Julien Minet

Soil Surface Water Content Estimation by Full-Waveform GPR Signal Inversion in the Presence of Thin Layers

We analyzed the effect of shallow thin layers on the estimation of soil surface water content using full-waveform inversion of off-ground ground penetrating radar (GPR) data. Strong dielectric contrasts are expected to occur under fast wetting or drying weather conditions, thereby leading to constructive and destructive interferences with respect to surface reflection. First, synthetic GPR data were generated and subsequently inverted considering different thin-layer model configurations. The resulting inversion errors when neglecting the thin layer were quantified, and then, the possibility to reconstruct these layers was investigated. Second, laboratory experiments reproducing some of the numerical experiment configurations were conducted to assess the stability of the inverse solution with respect to actual measurement and modeling errors. Results showed that neglecting shallow thin layers may lead to significant errors on the estimation of soil surface water content(¿¿>0.03 m3/m3), depending on the contrast. Accounting for these layers in the inversion process strongly improved the results, although some optimization issues were encountered. In the laboratory, the proposed full-waveform method permitted to reconstruct thin layers with a high resolution up to 2 cm and to retrieve the soil surface water content with an rmse less than 0.02 m3/m3, owing to the full-waveform inverse modeling. These results suggest that the proposed GPR approach is promising for field-scale mapping of soil surface water content of nondispersive soils with low electrical conductivity and for instances when soil layering is encountered.

Full-Waveform Modelling and Inversion of Ground-Penetrating Radar Data for Non-invasive Characterisation of Soil Hydrogeophysical Properties

We present a new technique for real-time, proximal sensing of the soil hydrogeophysical properties using ground-penetrating radar (GPR). The radar system is based on international standard vector network analyser technology, thereby setting up stepped-frequency continuous-wave GPR. The radar is combined with an off-ground, ultra-wideband, and highly directional horn antenna acting simultaneously as transmitter and receiver. Full-waveform forward modelling of the radar signal includes antenna propagation phenomena through a system of linear transfer functions in series and parallel. The system takes into account antenna–soil interactions and assumes the air–subsurface compartments as a three-dimensional multilayered medium, for which Maxwell’s equations are solved exactly. We provide an efficient way for estimating the spatial Green’s function as a solution of Maxwell’s equations from its spectral counterpart by deforming the integration path in the complex plane of the integration variable. Signal inversion is formulated as a complex least squares problem and is solved iteratively using the global multilevel coordinate search optimisation algorithm combined with the local Nelder–Mead simplex method. The electromagnetic model has unprecedented accuracy for describing the GPR signal in controlled laboratory conditions, providing accurate estimates for both soil dielectric permittivity and electrical conductivity. The proposed method has been specifically designed for the retrieval of soil surface dielectric permittivity and correlated surface water content, which has been validated in field conditions. We also show that constraining the electromagnetic inverse problem using hydrodynamic modelling theoretically permits retrieval of the soil hydraulic properties and reconstruction of continuous vertical water content profiles from time-lapse GPR data. The proposed method shows great promise for field-scale, high-resolution digital soil mapping, and thereby for bridging the spatial-scale gap between ground truthing based on soil sampling or local probes and airborne and spaceborne remote sensing.

Characterization of layered media using full-waveform inversion of proximal GPR data

Full-waveform inversion of proximal ground penetrating radar (GPR) data is used to determine the electromagnetic properties of layered media. The radar system consists of a vector network analyzer combined with an off-ground horn antenna operating at ultra wideband. The GPR wave propagation is modeled for a multilayered medium using a recursive Greens function computed in the frequency domain. The antenna and its interactions with the layered medium are modeled using a linear system of complex transfer functions. GPR signals were acquired in laboratory above a two-layered sand medium and two concrete slabs separated by a thin air layer (simulating a fracture). Subsequent inversions permit to retrieve the electromagnetic properties and the dimensions of these thin-layered media. For humid sand, GPR-derived dielectric permittivities showed a good agreement (RMSE = 1.65) with measured volumetric water contents. Dimensions of the three-layered concrete medium could be retrieved with a millimetric accuracy. The method is promising for the non-destructive characterization of multilayered media, including thin layers, owing to the full-waveform inversion of the radar data in a large frequency bandwidth.

Ground-penetrating radar for correlation analysis of temporal soil moisture stability and land-slope

Knowledge of temporal surface soil moisture variability is an useful key in agriculture, surface hydrology and meteorology. In that respect, ground-penetrating radar (GPR) is a non-invasive and promising tool for high-resolution and large scale characterization. In the case of quantitative analysis, off-ground GPR signal modeling and full-waveform inversion has shown a great potential during the last decade. In this research, we applied GPR for time-laps measurements in an agricultural field along a 320 m single transect with a significant land-slope for about 3 months. A 200-2000 MHz TEM-horn antenna situated 1.1 m above the ground, connected to a vector network analyzer (VNA) was used as an off-ground frequency-domain GPR. The accurate positioning was done using a differential GPS. All systems were mounted on a 4-wheels vehicle for realtime and automated mapping. The calibration of the antenna and using the GPR signal inversion permitted to the ground surface relative dielectric permittivity. Topps model was used for transformation of the relative dielectric permittivity to soil moisture. The temporal stability of the field-average soil moisture was computed by indicators based on the relative difference of the soil moisture to the field-average. The results showed an excellent correlation amount of -0.9905 for temporal stability of soil moisture and slope variability.