Articles | Volume 21, issue 10
Hydrol. Earth Syst. Sci., 21, 5375–5383, 2017
Hydrol. Earth Syst. Sci., 21, 5375–5383, 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.

Related authors

Added value of geophysics-based soil mapping in agro-ecosystem simulations
Cosimo Brogi, Johan A. Huisman, Lutz Weihermüller, Michael Herbst, and Harry Vereecken
SOIL, 7, 125–143,,, 2021
Short summary
Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling, and a satellite–model fusion approach
Oliver Miguel López Valencia, Kasper Johansen, Bruno José Luis Aragón Solorio, Ting Li, Rasmus Houborg, Yoann Malbeteau, Samer AlMashharawi, Muhammad Umer Altaf, Essam Mohammed Fallatah, Hari Prasad Dasari, Ibrahim Hoteit, and Matthew Francis McCabe
Hydrol. Earth Syst. Sci., 24, 5251–5277,,, 2020
Short summary
SKRIPS v1.0: a regional coupled ocean–atmosphere modeling framework (MITgcm–WRF) using ESMF/NUOPC, description and preliminary results for the Red Sea
Rui Sun, Aneesh C. Subramanian, Arthur J. Miller, Matthew R. Mazloff, Ibrahim Hoteit, and Bruce D. Cornuelle
Geosci. Model Dev., 12, 4221–4244,,, 2019
Short summary
K. Johansen, M. J. L. Morton, Y. Malbeteau, B. Aragon, S. Al-Mashharawi, M. Ziliani, Y. Angel, G. Fiene, S. Negrao, M. A. A. Mousa, M. A. Tester, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 407–411,,, 2019
Bayesian inference of earthquake rupture models using polynomial chaos expansion
Hugo Cruz-Jiménez, Guotu Li, Paul Martin Mai, Ibrahim Hoteit, and Omar M. Knio
Geosci. Model Dev., 11, 3071–3088,,, 2018
Short summary

Related subject area

Subject: Vadose Zone Hydrology | Techniques and Approaches: Stochastic approaches
Covariance resampling for particle filter – state and parameter estimation for soil hydrology
Daniel Berg, Hannes H. Bauser, and Kurt Roth
Hydrol. Earth Syst. Sci., 23, 1163–1178,,, 2019
Short summary
State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
Hongjuan Zhang, Harrie-Jan Hendricks Franssen, Xujun Han, Jasper A. Vrugt, and Harry Vereecken
Hydrol. Earth Syst. Sci., 21, 4927–4958,,, 2017
Short summary
Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction
Roland Baatz, Harrie-Jan Hendricks Franssen, Xujun Han, Tim Hoar, Heye Reemt Bogena, and Harry Vereecken
Hydrol. Earth Syst. Sci., 21, 2509–2530,,, 2017
Short summary
Kalman filters for assimilating near-surface observations into the Richards equation – Part 1: Retrieving state profiles with linear and nonlinear numerical schemes
G. B. Chirico, H. Medina, and N. Romano
Hydrol. Earth Syst. Sci., 18, 2503–2520,,, 2014
Kalman filters for assimilating near-surface observations into the Richards equation – Part 2: A dual filter approach for simultaneous retrieval of states and parameters
H. Medina, N. Romano, and G. B. Chirico
Hydrol. Earth Syst. Sci., 18, 2521–2541,,, 2014

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.