Spectroscopy has shown capabilities of predicting certain soil properties. Hence, it is a promising avenue to complement traditional wet chemistry analysis that is costly and time-consuming. This study focuses on the comparison of two Vis-NIR instruments of different resolution to assess the effect of the resolution on the ability of an instrument to predict various soil properties. In this study, 798 air dried and compressed soil samples representing different agro-climatic conditions across Québec (Canada) were analyzed using Vis-NIR spectroscopy. Vis-NIR spectra of all soil samples were collected using a laboratory setup of a field spectrometer operating in the range from 350 - 2200 nm (P4000, Veris Technologies, Salina, Kansas, USA) and the ASD FieldSpec® 4 Standard-Res Spectroradiometer (Malvern Panalytical Ltd, Malvern, United Kingdom) operating from 350 - 2500 nm. In addition to the analytical techniques, successful prediction of soil properties depends on sensor calibration. In this research, three spectral pre-processing methods were compared (standard normal variate, first and second derivatives, all with a Savitzky-Golay filter), the results were produced using partial least squares regression (PLSR) and the models were selected according to the R2of a 15-fold cross-validation. The results of each combination of soil property (extractable P, K, Ca, Mg, Al, SOM and CEC), data calibration method and instrument were assessed in terms of RMSE of the prediction and the R2for the linear regression between measured and predicted values. FieldSpec gave better predictions for K (R2 = 0.34, RMSE = 145 kg/ha), Al (R2 = 0.60, RMSE = 164 ppm), SOM (R2= 0.69, RMSE = 0.97%) and CEC (R2= 0.62, RMSE = 2.94 cmolc/kg) and Veris gave better predictions for P (R2= 0.11, RMSE = 142 kg/ha), Ca (R2 = 0.63, RMSE = 1260 kg/ha) and Mg   (R2 = 0.58, RMSE = 232 kg/ha). It was not possible to conclude which instrument performs better.