Knowledge of the spatial distribution of soil textural properties at the watershed scale is important for understanding spatial patterns of water movement, and in determining soil moisture storage and soil hydraulic transport properties. Capturing the heterogeneous nature of the subsurface without exhaustive and costly sampling presents a significant challenge. Soil scientists and geologists have adapted geophysical methods that measure a surrogate property related to the vital underlying process. Apparent electrical conductivity (ECa) is such a proxy, providing a measure of charge mobility due to application of an electric field, and is highly correlated to the electrical conductivity of the soil solution, clay percentage, and water content. Electromagnetic induction (EMI) provides the possibility of obtaining high resolution images of ECa across a landscape to identify subtle changes in subsurface properties. The aim of this study was to better characterize subsurface textural properties using EMI mapping and geostatistical analysis techniques. The effect of variable temperature environments on EMI instrumental response, and ECa - depth relationship were first determined. Then a procedure of repeated EMI mapping at varying soil water content was developed and integrated with temporal stability analysis to capture the time invariant properties of spatial soil texture on an agricultural field. In addition, an EMI imaging approach of densely sampling the subsurface of the Reynolds Mountain East watershed was presented using kriging to interpolate, and Sequential Gaussian Simulation to estimate the uncertainty in the maps. Due to the relative time-invariant characteristics of textural properties, it was possible to correlate clay samples collected over three seasons to ECa data of one mapping event. Kriging methods [ordinary kriging (OK), cokriging (CK), and regression kriging (RK)] were then used to integrate various levels of information (clay percentage, ECa, and spatial location) to produce clay percentage prediction maps. Leave-one-out cross-validation showed that the multivariate estimation methods CK and RK, incorporating the better sampled surrogate ECa, were able to improve the RMSE by 7% and 28%, respectively, relative to OK. Electromagnetic induction measurements provide an important exhaustive layer of information that can improve the quality and resolution of soil property maps used in hydrological and environmental research.