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Spatiotemporal monitoring of soil moisture from EMI data using DCT-based Bayesian inference and neural network

  • Loop-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 nonlinear 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 timelapse EMCI to the soil moisture,we examined two approaches, namely,Loop-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 nonlinear 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 timelapse 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.show moreshow less

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Metadaten
Author: Davood MoghadasORCiD, Khan Zaib Jadoon, Matthew F. McCabe
DOI:https://doi.org/10.1016/j.jappgeo.2019.07.004
ISSN:0926-9851
Title of the source (English):Journal of Applied Geophysics
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2019
Volume/Year:169
First Page:226
Last Page:238
Faculty/Chair:Zentrale Einrichtungen / Forschungszentrum Landschaftsentwicklung und Bergbaulandschaften (FZLB)
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