Title: Mapping Conductivity-Depth Relationships by Combining Proximal and Penetrating ECa Sensors Authors
Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: April 18, 2011
Publication Date: May 15, 2011
Citation: Sudduth, K.A., Myers, D.B., Kitchen, N.R., Drummond, S.T. 2011. Mapping Conductivity-Depth Relationships by Combining Proximal and Penetrating ECa Sensors. In: Adamchuk, V.I., Viscarra, R.A., editors. Proceedings of the 2nd Global Workshp on Proximal Soil Sensing, May 15-18, 2011, Montreal, Canada. pp. 32-35. Interpretive Summary: Apparent soil electrical conductivity (ECa) has become widely used to map within-field soil variability. The main soil parameters affecting ECa vary from place to place, and maps made with commercial mobile ECa sensors have been related to variations in soil parameters such as salinity, texture or moisture. The resulting maps have then been used to guide site-specific management such as fertilizer application or seeding rate. Additionally, by combining data from multiple mobile ECa sensors and applying various mathematical and statistical techniques, it is possible to infer by-depth variations in soil properties. This information is particularly useful for understanding the dynamics of subsurface water movement across landscapes. However, existing analysis approaches are not always reliable due to the mathematical structure of the ECa datasets. Our goal in this research was to overcome this limitation by combining mobile ECa sensor data with point measurements of soil layer conductivity obtained using an ECa-equipped penetrometer. We found that the penetrometer ECa data improved our ability to create a good mathematical model of the soil profile and also provided an efficient way to obtain calibration data for the model. On a test dataset our procedure was better able to represent variations of ECa in the soil profile than previous methods. After further validation this new procedure could allow users of ECa sensors to obtain more accurate estimates of how soil properties vary and provide better information on which to base management decisions.
Technical Abstract: Apparent soil electrical conductivity (ECa), a widely used proximal soil sensing technology, could be made more useful through better calibration to subsurface variations in soil properties. In this research we develop methods to improve calibrations through combining by-depth measurements from an ECa penetrometer with data from mobile proximal ECa sensors. Penetrating ECa data facilitated visualization and parameterization of a more accurate soil-layer model and provided an efficient way to obtain model calibration data for topsoil depth determination in strongly-layered claypan soils. Further research is required to validate this approach for other profile properties and soils.