Estimating Soil Properties Distribution at a Restored Wetland Using Electromagnetic Imaging and Limited Soil Core Samples

Abstract

Electromagnetic imaging (EMI) provides an e cient approach for characterizing variations in soil physicochemical properties at a high spatial resolution. While EMI has been widely used to estimate variations in soil properties in agricultural, geotechnical, and contaminated sites, limited applications have been reported for wetlands. This study assesses the use of EMI for estimating soil property distributions at a restored wetland in northwestern Ohio, USA. We acquired spatial distribution of soil apparent electrical conductivity (ECa) and apparent magnetic susceptibility (MSa) via EMI over a 162,000 m 2 restored wetland using an EM-38-MK2 instrument towed behind a utility terrain vehicle equipped with a differential ground positioning system. We collected twenty-two undisturbed soil samples and analyzed them in the laboratory for soil moisture (SMC), organic matter (SOM), porosity, bulk density, and texture. A least squares linear regression model was used to compare the correlation between each soil property with measured ECa and MSa while ECa was used to predict the distribution of SMC and SOM using the statistical model validated using the leave-one-out technique. We observed strong correlations between soil texture, SMC, and SOM, and ECa with SOM showing a slightly dominant control. This study shows that ECa can predict the distribution of SMC and SOM in wetland soils to an accuracy of ~ 67-70% for these datasets. The spatial ECa patterns matched the USDA soil map for the site. This study validates the potential of extending EMI for characterizing wetland soil properties, improving sampling plans, and extrapolating soil property estimates to unsampled regions.

Combined self-potential and electromagnetic imaging provides insights into the spatial variation of wetland soil hydro-biogeochemical properties

“…Both sites are old farm fields restored to their original wetland conditions. The sites lie within the ancient Great Black Swamp region, one of the largest wetlands in Northwestern Ohio [1], [49], [53]. The region was drained in the mid to late 1800s to establish farmlands and has primarily been used to grow corn, wheat, and soybeans since the 1940s.…”

“…SP signals interpretation with respect to soil processes remains challenging, with no documented studies relating SP signals to wetland soil processes. However, apparent electrical conductivity (ECa) and magnetic susceptibility (MSa) have been widely used to characterize wetland soil properties, including soil texture, moisture, and organic matter contents [49]. Hydric soils in wetlands have high silt and clay contents, which correlates with high apparent electrical conductivity [50].…”

“…High organic matter and soil moisture content in wetland soils also increase their electrical conductivity [51]. With the wetland soil properties influencing the electrical conductivity and magnetic susceptibility, combining their measurements via electromagnetic imaging [49], [50] with SP could improve the interpretation of SP signals and enhance the overall characterization of wetland soil properties.…”

Hydraulic Bottom Friction and Aerodynamic Roughness Coefficients for Mangroves in Southwest Florida, USA

“…This process involves removing one record from the dataset, training the random forest on the remaining data, using that trained model to predict the target values for the held-out record, and repeating the cycle for every record in the dataset. LOOCV has been used in numerous applications of machine learning regression techniques to environmental studies, where the ground-truth data acquisition involves extensive field work [43][44][45] and thus produces small (by typical machine learning standards) training data sets. A bootstrapping validation method was not chosen since the RF algorithm itself relies heavily on bootstrapping when building the decision trees comprising the random forest.…”

Assessing the Impact of Brackish Water on Soil Salinization Using Time-Lapse Electromagnetic Induction Inversion

“…Vanderlinden et al [2] highlighted the potential of ECa to perform salinity monitoring at the field or 2 farm scale. Emmanuel et al [3] validated the potential of extending EMI for characterizing wetland soil properties, such as salinity, improving sampling plans, and interpolating soil property estimates to unsampled regions. Scudiero et al [4] performed ECa survey to identify locations that can be repeatedly sampled to infer the frequency distribution of soil salinity.…”