Reflectance spectroscopy has been adopted as a nondestructive method for material characterization in a wide range of applications. In this study, near infrared reflectance spectroscopy (NIR) was used to evaluate diverse soil properties for five different soil series of Entisol, Mollisol and Aridisol soil groups in northern Turkey, using 512 soil samples collected in 25x25 m sampling grid over a 32 ha (800x400m) area. Air-dried soil samples were scanned at 1-nm resolution from 350 to 2500 nm (near-infrared wavelengths) and calibrations between soil properties and soil reflectance were developed by using a cross validation procedure under partial least squares (PLS) regression and multivariate adaptive regression splines (MARS). The two data analysis methods were evaluated by using raw reflectance data and first derivative reflectance data separately and combined. The strongest correlations (r2values ) between reflectance and soil chemical properties were observed using PLS regression and first derivative data, for exchangeable Ca (0.85), cation exchange capacity (0.88), exchangeable Mg (0.78), organic C concentration (0.84), clay content (0.93), sand content (0.90) and CaC03 (0.82). Weaker correlations were observed for pH, electrical conductivity (EC) and exchangeable K and Na (0.55-0.65). Overall, PLS regression provided larger r2 values than MARS. Also, no improvement was obtained by using both first derivative and raw data combined, nor by increasing the number of soil samples processed from 250 to 512. In conclusion, cross-validated regression models were able to predict soil properties with variable predictive ability with the best results provided by PLS regression on first derivative reflectance data.