Articles | Volume 21, issue 10
Hydrol. Earth Syst. Sci., 21, 5375–5383, 2017
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.
Hydrol. Earth Syst. Sci., 21, 5375–5383, 2017
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 et al.
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Cited
10 citations as recorded by crossref.
- A web-based platform for terrestrial data repository from Chicken Creek catchment D. Moghadas et al. 10.1007/s12145-019-00385-0
- 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
- 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
- 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
- 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
10 citations as recorded by crossref.
- A web-based platform for terrestrial data repository from Chicken Creek catchment D. Moghadas et al. 10.1007/s12145-019-00385-0
- 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
- 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
- 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
- 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
Latest update: 22 Sep 2021
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...
