Integrated ground-penetrating radar and electromagnetic induction offer a non-destructive approach to predict soil bulk density in boreal podzolic soil

• Non-destructive approach to predict soil bulk density over larger areas. • Soil compaction influences geophysical responses. • Random forest approach identified important variables to predict bulk density. • Developed models show high accuracy in predicting bulk density. • These data Electromagnetic Induction and Ground-penetrating radar data can predict bulk density. Tillage and soil compaction affect soil properties, processes, and state variables influencing soil health, hydrodynamics, and crop growth. Assessing soil compaction levels using traditional methods, such as soil sampling and penetration resistance, is inefficient for scaling up from plot to field scales. Geophysical methods like Ground-penetrating Radar (GPR) and Electromagnetic Induction (EMI) are becoming prominent for assessing soil properties and state variables in agriculture due to their ability to overcome the limitations of traditional methods. However, a research gap exists in non-destructively estimating bulk density changes related to tillage and soil compaction. This study aimed to (1) assess the influence of soil compaction on GPR and EMI responses in boreal podzolic soil and (2) develop and evaluate prediction models to determine soil bulk density using GPR and EMI. The experiment was conducted by compacting loamy sand-textured soil using a lawn roller. GPR data were collected to determine the soil dielectric constant (K r ) and the direct ground wave amplitude (A DGW ), along with EMI-measured apparent electrical conductivity (EC a ) under three compaction levels (no, four and ten roller passes). Relationships between K r , A DGW and EC a and the average bulk density of 0–0.30 m depth at three compaction levels were tested. A Random Forest (RF) regression approach was employed to identify the most significant variables for predicting bulk density. Simple and multiple linear regression (SLR and MLR, respectively) models were developed using EC a and K r and were subsequently evaluated. Results revealed significant differences between the measured bulk density and geophysical data across the tested compaction levels. During the model development, SLR and MLR showed R 2 > 0.65, and the model evaluation showed a root mean square error of < 0.14 g/cm 3 . This study highlights the potential of using GPR and EMI for the non-destructive prediction of bulk density in the agricultural landscape. However, further research is needed to explore the applicability and limitations of this approach across varying water contents, electrical conductivities, and soil types.