Apparent electrical conductivity mapping in managed podzols using multi-coil and multi-frequency EMI sensor measurements

The research focused on utilizing apparent electrical conductivity (ECa) survey protocols in characterizing the spatial and temporal variability of soil physical and hydraulic properties in Western Newfoundland, Canada. In this study, two different non-invasive multi-coil and multi-frequency EMI sensors; CMD Mini-explorer and GEM-2, respectively were used to collect ECa data under different nutrient management systems at Pynn’s Brook Research Station, Pasadena. Results showed that due to the differences in investigation depths of the two EMI sensors, the linear regression models generated for SMC using the CMD Mini-explorer were statistically significant with the highest R² = 0.79 and the lowest RMSE = 0.015 m³ m⁻³ and not significant for GEM-2 with the lowest R² = 0.17 and RMSE = 0.045 m³ m⁻³. Furthermore, there is a significant relationship between the ECa mean relative differences (MRD) versus SMC MRD (R² = 0.33 to 0.70) for both multi-Coil and multi-Frequency sensors. In addition, the spatial variability of the ECa predicted soil properties are relatively consistent with lower variability compared to the measured soil properties. Conclusively, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for soil physical and hydraulic properties at the field scale

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

Precision agriculture (PA) involves the management of agricultural fields including spatial information of soil properties derived from apparent electrical conductivity (ECa) measurements. While this approach is gaining much attention in agricultural management, farmed podzolic soils are under-represented in the relevant literature. This study: (i) established the relationship between ECa and soil moisture content (SMC) measured using time domain reflectometry (TDR); and (ii) evaluated the estimated SMC with ECa measurements obtained with two electromagnetic induction (EMI) sensors, i.e., multi-coil and multi-frequency, using TDR measured SMC. Measurements were taken on several plots at Pynn’s Brook Research Station, Pasadena, Newfoundland, Canada. The means of ECa measurements were calculated for the same sampling location in each plot. The linear regression models generated for SMC using the CMD-MINIEXPLORER were statistically significant with the highest R2 of 0.79 and the lowest RMSE (root mean square error) of 0.015 m3 m−3 but were not significant for GEM-2 with the lowest R2 of 0.17 and RMSE of 0.045 m3 m−3; this was due to the difference in the depth of investigation between the two EMI sensors. The validation of the SMC regression models for the two EMI sensors produced the highest R2 = 0.54 with the lowest RMSE prediction = 0.031 m3 m−3 given by CMD-MINIEXPLORER. The result demonstrated that the CMD-MINIEXPLORER based measurements better predicted shallow SMC, while deeper SMC was better predicted by GEM-2 measurements. In addition, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for SMC mapping at the field scale