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In this study, we analyzed low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil electrical conductivity mapping. Indeed, in the lowest GPR frequency ranges, the soil surface reflexion coefficient depends more on the soil electrical conductivity than on its permittivity. Numerical experiments were conducted within the frequency range 15–45 MHz to analyze parameter sensitivities, the well-posedness of the inverse problem as well as the depth of sensitivity. The results show that the soil surface reflexion is significantly more sensitive to the soil electrical conductivity than the soil permittivity. Therefore, the conductivity can be retrieved using full-wave inversion within this frequency range, with a characterization depth varying from 0.5 to 1 m, depending on the soil properties. Yet, the permittivity also affects the results and should be accounted for in the inversion strategy. Field measurements were performed using low-frequency drone-borne radar with a 5-m half-wave dipole antenna, and electromagnetic induction (EMI) measurements with different depth sensitivities were conducted for comparison. Kriging interpolation was used to get maps from measurement points. The soil conductivity maps obtained by the proposed GPR and EMI are compliant in terms of absolute values and spatial patterns. This study demonstrated the capacity of low-frequency drone-borne GPR for fast, field-scale soil electrical conductivity mapping. [ABSTRACT FROM AUTHOR]

Drone-borne ground-penetrating radar (GPR) has proven to be efficient and promising for high-resolution topsoil moisture mapping at the field scale. However, in practice, the radar incident angle may change as a function of the terrain slope and unstable flying conditions. In this respect, we analyzed the effect of radar incident angle on full-wave inversion for soil permittivity characterization. Specifically, using the frequency-domain radar equation, antenna characteristic functions with different incident angles were determined for a horn antenna as an example. Then, numerical analyses and field measurements were performed to quantify the errors on the retrieved permittivity resulting from incident angle drifts. In agreement with the radar equation concept, we observed that the angle- and frequency-dependent global transmission functions of the antenna correspond well to its radiation pattern. Numerical and field results show that errors in the permittivity estimation can be very significant as a function of the antenna radiation pattern and the incident angle. Nevertheless, if the incident angle is known and the characteristic antenna functions are determined as a function of the incident angle, full-wave modeling and inversion remain accurate, provided that the signal-to-noise ratio remains sufficient. [ABSTRACT FROM AUTHOR]

Several data fusion approaches have been developed to optimize both resolution and characterization depth for multifrequency ground-penetrating radar (GPR). In this study, we propose a novel physically based method to merge radar data coming from antennas operating in different frequency ranges. The strategy relies on the removal of the source and antenna effects from GPR data and the subsequent fusion of the resulting signals, which are now normalized, in the frequency domain. The approach used to filter out antenna effects resorts to an intrinsic, closed-form solution of Maxwell's equations to describe the radar-antenna-medium system. We validated the multifrequency GPR data fusion approach through laboratory experiments with measurements performed in far- and near-field conditions above a copper plane and pipes buried at different depths in a sandbox. The results demonstrated the benefit of filtering the frequency-dependent antennas effects before data fusion. Enhanced radargrams were subsequently obtained as a result of the broadening of the spectral bandwidth. This physically based fusion approach appears to be very promising to improve subsurface imaging. [ABSTRACT FROM PUBLISHER]

Limestone has been a widely used building material for many years. Its degradation process has been strongly linked to its water content, to which the radar waves are sensitive. In this paper, the closed-form, near-field full-wave radar model of Lambot et al. is utilized for monitoring water ingress in limestone. This model is based on planar layered media Green's functions combined with an intrinsic representation of the antenna through global reflection and transmission functions. The Jonscher model is used to describe the frequency dependence of the limestone electromagnetic properties. Then, an inversion method based on a genetic algorithm is developed and optimized. The method is numerically validated by simulating electromagnetic wave propagation in medias, using a three-dimensional finite element software, namely high-frequency electromagnetic field simulation. Finally, the method is experimentally validated, with a stepped-frequency radar system and an ultrawide band antenna, during an imbibition process of a limestone slab. Results are compared to gammadensimetry measurements, a nuclear and semidestructive approach, performed as the reference. Analyses of the results show that the average relative difference between the estimated and the reference water front is 2.5%, thereby demonstrating the performance of the proposed nondestructive method. [ABSTRACT FROM PUBLISHER]

Multifrequency and multioffset ground-penetrating radar data acquisition modes are used to maximize the information content and parameter retrieval capabilities. However, they also increase the computational cost dedicated to the inversion procedure. In this paper, the impact of the number of frequencies and the multistatic configurations on the information retrieval capabilities is investigated through the response surface topographies of the objective functions. We resort to a full-wave-inversion procedure and a recently developed electromagnetic model which takes advantage of a closed-form solution of Maxwell's equations to describe the antenna–medium system. We show with numerical and laboratory experiments the possibility of reducing the number of frequencies from several hundreds to one or several tens of components without affecting the information retrieval capabilities. We also show through several scenarios that the presence of a perfect electrical conductor increases the number of frequencies required to ensure an acceptable retrieval of the subsurface properties whereas the conductivity of the first layer and the relative permittivity of the second layer do not affect it. The results highlight that information content analyses are important in order to study and optimize data acquisition and inversion procedures, and thereby the computation time. [ABSTRACT FROM AUTHOR]

Litter is acknowledged to affect microwave remote sensing data over forests, and accurate quantification of litter radiative properties is essential for proper retrieval of forest soil water content and forest canopy biophysical characteristics from satellite or airborne sensors. Furthermore, detailed characterization of forest soil organic horizons is of paramount importance for ecological studies and for carbon cycle and global changerelated studies. Ultrawideband ground-penetrating radar (GPR) data acquired above beech forest litter were used to analyze the effects of litter on the backscattering and investigate the ability of GPR to quantitatively characterize the properties of the forest floor. Radar measurements were performed over different litter configurations (i.e., single layers of recently fallen litter (OL) or of fragmented litter (OF), and combined OL-OF layers) and thicknesses. Radar data were processed through full-wave inversion. Over the considered frequency range (0.8-4.0 GHz), attenuation of the radar signal increased as both frequency and litter thickness increase, due to scattering and dielectric losses. These effects were accounted for in the radar model through frequency dependence of litter apparent electrical conductivity. Good agreement (RMSE = 9.5 × 10-3 m) was observed between estimated and measured litter thicknesses. Radar data inversions provided reliable estimates of litter electromagnetic properties, with relative dielectric permittivity values around 1.2 and 3.9 for OL and OF litters, respectively, and corresponding values of 0.032 and 0.080 Sm-1 for litter apparent electrical conductivity at 4.0 GHz. These results show great promise for the use of GPR for noninvasive characterization of forest litter. [ABSTRACT FROM PUBLISHER]

We present validation results of a new ground-penetrating radar (GPR) near-field model for determining the electrical properties and correlated water content of a sand using both frequency- and time-domain radars. The radar antennas are intrinsically characterized using an equivalent set of infinitesimal source/field points and characteristic functions of antennas, which were determined using measurements with the antenna at different distances from a copper plane. The antenna radiation was modeled using six source and field points, which was found to be a good compromise between high modeling accuracy and computing efficiency. We validated our model by inverting GPR data to predict the water content of a sand layer subject to seven levels of saturation. A soil dielectric mixing model was integrated into the full-wave GPR inverse modeling to directly estimate the water content and to account for the frequency dependence of the electrical properties. Although the quality of the fit slightly decreased as the antenna approached the sand surface, the results showed a close agreement between measured and modeled data, resulting in accurate estimation of the water content. The average errors of all water content estimates were 0.012 cm3/cm3 for the frequency domain and 0.016 cm3/cm3 for the time-domain GPR. However, the accuracy reduced when the sand became wet. By performing numerical simulations, we found that it is due to the vertical heterogeneity of soil moisture under the effect of the hydrostatic pressure. We also showed that the GPR inversion with the multilayered soil model could account for this heterogeneity and improved the agreement between the modeled and measured GPR data as well as the accuracy of soil moisture estimation. As for the frequency dependence of the electrical properties, in the frequency ranges of both GPR systems, while the dielectric permittivity was approximately constant, the apparent conductivity exponentially increased with increasing frequency. The success of the calibration and validation in laboratory conditions demonstrates a great potential for practical applications of the radar model, notably for the digital soil mapping and nondestructive testing of materials. [ABSTRACT FROM PUBLISHER]

A new near-field radar modeling approach for wave propagation in planar layered media is presented. The radar antennas are intrinsically modeled using an equivalent set of infinitesimal electric dipoles and characteristic, frequency-dependent, global reflection, and transmission coefficients. These coefficients determine through a plane wave decomposition wave propagation between the radar reference plane, point sources, and field points. The interactions between the antenna and layered medium are thereby inherently accounted for. The fields are calculated using 3-D Green's functions. We validated the model using an ultrawideband frequency-domain radar with a transmitting and receiving Vivaldi antenna operating in the range 0.8-4 GHz. The antenna characteristic coefficients are obtained from near- and far-field measurements over a copper plane. The proposed model provides unprecedented accuracy for describing near-field radar measurements collected over a water layer, the frequency-dependent electrical properties of which were described using the Debye model. Layer thicknesses could be retrieved through full-wave inversion. The proposed approach demonstrated great promise for nondestructive testing of planar materials and digital soil mapping using ground-penetrating radar. [ABSTRACT FROM AUTHOR]