Low frequency electromagnetic induction (EMI) is becoming a useful tool for soil characterization due to its fast measurement capability and sensitivity to soil moisture and salinity. In this research, a new EMI system (the CMD mini‐Explorer) is used for subsurface characterization of soil salinity in a drip irrigation system via a joint inversion approach of multiconfiguration EMI measurements. EMI measurements were conducted across a farm where Acacia trees are irrigated with brackish water. In situ measurements of vertical bulk electrical conductivity (σ_b) were recorded in different pits along one of the transects to calibrate the EMI measurements and to compare with the modeled electrical conductivity (σ) obtained by the joint inversion of multiconfiguration EMI measurements. Estimates of σ were then converted into the universal standard of soil salinity measurement (i.e., electrical conductivity of a saturated soil paste extract – EC_e). Soil apparent electrical conductivity (EC_a) was repeatedly measured with the CMD mini‐Explorer to investigate the temperature stability of the new system at a fixed location, where the ambient air temperature increased from 26°C to 46°C. Results indicate that the new EMI system is very stable in high temperature environments, especially above 40°C, where most other approaches give unstable measurements. In addition, the distribution pattern of soil salinity is well estimated quantitatively by the joint inversion of multicomponent EMI measurements. The approach of joint inversion of EMI measurements allows for the quantitative mapping of the soil salinity distribution pattern and can be utilized for the management of soil salinity.

We tested an off-ground ground-penetrating radar (GPR) system at a fixed location over a bare agricultural field to monitor the soil freeze-thaw cycles over a snow-covered surface. The GPR system consisted of a monostatic horn antenna combined with a vector network analyzer, providing an ultra-wideband stepped-frequency continuous-wave radar. An antenna calibration experiment was performed to filter antenna and back scattered effects from the raw GPR data. Near the GPR setup, sensors were installed in the soil to monitor the dynamics of soil temperature and dielectric permittivity at different depths. The soil permittivity was retrieved via inversion of time domain GPR data focused on the surface reflection. Significant effects of soil dynamics were observed in the time-lapse GPR, temperature and dielectric permittivity measurements. In particular, five freeze and thaw events were clearly detectable, indicating that the GPR signals respond to the contrast between the dielectric permittivity of frozen and thawed soil. The GPR-derived permittivity was in good agreement with sensor observations. Overall, the off-ground nature of the GPR system permits non-invasive time-lapse observation of the soil freeze-thaw dynamics without disturbing the structure of the snow cover. The proposed method shows promise for the real-time mapping and monitoring of the shallow frozen layer at the field scale.

Planning for use of a dune field aquifer for managed aquifer recharge (MAR) requires that hydraulic properties need to be estimated over a large geographic area. Saturated hydraulic conductivity of dune sands is commonly estimated from grain size distribution data by employing some type of empirical equation. Over 50 samples from the Wadi Khulays dune field in Western Saudi Arabia were collected and the grain size distribution, porosity, and hydraulic conductivity were measured. An evaluation of 20 existing empirical equations showed a generally high degree of error in the predicted compared to the measured hydraulic conductivity values of these samples. Statistical analyses comparing estimated versus measured hydraulic conductivity demonstrated that there is a significant relationship between hydraulic conductivity and mud percentage (and skewness). The modified Beyer equation, which showed a generally low prediction error, was modified by adding a second term fitting parameter related to the mud concentration based on 25 of the 50 samples analyzed. An inverse optimization process was conducted to quantify the fitting parameter and a new empirical equation was developed. This equation was tested against the remaining 25 samples analyzed and produced an estimated saturated hydraulic conductivity with the lowest error of any empirical equation. This methodology can be used for large dune field hydraulic conductivity estimation and reduce planning costs for MAR systems.