Improving the relationship between soil texture and large-scale electromagnetic induction surveys using a direct current electrical resistivity calibration

Abstract. Ground-based electromagnetic induction (EMI) surveys can be used to infer soil properties and (by extension) support nutrient loss risk assessments of agricultural fields. The transport of nutrients from an agricultural field to surrounding surface waters depends on the hydrologic connectivity between the two systems, largely controlled by soil texture. Preexisting soil texture maps and associated soil drainage classifications are often used as proxy information to assess the potential for lateral migration of nutrients in the groundwater; however, the resolution of these maps is inadequate at the scale of individual fields. In this study, we evaluated whether the relationship between EMI data and soil texture was improved by calibrating the apparent electrical conductivity measured by an EMI sensor with a 2D electrical resistivity imaging (ERI) survey. The joint geophysical survey was performed across a ~1-ha field in Princess Anne, Maryland, United States. A calibration-inversion-comparison framework is presented that calibrates the EMI measurements using ERI conductivity models and subsequently inverts the EMI data. A robust validation scheme compared the calibrated and not calibrated EMI conductivity models against grain size, core-scale conductivity measurements and an ERI survey performed roughly 80 m from the first. This study shows that the calibration of EMI data with an ERI profile is significantly improves the quantitative relationship between EMI-derived electrical conductivity and representative soil properties, ensuring a finer-resolution proxy soil map for evaluating subsurface nutrient transport from agricultural fields.