Using geophysical techniques to characterize tillage effect on soil properties

Tillage practices influence physical, chemical, and biological soil properties, which also affect soil quality and consequently plant growth. In this study, the main objective was to evaluate the effect of different tillage systems on soil physical properties by using geophysical methods, namely, ground-penetrating radar (far-field and near-field GPR), capacitance probes (ThetaProbe and 5TE), electromagnetic induction (EMI) (Profiler and EM38), soil sampling, and by soil penetrometer. Since 2005, three contrasting tillage systems were applied on different plots of an agricultural field: i) conventional tillage (CT) with mouldboard ploughing to 27 cm depth, ii) deep loosening tillage (DL) with a heavy tine cultivator to 30 cm depth, and iii) reduced tillage (RT) with a spring tine cultivator to 10 cm depth. The geophysical and soil strength measurements were performed in April 2010. We observed that tillage influences the soil resistance (deeper tillage decreases soil resistance), which could be partly seen in the radar data. Soil water content reference measurements (capacitance probes and soil sampling) were in a relatively good agreement with the water content estimates from far-field GPR. We also observed that the tillage influences surface water content. Mean surface water content was significantly lower for CT than for DL and RT, which was partly explained by lower macropore connectivity between the topsoil and the deeper layers after conventional tillage. This study confirms the potential of GPR and EMI sensors for soil physical properties determination at the field scale and for the characterization of agricultural management practices

Improving soil moisture profile reconstruction from ground-penetrating radar data: a maximum likelihood ensemble filter approach

The vertical profile of shallow unsaturated zone soil moisture plays a key role in many hydro-meteorological and agricultural applications. We propose a closed-loop data assimilation procedure based on the maximum likelihood ensemble filter algorithm to update the vertical soil moisture profile from time-lapse ground-penetrating radar (GPR) data. A hydrodynamic model is used to propagate the system state in time and a radar electromagnetic model and petrophysical relationships to link the state variable with the observation data, which enables us to directly assimilate the GPR data. Instead of using the surface soil moisture only, the approach allows to use the information of the whole soil moisture profile for the assimilation. We validated our approach through a synthetic study. We constructed a synthetic soil column with a depth of 80 cm and analyzed the effects of the soil type on the data assimilation by considering 3 soil types, namely, loamy sand, silt and clay. The assimilation of GPR data was performed to solve the problem of unknown initial conditions. The numerical soil moisture profiles generated by the Hydrus-1D model were used by the GPR model to produce the "observed" GPR data. The results show that the soil moisture profile obtained by assimilating the GPR data is much better than that of an open-loop forecast. Compared to the loamy sand and silt, the updated soil moisture profile of the clay soil converges to the true state much more slowly. Decreasing the update interval from 60 down to 10 h only slightly improves the effectiveness of the GPR data assimilation for the loamy sand but significantly for the clay soil. The proposed approach appears to be promising to improve real-time prediction of the soil moisture profiles as well as to provide effective estimates of the unsaturated hydraulic properties at the field scale from time-lapse GPR measurements

Estimating soil hydraulic properties using L-band radiometer and ground-penetrating radar

peer reviewedIn this study, we experimentally analyze the feasibility of estimating the soil hydraulic properties from L-band radiometer and ground-penetrating radar (GPR) data. L-band radiometer and ultrawideband off-ground GPR measurements were performed above a sand box in hydrostatic equilibrium with a water table located at different depths. The results of the inversions showed that the radar and radiometer signals contain sufficient information to estimate the soil water retention curve and its related hydraulic parameters with a relatively good accuracy compared to time-domain reflectometry estimates. However, an accurate estimation of the hydraulic parameters was only obtained by considering the saturated water content parameter as known during the inversion. © 2012 IEEE

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

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 coal-clay, 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

Pet soil thickness and carbon storage in the Belgian High Fens: insights from multi-sensor UAV remote sensing

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Estimation of root water uptake parameters by inverse modeling with soil water content data

In this paper we have tested the feasibility of the inverse modeling approach to derive root water uptake parameters (RWUP) from soil water content data using numerical experiments for three differently textured soils and for an optimal drying period. The RWUP of interest are the rooting depth and the bottom root length density. In a first step, a thorough sensitivity analysis was performed. This showed that soil water content dynamics is relatively insensitive to RWUP and that the sensitivity depends on the texture of the considered soil. For medium-fine textured soil, the sensitivity is particularly low due to relatively high unsaturated hydraulic conductivity values. These ones allow a “compensating effect” to occur, i.e., vertical unsaturated water fluxes overshadowing in some way the root water uptake. In a second step, we analyzed the well-posedness of the solution (stability and nonuniqueness) when only RWUP are optimized. For this case, the inverse problem is clearly ill-posed except for the estimation of the rooting depth parameter for coarse and the very fine textured soils. In a third step, we addressed the case where RWUP are estimated simultaneously with additional parameters of the system (i.e., with soil hydraulic parameters). For this case, our study showed that the inverse problem is well-posed for the coarse and very fine textured soils, allowing for the estimation of both RWUP of interest provided that a powerful global optimization algorithm is used. On the contrary, the estimation of RWUP is unfeasible for medium-fine textured soil due to the “compensating effect” of the vertical unsaturated water flows. In conclusion, we can state that the inverse modeling approach can be applied to derive RWUP for some soils (coarse and very fine textured) and that the feasibility is strongly improved if the RWUP are simultaneously optimized with additional parameters. Nevertheless, more detailed research is needed to apply the inverse modeling approach to real cases for which additional issues are likely to be encountered such as soil heterogeneity and root dynamics

Creating FDTD models of commercial GPR antennas using Taguchi’s optimisation method

Very few researchers have developed numerical models of ground-penetrating radar (GPR) that include realistic descriptions of both the antennas and the subsurface. This is essential to be able to accurately predict responses from near-surface, near-field targets. We have developed a detailed 3D finite-difference time-domain models of two commercial GPR antennas — a Geophysical Survey Systems, Inc. (GSSI) 1.5-GHz antenna and a MALÅ Geoscience 1.2-GHz antenna — using simple analyses of the geometries and the main components of the antennas. Values for unknown parameters in the antenna models (due to commercial sensitivity) were estimated by using Taguchi's optimization method, resulting in a good match between the real and modeled crosstalk responses in free space. Validation using a series of oil-in-water emulsions to simulate the electrical properties of real materials demonstrated that it was essential to accurately model the permittivity and dispersive conductivity. When accurate descriptions of the emulsions were combined with the antenna models, the simulated responses showed very good agreement with real data. This provides confidence for use of the antenna models in more advanced studies

Investigating the role of changing permafrost hydrology on carbon and nutrient fluxes using very-high resolution remote sensing and geochemical techniques (Stordalen, Sweden)

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