Technical Abstract: Spatial variability has a profound influence on solute transport in the vadose zone, soil quality assessment, and site-specific crop management. Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity (ECa) is a potential means of characterizing the spatial variability of any soil property that influences ECa including sol salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. Arguably the most significant step in the protocols for characterizing spatial variability with ECa-directed soil sampling is the statistical sampling design, which consists of model- and design-based sampling strategies such as response surface sampling (RSS) and stratified random sampling (SRS), respectively. The primary objective of this study was to compare model- and design-based sampling strategies to evaluate if one sampling strategy outperformed the other or if both strategies were equal in performance. Three different model validation tests were used to verify that the regression equation estimated from the RSS data produced accurate and unbiased predictions of the log salinity levels at the independently chosen SRS sites. The model validation tests show that the RSS design can be reliably used to estimate the ln function between ECa and soil salinity and can provide an accurate and unbiased prediction of the validation sample sites chosen by the SRS design. Design optimality scores indicate that the use of the RSS design should facilitate the estimation of a more accurate regression model; i.e., the RSS approach should allow for better model discrimination, more precise parameter estimates, and smaller prediction variances. Even though a model-based sampling design, such as RSS, has been less prevalent in the literature, it is concluded from the comparison that there is no reason to refrain from its use and in fact warrants greater consideration.