Modeling of ground-penetrating Radar for accurate characterization of subsurface electric properties

Lambot, Sébastien; Slob, Evert; Bosch, I. van den; Stockbroeckx, B.; Vanclooster, Marnik

doi:10.1109/tgrs.2004.834800

Cited by 427 publications

(455 citation statements)

References 52 publications

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“…Few studies have applied such an off-ground GPR approach for soil moisture sensing in field conditions (Chanzy et al, 1996;Redman et al, 2002;Or, 2003, 2005). Based on a full-waveform inversion of the GPR data and an accurate GPR system modeling, the off-ground GPR system developed by Lambot et al (2004Lambot et al ( , 2006b) has shown excellent potentialities for surface soil moisture sensing and mapping in field conditions (Weihermüller et al, 2007;Lambot et al, 2008;Jadoon et al, 2010a;Jonard et al, 2010;Minet et al, 2011). The method relies on an accurate radar model that, in particular, accounts for the antenna and antenna-soils interactions.…”

Section: Introductionmentioning

confidence: 99%

Validation of ground penetrating radar full-waveform inversion for field scale soil moisture mapping

Minet

Bogaert

Vanclooster

et al. 2012

Journal of Hydrology

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Ground penetrating radar (GPR) is an efficient method for soil moisture mapping at the field scale, bridging the scale gap between small-scale invasive sensors and large-scale remote sensing instruments. Nevertheless, commonly-used GPR approaches for soil moisture characterization suffer from several limitations and the determination of the uncertainties in GPR soil moisture sensing has been poorly addressed. Herein, we used an advanced proximal GPR method based on full-waveform inversion of ultra-wideband radar data for mapping soil moisture and uncertainties in the soil moisture maps were evaluated by three different methods. First, GPRderived soil moisture uncertainties were computed from the GPR data inversion, according to measurements and modeling errors and to the sensitivity of the electromagnetic model to soil moisture. Second, the reproducibility of the soil moisture mapping was evaluated. Third, GPR-derived soil moisture was compared with ground-truth measurements (soil core sampling). The proposed GPR method appeared to be highly precise and accurate, with spatially averaged GPR inversion uncertainty of 0. and an uncertainty of 0.0233 m 3 m −3 when compared with ground-truth measurements. These uncertainties were mapped and appeared to be related to some local model inadequacies and to small-scale variability of soil moisture. In a soil moisture mapping framework, the interpolation was found to be the determinant source of the observed uncertainties. The proposed GPR method was proven to be largely reliable in terms of accuracy and precision and appeared to be highly efficient for soil moisture * julien.minet@uclouvain.be mapping at the field scale.

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Section: Introductionmentioning

confidence: 99%

Validation of ground penetrating radar full-waveform inversion for field scale soil moisture mapping

Minet

Bogaert

Vanclooster

et al. 2012

Journal of Hydrology

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“…Before each set of measurement, the VNA was calibrated at the connection between the coaxial cable and the antenna using a standard open-short-match calibration kit. The radar data can be described using the antenna-medium model developed by Lambot et al [30]. In this model, the far-field radar antenna is modelled using frequency-dependent global reflection and transmission coefficients and Green functions are used to model the frequency response of the soil medium.…”

Section: Ground-penetrating Radar Measurements Of Soil Surface Roughnessmentioning

confidence: 99%

“…Figure 9 depicts the measured Green's function (computed from the radar data S 11 using the antenna-medium model [30]) in the time domain. The soil surface reflection can be clearly observed between 3 and 4 ns.…”

Section: Soil Roughness Parametermentioning

confidence: 99%

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

et al. 2018

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Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils.

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“…Other popular processing techniques involve forward and inverse modeling based on physical models and approximate numerical solutions to Maxwell's equations [21,22]. Although these methods have shown good results at characterizing the subsurface, they can be difficult to apply to different imaging scenarios and can be computationally intensive.…”

Section: Classical Approachesmentioning

confidence: 99%

“…Although these methods have shown good results at characterizing the subsurface, they can be difficult to apply to different imaging scenarios and can be computationally intensive. The simplifying assumption of far-field propagation in [21] would not be valid for the GPR horizon control sensor due to the ground-coupled antenna requirement.…”

Section: Classical Approachesmentioning

confidence: 99%

Near-surface Interface Detection for Coal Mining Applications using Bispectral Features and GPR

Strange

Ralston

Chandran

2005

Subsurf Sens Technol Appl

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The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coalclay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range. KEY WORDSGPR, bispectrum, interface detection, horizon control, coal mining.

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Modeling of ground-penetrating Radar for accurate characterization of subsurface electric properties

Cited by 427 publications

References 52 publications

Validation of ground penetrating radar full-waveform inversion for field scale soil moisture mapping

Validation of ground penetrating radar full-waveform inversion for field scale soil moisture mapping

Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment

Near-surface Interface Detection for Coal Mining Applications using Bispectral Features and GPR

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