Use of electromagnetic induction (EMI) sensors along with geospatial modeling provide a better opportunity for understanding spatial distribution of soil properties and crop yields on a landscape level and to map site-specific management zones. The first objective of this research was to evaluate the relationship of crop yields, soil properties and apparent electrical conductivity (ECa) at different topographic positions (shoulder, backslope, and deposition slope). The second objective was to examine whether the correlation of ECa with soil properties and crop yields on a watershed scale can be improved by considering topography in modeling ECa and soil properties compared to a whole field scale with no topographic separation. This study was conducted in two headwater agricultural watersheds in southern Illinois, USA. The experimental design consisted of three basins per watershed and each basin was divided into three topographic positions (shoulder, backslope and deposition) using the Slope Position Classification model in ESRI ArcMap. A combine harvester equipped with a GPS-based recording system was used for yield monitoring and mapping from 2012 to 2015. Soil samples were taken at depths from 0–15 cm and 15–30 cm from 54 locations in the two watersheds in fall 2015 and analyzed for physical and chemical properties. The ECa was measured using EMI device, EM38-MK2, which provides four dipole readings ECa-H-0.5, ECa-H-1, ECa-V-0.5, and ECa-V-1. Soybean and corn yields at depositional position were 38% and 62% lower than the shoulder position in 2014 and 2015, respectively. Soil pH, total carbon (TC), total nitrogen (TN), Mehlich-3 Phosphorus (P), Bray-1 P and ECa at depositional positions were significantly higher compared to shoulder positions. Corn and soybeans yields were weakly to moderately (<±0.75) correlated with ECa. At the deposition position at the 0–15 cm depth ECa-H-0.5 was weakly correlated (r < ±0.50) with soil pH and was moderately correlated (r = ±0.50–±0.75) with organic matter (OM), calcium (Ca) and sulfur (S). Slope variation from 1%–20% at the research site had a strong influence on soil properties at watershed scale. When data from all topographic positions were combined together in all basins spatial interpolation between Mehlich-3 P and ECa-H-0.5 resulted in a larger cross validation RMSE compared to individual shoulder and backslope positions. Results demonstrated that topographic position should be considered while making correlations of ECa with soil properties. Methods of delineating topography positions presented in this paper can easily be replicated on other fields with similar landscape characteristics and EMI sensor based survey techniques can certainly improve and help in making detailed prediction maps of soil properties.