Inferring the Location of Preferential Flow Paths of a Leachate Plume by Using a DUALEM‐421 and a Quasi‐Three‐Dimensional Inversion Model

et al. 2017

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Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (ECa – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.

Section: Resultsmentioning

confidence: 99%

Field level digital mapping of soil mineralogy using proximal and remote‐sensed data

Nagra

Burkett

et al. 2017

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“…Alternatively, the collection of σ a data could be undertaken on 5‐m transects as opposed to the 25 m used here. In the latter case, a 1‐D spatially constrained quasi‐3‐D inversion algorithm could be employed (Triantafilis et al ., ).…”

Section: Resultsmentioning

confidence: 99%

Spatial prediction of the exchangeable sodium percentage at multiple depths using electromagnetic inversion modelling

Davies

Santos

2014

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Agriculture in the semi‐arid and arid areas of the world requires irrigation. However, in these areas, soils naturally contain large amounts of sodium (sodic) which can cause amongst other things, surface crusting on the topsoil or structural instability in the subsoil. The exchangeable sodium percentage (ESP) needs to be mapped to guide the application of gypsum. Whilst geostatistical techniques, such as ordinary, co‐ and 3‐D kriging have been used, they have often been criticized because they are unable to take into account soil knowledge concerning distribution, processes and factors of formation. The use of digital soil mapping methods which couple remote or proximally sensed data with soil information is increasingly becoming useful because of the production of high‐resolution ancillary data. In this study, we first invert (using EM4Soil software) the electrical conductivity (σa –mS/m) of DUALEM‐421 data collected along a single transect. In doing this, we generate a 2‐dimensional electromagnetic conductivity image (EMCI). We couple the estimates of electrical conductivity (σ – mS/m) at 0.30 m depth increments down to 1.5 m with measured soil ESP. We compare the results of inversion using various possible coil array configurations of the DUALEM‐421 to determine a suitable set of data. We conclude that the use of the DUALEM‐41 is optimal (r2 = 0.70). We use the calibration to estimate ESP along adjacent transects where we also generate EMCI. We are thus able to estimate ESP at various depths across a clay plain and an associated prior stream channel. We conclude that the collection of additional transects of DUALEM‐421 data as well as the use of a quasi‐3‐D inversion modelling approach would improve prediction.

“…First, quasi‐2D inversions were conducted using the EC a data collected on each day at the 22 measurement locations. To determine the best EMCI with respect to θ , EM4Soil was run with different parameters (Triantafilis & Monteiro Santos, ; Triantafilis et al ., ,b; Huang et al ., ). This included forward modelling, either based on the cumulative function (CF) (McNeill, ) or the full solution (FS) of electromagnetic fields in a layered earth (Keller & Frischknecht, ), inversion algorithms (i.e.…”

Section: Methodsmentioning

confidence: 99%

Potential to predict depth‐specific soil–water content beneath an olive tree using electromagnetic conductivity imaging

Martínez

Vanderlinden

et al. 2018

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Efficient monitoring of soil moisture is becoming increasingly important. To understand soil–plant–water dynamics, we evaluate the potential of using a multiple‐coil‐array electromagnetic induction instrument and inversion software to map soil moisture beneath an olive tree. On twelve different days, we collected apparent electrical conductivity (ECa) data using a DUALEM‐21S and the volumetric soil moisture (θ) using a bank of soil moisture sensors on opposite sides of the tree. Using EM4Soil, we inverted the ECa data on five of the days and established a site‐specific calibration between estimates of true electrical conductivity (σ) and θ. The strongest calibration relationship between σ and θ (R2 = 0.65) was obtained for a full‐solution, S2 algorithm and damping factor of 1.2. A leave one out cross‐validation (LOOCV) showed the calibration was robust, with a root mean square error (RMSE) of 0.046 m3/m3, a mean error (ME) of 0.001 m3/m3 and a Lin's concordance of 0.72. We subsequently evaluated the calibration relationship on the seven remaining days and over a drying period of 120 days. This approach provides information about the temporal evolution of θ by a LOOCV of validation with a RMSE of 0.037, ME of −0.003 and a Lin's concordance of 0.54. Improvement could be achieved by aligning the DUALEM‐21S in the same orientation as the sensors, with time‐lapse inversion also being advantageous.