Articles | Volume 21, issue 10
https://doi.org/10.5194/hess-21-5375-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-5375-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Oct 2017
Research article |
| 26 Oct 2017
Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo
Khan Zaib Jadoon
CORRESPONDING AUTHOR
Department of the Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
now at: Department of Civil Engineering, International Islamic University, Islamabad 44000, Pakistan
Muhammad Umer Altaf
Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Earth Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Matthew Francis McCabe
Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Ibrahim Hoteit
Earth Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Nisar Muhammad
Department of the Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
Davood Moghadas
Brandenburg University of Technology, Research Center Landscape Development and Mining Landscapes, 03046 Cottbus, Germany
Lutz Weihermüller
Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, GmbH, 52425 Jülich, Germany
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Cited
14 citations as recorded by crossref.
- Prediction of soil salinity and sodicity using electromagnetic conductivity imaging A. Paz et al. 10.1016/j.geoderma.2019.114086
- Mapping soil salinity using electromagnetic conductivity imaging—A comparison of regional and location‐specific calibrations M. Farzamian et al. 10.1002/ldr.3317
- Assessing soil moisture variability in a vineyard via frequency domain electromagnetic induction data L. De Carlo et al. 10.3389/fsoil.2023.1290591
- Combined Geophysical Methods in Extreme Environments—An Example from the Dead Sea M. Lazar et al. 10.3390/rs16111978
- Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network D. Moghadas et al. 10.1016/j.jappgeo.2019.07.004
- Soil electrical conductivity imaging using a neural network-based forward solver: Applied to large-scale Bayesian electromagnetic inversion D. Moghadas et al. 10.1016/j.jappgeo.2020.104012
- A web-based platform for terrestrial data repository from Chicken Creek catchment D. Moghadas et al. 10.1007/s12145-019-00385-0
- Modified ECa – ECe protocols for mapping soil salinity under micro-irrigation D. Corwin et al. 10.1016/j.agwat.2022.107640
- Improved linear inversion of low induction number electromagnetic data S. Parnow et al. 10.1093/gji/ggaa531
- Probabilistic Inversion of Multiconfiguration Electromagnetic Induction Data Using Dimensionality Reduction Technique: A Numerical Study D. Moghadas 10.2136/vzj2018.09.0183
- One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network D. Moghadas 10.1093/gji/ggaa161
- Assessing the dynamics of soil salinity with time-lapse inversion of electromagnetic data guided by hydrological modelling M. Farzamian et al. 10.5194/hess-25-1509-2021
- Landscape-scale mapping of soil salinity with multi-height electromagnetic induction and quasi-3d inversion in Saharan Oasis, Tunisia M. Farzamian et al. 10.1016/j.agwat.2023.108330
- The Influence of Geostatistical Prior Modeling on the Solution of DCT-Based Bayesian Inversion: A Case Study from Chicken Creek Catchment D. Moghadas & J. Vrugt 10.3390/rs11131549
14 citations as recorded by crossref.
- Prediction of soil salinity and sodicity using electromagnetic conductivity imaging A. Paz et al. 10.1016/j.geoderma.2019.114086
- Mapping soil salinity using electromagnetic conductivity imaging—A comparison of regional and location‐specific calibrations M. Farzamian et al. 10.1002/ldr.3317
- Assessing soil moisture variability in a vineyard via frequency domain electromagnetic induction data L. De Carlo et al. 10.3389/fsoil.2023.1290591
- Combined Geophysical Methods in Extreme Environments—An Example from the Dead Sea M. Lazar et al. 10.3390/rs16111978
- Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network D. Moghadas et al. 10.1016/j.jappgeo.2019.07.004
- Soil electrical conductivity imaging using a neural network-based forward solver: Applied to large-scale Bayesian electromagnetic inversion D. Moghadas et al. 10.1016/j.jappgeo.2020.104012
- A web-based platform for terrestrial data repository from Chicken Creek catchment D. Moghadas et al. 10.1007/s12145-019-00385-0
- Modified ECa – ECe protocols for mapping soil salinity under micro-irrigation D. Corwin et al. 10.1016/j.agwat.2022.107640
- Improved linear inversion of low induction number electromagnetic data S. Parnow et al. 10.1093/gji/ggaa531
- Probabilistic Inversion of Multiconfiguration Electromagnetic Induction Data Using Dimensionality Reduction Technique: A Numerical Study D. Moghadas 10.2136/vzj2018.09.0183
- One-dimensional deep learning inversion of electromagnetic induction data using convolutional neural network D. Moghadas 10.1093/gji/ggaa161
- Assessing the dynamics of soil salinity with time-lapse inversion of electromagnetic data guided by hydrological modelling M. Farzamian et al. 10.5194/hess-25-1509-2021
- Landscape-scale mapping of soil salinity with multi-height electromagnetic induction and quasi-3d inversion in Saharan Oasis, Tunisia M. Farzamian et al. 10.1016/j.agwat.2023.108330
- The Influence of Geostatistical Prior Modeling on the Solution of DCT-Based Bayesian Inversion: A Case Study from Chicken Creek Catchment D. Moghadas & J. Vrugt 10.3390/rs11131549
Latest update: 20 Nov 2024
Short summary
In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity in an agriculture field irrigated with a drip irrigation system. Electromagnetic model parameters and uncertainty were estimated using adaptive Bayesian Markov chain Monte Carlo (MCMC). Application of the MCMC-based inversion to the synthetic and field measurements demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil.
In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity...
