Identifying and characterizing the spatial patterns in soil moisture variability under different land use conditions is crucial for agriculture, forestry, civil and environmental engineering. Yet employing multi-frequency electromagnetic induction (EMI) techniques to carry out this task is under-represented in boreal podzolic soils. This study: (i) compared four frequencies (2.8 ~ 80 kHz) for shallow mapping of soil moisture measured with a time-domain reflectometry at 0 – 20 cm soil depth under three different land-use conditions (agricultural land, field road, and a recently cleared natural forest), (ii) developed a relationship between apparent electrical conductivity (ECa) measured using multi-frequency EMI (GEM-2) and soil moisture and (iii) assessed the effectiveness of ECa as an auxiliary variable in predicting soil moisture variations under different land use conditions. The means of ECa measurements were calculated for the exact sampling location (ground truth data) in each land use condition at a research site, Pasadena, Newfoundland. Soil moisture–ECa linear regression models for the three land-use conditions were only statistically significant for 38.3 kHz frequency and were further analyzed. Further statistical analysis revealed that ECa was primarily controlled by soil moisture for the three land-use conditions, with the natural forest possessing the highest mean ECa and soil moisture. Geostatistical analysis revealed that cokriging ECa with less densely collected soil moisture improved the characterization accuracy of soil moisture variability across the different land use conditions. These results reveal the effectiveness of the georeferenced MF–EMI technique to rapidly assess intra-field soil moisture variability under different land uses.

The complex nature of podzolic soils makes investigating their subsurface challenging. Near-surface geophysical techniques, like electromagnetic induction (EMI), offer significant assistance in studying podzolic soils. Multi-coil (MC-EMI) and multi-frequency (MF-EMI) sensors were selected to maximize soil water content (SWC) prediction in this study. The objectives were to (i) compare apparent electrical conductivity (ECa) measurements from the MC and MF-EMI sensors under different land use conditions, (ii) investigate the spatial variation of ECa, SWC, texture, soil organic matter (SOM), and bulk density (BD) under different land use conditions, and (iii) use statistical and geostatistical analysis to evaluate the effectiveness of ECa measurements in characterizing SWC under different land use conditions, considering the texture, SOM, and BD contents. The study found that MC-EMI had statistically significant relations (p-value  0.05) with SWC relative to the MF-EMI. Multiple linear regression (MLR) models were also shown to be more effective in representing SWC variations (higher coefficient of determination and lower root mean square error) than simple linear regression models. MC-EMI sensor provided better SWC predictions compared to the MF-EMI sensor, possibly due to larger sampling depths differences between time domain reflectometry measured SWC (SWCTDR) and MF-EMI sensor than those between SWCTDR and MC-EMI sensor. Lastly, cokriging of measured SWC was found to offer more accurate maps than cokriging of predicted SWC obtained from MLR across different land use conditions. The study has shown that EMI may not be highly effective for shallow depths, and ECa can be affected by various soil properties, making it difficult to extrapolate other parameters. However, EMI still shows promise as a reliable method for predicting SWC in boreal podzolic soils. Research into EMI's usefulness for this purpose has yielded promising results, as indicated in this study. Further investigation is needed to fully harness the potential of this promising technique.