We describe a regression-based statistical methodology suitable for predicting field scale spatial salinity (ECe) conditions from rapidly acquired electromagnetic induction (ECga) data. This technique uses multiple linear regression (MLR) models to estimate soil salinity from ECa data. This technique uses multiple linear regression (MLR) models to estimate soil salinity from ECa survey data. The MLR models incorporate multiple ECa measurements and trend surface parameters to increase the prediction accuracy and can be fitted from limited amounts of ECe calibration data. This estimate technique is compared to some commonly recommended cokriging techniques, with respect to statistical modeling assumptions, calibration sample size requirements, and prediction capabilities. We show that MLR models are theoretically equivalent to and cost-effective relative to cokriging for estimating a spatilly distributed random variable when the residuals from the regression model are spatially uncorrelated. MLR modeling and prediction techniques are demonstrated with data from three salinity surveys. ¿ American Geophysical Union 1995