Calibration of an electromagnetic induction sensor with time-domain reflectometry data to monitor rootzone electrical conductivity under saline water irrigation

2016 ◽  
Vol 67 (6) ◽  
pp. 737-748 ◽  
Author(s):  
A. Coppola ◽  
K. Smettem ◽  
A. Ajeel ◽  
A. Saeed ◽  
G. Dragonetti ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Time Domain ◽  
Electromagnetic Induction ◽  
Saline Water ◽  
Time Domain Reflectometry ◽  
Saline Water Irrigation ◽  
Induction Sensor

scholarly journals Soil electrical conductivity estimated by time domain reflectometry and electromagnetic induction sensors: Accounting for the different sensor observation volumes

2017 ◽  
Vol 48 (4) ◽  
pp. 223-234 ◽  
Author(s):  
Ali Saeed ◽  
Alessandro Comegna ◽  
Giovanna Dragonetti ◽  
Nicola Lamaddalena ◽  
Angelo Sommella ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Spatial Scale ◽  
Time Domain ◽  
Electromagnetic Induction ◽  
Root Zone ◽  
Saline Water ◽  
Time Domain Reflectometry ◽  
Regression Equations ◽  
Time Dynamics ◽  
Observation Volume

This paper dealt with the calibration of an EMI sensor for monitoring the time dynamics of root zone salinity under irrigation with saline water. Calibration was based on an empirical multiple regression approach largely adopted in the past and still applied in practice for its relative simplicity. Compared to the more complex inversion approaches, it requires an independent dataset of local σb measured within discrete depth intervals, to be compared to horizontal and vertical electrical conductivity (ECaH and ECaV) readings for estimating the parameters of the empirical regression equations. In this paper, we used time domain reflectometry (TDR) readings to replace direct sampling for these local σb measurements. When using this approach, there is the important issue of taking into account the effect of the different sensor observation volumes, making the readings not immediately comparable for empirical calibration. Accordingly, a classical Fourier’s filtering technique was applied to remove the high frequency part (at small spatial scale) of the original data variability, which, due to the different observation volume, was the main source of dissimilarity between the two datasets. Thus, calibration focused only on the lower frequency information, that is, the information at a spatial scale larger than the observation volume of the sensors. By this analysis, we showed and quantified the degree to which the information of the set of TDR readings came from a combination of local and larger scale heterogeneities and how they have to be manipulated for use in EMI electromagnetic induction sensor calibration.

scholarly journals Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

2017 ◽  
Author(s):  
Giovanna Dragonetti ◽  
Alessandro Comegna ◽  
Ali Ajeel ◽  
Gian Piero Deidda ◽  
Nicola Lamaddalena ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Time Domain ◽  
Electromagnetic Induction ◽  
Root Zone ◽  
Ground Truth ◽  
Time Domain Reflectometry ◽  
Data Series ◽  
Actual Distribution ◽  
Ground Truth Data ◽  
Soil Measurements

Abstract. This paper deals with the issue of monitoring the horizontal and vertical distribution of bulk electrical conductivity, σb, in the soil root zone by using Electromagnetic Induction (EMI) sensors under different water and salinity conditions. In order to deduce the actual distribution of depth-specific σb from EMI depth-weighted apparent electrical conductivity (ECa) measurements, we inverted the signal by using a regularized 1D 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 reconstruction. 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 30 m long and 2.8 m wide, cultivated with green bean and irrigated with water at two different salinity levels and using two different irrigation volumes, to induce different salinity and water contents within the soil profile. For each transect, 26 regularly spaced monitoring sites (1 m apart) were selected for soil measurements using a Geonics EM-38 and a Tektronix Reflectometer. Despite the original discrepancies in the EMI and TDR data, we found a significantly high correlation of the means and standard deviations of the two data series, especially after filtering the TDR data. Based on these findings, the paper introduces a novel methodology to calibrate EMI-based electrical conductivity via TDR direct measurements by simply using the statistics of the two data series.

scholarly journals Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

2018 ◽  
Vol 22 (2) ◽  
pp. 1509-1523 ◽  
Author(s):  
Giovanna Dragonetti ◽  
Alessandro Comegna ◽  
Ali Ajeel ◽  
Gian Piero Deidda ◽  
Nicola Lamaddalena ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Spatial Distribution ◽  
Time Domain ◽  
Electromagnetic Induction ◽  
Root Zone ◽  
Linear Mapping ◽  
Time Domain Reflectometry ◽  
Data Series ◽  
Actual Distribution ◽  
Ground Truth Data

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 30 m long and 2.8 m 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 (1 m 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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