Proximal sensing using diffuse-reflectance near-infrared spectroscopy has demonstrated substantial potential for rapid and accurate estimation of selected soil properties for various applications. Many of these soil properties are diagnostic for the purpose of soil classification and survey. There is increasing interest in complementing the current methodologies employed in initial mapping and soil survey updating process using proximal sensing, especially with regard to digital and predictive soil mapping. This study focused on developing an accurate method for estimating in situ the spatial distribution of selected soil chemical and physical properties within a soil profile. An unaligned sampling grid (1.2 m × 0.2 m) with a sampling interval of 0.05 m (n = 124 sample points) was used to predict the spatial distribution of selected soil properties throughout the profile of one pedon in Allegany County, NY. An efficient data processing flow was also developed for estimating the spatial distribution of soil properties using PLS1 regression and ordinary kriging. While the estimated soil property values were generally within acceptable error tolerances, a larger sample size and a more robust prediction model would strengthen prediction accuracy. The results of this study suggest that advanced proximal sensing techniques, both laboratory-based and in situ, are useful for soil survey and serve to complement existing field soil survey methods.