Electromagnetic induction (EMI) systems measure the soil apparent electrical conductivity (ECa), which is related to the soil water content, texture, and salinity changes. Large-scale EMI measurements often show relevant areal ECa patterns, but only few researchers have attempted to resolve vertical changes in electrical conductivity that in principle can be obtained using multiconfiguration EMI devices. In this work, we show that EMI measurements can be used to determine the lateral and vertical distribution of the electrical conductivity at the field scale and beyond. Processed ECa data for six coil configurations measured at the Selhausen (Germany) test site were calibrated using inverted electrical resistivity tomography (ERT) data from a short transect with a high ECa range, and regridded using a nearest neighbor interpolation. The quantitative ECa data at each grid node were inverted using a novel three-layer inversion that uses the shuffled complex evolution (SCE) optimization and a Maxwell-based electromagnetic forward model. The obtained 1-D results were stitched together to form a 3-D subsurface electrical conductivity model that showed smoothly varying electrical conductivities and layer thicknesses, indicating the stability of the inversion. The obtained electrical conductivity distributions were validated with low-resolution grain size distribution maps and two 120 m long ERT transects that confirmed the obtained lateral and vertical large-scale electrical conductivity patterns. Observed differences in the EMI and ERT inversion results were attributed to differences in soil water content between acquisition days. These findings indicate that EMI inversions can be used to infer hydrologically active layers.
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Abstract. The spatial distribution and accessibility of organic matter (OM) to soil microbes in aggregatesdetermined by the fine-scale, 3-D distribution of OM, pores and mineral phases -may be an important control on the magnitude of soil heterotrophic respiration (SHR). Attempts to model SHR on fine scales requires data on the transition probabilities between adjacent pore space and soil OM, a measure of microbial accessibility to the latter. We used a combination of osmium staining and synchrotron X-ray computed tomography (CT) to determine the 3-D (voxel) distribution of these three phases (scale 6.6 µm) throughout nine aggregates taken from a single soil core (range of organic carbon (OC) concentrations: 4.2-7.7 %). Prior to the synchrotron analyses we had measured the magnitude of SHR for each aggregate over 24 h under controlled conditions (moisture content and temperature). We test the hypothesis that larger magnitudes of SHR will be observed in aggregates with (i) shorter length scales of OM variation (more aerobic microsites) and (ii) larger transition probabilities between OM and pore voxels.After scaling to their OC concentrations, there was a 6-fold variation in the magnitude of SHR for the nine aggregates. The distribution of pore diameters and tortuosity index values for pore branches was similar for each of the nine aggregates. The Pearson correlation between aggregate surface area (normalized by aggregate volume) and normalized headspace C gas concentration was both positive and reasonably large (r = 0.44), suggesting that the former may be a factor that influences SHR. The overall transition probabilities between OM and pore voxels were between 0.07 and 0.17, smaller than those used in previous simulation studies. We computed the length scales over which OM, pore and mineral phases vary within each aggregate using 3-D indicator variograms. The median range of models fitted to variograms of OM varied between 38 and 175 µm and was generally larger than the other two phases within each aggregate, but in general variogram models had ranges < 250 µm. There was no evidence to support the hypotheses concerning scales of variation in OM and magnitude of SHR; the linear correlation was 0.01. There was weak evidence to suggest a statistical relationship between voxel-based OM-pore transition probabilities and the magnitudes of aggregate SHR (r = 0.12). We discuss how our analyses could be extended and suggest improvements to the approach we used.
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