MAPPING CLAY CONTENT VARIATION USING ELECTROMAGNETIC INDUCTION TECHNIQUES

Authors: TRIANTAFILIS, JOHN - U NEW SOUTH WALES AU.; LESCH, SCOTT - UC RIVERSIDE, CA

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/15/2004
Publication Date: 3/1/2005
Citation: Triantafilis, J., Lesch, S.M. 2005. Mapping clay content variation using electromagnetic induction techniques. Computers and Electronics in Agriculture. 46:203-237.

Interpretive Summary: This research describes the application of statistical classification and hierarchical modeling strategies for predicting the (0-7 m) average clay content in the Trangie valley (Australia) from electromagnetic induction (EM) sensors. In this study, a fuzzy k-means classification strategy was used to first partition the study area into differing hydrogeologic regions. Model-based, spatial response surface sampling designs were then used to select soil calibration sampling locations within each region (using the EM data as the covariate information). The goal of this research was two-fold: (i) to examine if the EM data could be used to accurately map the clay content, and (ii) to determine if the precision (i.e., accuracy) of these predictions could be improved by incorporating a pre-classification strategy into the modeling approach. The results suggest that the clay content was accurately mapped (by the EM survey data), but also that the pre-classification analysis did not improve the precision of the estimates.

Technical Abstract: In this study a methodology is outlined for mapping spatial distribution of bulk soil average clay content (% clay) to a depth of 7 m using EM34 measurements. The study was conducted southeast of Trangie in the lower Macquarie valley of New South Wales, Australia. Two EM sensors were employed. To provide deep bulk-soil EM measurements, an EM34 was used in the horizontal dipole mode at coil configurations of 10, 20, and 40 m (respectively designated, EM34-10, EM34-20, and EM34-40). For shallower bulk soil EM measurements, an EM38 was used in vertical and horizontal modes (EM38-v and EM38-h, respectively). A total of 755 locations were measured on a grid of approximately 0.5 km. In order to classify the EM34 data into broad physiographic and hydrogeological units, fuzzy k-means (FKM) classification was applied. A spatial response surface sampling (SRSS) design was invoked to select sampling sites within each of the four regular and one Extragrade class. From 40 calibration holes (i.e., 8 from each class) soil samples were taken at 1 m intervals from the soil surface to a depth of 7 m. Each sample was analyzed for clay content then averaged for a 0'7 m clay content (% clay) for each hole. In order to predict the % clay across the landscape a hierarchical spatial regression model (HSR) was developed using a composite signal variable [i.e., ln(EM34-10) + ln(EM34-40) + ln(EM38-h)] and first-order trend surface components (i.e., Easting and Northing). The final map of % clay generally reflects the known surface clay content and provides information about the spatial distribution of subsurface % clay variability. We conclude that although the FKMe analysis did not result in an improved calibration within each class, the approach delineated similar clusters of signal readings that were useful in providing a framework to determine a soil sampling design that accounted for variations in physiography and hydrogeology.