To investigate the relationship of the soil organic matter (SOM) content to the electrical conductivity (EC), pH, and Fe content, we collected 110 samples at the Ebinur Lake Reserve in August 2017 and measured the soil reflectance spectra, SOM content, EC, Fe content, and pH. We performed three kinds of pre-treatments, including Savitzky-Golay (SG) smoothing, multiplicative scatter correction (MSC), and first-order differentiation (FD), on the original spectrum and then performed a principal-component analysis of the spectral data. The eigenvalues of the first five principal components were selected as the spectral variables. Strategy I used the original spectrum, performed SG-MSC and SG-MSC-FD on it, and employed the original spectrum as a control group. Strategy II used the soil covariates (EC, Fe, pH) as the input variables. Strategy III combined strategy I and strategy II. Predictions of the SOM content were obtained for all three strategies using partial least squares regression. The results show that predictions based on the pre-processed spectral data (for the verification set, the coefficient of determination was R²=0.66-0.82) were better than those based on the soil covariates as the prediction variables (for the verification set, the coefficient of determination was R²=0.40) and that combining the soil covariates and spectral data significantly improved the spectral-prediction accuracy for SOM (for the best verification set, R²=0.88). Pre-processing the spectral data effectively enhanced the potential spectral information and improved the predictive accuracy of the model. In summary, the combination of visible-near-infrared spectral information and soil covariates effectively improves the predictive performance of SOM models.