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dc.contributor.authorMoghadas, Davood
dc.contributor.authorJadoon, Khan Zaib
dc.contributor.authorMcCabe, Matthew
dc.date.accessioned2019-08-07T11:36:48Z
dc.date.available2019-08-07T11:36:48Z
dc.date.issued2019-07-10
dc.identifier.citationMoghadas, D., Jadoon, K. Z., & McCabe, M. F. (2019). Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network. Journal of Applied Geophysics, 169, 226–238. doi:10.1016/j.jappgeo.2019.07.004
dc.identifier.doi10.1016/j.jappgeo.2019.07.004
dc.identifier.urihttp://hdl.handle.net/10754/656398
dc.description.abstractLoop-loop electromagnetic induction (EMI) has proven to be efficient for fast and real-time soil apparent electrical conductivity (ECa) measurements. It is important to develop robust and accurate inversion strategies to obtain soil electromagnetic conductivity image (EMCI) from ECa data. Moreover, obtaining an accurate non-linear relationship between subsurface electrical conductivity (σ) and water content (θ) plays a key role for soil moisture monitoring using EMI. Here, we incorporated probabilistic inversion of multi-configuration ECa data with dimensionality reduction technique through the discrete cosine transform (DCT) using training image (TI)-based parametrization to retrieve soil EMCI. The ECa data were measured repeatedly along a 10 m transect using a CMD mini-Explorer sensor. Time-lapse reference data were collected as well to benchmark the inversion results and to find the in-situ relationship between σ and θ. To convert the inversely estimated time-lapse EMCI to the soil moisture, we examined two approaches, namely, Rhoades et al. (1976) model and artificial neural network (ANN). The proposed inversion strategy estimated the soil EMCI with an excellent agreement with the reference counterpart. Moreover, the ANN approach demonstrated superiorities than the commonly used petrophysical model of Rhoades et al. (1976) to obtain spatiotemporal images of θ from time-lapse EMCI. The results demonstrated that incorporation of the DCT-based probabilistic inversion of ECa data with the ANN approach offers a great promise for accurate characterization of the temporal wetting front and root zone soil moisture.
dc.description.sponsorshipThis work was supported by the Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST, Saudi Arabia) in collaboration with the Brandenburg University of Technology Cottbus - Senftenberg (BTU, Germany). The first author kindly acknowledges Philippe Renard and Julien Straubhaar (University of Neuchâtel) for providing the DeeSse simulation code. Matthew F. McCabe was funded by the King Abdullah University of Science and Technology.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0926985119303453
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Applied Geophysics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Applied Geophysics, [[Volume], [Issue], (2019-07-10)] DOI: 10.1016/j.jappgeo.2019.07.004 . © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectElectromagnetic induction
dc.subjectSoil moisture
dc.subjectDiscrete cosine transform
dc.subjectMultiple point statistics
dc.subjectBayesian inference
dc.subjectArtificial neural network
dc.titleSpatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network
dc.typeArticle
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.identifier.journalJournal of Applied Geophysics
dc.eprint.versionPost-print
dc.contributor.institutionResearch Center Landscape Development and Mining Landscapes, Brandenburg University of Technology, D-03046 Cottbus, Germany
dc.contributor.institutionDepartment of the Civil Engineering, International Islamic University, Islamabad 44000, Pakistan
kaust.personMcCabe, Matthew
kaust.acknowledged.supportUnitWater Desalination and Reuse Center
dc.date.published-online2019-07-10
dc.date.published-print2019-10


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NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Applied Geophysics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Applied Geophysics, [[Volume], [Issue], (2019-07-10)] DOI: 10.1016/j.jappgeo.2019.07.004 . © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Except where otherwise noted, this item's license is described as NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Applied Geophysics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Applied Geophysics, [[Volume], [Issue], (2019-07-10)] DOI: 10.1016/j.jappgeo.2019.07.004 . © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/