Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software
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 propert...
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MDPI AG
2019-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/18/3936 |
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| author | Tibet Khongnawang Ehsan Zare Dongxue Zhao Pranee Srihabun John Triantafilis |
| author_facet | Tibet Khongnawang Ehsan Zare Dongxue Zhao Pranee Srihabun John Triantafilis |
| author_sort | Tibet Khongnawang |
| collection | DOAJ |
| description | 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 (EC<sub>a</sub>—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 EC<sub>a</sub> in horizontal (EC<sub>ah</sub>) and vertical (EC<sub>av</sub>) 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 EC<sub>ah</sub> and EC<sub>av</sub> data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between EC<sub>a</sub> and soil properties was between EC<sub>ah</sub> and subsoil clay (R<sup>2</sup> = 0.43) and subsoil CEC (R<sup>2</sup> = 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 (R<sup>2</sup> = 0.65) and CEC (R<sup>2</sup> = 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 EC<sub>a</sub> 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. |
| first_indexed | 2024-04-11T13:45:22Z |
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| spelling | doaj.art-4778ec4d47c9405ca8f6b0f87add8b092022-12-22T04:21:06ZengMDPI AGSensors1424-82202019-09-011918393610.3390/s19183936s19183936Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion SoftwareTibet Khongnawang0Ehsan Zare1Dongxue Zhao2Pranee Srihabun3John Triantafilis4School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW 2052, AustraliaSchool of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW 2052, AustraliaSchool of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW 2052, AustraliaSchool of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW 2052, AustraliaSchool of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW 2052, AustraliaMost 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 (EC<sub>a</sub>—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 EC<sub>a</sub> in horizontal (EC<sub>ah</sub>) and vertical (EC<sub>av</sub>) 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 EC<sub>ah</sub> and EC<sub>av</sub> data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between EC<sub>a</sub> and soil properties was between EC<sub>ah</sub> and subsoil clay (R<sup>2</sup> = 0.43) and subsoil CEC (R<sup>2</sup> = 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 (R<sup>2</sup> = 0.65) and CEC (R<sup>2</sup> = 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 EC<sub>a</sub> 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.https://www.mdpi.com/1424-8220/19/18/3936Three-dimensional mappingquasi-3D inversion algorithmcation exchange capacityclay contentsandy infertile soil |
| spellingShingle | Tibet Khongnawang Ehsan Zare Dongxue Zhao Pranee Srihabun John Triantafilis Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software Sensors Three-dimensional mapping quasi-3D inversion algorithm cation exchange capacity clay content sandy infertile soil |
| title | Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software |
| title_full | Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software |
| title_fullStr | Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software |
| title_full_unstemmed | Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software |
| title_short | Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software |
| title_sort | three dimensional mapping of clay and cation exchange capacity of sandy and infertile soil using em38 and inversion software |
| topic | Three-dimensional mapping quasi-3D inversion algorithm cation exchange capacity clay content sandy infertile soil |
| url | https://www.mdpi.com/1424-8220/19/18/3936 |
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