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 properties, elect...
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| Published in: | Sensors (Basel, Switzerland) Vol. 19; no. 18; p. 3936 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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.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. |
| Author | Zhao, Dongxue Srihabun, Pranee Zare, Ehsan Triantafilis, John Khongnawang, Tibet |
| AuthorAffiliation | 1 School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW 2052, Australia 2 Land Development Regional Office 5, Land Development Department, Khon Kaen 40000, Thailand |
| AuthorAffiliation_xml | – name: 2 Land Development Regional Office 5, Land Development Department, Khon Kaen 40000, Thailand – name: 1 School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW 2052, Australia |
| Author_xml | – sequence: 1 givenname: Tibet orcidid: 0000-0002-0231-4818 surname: Khongnawang fullname: Khongnawang, Tibet – sequence: 2 givenname: Ehsan surname: Zare fullname: Zare, Ehsan – sequence: 3 givenname: Dongxue surname: Zhao fullname: Zhao, Dongxue – sequence: 4 givenname: Pranee surname: Srihabun fullname: Srihabun, Pranee – sequence: 5 givenname: John surname: Triantafilis fullname: Triantafilis, John |
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| CitedBy_id | crossref_primary_10_1016_j_jhydrol_2021_127232 crossref_primary_10_3390_s19194293 crossref_primary_10_1016_j_agwat_2020_106705 crossref_primary_10_1016_j_catena_2021_105702 crossref_primary_10_3390_rs14184448 crossref_primary_10_1016_j_still_2020_104618 crossref_primary_10_1016_j_chemosphere_2024_142425 crossref_primary_10_3390_app122010550 crossref_primary_10_1016_j_catena_2021_105919 crossref_primary_10_3390_s19235280 crossref_primary_10_1080_10106049_2022_2076913 crossref_primary_10_1016_j_geoderma_2020_114611 crossref_primary_10_1016_j_geoderma_2020_114852 crossref_primary_10_1016_j_catena_2021_105791 crossref_primary_10_3390_soilsystems4020025 crossref_primary_10_1016_j_compag_2022_107409 |
| Cites_doi | 10.1002/jpln.200625130 10.1071/SR09149 10.1111/j.1365-2389.1992.tb00128.x 10.1097/00010694-200106000-00007 10.20944/preprints201706.0075.v1 10.2134/agronmonogr9.1 10.1016/S0016-7061(00)00025-2 10.1111/gwat.12231 10.1016/j.scitotenv.2017.05.074 10.2136/sssaj2003.9190 10.1016/S0960-1481(01)00005-2 10.2136/sssaj2005.0273 10.1111/sum.12261 10.1016/0016-7061(95)00007-B 10.1016/j.catena.2018.05.001 10.1071/SR08240 10.1111/j.1475-2743.2011.00352.x 10.2136/sssaj2018.03.0100 10.1007/s11368-012-0483-3 10.1152/advan.00006.2009 10.1016/0166-6622(90)80174-3 10.1016/j.catena.2017.07.002 10.1190/1.1442303 10.2136/sssaj2006.0054 10.1071/SR9870021 10.1190/1.3537834 10.1111/j.1365-2389.1980.tb02084.x 10.1190/geo2011-0507.1 10.2136/sssaj2015.06.0238 10.1016/j.geoderma.2019.01.033 10.2307/2532051 10.1016/S0016-7061(01)00074-X 10.2134/agronj2003.4720 |
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| SubjectTerms | cation exchange capacity clay content Geophysics Laboratories Particle size quasi-3D inversion algorithm Salinity sandy infertile soil Seasons Software Sugarcane Three-dimensional mapping |
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| Title | Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software |
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