Mapping patterns of soil properties and soil moisture using electromagnetic induction to investigate the impact of land use changes on soil processes

As highlighted by many authors, classical or geophysical techniques for measuring soil moisture such as destructive soil sampling, neutron probes or Time Domain Reflectometry (TDR) have some major drawbacks. Among other things, they provide point scale information, are often intrusive and time-consuming. ElectroMagnetic Induction (EMI) instruments are often cited as a promising alternative hydrogeophysical methods providing more efficiently soil moisture measurements ranging from hillslope to catchment scale. The overall objective of our research project is to investigate whether a combination of geophysical techniques at various scales can be used to study the impact of land use change on temporal and spatial variations of soil moisture and soil properties. In our work, apparent electrical conductivity (ECa) patterns are obtained with an EM multiconfiguration system. Depth profiles of ECa were subsequently inferred through a calibration-inversion procedure based on TDR data. The obtained spatial patterns of these profiles were linked to soil profile and soil water content distributions. Two catchments with contrasting land use (agriculture vs. natural forest) were selected in a subtropical region in the south of Brazil. On selected slopes within the catchments, combined EMI and TDR measurements were carried out simultaneously, under different atmospheric and soil moisture conditions. Ground-truth data for soil properties were obtained through soil sampling and auger profiles. The comparison of these data provided information about the potential of the EMI technique to deliver qualitative and quantitative information about the variability of soil moisture and soil properties

Managing agricultural fields: from observation to prediction

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Spatio-temporal drivers of soil and ecosystem carbon fluxes at field scale in an upland grassland in Germany

Ecosystem carbon (C) fluxes in terrestrial ecosystems are affected by varying environmental conditions (e.g. soil heterogeneity and the weather) and land management. However, the interactions between soil respiration (Rs) and net ecosystem exchange (NEE) and their spatio-temporal dependence on environmental conditions and land management at field scale is not well understood. We performed repeated C flux measurement at 21 sites during the 2013 growing season in a temperate upland grassland in Germany, which was fertilized and cut three times according to the agricultural practice typical of the region. Repeated measurements included determination of NEE, Rs, leaf area index (LAI), meteorological conditions as well as physical and chemical soil properties. Temporal variability of Rs was controlled by air temperature, while LAI influenced the temporal variability of NEE. The three grass cuts reduced LAI and affected NEE markedly. More than 50% of NEE variability was explained by defoliation at field scale. Additionally, soil heterogeneity affected NEE, but to a lower extent (>30%), while Rs remained unaffected. We conclude that grassland management (i.e. repeated defoliation) and soil heterogeneity affects the spatio-temporal variability of NEE at field scale

Calibration and large-scale inversion of multi-configuration electromagnetic induction data for vadose zone characterization

Frequency-domain electromagnetic induction (EMI) devices measure a secondary magnetic field superimposed by the transmitted primary magnetic field in current conducting media. Commercially available systems convert this magnetic field ratio into an apparent electrical conductivity (ECa), not concretely stated but probably with a linear approximation assuming low induction numbers (LIN). In the LIN-based conversion, errors were observed between the true ground electrical conductivity ($\sigma(z_{i}$)) and ECa such that the present thesis introduces an improved non-linear exact ECa conversion (EEC) approach that can be used beyond the LIN approximation. Until recently, the EMI method was used for qualitative data interpretations because quantitative ECa values were often not obtained. For example, the operator or the field setup generated additional magnetic fields being measured by the EMI device that shift the recorded ECa. To eliminate the shifts, a post-calibration is required. Here, a cross-correlation between measured and predicted EMI-ECa values resulted in calibration parameters that were applied to the EMI data such that quantitative ECa values were obtained. To predict the EMI device specific ECa values, a Maxwell-based electromagnetic forward model (EM-FM) used $\sigma(z_{i}$) obtained from inverted electrical resistivity tomography (ERT) or inverted vertical electrical sounding (VES) data. Analyzing several post-calibrations based on ERT, coefficients of determination of R$^{2}$ > 0.75 were obtained when the data range along a calibration line exceeded 3 mS/m and when the ground electrical conductivity was larger than 5 mS/m. Using derived calibrations of different test sites, universal calibration parameters were obtained that allowed postcalibrations without an ERT reference line. Combining the introduced EEC with the modeling using the EM-FM that assumes horizontal layers in a multi-layer inversion of the post-calibrated EMI data, no errors were introduced anymore such that these methods can be applied also for high electrical conductive, e.g., saline areas, where the LIN approximation is no longer valid. Large-scale EMI measurements often reflect relevant subsurface patterns, but only few researchers have attempted to resolve the vertical changes in electrical conductivity [...

Large-Scale 3D Multi-Layer Electromagnetic Induction Data Inversion

Multi-coil electromagnetic induction (EMI) systems sense different depth levels and offer the potential to estimate the vertical subsurface electrical conductivity distribution with a high spatial resolution. However, due to the complicated and overlapping sensitivity functions of each coil configuration, it is not straightforward to characterize a layered subsurface. Moreover, EMI measurements are influenced by external conditions such as the operator, field set-up, cables or any other current conducting material close the system, such that the recorded value is shifted, which hinders a quantitative interpretation of the measured apparent electrical conductivities (ECa). Therefore, measured ECa need to be calibrated and inverted to obtain a reliable layered subsurface electrical model. The calibration is performed by a linear regression between predicted ECa, obtained from an electromagnetic (EM) forward model that numerically solves the Maxwell equations to predict ECa using inverted electrical resistivity tomography (ERT) data recorded at a small transect as input, and collocated measured EMI-ECa. The coil specific regression parameters are then used to calibrate the large-scale EMI data. Next, the calibrated multi-coil EMI data are re-gridded to a common grid, such that a one dimensional (1D) multi-layer conductivity inversion can be performed. To invert the quantitative ECa, we use a parallelized version of the shuffled complex evolution (SCE) optimization and minimize the misfit between the measured and modelled data obtained from the full-solution EM forward model using the L1-norm while assuming a horizontally layered earth. The obtained 1D-models at each grid node are stitched together to form a 3D subsurface volume. We applied this method to a data set obtained at an experimental field covering an area of 11400 m2. The smoothly changing lateral and vertical electrical conductivity model was validated with grain size distribution maps and two previously measured 120 m long ERT transects. Overall, the subsurface model obtained with the quasi-3D EMI inversion and the independent ERT inversions showed similar subsurface structures. Differences in absolute electrical conductivity values within certain layers are probably due to the varying soil moisture content. These findings indicate that EMI can be successfully used to quantitatively characterize the lateral and vertical electrical conductivity structures at the field scale and beyond