Title: Estimating depth to argillic soil horizons using apparent electrical conductivity Authors
Submitted to: Journal of Environmental & Engineering Geophysics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 23, 2010
Publication Date: September 1, 2010
Citation: Sudduth, K.A., Kitchen, N.R., Myers, D.B., Drummond, S.T. 2010. Estimating depth to argillic soil horizons using apparent electrical conductivity. Journal of Environmental & Engineering Geophysics. 15(3):135-146. Interpretive Summary: As farmers have become familiar with the technologies of precision agriculture, such as yield monitors and GPS, there has been increasing interest in being able to map soil variability within fields. One on-the-go soil sensing technology is soil apparent electrical conductivity (EC), for which there are several sensors commercially available. Each of the commercial sensors measures EC as a single reading averaged over a measurement depth, and measurement depth is different for the different sensors. We wanted to find which sensor data would give the best picture of topsoil depth variability for claypan soils commonly found in several states of the U.S. Midwest, because topsoil depth is an important factor in the productivity of these soils. We collected data with three commercial EC sensors on two fields in Missouri and related the data to topsoil depth with two different methods, one using statistical analysis and one using calculations based on the theoretical response functions of the sensors. We found that topsoil depth estimates by two methods were very similar and that the EC sensors with medium measurement depths gave the best results. We also found that better estimates could be obtained when combining data from multiple sensors. These results will benefit users of EC instruments by providing methods to calculate soil variables from the EC data. The results will also benefit scientists and extension personnel who may need to recommend the best EC instrument to use in a particular situation.
Technical Abstract: Maps of apparent electrical conductivity (ECa) of the soil profile are widely used in precision agriculture practice and research. A number of ECa sensors are commercially available, each with a unique response function (i.e., the relative contribution of soil at each depth to the integrated ECa reading). In past research we estimated depth to an argillic horizon (i.e., topsoil depth, TD) on claypan soils by fitting empirical equations to ECa data from such sensors. The objective of this research was determine if TD estimates could be improved by combining data from multiple ECa sensors and by solving for TD by inverting a two-layer soil model incorporating instrument response functions. Data were obtained with three sensors having five different ECa depth-response functions (Veris 3150, Geonics EM38 vertical dipole mode, and DUALEM-2S) on two Missouri claypan-soil fields. Soil cores were also obtained in each field to provide calibration and validation TD data. Using a numerical optimization approach, response-function calibration models were developed for ECa variables individually and in combination. Similarly, linear regression was applied to single and multiple variables. Root mean square error of validation (RMSEv) of single-variable TD estimates varied from 19 to 26 cm, with better results generally obtained for those variables with moderately deep ECa response functions. Results from the model-based approach were very similar to those obtained by regressing TD on ECa-1. The best calibrations using multiple variables in model inversion or regression were somewhat better than those using single variables, with RMSEv of 15 cm and 18 cm, respectively. For all approaches, occurrences of highest TD error were localized to one area of one field, possibly because soils in this area violated the model assumption of spatially heterogeneous soil layer conductivity. A model solution that allowed layer conductivities to vary spatially should be investigated for possible improvements in TD estimation.