Articles | Volume 21, issue 10
https://doi.org/10.5194/hess-21-5375-2017
https://doi.org/10.5194/hess-21-5375-2017
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, Muhammad Umer Altaf, Matthew Francis McCabe, Ibrahim Hoteit, Nisar Muhammad, Davood Moghadas, and Lutz Weihermüller

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by Editor and Referees) (19 Dec 2016) by Monica Riva
AR by Khan Zaib Jadoon on behalf of the Authors (26 Jan 2017)  Author's response   Manuscript 
ED: Publish subject to revisions (further review by Editor and Referees) (08 Feb 2017) by Monica Riva
AR by Khan Zaib Jadoon on behalf of the Authors (15 May 2017)  Manuscript 
ED: Referee Nomination & Report Request started (06 Jun 2017) by Monica Riva
RR by Anonymous Referee #1 (20 Jun 2017)
ED: Publish subject to minor revisions (further review by Editor) (06 Jul 2017) by Monica Riva
AR by Khan Zaib Jadoon on behalf of the Authors (16 Jul 2017)  Author's response   Manuscript 
ED: Publish as is (23 Jul 2017) by Monica Riva
AR by Khan Zaib Jadoon on behalf of the Authors (09 Aug 2017)
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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.