An empirical model and an inversion technique for radar scattering from bare soil surfaces

Oh, Yisok; Sarabandi, Kamal; Ulaby, F. T.

doi:10.1109/36.134086

Cited by 1,130 publications

(618 citation statements)

References 8 publications

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“…Again, Zribi and Dechambre (2002) reported that a number of analytical models have been proposed, but the inversion approach to retrieve roughness parameters was not accurately developed. As mentioned in the literature (Oh et al 1992, Dubois et al 1995, Zribi et al 2000, Baghdadi et al 2002, Singh 2005, Zribi et al 2005a, b, Rahman et al 2007) many inversion studies for roughness parameter estimation are based on the relation between the scattering coefficient and roughness parameters. On the other hand, researchers are using alternative approaches such as the fractal and power law technique for describing natural surfaces.…”

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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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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“…Hence, a large number of experimental investigations on the backscattering of electromagnetic fields from rough surfaces have been conducted and reported in the last 50 years. These investigations enabled, on the one hand, the improvement of the theoretical models to more accurately assess the roughness and the humidity (via the dielectric constant) of soil and, on the other hand, to develop of new empirical or semi-empirical models, such as the Oh-model [2], or the Dubois model [3]. However, few controlled experimental measurements have been performed for the forward scattering case or the bistatic case.…”

Section: Introductionmentioning

confidence: 99%

Surface Parameter Estimation Using Bistatic Polarimetric X-Band Measurements

Khadhra

2012

PIER B

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Abstract-The main purpose of this paper is to separately estimate the important surface parameters (soil moisture and roughness) by using full polarimetric bistatic measurements. The results provide a basis for new satellite application of future bistatic measurement systems such as the TanDEM-X satellite mission.Initially, bistatic X-band measurements, which have been recorded in the Bistatic Measurement Facility (BMF) at the DLR Oberpfaffenhofen, Microwaves and Radar Institute, will be presented. The bistatic measurement sets are composed of soils with different well-known statistical roughness scales and different moistures. The BMF has been calibrated using the Isolated Antenna Calibration Technique (IACT). The validation of the calibration was achieved by measuring the reflectivity of fresh water. In the second part, the assessment of the surface parameters (soil moisture and surface roughness) using the well calibrated data introduced in the former related part, will be detailed. The validation of the specular algorithm by estimating the soil moisture of two surfaces with different roughness scales will be reported. Additionally, a new technique using the coherent term of the Integral Equation Method (IEM) to estimate the soil roughness will be presented, as well as the sensitivity of phase and reflectivity with regard to moisture variation and therefore the penetration depth was evaluated. Current results demonstrate a non-linear relationship

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“…Under this approximation, both co-and cross-polarization ratios can be derived as Here, ε is the dielectric constant, θ is the incidence angle, and y h is the azimuth slope. The ground azimuth slope causes the polarization rotation as shown in equation (1). Unlike the SPM, the cross polarization component can be obtained using the tilted Bragg approximation as expressed in (2).…”

Section: A Soil Moisture Estimation Algorithm Using the Tilted Brmentioning

confidence: 99%

“…Oh et al [1] developed an empirical model based on polarimetric radar measurements and the scattering behavior in limiting cases. Another empirical algorithm for estimating soil moisture was derived by Dubois et al using scatterometer data [2].…”

Section: Comparison With Existing Soil Moisture Algorithmsmentioning

confidence: 99%

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A soil moisture algorithm using tilted Bragg approximation

Kim¹,

Zyl²,

Shi³

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)

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A successful soil moisture algorithm using radar data must identify the soil moisture effect and the surface roughness dependence explicitly since rough surface scattering depends on both the roughness and the dielectric constant of an imaged surface. For bare surfaces, several algorithms have been developed to estimate soil moisture using polarimetric radar data. These algorithms were empirically derived from either experimental data or numerical data instead of starting from rough surface scattering theories. In this paper, we present a soil moisture algorithm theoretically derived using the tilted Bragg approximation. With appropriate approximations using the tilted Bragg theory, we have derived both co-and cross-polarization ratios. Then, a soil moisture algorithm is developed based on these two ratios. This new algorithm is compared with the existing empirical methods using input data from the IEM (Integral Equation Method) and experimental radar data. We also briefly discuss the effect of vegetation on this soil moisture algorithm.

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An empirical model and an inversion technique for radar scattering from bare soil surfaces

Cited by 1,130 publications

References 8 publications

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

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

Surface Parameter Estimation Using Bistatic Polarimetric X-Band Measurements

A soil moisture algorithm using tilted Bragg approximation

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