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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 2
Hydrol. Earth Syst. Sci., 22, 1509-1523, 2018
https://doi.org/10.5194/hess-22-1509-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 22, 1509-1523, 2018
https://doi.org/10.5194/hess-22-1509-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 Feb 2018

Research article | 28 Feb 2018

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Giovanna Dragonetti1, Alessandro Comegna2, Ali Ajeel2, Gian Piero Deidda3, Nicola Lamaddalena1, Giuseppe Rodriguez4, Giulio Vignoli3,5, and Antonio Coppola2 Giovanna Dragonetti et al.
  • 1Mediterranean Agronomic Institute (MAIB) – Land and Water Department, Valenzano (Bari), Italy
  • 2University of Basilicata, School of Agricultural, Forestry and Environmental Sciences – Hydraulics and Hydrology Division, Potenza, Italy
  • 3Dipartimento di Ingegneria Civile, Ambientale e Architettura, Università di Cagliari, Cagliari, Italy
  • 4Dipartimento di Matematica e Informatica, Università di Cagliari, Cagliari, Italy
  • 5Groundwater and Quaternary Geology Mapping Department, Geological Survey of Denmark and Greenland, Aarhus, Denmark

Abstract. This paper deals with the issue of monitoring the spatial distribution of bulk electrical conductivity, σb, in the soil root zone by using electromagnetic induction (EMI) sensors under different water and salinity conditions. To deduce the actual distribution of depth-specific σb from EMI apparent electrical conductivity (ECa) measurements, we inverted the data by using a regularized 1-D inversion procedure designed to manage nonlinear multiple EMI-depth responses. The inversion technique is based on the coupling of the damped Gauss–Newton method with truncated generalized singular value decomposition (TGSVD). The ill-posedness of the EMI data inversion is addressed by using a sharp stabilizer term in the objective function. This specific stabilizer promotes the reconstruction of blocky targets, thereby contributing to enhance the spatial resolution of the EMI results in the presence of sharp boundaries (otherwise smeared out after the application of more standard Occam-like regularization strategies searching for smooth solutions). Time-domain reflectometry (TDR) data are used as ground-truth data for calibration of the inversion results. An experimental field was divided into four transects 30m long and 2.8m wide, cultivated with green bean, and irrigated with water at two different salinity levels and using two different irrigation volumes. Clearly, this induces different salinity and water contents within the soil profiles. For each transect, 26 regularly spaced monitoring soundings (1m apart) were selected for the collection of (i) Geonics EM-38 and (ii) Tektronix reflectometer data. Despite the original discrepancies in the EMI and TDR data, we found a significant correlation of the means and standard deviations of the two data series; in particular, after a low-pass spatial filtering of the TDR data. Based on these findings, this paper introduces a novel methodology to calibrate EMI-based electrical conductivities via TDR direct measurements. This calibration strategy consists of a linear mapping of the original inversion results into a new conductivity spatial distribution with the coefficients of the transformation uniquely based on the statistics of the two original measurement datasets (EMI and TDR conductivities).

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The paper aims to infer the bulk electrical conductivity distribution in the root zone from EMI readings. TDR measurements were used as ground-truth data to evaluate the goodness of the estimations by EMI inversion. The approach is based on the mean and standard deviation of the EMI and TDR series. It looks for the physical reasons for the differences between EMI- and TDR-based electrical conductivity and provides a correction of the bias based on the statistical sources of the discrepancies.
The paper aims to infer the bulk electrical conductivity distribution in the root zone from EMI...
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