A comparison between soil roughness statistics used in surface scattering models derived from mechanical and laser profilers

Mattia, F.; Davidson, Malcolm; Toan, Thuy Le; D'Haese, Cécile; Verhoest, Niko; Gatti, A.M.; Borgeaud, M.

doi:10.1109/tgrs.2003.813359

Cited by 85 publications

(74 citation statements)

References 15 publications

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“…Extensive surveys of natural terrain [26] show the following: 1) H tends to have a relatively narrow range (e.g., 0.3 to 0.7) about a value of 0.5, termed as "Brownian"; 2) there may be different values of H for different ranges of horizontal scale ("multifractaf behavior); and 3) there is often a plateau, or roll-off, in the value of <x(L) at a profile length anywhere from tens of centimeters to several meters. Some authors suggest hybrid surface descriptions incorporating large-scale tilts and smaller scale self-affine roughness [21] or a combination of scale-independent small-scale roughness and larger scale selfaffine behavior [22], [24], but these introduce unnecessary complexity relative to a multifractal approach (for example, with H approaching zero for a horizontal scale range over which is observed a nearly constant rms height).…”

Section: B Self-affine Descriptionmentioning

confidence: 99%

“…The problem arises in not knowing from relatively short profiles (e.g., the 1-m length used by many investigators) whether this roll-off has been reached, but for relatively smooth surfaces this is probably a reasonable assumption. Possible errors in estimating self-affine descriptive parameters from topographic data sets limited in number and/or sampling length are further addressed in [19], [24], [26], and [33].…”

Section: B Self-affine Descriptionmentioning

confidence: 99%

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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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Section: B Self-affine Descriptionmentioning

confidence: 99%

Section: B Self-affine Descriptionmentioning

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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“…Bryant et al, 2007;Lievens et al, 2009), or to a failure of the backscatter models in describing the complexity of surface roughness on the other hand (e.g. Mattia et al, 2003;Wagner et al, 2007). Most physically-based models such as the IEM assume that surface roughness is a single-scale random stationary process characterised in terms of the Root Mean Square (RMS) height, s, the correlation length, l, and an autocorrelation function (ACF) (Fung et al, 1992).…”

Section: Introductionmentioning

confidence: 99%

“…Most physically-based models such as the IEM assume that surface roughness is a single-scale random stationary process characterised in terms of the Root Mean Square (RMS) height, s, the correlation length, l, and an autocorrelation function (ACF) (Fung et al, 1992). However, natural surfaces are generally non-stationary and should be regarded as a superposition of single-and multiscale processes, respectively related to agricultural tillage effects and long-term shaping (Davidson et al, 2000;Mattia et al, 2003). As a consequence, the parameterisation of roughness in terms of s, l, and ACF is problematic (see Verhoest et al (2008) for a topical review) and often reported as being the main error source contributing to poor soil moisture retrieval results (e.g.…”

Section: Introductionmentioning

confidence: 99%

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Lievens

Verhoest

Keyser

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of fieldmeasured roughness. This paper analyses effective roughness parameters derived from C-and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that parameters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at Cand L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

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“…Various studies have contributed to the use of a more complete description of soil surface roughness for forward studies [22][23][24][25][26][27][28][29][30]. Zribi et al [26] introduced fractal and Brownian approaches to describe the correlation function, whereas Li et al [20] proposed a general power law description of roughness spectra.…”

Section: Introductionmentioning

confidence: 99%

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

Gorrab

Zribi

Baghdadi³

et al. 2015

Remote Sensing

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Abstract:The aim of this paper is to analyze the potential of X-band SAR measurements (COSMO-SkyMed and TerraSAR-X) made over bare soils for the estimation of soil moisture and surface geometry parameters at a semi-arid site in Tunisia (North Africa). Radar signals acquired with different configurations (HH and VV polarizations, incidence angles of 26° and 36°) are statistically compared with ground measurements (soil moisture and roughness parameters). The radar measurements are found to be highly sensitive to the various soil parameters of interest. A linear relationship is determined for the radar signals as a function of volumetric soil moisture, and a logarithmic correlation is observed between the radar signals and three surface roughness parameters: the root mean square height (Hrms), the parameter Zs = Hrms 2 /l (where l is the correlation length) and the parameter Zg = Hrms × (Hrms/l) α (where α is the power of the surface height correlation function). The highest dynamic sensitivity is observed for Zg at high incidence angles. Finally, the performance of different physical and semi-empirical backscattering models (IEM, Baghdadi-calibrated IEM and Dubois models) is compared with SAR measurements. The results provide an indication of the limits of validity of the IEM and Dubois models, for various radar configurations and roughness conditions. Considerable improvements in OPEN ACCESSRemote Sens. 2015, 7 748 the IEM model performance are observed using the Baghdadi-calibrated version of this model.

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A comparison between soil roughness statistics used in surface scattering models derived from mechanical and laser profilers

Cited by 85 publications

References 15 publications

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

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

Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

Potential of X-Band TerraSAR-X and COSMO-SkyMed SAR Data for the Assessment of Physical Soil Parameters

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