Understanding the spatial variability of soil chemical and physical attributes allows for the optimization of the profitability of nutrient and water management for crop development. Considering the advantages and accessibility of various types of multi-sensor platforms capable of acquiring large sensing data pertaining to soil information across a landscape, this study compares data obtained using four common soil mapping systems: 1) topography obtained using a real-time kinematic (RTK) global navigation satellite system (GNSS) receiver, 2) apparent soil electrical conductivity obtained using an electromagnetic induction instrument with topographic data, 3) combination of apparent soil electrical conductivity obtained using galvanic contact resistivity sensing, subsurface soil reflectance and direct soil pH measurements with topographic data, and  4) passive gamma-ray spectroscopy with topographic data with regards to their capability to predict six soil properties: clay content, cation exchange capacity (CEC), soil pH, soil organic matters (SOM) content, extractable potassium (P) and phosphorus (K) levels. These systems were used to map two agricultural fields: NX (45 ha) and ST (40 ha) in northeastern Ontario, Canada. It was shown that sensor combinations produced lower prediction errors as compared to individual sensors.