On the characterization of agricultural soil roughness for radar remote sensing studies

Davidson, Malcolm; Toan, Thuy Le; Mattia, F.; Satalino, G.; Manninen, Terhikki; Borgeaud, M.

doi:10.1109/36.841993

Cited by 248 publications

(169 citation statements)

References 16 publications

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“…In these experiments, 25 m long roughness profiles were acquired on agricultural fields with different tillage conditions. A thorough analysis of the complete data set of roughness profiles is reported by Davidson et al [2000]. In this paper, it is shown that over distances >5-10 m, multiscale features clearly characterize the surface correlation properties.…”

Section: Experimental Datamentioning

confidence: 99%

An analytical, numerical, and experimental study of backscattering from multiscale soil surfaces

Mattia¹,

Toan²,

Davidson³

2001

Radio Science

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Section: Experimental Datamentioning

confidence: 99%

An analytical, numerical, and experimental study of backscattering from multiscale soil surfaces

Mattia¹,

Toan²,

Davidson³

2001

Radio Science

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“…According to in situ measurements [32] and [33], the fractal dimension D of natural soil surface profiles is usually smaller than about 1.5.…”

Section: Numerical Results and Discussmentioning

confidence: 99%

“…(ii) Find X (j) 13b (for TM case) or X (j) 23b (for TE case) over Region 3 by the backward propagation principle; just like FBM/SAA for PEC rough surface, the FBM/SAA is used to calculate the backward components of induced currents along the CSSPOSII through small matrix D 33 (for TE case) or C 33 (for TM case).…”

Section: Fbm/saa For Dielectric Rough Surface With a Conducting Sspomentioning

confidence: 99%

Bistatic Scattering From Rough Dielectric Soil Surface With a Conducting Object With Arbitrary Closed Contour Partially Buried by Using the FBM/Saa Method

Li¹

2007

PIER

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Abstract-A hybrid approach of the forward-backward method (FBM) with spectral accelerate algorithm (SAA) and Monte Carlo method is developed in this paper. It is applied to numerical simulation of bistatic scattering from one-dimensional arbitrary dielectric constant soil surface with a conducting object with arbitrary closed contour partially buried under both the horizontal and vertical polarization tapered wave incidence at low grazing angle. The energy conservation has been checked for the FBM/SAA. Numerical simulations of bistatic scattering at low grazing angle have been discussed in this paper.

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“…The experimental soil profiles are proved to be locally fractal over a spatial range of a few centimetres (Zribi et al 2005b), the soil surfaces can be considered as fractal surfaces. Also, natural surfaces are often better described using random fractals instead of stationary single scale processes (Davidson et al 2000). It should be emphasized that the fractal dimension (D) can easily be estimated with the help of SAR scattering coefficient values.…”

Section: Introductionmentioning

confidence: 99%

The potential application of fractal approach for surface roughness retrieval: A study for simulated surfaces

Pant

Singh

Srivastava

2010

Geomatics, Natural Hazards and Risk

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Natural surfaces can be modelled with fractals because fractals properly account for scale invariance and self similarity of these surfaces. The well-known measure, i.e. fractal dimension (D), is the property used to describe the roughness of fractal surfaces. Retrieving the Earth's surface roughness with satellite images, particularly synthetic aperture radar (SAR) images, is an interesting and challenging task. Consequently, many researchers are using electromagnetic models, various inversion techniques, semi-empirical models to retrieve the roughness parameters, i.e. RMS surface height (s) and autocorrelation length (l). Most of the models require some a priori information or some given values to solve the equations and retrieve l and s. Uncertainty still exists to retrieve these parameters with minimum or no a priori information. Therefore, in this paper, the fractal dimension approach has been applied to correlate l and s with fractal properties for development of a surface parameter retrieval algorithm. For this purpose, 1500 synthetic surfaces for known l and s have been generated, and their fractal dimension has been computed. D has also been computed after introducing Gaussian and speckle noise to the generated surfaces. The analysis among D, l and s shows the potentiality of relationship among these parameters and is helpful in developing a relationship among them by which l and s can be retrieved. The values of l and s are retrieved with the help of a look-up table for the synthetic surfaces which can be extended for retrieval of roughness parameters from the SAR images.

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On the characterization of agricultural soil roughness for radar remote sensing studies

Cited by 248 publications

References 16 publications

An analytical, numerical, and experimental study of backscattering from multiscale soil surfaces

An analytical, numerical, and experimental study of backscattering from multiscale soil surfaces

Bistatic Scattering From Rough Dielectric Soil Surface With a Conducting Object With Arbitrary Closed Contour Partially Buried by Using the FBM/Saa Method

The potential application of fractal approach for surface roughness retrieval: A study for simulated surfaces

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