Modelisation of roughness and microwave scattering of bare soil surfaces based on fractal Brownian geometry

Zribi, Mehrez; Paille, J.; Ciarletti, Valérie; Taconet, O.; Boissard, P.; Chapron, M.; Rabin, Bryan

doi:10.1109/igarss.1998.691354

Cited by 8 publications

(6 citation statements)

References 2 publications

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“…As profile lengths increase, neither Gaussian nor exponential ACF are appropriate due to multi-scale features Davidson et al, 2000). A Brownian random fractional ACF was introduced by Zribi et al (1998) to incorporate multi-scale processes into the IEM (Mattia and Le Toan, 1999;Zribi et al, 2000). The empirical form of the fractal ACF for all horizontal positions x > 0 is given by:…”

Section: Surface Roughness Statisticsmentioning

confidence: 99%

Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications

Kornelsen

Coulibaly

2013

Journal of Hydrology

266

131

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Section: Surface Roughness Statisticsmentioning

confidence: 99%

Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications

Kornelsen

Coulibaly

2013

Journal of Hydrology

266

131

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“…[22,53,182-184]). Although the latter helps in better understanding the backscattering process, its application involves measurements of more complicated roughness parameters, causing difficulties for inverting the radar signal [52,54].…”

Section: Alternative Approaches To the Roughness Problemmentioning

confidence: 99%

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

Verhoest

Lievens

Wagner

et al. 2008

Sensors

288

234

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Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.

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“…This is not the case of an isotropic Gaussian surface but it holds, in the c c l sense, for "1/f"-processes called after their PSD that is proportional to β ν 1 . In situ measurements have also shown that natural soils are self-affine on a wide range of scale length [10]. Both properties are typical for Fractional Brownian Motion (FBM).…”

Section: Large Scale Roughnessmentioning

confidence: 94%

Polarimetric Surface Scattering Model for Surface Parameter Inversion

Breuer¹,

Hajnsek²,

Allain³

et al. 2003

SAR Image Analysis, Modeling, and Techniques V

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The sensitivity of microwave scattering to the dielectric properties and the geometrical structure of bare soil surfaces makes radar remote sensing a challenge for a wide range of environmental issues related to the condition of natural surfaces. Polarimetry plays an important role as it allows a direct or indirect separation of surface parameters, namely the volumetric soil moisture content and the geometrical surface properties [1,2]. The aim of this study is to investigate two different distributions used for the description of rough surfaces in order to extend the validity range of the Small Perturbation Model. The first approach considered a uniform distribution of angles corresponding to a certain surface roughness range, taking only the rms height into account [3,4]. The still opening question is the influence of the surface correlation length on the quantitative estimation of surface parameters which will be addressed in this study. For the investigation we chose a Gaussian and a Fractional Brownian Motion distribution which are accounting for the surface correlation length.

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Modelisation of roughness and microwave scattering of bare soil surfaces based on fractal Brownian geometry

Cited by 8 publications

References 2 publications

Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications

Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

Polarimetric Surface Scattering Model for Surface Parameter Inversion

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