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The measurement accuracy of low‐cost electromagnetic soil water content sensors is often deteriorated by temperature and soil bulk electrical conductivity effects. This study aimed to quantify these effects for the ECH2O EC‐5 and 5TE sensors and to derive and test correction functions. In a first experiment, the temperature of eight reference liquids with permittivity ranging from 7 to 42 was varied from 5 to 40°C. Both sensor types showed an underestimation of permittivity for low temperature (5–25°C) and an overestimation for high temperature (25–40°C). Next, NaCl was added to increase the conductivity of the reference liquids (up to ∼2.5 dS m−1 for a permittivity of 26–42, up to ∼1.5 dS m−1 for a permittivity of 22–26). The permittivity measured with both sensors showed a strong and complicated dependence on electrical conductivity, with both under‐ and overestimation of permittivity. Using these experimental data, we derived empirical correction functions. The performance of the correction functions for the 5TE sensor was evaluated using coarse sand and silty clay loam soil samples. After correcting for temperature effects, the measured permittivity corresponded well with theoretical predictions from a dielectric mixing model for soil with low electrical conductivity. The conductivity correction function also improved the accuracy of the soil moisture measurements, but only within the validity range of this function. Finally, both temperature and electrical conductivity of the silty clay loam were varied and a sequential application of both correction functions also resulted in permittivity measurements that corresponded well with model predictions.

Electrophysiological activity in the human brain generates a small magnetic field from the spatial superposition of individual neuronal source currents. At a distance of about 15 mm from the scalp, the observed field is of the order of 10−13 to 10−12 T peak-to-peak. This measurement process is termed magnetoencephalography (MEG). In order to minimize instrumental noise, the MEG is usually detected using superconducting flux transformers, coupled to SQUID (superconducting quantum interference device) sensors. Since MEG signals are also measured in the presence of significant environmental magnetic noise, flux transformers must be designed to strongly attenuate environmental noise, maintain low instrumental noise and maximize signals from the brain. Furthermore, the flux transformers must adequately sample spatial field variations if the brain activity is to be imaged. The flux transformer optimization for maximum brain signal-to-noise ratio (SNR) requires analysis of the spatial and temporal properties of brain activity, the environmental noise and how these signals are coupled to the flux transformer. Flux transformers that maximize SNR can detect the smallest brain signals and have the best ability to spatially separate dipolar sources. An optimal flux transformer design is a synthetic higher-order gradiometer based on relatively short-baseline first-order radial gradiometer primary sensors.

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