 publications

Modeling polarimetric radar scattering from the lunar surface: Study on the effect of physical properties of the regolith layer

Modeling polarimetric radar scattering from the lunar surface: Study on the effect of physical properties of the regolith layer

Scale-Dependent Surface Roughness Behavior and Its Impact on Empirical Models for Radar Backscatter

Radar Backscatter from Mars: Properties of Rock-Strewn Surfaces

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