Submitted to: European Conference on Precision Agriculture Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 2/15/2017
Publication Date: 7/16/2017
Citation: Sudduth, K.A., Kitchen, N.R., Drummond, S.T. 2017. Inversion of soil electrical conductivity data to estimate layered soil properties. European Conference on Precision Agriculture Proceedings. 8(2)433-438. 2017. Interpretive Summary: Bulk apparent soil electrical conductivity (ECa) is the most widely used type of soil sensing used in precision agriculture. Soil ECa relates to multiple soil properties, including clay content, water content, and salt content (salinity), and farmers and others have found it useful for mapping variations within fields. What has been more difficult is developing numeric relationships between ECa readings and important soil properties. In part, this is because ECa readings represent the entire profile, while soil properties are most often determined for specific soil layers. In this research we investigated a mathematical approach known as model inversion to calculate individual layer ECa values for the same layers where soil property data were available. Input data was from four fields in Missouri with variable soils. The inversion process provided layer values that were strongly related to measured layer ECa at calibration points established in each field. Layer conductivity data successfully estimated soil texture in two fields but not in the third field where soil data were available. Further examination of this approach is warranted to potentially provide improved ways to estimate depth-variable soil properties using ECa. If proven successful, the inversion approach could help provide soil datasets important for agronomic and environmental modelling, potentially benefiting producers, agribusiness entities, and the general public.
Technical Abstract: CBulk apparent soil electrical conductivity (ECa) sensors respond to multiple soil properties, including clay content, water content, and salt content (i.e., salinity). They provide a single sensor value for an entire soil profile down to a sensor-dependent measurement depth, weighted by a nonlinear response function. Because of this, it is generally difficult to elucidate strong relationships between ECa and the measured properties of individual soil layers. This research investigated inversion of the equations that govern the ECa-depth response relationship to reconstruct the soil conductivity in profile layers using data collected in multiple fields in the Midwest US. Layer conductivities obtained by inversion were first validated by comparison with true conductivities measured as a function of depth with an ECa-sensing penetrometer. Then, the validated layer conductivities were related to laboratory- measured soil properties. Inversion worked well but sometimes required iterative adjustment of initial conditions and other input parameters to obtain best results. Strong linear relationships (r2 = 0.76) were obtained between inversion-estimated and measured layer conductivity data in all cases, sometimes with a truncated depth range. Layer conductivity data successfully estimated soil texture fractions in the two alluvial fields examined. This was not the case for a claypan soil field, where there appeared to be parameters other than texture strongly affecting the EC response. Further examination of this approach is warranted to potentially provide improved ways to estimate depth-variable soil properties using ECa.