Validation of Near-Field Ground-Penetrating Radar Modeling Using Full-Wave Inversion for Soil Moisture Estimation

• Published in: IEEE transactions on geoscience and remote sensing Vol. 52; no. 9; pp. 5483 - 5497
• Main Authors: Anh Phuong Tran, Andre, Frederic, Lambot, Sebastien
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Antenna measurements; Antennas; Computational modeling; Electrical properties; Full-wave inverse modeling; Green's functions; Ground penetrating radar; ground-penetrating radar (GPR); Heterogeneity; Hydrostatic pressure; Mathematical models; Moisture content; near-field; Nondestructive testing; Radar; Radar antennas; Sand; Sand & gravel; Sensors; Soil; Soil moisture; Water content; water content; Full-wave inverse modeling; near-field; Green's functions; ground-penetrating radar (GPR)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2013.2289952

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 cm 3 /cm 3 for the frequency domain and 0.016 cm 3 /cm 3 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.