Low-Pass Filters for a Temperature Drift Correction Method for Electromagnetic Induction Systems.

Electromagnetic induction (EMI) systems are used for mapping the soil's electrical conductivity in near-surface applications. EMI measurements are commonly affected by time-varying external environmental factors, with temperature fluctuations being a big contributing factor. This makes it challenging to obtain stable and reliable data from EMI measurements. To mitigate these temperature drift effects, it is customary to perform a temperature drift calibration of the instrument in a temperature-controlled environment. This involves recording the apparent electrical conductivity (ECa) values at specific temperatures to obtain a look-up table that can subsequently be used for static ECa drift correction. However, static drift correction does not account for the delayed thermal variations of the system components, which affects the accuracy of drift correction. Here, a drift correction approach is presented that accounts for delayed thermal variations of EMI system components using two low-pass filters (LPF). Scenarios with uniform and non-uniform temperature distributions in the measurement device are both considered. The approach is developed using a total of 15 measurements with a custom-made EMI device in a wide range of temperature conditions ranging from 10 °C to 50 °C. The EMI device is equipped with eight temperature sensors spread across the device that simultaneously measure the internal ambient temperature during measurements. To parameterize the proposed correction approach, a global optimization algorithm called Shuffled Complex Evolution (SCE-UA) was used for efficient estimation of the calibration parameters. Using the presented drift model to perform corrections for each individual measurement resulted in a root mean square error (RMSE) of <1 mSm-1 for all 15 measurements. This shows that the drift model can properly describe the drift of the measurement device. Performing a drift correction simultaneously for all datasets resulted in a RMSE <1.2 mSm-1, which is considerably lower than the RMSE values of up to 4.5 mSm-1 obtained when using only a single LPF to perform drift corrections. This shows that the presented drift correction method based on two LPFs is more appropriate and effective for mitigating temperature drift effects.

Evaluation of Three Soil Moisture Profile Sensors Using Laboratory and Field Experiments.

Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m3 m-3 per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m3 m-3) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future.

Monitoring of in-situ chemical oxidation for remediation of diesel-contaminated soil with electrical resistivity tomography.

In-situ chemical oxidation (ISCO) with persulfate, an electrically conductive oxidant, provides a powerful signal for noninvasive geophysical techniques to characterize the remediation process of hydrocarbon contaminants. In this study, remediation with ISCO is conducted in laboratory sandboxes to evaluate the ability of electrical resistivity tomography (ERT) for monitoring the base-activated persulfate remediation process of diesel-contaminated soil. It was found that the resistivity of contaminated sand significantly decreased from 846 Ω·m to below 10 Ω·m after persulfate injection, and all measured chemical parameters showed a noticeable increase. Natural degradation and contamination plume migration were not evident in a reference sandbox without treatment. The area with a resistivity ratio < 0.95 based on imaging before and after injection indicated downward migration of the oxidation plume due to density-driven flow. A comparison between remediation and reference sandboxes showed that the observed resistivity decrease can be due to both contaminant degradation as well as the oxidation plume itself in the contaminated source zone. In contrast, the resistivity decrease in the area with low contamination concentration is attributed to the oxidation plume alone. The derived relationships between resistivity and contaminant indicators further emphasize that the contribution of contaminant consumption to resistivity change in the source area is 25.6%, while it is <16% in the low or non-contaminated area. Although this study showed that resistivity is not solely affected by the chemical transformation of diesel components, it can be combined with sampling data to allow an assessment of the effectiveness of ISCO treatment and to identify target areas for subsequent treatment.

Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management.

In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near real-time sensing of soil moisture to investigate seasonal and event dynamics of soil moisture patterns. It is also discussed how WSN measurements of soil measurements contribute to the validation and downscaling of satellite data and non-invasive geophysical instruments as well as the validation of distributed hydrological models. Finally, future perspectives for WSN measurements of soil moisture are highlighted, which includes the improved integration of real-time WSN measurements with other information sources using the latest wireless communication techniques and cyberinfrastructures.

Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems.

Data measured using electromagnetic induction (EMI) systems are known to be susceptible to measurement influences associated with time-varying external ambient factors. Temperature variation is one of the most prominent factors causing drift in EMI data, leading to non-reproducible measurement results. Typical approaches to mitigate drift effects in EMI instruments rely on a temperature drift calibration, where the instrument is heated up to specific temperatures in a controlled environment and the observed drift is determined to derive a static thermal apparent electrical conductivity (ECa) drift correction. In this study, a novel correction method is presented that models the dynamic characteristics of drift using a low-pass filter (LPF) and uses it for correction. The method is developed and tested using a customized EMI device with an intercoil spacing of 1.2 m, optimized for low drift and equipped with ten temperature sensors that simultaneously measure the internal ambient temperature across the device. The device is used to perform outdoor calibration measurements over a period of 16 days for a wide range of temperatures. The measured temperature-dependent ECa drift of the system without corrections is approximately 2.27 mSm-1K-1, with a standard deviation (std) of only 30 µSm-1K-1 for a temperature variation of around 30 K. The use of the novel correction method reduces the overall root mean square error (RMSE) for all datasets from 15.7 mSm-1 to a value of only 0.48 mSm-1. In comparison, a method using a purely static characterization of drift could only reduce the error to an RMSE of 1.97 mSm-1. The results show that modeling the dynamic thermal characteristics of the drift helps to improve the accuracy by a factor of four compared to a purely static characterization. It is concluded that the modeling of the dynamic thermal characteristics of EMI systems is relevant for improved drift correction.

Pore-Scale Mechanisms for Spectral Induced Polarization of Calcite Precipitation Inferred from Geo-Electrical Millifluidics.

Spectral induced polarization (SIP) has the potential for monitoring reactive processes in the subsurface. While strong SIP responses have been measured in response to calcite precipitation, their origin and mechanism remain debated. Here we present a novel geo-electrical millifluidic setup designed to observe microscale reactive transport processes while performing SIP measurements. We induced calcite precipitation by injecting two reactive solutions into a porous medium, which led to highly localized precipitates at the mixing interface. Strikingly, the amplitude of the SIP response increased by 340% during the last 7% increase in precipitate volume. Furthermore, while the peak frequency in SIP response varied spatially over 1 order of magnitude, the crystal size range was similar along the front, contradicting assumptions in the classical grain polarization model. We argue that the SIP response of calcite precipitation in such mixing fronts is governed by Maxwell-Wagner polarization due to the establishment of a precipitate wall. Numerical simulations of the electric field suggested that spatial variation in peak frequency was related to the macroscopic shape of the front. These findings provide new insights into the SIP response of calcite precipitation and highlight the potential of geoelectrical millifluidics for understanding and modeling electrical signatures of reactive transport processes.

On the Accuracy of Factory-Calibrated Low-Cost Soil Water Content Sensors.

Soil water content (SWC) monitoring is often used to optimize agricultural irrigation. Commonly, capacitance sensors are used for this task. However, the factory calibrations have been often criticized for their limited accuracy. The aim of this paper is to test the degree of improvement of various sensor- and soil-specific calibration options compared to factory calibrations by taking the 10HS sensor as an example. To this end, a two-step sensor calibration was carried out. In the first step, the sensor response was related to dielectric permittivity using calibration in media with well-defined permittivity. The second step involved the establishment of a site-specific relationship between permittivity and soil water content using undisturbed soil samples and time domain reflectometry (TDR) measurements. Our results showed that a model, which considered the mean porosity and a fitted dielectric permittivity of the solid phase for each soil and depth, provided the best fit between bulk permittivity and SWC. Most importantly, it was found that the two-step calibration approach (RMSE: 1.03 vol.%) provided more accurate SWC estimates compared to the factory calibration (RMSE: 5.33 vol.%). Finally, we used these calibrations on data from drip-irrigated almond and apple orchards and compared the factory calibration with our two-step calibration approach.

Meteorological and soil surface effects in gamma radiation time series - Implications for assessment of earthquake precursors.

Monitoring of environmental radioactivity for the purpose of earthquake prediction requires the discrimination of anomalies of non-tectonic origin from seismically-induced anomalies. This is a challenging task as time series of environmental radioactivity display a complex temporal pattern reflecting a wide range of different physical processes, including meteorological and surface effects. The present study is based on the detailed time series of gamma radiation from the Eastern North Atlantic (ENA) site in the Azores, and on very high resolution precipitation intensity and soil moisture time series. The results show that an abrupt shift in the average level of the gamma radiation time series previously reported as a potential earthquake precursor can also be explained by a corresponding abrupt change in soil moisture. It was concluded that the reduction of false positive earthquake precursors requires the detailed assessment of both precipitation and soil moisture conditions at high temporal resolution.

Spectral induced polarization of Na-montmorillonite dispersions.

Montmorillonite (Mt) clays have a high specific surface area and surface charge, which confer them remarkable adsorption properties. Nevertheless, their electrochemical and aggregation behavior are not completely elucidated because of the complexity of their microstructural and interfacial properties. In this work, the conductive and dispersive properties of Na-Mt suspensions of weight fractions 0.5-5.2% were investigated for the first time using the spectral induced polarization method. A four-electrode system was used to reduce errors introduced by electrode polarization and contact resistances. Complex conductivity spectra in the low-frequency range of 0.1Hz to 45kHz were successfully described using a triple layer model of the basal surface of Mt and a complex conductivity model that considers conduction of the diffuse layer and polarization of the Stern layer. Aggregate size distributions were inferred from inverted relaxation time distributions. We found that the negative and permanent surface charge of the basal plane of Na-Mt controls its quadrature (imaginary) conductivity, which is not very sensitive to pH and salinity (NaCl) in the 100Hz to 45kHz frequency range. For lower frequencies, the sudden increase of the quadrature conductivity at the highest salinities was explained by considering coagulation of Na-Mt particles.

Effective Calibration of Low-Cost Soil Water Content Sensors.

Soil water content is a key variable for understanding and modelling ecohydrological processes. Low-cost electromagnetic sensors are increasingly being used to characterize the spatio-temporal dynamics of soil water content, despite the reduced accuracy of such sensors as compared to reference electromagnetic soil water content sensing methods such as time domain reflectometry. Here, we present an effective calibration method to improve the measurement accuracy of low-cost soil water content sensors taking the recently developed SMT100 sensor (Truebner GmbH, Neustadt, Germany) as an example. We calibrated the sensor output of more than 700 SMT100 sensors to permittivity using a standard procedure based on five reference media with a known apparent dielectric permittivity (1 < Ka < 34.8). Our results showed that a sensor-specific calibration improved the accuracy of the calibration compared to single "universal" calibration. The associated additional effort in calibrating each sensor individually is relaxed by a dedicated calibration setup that enables the calibration of large numbers of sensors in limited time while minimizing errors in the calibration process.