Hyperspectral remote sensing data with more than one hundred bands and very high spectral and spatial resolutions can help facilitate soil classification detail not achieved through traditional remote sensing methods. One of the objectives of this study focused on establishing quantitative relationships among hyperspectral data, selected soil nutrients, soil texture classes, and cation exchange capacity (CEC), as well as between soil sample points and spectral data. These analyses were accomplished by implementing multiple regression analyses among soil texture parameters, selected soil nutrients, CEC, and spectral reflectances of bands. Based on the results, the hypothesis supported the existence of significant statistical-spectral relationships between soil texture classes, selected soil nutrients, and CEC and hyperspectral data at 95% confidence interval for multiple regression with R2 values greater than 0.68. Analyses of the results helped with improving the precision of soil databases that potentially could be useful for rural taxation purposes, as well as devising a soil sampling scheme, which could be important for applications pertaining to precision agriculture.