Comparing the EM38DD and DUALEM‐21S Sensors for Depth‐to‐Clay Mapping

Abstract

Geophysical instruments show great potential for the detailed quantification of soil stratigraphy. In this study, two electromagnetic induction sensors were evaluated on their capacity to map small‐scale variations of the depth to the interface (zin) in a two‐layered soil. On a 2‐ha study site, zin between the silty topsoil and the contrasting clayey subsoil was modeled first by relating the two apparent electrical conductivity (ECa) measurements of the EM38DD sensor to observations of zin obtained by augering. A substantial number of these calibration observations was needed, however, to account for the modeling parameters. To avoid this step, an entirely noninvasive procedure was proposed based on one survey with the DUALEM‐21S sensor. This sensor simultaneously records four ECa values with different coil configurations. These measurements correspond to four different depth response functions that allow modeling zin without calibration observations. The only assumption was a two‐layered soil profile. The zin predictions were validated with 24 independent depth observations. Both procedures resulted in equal correlation coefficients (0.85) between predicted and measured zin and average estimation errors (0.26 m). This indicated that both sensors allowed the accurate mapping of the depth to a contrasting textural layer. With the EM38DD, calibration observations are needed, whereas the four different coil conurations of the DUALEM‐21S sensor provided sufficient information to predict the interface depth without augering.

The use of electromagnetic induction techniques in soils studies

“…Each of the aforementioned sensors has distinct operational advantages and disadvantages (Sudduth et al, 2003). Comparative studies have generally revealed close similarities between EC a data collected with different sensors (Doolittle et al, 2001(Doolittle et al, , 2002aSaey et al, 2009a;Sudduth et al, 1999Sudduth et al, , 2003Urdanoz and Aragüés, 2012). However, differences in sensor calibration, depth sensitivity and volume of soil material measured will affect measurements and result in slightly different EC a values.…”

Functional homogeneous zones (fHZs) in viticultural zoning procedure: an Italian case study on Aglianico vine

“…In this study the apparent soil electric conductivity (ECa), was carried out by electromagnetic induction (EMI) sensors, which represents a very useful tool for identifying soil map units and soil properties in respect of clay content (Morari et al, 2009), soil depth (Saey et al, 2009), water content (Davies, 2004;Cousin et al, 2009;Lück et al, 2009;Tromp-van Meerveld and McDonnell, 2009) and water salinity (Doolittle et al, 2001).…”

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

“…Examples of the application of EMI-based ECa measurements include soil salinity (e.g., Doolittle et al, 2001;Heilig et al, 2011), spatial patterns of soil texture (e.g., Abdu et al, 2008;Heil and Schmidhalter, 2012), lateral boundaries between soil types (e.g., Anderson-Cook et al, 2002;James et al, 2003), depth of clay-rich layers (e.g., Saey et al, 2009;Doolittle at al., 1994), clay content (e.g., King et al, 2005;Weller et al, 2007), soil compaction (e.g., Al-Gaadi, 2012;Islam et al, 2014), soil CEC (e.g., Headley et al, 2004;Triantafilis et al, 2009), soil organic carbon (e.g., Martinez et al, 2009;Altdorff et al, 2016), assessment of soil quality (e.g., Johnson et al, 2001;Corwin and Lesch, 2005), detection of buried services (e.g., Won and Huang, 2004;El-Quady et al, 2014), and mapping of active layer thickness in permafrost areas (e.g., Hauck and Kneisel, 2008;Dafflon et al, 2013). ECa measurements are widely used in the context of precision agriculture for, e.g., refining existing soil maps (e.g., Doolittle et al, 2008;Martini et al, 2013), precision farming (e.g., Lück et al, 2009;Scudiero et al, 2015), and harvest zoning (e.g., Frogbrook and Oliver, 2007;Priori et al, 2013).…”