Submitted to: Bouyoucos Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2009
Publication Date: 9/8/2009
Citation: Sudduth, K.A., Kitchen, N.R., Myers, D.B., Drummond, S.T. 2009. Geophysical Sensing Applications on Claypan Soils [abstract]. In. Proceedings of Bouyoucos Conference for the Advancement of Geophysical Technologies Applied to Agroecosystems.
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, either individually or in combination. The objective of this research was to take a more theoretical approach, estimating TD using a two-layer soil model incorporating data from different ECa sensors along with their published 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 each ECa dataset. Best TD estimates (root mean square error of validation, RMSEv = 19 cm) were obtained with the shallower reading from the DUALEM-2S, while the least accurate estimates were obtained from the Veris datasets (RMSEv = 24-25 cm). Results from the model-based approach were very similar to those obtained by regressing TD on ECa-1. For both approaches, most occurrences of high TD error were localized in one area of one field, possibly due to the presence of subsoil features of lower conductivity that violated the two-layer assumption. TD error in this area was reduced by including additional ECa-1 terms from multiple sensors in the regression approach.