This study compares acid digestion and temperature ramping methods for obtaining soil organic carbon (SOC) reference data to train Fourier Transform Near Infrared (FT-NIR) models in carbonate-rich Saskatchewan agricultural soils. FT-NIR spectra were measured on soil samples (n = 431) from carbonate-rich Dark Brown Chernozem soil, with quantification of inorganic and organic carbon. Spectra were transformed using continuous wavelet transform and analyzed using cubist regression tree models. Models were built using a 70:30 train-test split validation approach. Spectral feature selection, wavelet scale, model and hyperparameter optimization were conducted using 5-fold cross-validation analysis on the training dataset. All validation metrics were calculated using the testing dataset. The temperature ramping method identified outliers with SIC greater than 1.5%, which were not detected using the acid digestion method. SOC prediction accuracy was higher using temperature ramping data (r2 = 0.66, ccc = 0.78) compared to acid digestion data (r2 = 0.44, ccc = 0.64), while TC prediction accuracy was similar for both methods (r2 = 0.58, ccc = 0.71). Removing samples with high carbonate (SIC > 1.5%) improved SOC and TC prediction accuracy using temperature ramping data (r2 = 0.71, ccc = 0.80 for SOC; r2 = 0.64; ccc = 0.75 for TC), but not when using acid digestion method. This study suggests that high carbonate content may negatively affects SOC model accuracy, especially when relying upon acid digestion methods for reference SOC data.