Articles | Volume 21, issue 10
Hydrol. Earth Syst. Sci., 21, 5375–5383, 2017
https://doi.org/10.5194/hess-21-5375-2017
Hydrol. Earth Syst. Sci., 21, 5375–5383, 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 et al.

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Cited articles

Altaf, M. U., Butler, T., Mayo, T., Luo, X., Dawson, C., Heemink, A. W., and Hoteit, I.: A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation, Mon. Weather Rev., 142, 2899–2914, 2014.
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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.