Modeling soil electrical conductivity–depth relationships with data from proximal and penetrating ECa sensors

Abstract

Apparent soil electrical conductivity (ECa), a widely used proximal soil sensing technology, is related to several important soil properties, including salinity, clay content, and bulk density. Particularly in layered soils, interpretation of ECa variations would be enhanced with better calibrations to depth-wise variations in these soil properties. Thus, the objective of this research was to combine point measurements of layer conductivity obtained using ECa-equipped penetrometers with mapped ECa data from proximal sensors for improved quantification of conductivity–depth relationships in layered soils. Data were collected from a set of large plots on a claypan-soil landscape in central Missouri which had been managed in grain and perennial grass cropping systems for 20years. Soil ECa variation with depth was represented by both two- and three-layer models that were visualized and parameterized with the use of penetrometer ECa data. The three-layer model provided a more realistic representation of the soil profiles in the study area and provided similar topsoil depth estimation accuracy as the commonly used two layer model. Penetrometer ECa data also provided efficient and accurate (r2=0.92) estimation of calibration-point topsoil depth as an alternative to soil coring and manual determination. A key to accurate model calibration was selection of points from areas of spatially-homogeneous proximal ECa. Combining penetrometer ECa with proximal ECa data improved modeling of conductivity–depth relationships in terms of model selection, model parameterization, and model calibration.