Correcting the Temperature Influence on Soil Capacitance Sensors Using Diurnal Temperature and Water Content Cycles

The influence of temperature on the dielectric permittivity of soil is the result of counteracting effect that depends on the soil’s composition and mineralogy. In this paper, laboratory experiments showed that for a given water content, the soil dielectric permittivity was linearly related to the temperature, with a slope (α) that varied between samples taken in the same soil. These variations are difficult to predict and therefore, a simple and straightforward algorithm was designed to estimate α  based on the diurnal patterns of both the measured dielectric permittivity and the soil temperature. The underlying idea is to assume that soil water content variations can be known with a reasonable accuracy over an appropriate time window within a day. This allows determining the contribution of the soil water content to the dielectric permittivity variations and then, the difference with the observed measurements is attributed to the soil temperature. Implementation of the correction methods in a large number of experiments significantly improved the physical meaning of the temporal evolution of the soil water content as the daily cycles for probes located near the surface or the long-term variations for more deeply installed probes

MR-based measurements and simulations of the magnetic field created by a realistic transcranial magnetic stimulation (TMS) coil and stimulator

Transcranial magnetic stimulation (TMS) is an emerging technique that allows non-invasive neurostimulation. However, the correct validation of electromagnetic models of typical TMS coils and the correct assessment of the incident TMS field (BTMS) produced by standard TMS stimulators are still lacking. Such a validation can be performed by mapping BTMS produced by a realistic TMS setup. In this study, we show that MRI can provide precise quantification of the magnetic field produced by a realistic TMS coil and a clinically used TMS stimulator in the region in which neurostimulation occurs. Measurements of the phase accumulation created by TMS pulses applied during a tailored MR sequence were performed in a phantom. Dedicated hardware was developed to synchronize a typical, clinically used, TMS setup with a 3-T MR scanner. For comparison purposes, electromagnetic simulations of BTMS were performed. MR-based measurements allow the mapping and quantification of BTMS starting 2.5 cm from the TMS coil. For closer regions, the intra-voxel dephasing induced by BTMS prohibits TMS field measurements. For 1% TMS output, the maximum measured value was ~0.1 mT. Simulations reflect quantitatively the experimental data. These measurements can be used to validate electromagnetic models of TMS coils, to guide TMS coil positioning, and for dosimetry and quality assessment of concurrent TMS-MRI studies without the need for crude methods, such as motor threshold, for stimulation dose determination

Joint estimation of soil moisture profile and hydraulic parameters by ground-penetrating radar data assimilation with maximum likelihood ensemble filter

Ground-Penetrating Radar (GPR) has recently become a powerful geophysical technique to characterize soil moisture at the field scale. We developed a data assimilation scheme to simultaneously estimate the vertical soil moisture profile and hydraulic parameters from time-lapse GPR measurements. The assimilation scheme includes a soil hydrodynamic model to simulate the soil moisture dynamics, a fullwave electromagnetic wave propagation model, and petrophysical relationship to link the state variable with the GPR data and a maximum likelihood ensemble assimilation algorithm. The hydraulic parameters are estimated jointly with the soil moisture using a state augmentation technique. The approach allows for the direct assimilation of GPR data, thus maximizing the use of the information. The proposed approach was validated by numerical experiments assuming wrong initial conditions and hydraulic parameters. The synthetic soil moisture profiles were generated by the Hydrus-1D model, which then were used by the electromagnetic model and petrophysical relationship to create ‘‘observed’’ GPR data. The results show that the data assimilation significantly improves the accuracy of the hydrodynamic model prediction. Compared with the surface soil moisture assimilation, the GPR data assimilation better estimates the soil moisture profile and hydraulic parameters. The results also show that the estimated soil moisture profile in the loamy sand and silt soils converge to the ‘‘true’’ state more rapidly than in the clay one. Of the three unknown parameters of the Mualem-van Genuchten model, the estimation of n is more accurate than that of a and Ks. The approach shows a great promise to use GPR measurements for the soil moisture profile and hydraulic parameter estimation at the field scale