Articles | Volume 21, issue 10
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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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.
Anderson, W. L.: Numerical integration of related Hankel transforms of orders 0 and by adaptive digital filtering, Geophysics, 44, 1287–1305, 1979.
Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal Proces., 50, 174–188, 2002.
Callegary, J. B., Ferre, T. P. A., and Groom, R. W.: Vertical spatial sensitivity and exploration depth of low-induction-number electromagnetic-induction instruments, Vadose Zone J., 6, 158–167, 2007.
Cook, P. G. and Walker, G. R.: Depth profiles of electrical-conductivity from linear-combinations of electromagnetic induction measurements, Soil Sci. Soc. Am. J., 56, 1015–1022, 1992.
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.