Mapping Clay Content across Boundaries at the Landscape Scale with Electromagnetic Induction

Abstract

Detailed information on soil textural heterogeneity is essential for land management and conservation. It is well known that in individual fields, measurement of the soil's apparent electrical conductivity (ECa) offers an opportunity to map the clay content of soils with free drainage under a humid climate. At the catchment scale, however, units of different land management and differing sampling dates add variation to ECa and constrain the mapping across field boundaries. We analyzed their influence and compared three approaches for applying electromagnetic induction (EMv) to clay‐content mapping at the landscape scale across the boundaries of individual fields and different sampling dates. In the study region, a separate calibration of the relation between clay and ECa for each field and sampling date (fieldwise calibration) yielded satisfactory clay‐content predictions only if the costly precondition of sufficient calibration points for each field was fulfilled. We propose a method (nearest‐neighbors ECa correction) for unifying ECa across boundaries based only on the ECa data themselves, and the assumption of continuity of textural properties at field boundaries, which was fulfilled in the landscape studied. Prediction is calibrated once for the entire landscape, which allows a reduced set of calibration points. The coefficient of determination for predicting clay content (here, including silt <4 μm) was improved from R2 = 0.66 (no correction for land use and sampling date) to R2 = 0.85 (n = 46). With the method developed, ECa offers a powerful and cheap method of clay‐content mapping in agricultural landscapes.

The use of electromagnetic induction techniques in soils studies

“…Apparent conductivity has also been used as a surrogate measure of soil water content (Allred et al, 2005;Brevik et al, 2006;Hezarjaribi and Sourell, 2007;Huth and Poulton, 2007;Kachanoski et al, 1988Kachanoski et al, , 1990Khakural et al, 1998;Korsaeth et al, 2008;Mueller et al, 2003;Sheets and Hendrickx, 1995;Tromp-van Meerveld and McDonnell, 2009;Waine et al, 2000), soil texture (Heil and Schmidhalter, 2012;James et al, 2003;Saey et al, 2012a;White et al, 2012); and clay content (Cockx et al, 2009;Harvey and Morgan, 2009;King et al, 2005;Mueller et al, 2003;Sommer et al, 2003;Weller et al, 2007;Wienhold and Doran, 2008;Williams and Hoey, 1987). Electromagnetic induction has been used to assess difference in lithology and mineralogy (Bourgault and Rabenhorst, 2012;Doolittle et al, 2005Doolittle et al, , 2013, soil compaction (Al-Gaadi, 2012; Sudduth et al, 2010); CEC (Korsaeth et al, 2008;Triantafilis et al, 2009;), exchangeable Ca and Mg (McBride et al, 1990), CaCO 3 (Vitharana et al, 2008b); soil pH (Bianchini and Mallarino, 2002;Dunn and Beecher, 2007;Van Meirvenne et al, 2013;Vitharana et al, 2008b;Wienhold and Doran, 2008), soil organic carbon …”

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).…”

Understanding of morphometric features for adequate water resource management in arid environments

“…Schloeder et al (2001) observed that ordinary Kriging and inverse distance weighting were similarly accurate and effective methods, while thin-plate smoothing splines with tension were not. Weller et al (2007) concluded not only that the predications for Kriging were not satisfied by the Kriging method but also that it was as good as any other radial base function interpolation.…”