Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

Abstract

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (ECp – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R2 = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (ECe – dS/m). Our interpretation based on estimates of ECe suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.

Mapping soil salinity using electromagnetic conductivity imaging—A comparison of regional and location‐specific calibrations

“…σ can be correlated using linear regression (LR) with measured EC e at any depth. For example, Stockmann, Huang, Minasny, and Triantafilis (2017) showed how such an LR could be used to predict dryland EC e down a catenary sequence in the Hunter Valley, Australia. A similar approach was used to calibrate and map EC e across a small alluvial-estuarine area influenced by acid sulfate soil (Goff, Huang, Wong, Monteiro Santos, & Triantafilis, 2014).…”

Quantitative mapping of soil salinity using the DUALEM‐21S instrument and EM inversion software

“…Recently, methods and techniques have been shown to be capable of accounting for this vertical change through the use of EM inversion software. In 2‐D, this includes the development of electromagnetic conductivity images (EMCI) to map salinity associated with gilgai landscapes (Huang, Mokhtari, Cohen, Monteiro Santos, & Triantafilis, ), across an alluvial–estuarine area (Goff, Huang, Wong, Monteiro Santos, Wege, & Triantafilis, ) and down a toposequence (Stockmann, Huang, Minasny, & Triantifilis, ). In 3‐D, salinity has also been mapped using EMCI within a field affected by dryland salinity (Huang, Kilminster, Barrett‐lennard, & Triantifilis, ) and in an irrigated field (Zare, Huang, Monteiro Santos, & Triantafilis, ).…”

Reconnaissance scale mapping of salinity in three‐dimensions using EM38 and EM34 data and inversion modelling

“…As such, the intercept (4.1253) of their calibration was much larger than herein, where the range in EC e was much smaller (0–10.31 dS m −1 ). Interestingly, Stockmann et al (2017) found that while the intercept (−0.079; 0.046 × σ − 0.079) was equivalent to the one herein the slope (0.046) was almost three times larger for the calibration between EC e and σ estimated from inversion of DUALEM‐421 EC a measured down a catenary sequence in the nearby Hunter Valley and on duplex soil types. We attribute this to the more highly weathered nature of the clays, which characterise the Australian Soil Orders (i.e., Kurosols, Chromosols and Dermosols) of the Pokolbin Soil Landscape (Kovac & Lawrie, 1991) and derived from parent rocks of Permian age (e.g., mudstones).…”

“…In addition, EC e has been mapped for average rootzone or discrete depths. Recently, DUALEM-421 data (Stockmann et al, 2017) or using two instruments (EM38 and EM31) in concert (Triantafilis & Monteiro Santos, 2010), have been inverted to true electrical conductivity (σ, mS m −1 ) at various depths using a quasi-two dimensional (Q-2D) inversion software (EM4Soil, EMTOMO, 2014); where σ has been directly correlated to EC e at the same depths, to develop a LR calibration. Most recently, Zare et al (2015) showed that using a quasi-three dimensional (Q-3D) inversion of DUALEM-421 and EM34 could be employed to map soil EC e adjacent to a large water reservoir.…”