Three-dimensional imaging of active acid sulfate soil using a DUALEM-21S and EM inversion software.

One of the major environmental issues in Finland is the presence of large tracts of acid sulfate soil (ASS) landscapes along the coast. Accurately identifying the distribution of ASS sediments, and in particular soil pH, is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) with various ancillary information. Although electromagnetic (EM) induction data has shown potential in mapping ASS, few studies have been conducted to map the spatial distribution of pH at different depths. In this study, a DUALEM-21S was used to collect apparent soil electrical conductivity (ECa) data across a 23-ha field near Vaasa, which lies along the western coast of Finland. A quasi-3D inversion algorithm was used to calculate the estimated true electrical conductivity (σ - mS m-1). A calibration relationship was developed between σ and incubation-pH measured at various depths from topsoil (0-0.2 m), subsurface (0.2-0.4 m) and subsoil (e.g. 0.4-0.6 and 1.8-2 m) using an artificial neural network (ANN) model. The performance of the ANN model was good given the large R2 values for calibration (0.72) and validation (0.65). It was concluded that the combination of ECa data and quasi-3D inversion algorithm (in EM4Soil) was able to map the spatial distribution of incubation-pH associated within an ASS landscape. The approach has the potential to be applied across the coastal areas of Finland and elsewhere to map incubation-pH and identify active-ASS areas and thereby improve the management of these areas.

Mapping cation exchange capacity using a Veris-3100 instrument and invVERIS modelling software.

The cation exchange capacity (CEC) is one of the most important soil properties as it influences soil's ability to hold essential nutrients. It also acts as an index of structural resilience. In this study, we demonstrate a method for 3-dimensional mapping of CEC across a study field in south-west Spain. We do this by establishing a linear regression (LR) between the calculated true electrical conductivity (σ - mS/m) and measured CEC (cmol(+)/kg) at various depths. We estimate σ by inverting Veris-3100 data (ECa - mS/m) collected along 47 parallel transects spaced 12m apart. We invert the ECa data acquired from both shallow (0-0.3m) and deep (0-0.9m) array configurations, using a quasi-three-dimensional inversion algorithm (invVeris V1.1). The CEC data was acquired at 40 locations and from the topsoil (0-0.3m), subsurface (0.3-0.6m) and subsoil (0.6-0.9m). The best LR between σ and CEC was achieved using S2 inversion algorithm using a damping factor (λ)=18. The LR (CEC=1.77+0.33×σ) had a large coefficient of determination (R2=0.89). To determine the predictive capability of the LR, we validated the model using a cross-validation. Given the high accuracy (root-mean-square-error [RMSE]=1.69 cmol(+)/kg), small bias (mean-error [ME]=-0.00cmol(+)/kg) and large coefficient of determination (R2=0.88) and Lin's concordance (0.94), between measured and predicted CEC and at various depths, we conclude we were well able to predict the CEC distribution in topsoil and the subsurface. However, the predictions made in the subsoil were poor due to limited data availability in areas where ECa changed rapidly from small to large values. In this regard, improvements in prediction accuracy can be achieved by collection of ECa in more closely spaced transects, particularly in areas where ECa varies over short spatial scales.

Mapping soil salinity and a fresh-water intrusion in three-dimensions using a quasi-3d joint-inversion of DUALEM-421S and EM34 data.

In order to understand the drivers of topsoil salinization, the distribution and movement of salt in accordance with groundwater need mapping. In this study, we described a method to map the distribution of soil salinity, as measured by the electrical conductivity of a saturated soil-paste extract (ECe), and in 3-dimensions around a water storage reservoir in an irrigated field near Bourke, New South Wales, Australia. A quasi-3d electromagnetic conductivity image (EMCI) or model of the true electrical conductivity (σ) was developed using 133 apparent electrical conductivity (ECa) measurements collected on a 50m grid and using various coil arrays of DUALEM-421S and EM34 instruments. For the DUALEM-421S we considered ECa in horizontal coplanar (i.e., 1mPcon, 2mPcon and 4mPcon) and vertical coplanar (i.e., 1mHcon, 2mHcon and 4mHcon) arrays. For the EM34, three measurements in the horizontal mode (i.e., EM34-10H, EM34-20H and EM34-40H) were considered. We estimated σ using a quasi-3d joint-inversion algorithm (EM4Soil). The best correlation (R2=0.92) between σ and measured soil ECe was identified when a forward modelling (FS), inversion algorithm (S2) and damping factor (λ=0.2) were used and using both DUALEM-421 and EM34 data; but not including the 4m coil arrays of the DUALEM-421S. A linear regression calibration model was used to predict ECe in 3-dimensions beneath the study field. The predicted ECe was consistent with previous studies and revealed the distribution of ECe and helped to infer a freshwater intrusion from a water storage reservoir at depth and as a function of its proximity to near-surface prior stream channels and buried paleochannels. It was concluded that this method can be applied elsewhere to map the soil salinity and water movement and provide guidance for improved land management.

Mapping water table depth using geophysical and environmental variables.

Despite its importance, accurate representation of the spatial distribution of water table depth remains one of the greatest deficiencies in many hydrological investigations. Historically, both inverse distance weighting (IDW) and ordinary kriging (OK) have been used to interpolate depths. These methods, however, have major limitations: namely they require large numbers of measurements to represent the spatial variability of water table depth and they do not represent the variation between measurement points. We address this issue by assessing the benefits of using stepwise multiple linear regression (MLR) with three different ancillary data sets to predict the water table depth at 100-m intervals. The ancillary data sets used are Electromagnetic (EM34 and EM38), gamma radiometric: potassium (K), uranium (eU), thorium (eTh), total count (TC), and morphometric data. Results show that MLR offers significant precision and accuracy benefits over OK and IDW. Inclusion of the morphometric data set yielded the greatest (16%) improvement in prediction accuracy compared with IDW, followed by the electromagnetic data set (5%). Use of the gamma radiometric data set showed no improvement. The greatest improvement, however, resulted when all data sets were combined (37% increase in prediction accuracy over IDW). Significantly, however, the use of MLR also allows for prediction in variations in water table depth between measurement points, which is crucial for land management.