Digital regolith mapping of clay across the Ashley irrigation area using electromagnetic induction data and inversion modelling

Zhao

et al. 2019

Sensors

Self Cite

Most cultivated upland areas of northeast Thailand are characterized by sandy and infertile soils, which are difficult to improve agriculturally. Information about the clay (%) and cation exchange capacity (CEC—cmol(+)/kg) are required. Because it is expensive to analyse these soil properties, electromagnetic (EM) induction instruments are increasingly being used. This is because the measured apparent soil electrical conductivity (ECa—mS/m), can often be correlated directly with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay and CEC. In this study, we explore the potential to use this approach and considering a linear regression (LR) between EM38 acquired ECa in horizontal (ECah) and vertical (ECav) modes of operation and the soil properties at each of these depths. We compare this approach with a universal LR relationship developed between calculated true electrical conductivity (σ—mS/m) and laboratory measured clay and CEC at various depths. We estimate σ by inverting ECah and ECav data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between ECa and soil properties was between ECah and subsoil clay (R2 = 0.43) and subsoil CEC (R2 = 0.56). We concluded these LR were unsatisfactory to predict clay or CEC at any of the three depths, however. In comparison, we found that a universal LR could be established between σ with clay (R2 = 0.65) and CEC (R2 = 0.68). The LR model validation was tested using a leave-one-out-cross-validation. The results indicated that the universal LR between σ and clay at any depth was precise (RMSE = 2.17), unbiased (ME = 0.27) with good concordance (Lin’s = 0.78). Similarly, satisfactory results were obtained by the LR between σ and CEC (Lin’s = 0.80). We conclude that in a field where a direct LR relationship between clay or CEC and ECa cannot be established, can still potentially be mapped by developing a LR between estimates of σ with clay or CEC if they all vary with depth.

Section: Conclusion and Discussionmentioning

confidence: 99%

Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software

Khongnawang

Zhao

et al. 2019

Sensors

Self Cite

“…Although EM instruments only provide a bulk measurement of EC a , inversion is providing useful estimates of soil true electrical conductivity (σ, mS m −1 ), which can be used to calibrate soil properties at the same depths. Examples include calibration for soil salinity (Zare, Huang, Monteiro Santos, & Triantafilis, 2015), clay (Zhao et al., 2019), and cation exchange capacity (Koganti et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Time‐lapse imaging of soil moisture using electromagnetic conductivity imaging: Wetting phase

Soil Science Soc of Amer J

Arshad

et al. 2021

Self Cite

The Vertosols of the lower Namoi valley in New South Wales, Australia, have been extensively developed for irrigated cotton (Gossypium hirsutum L.) production. However, there are competing demands for water from mining, environmental needs, and agriculture, with climate change forecasts suggesting reduced rainfall and hotter climates. This means irrigators need to improve water efficiency, which requires methods to monitor the soil volumetric water content (θ, m 3 m -3 ). The aim of this study is to assess if a multicoil electromagnetic instrument (DUALEM-421), which measures soil apparent electrical conductivity (EC a , mS m -1 ), can be used to value add to limited measurements of θ and allow extrapolation along a uniformly heavy clay irrigation furrow. Specifically, we seek to invert EC a using EM4Soil software to generate electromagnetic conductivity images, whereby the estimates of soil true electrical conductivity (σ, mS m -1 ) in the topsoil, subsurface, and subsoil are correlated with θ measured by Decagon GS3 sensors installed at the same depths. We also compared the usefulness of inversion of different combinations of DUALEM EC a : DUALEM-1, DUALEM-2, and DUALEM-21. We found estimated σ from inversion of DUALEM-421 EC a had good agreement (i.e., Lin's concordance = .84) with measured soil bulk electrical conductivity (σ b , mS m -1 ). We explored the relationship between DUALEM-421 estimated σ and θ using an artificial neural network. The predictions showed the model had good coefficient of determination (R 2 = .74) with good accuracy (RMSE = 0.04 m 3 m -3 ) and demonstrated good agreement between measured and predicted θ (Lin's = .84).

“…For example, CEC less than 20 cmol(+)kg −1 indicates poor shrink-swell, whereas CEC between 20 and 40 cmol(+)kg −1 suggests moderate shrink-swell potential. Moreover, the electrical conductivity of saturated soil paste extracts (EC e -dS m −1 ) can indicate recharge areas as a function of non-saline (EC e < 2 dS m −1 ) and discharge areas in highly saline (8)(9)(10)(11)(12)(13)(14)(15)(16) dS m −1 ) locations. Unfortunately, extensive soil characterisation is challenging, owing to time and the cost-prohibitive nature of sampling and analysis.…”

Section: Introductionmentioning

confidence: 99%

“…However, given depth-specific information is required, newer approaches are essential to predict soil properties with the depth. A more recent study [10] showed how the inversion of Geonics (Geonics Ltd., Ontario, Canada) EM38 and EM34 EC a using EM4Soil software (EMTOMO, Lisboan, Portugal) can characterise clay at 1-m depth increments up to 12 m and across 40,000 ha in the nearby lower Gwydir Valley. In another study, Khognawang et al (2019) [11] used depth-specific estimates of true electrical conductivity (σ-mS m −1 ) inverted from EC a collected from an EM38 in order to develop a linear regression model to predict CEC at the field level and at 0.3-m increments to 0.9-m. Herein, we aim to characterise soil variation by depth along a 4-km section of a farm supply channel by (i) collecting DUALEM-421 EC a data; (ii) using EC a to inform a soil sample site selection; (iii) using the EM4soil software package to make electromagnetic conductivity images (EMCI) from the inversion of EC a ; (iv) determine the best set of inversion parameters (e.g., forward modelling, inversion algorithm and damping factor) to account for soil physical (clay and sand content) and chemical (EC e and CEC) properties and (v) use a support vector machine (SVM) to predict the soil properties from σ and understand how two-dimensional images can be used to identify more permeable prior stream channels and the implication for water management.…”

Section: Introductionmentioning

confidence: 99%

Identifying Potential Leakage Zones in an Irrigation Supply Channel by Mapping Soil Properties Using Electromagnetic Induction, Inversion Modelling and a Support Vector Machine

Khongnawang

et al. 2020

Soil Systems

Self Cite

The clay alluvial plains of Namoi Valley have been intensively developed for irrigation. A condition of a license is water needs to be stored on the farm. However, the clay plain was developed from prior stream channels characterised by sandy clay loam textures that are permeable. Cheap methods of soil physical and chemical characterisations are required to map the supply channels used to move water on farms. Herein, we collect apparent electrical conductivity (ECa) from a DUALEM-421 along a 4-km section of a supply channel. We invert ECa to generate electromagnetic conductivity images (EMCI) using EM4Soil software and evaluate two-dimensional models of estimates of true electrical conductivity (σ—mS m−1) against physical (i.e., clay and sand—%) and chemical properties (i.e., electrical conductivity of saturated soil paste extract (ECe—dS m−1) and the cation exchange capacity (CEC, cmol(+) kg−1). Using a support vector machine (SVM), we predict these properties from the σ and depth. Leave-one-site-out cross-validation shows strong 1:1 agreement (Lin’s) between the σ and clay (0.85), sand (0.81), ECe (0.86) and CEC (0.83). Our interpretation of predicted properties suggests the approach can identify leakage areas (i.e., prior stream channels). We suggest that, with this calibration, the approach can be used to predict soil physical and chemical properties beneath supply channels across the rest of the valley. Future research should also explore whether similar calibrations can be developed to enable characterisations in other cotton-growing areas of Australia.