Understanding sensitive wetlands often requires non-invasive methods to characterize their complex geological structure and hydrogeological parameters. Here, geoelectrical characterization is explored by employing frequency-domain electromagnetic induction (EMI) at a site previously characterized by extensive intrusive measurements and 3D electrical resistivity tomography (ERT). This work investigates the performance of several approaches to obtain structural information from EMI data and sharp and smooth inversions. Additionally, the hydrological information content of EMI data is investigated using correlation with piezometric measurements, established petrophysical relationships, and synthetic modeling. EMI measurements were dominated by peat thickness and were relatively insensitive to both topography and depth to bedrock. An iso-conductivity method for peat depth estimation had a normalized mean absolute difference (NMAD) of 23.5%, and although this performed better than the sharp inversion algorithm (NMAD = 73.5%), a multi-linear regression approach achieved a more accurate prediction with only 100 measurements (NMAD = 17.8%). In terms of hydrological information content, it was not possible to unravel correlation causation at the site, however, synthetic modeling demonstrates that the EMI measurements are predominantly controlled by the electrical conductivity of the upper peat pore-water and not the thickness of the unsaturated zone or the lower peat pore-water conductivity. Additionally, a priori information significantly improves the potential for time-lapse applications in similar environments. This study provides an objective overview and insights for future EMI applications in similar environments. It also covers areas seldom investigated in EMI studies, e.g. error quantification and the depth of investigation of ERT models used for EMI calibration.

Wheat is one of the most widely grown crops and it plays an important role in food production. Currently there is considerable interest in identifying traits that contribute to high yields. Trait selection has mainly focused on the above-ground part of the plant neglecting below-ground processes. Climate change and the greater uncertainty in weather conditions challenge our current food system and create the need to select more varieties with grater resilience against the effects of climate variation. The root system of the plant plays a key part in this resilience, but it is difficult to study at the field-scale which is essential for the effective selection of breeding lines. Geophysical tools such as electromagnetic induction (EMI) and electrical resistivity tomography (ERT) offer the possibility to study the below-ground phenotype of the plant in a non-destructive and high-throughput manner. In this study, changes in soil moisture induced by root water uptake are monitored using time-lapse ERT/EMI surveys. These methods were applied at two scales: (a) at a high-spatial resolution where hundreds of wheat varieties were monitored monthly using EMI in a wheat breeding field trial and (b) at high-temporal resolution where hourly ERT measurements were collected along with above-ground phenotyping traits on a few plots with a field facility (Field Scanalyzer). Coupling these geophysically-based below-ground data with above-ground canopy measurements can increase our understanding of the crop response to its environment. Good correlation was found between leaf area index (LAI) and soil drying inferred from EMI measurements for the high-spatial experiment (a). The ERT monitoring experiment (b) accurately showed the dynamics of two different nitrogen treatments, their interactions with weather conditions and their correlation with above-ground crop growth. Coupling geophysically-based below-ground measurements with above-ground data allows the increased understanding of the whole plant phenotype. This might help to identify useful traits to select for increased crop yield and resilience.