In this paper, the soil salt content (SSC) and the associated spectral reflectance were measured and analyzed during the microbial remediation process of saline soil. The two methods including extremums of correlation coefficients and the different ranges of correlation coefficients were used to find the optimal sensitive bands of SSC for eight spectral data sets covering the raw spectral reflectance, the smoothed spectral reflectance and six different pre-processing transformations of spectral data of saline soil. With this basis, partial least squares regression (PLSR) was used to build relational models between SSC and spectral reflectance based on full bands (400～1　650 nm) and optimal bands, respectively. The results showed that the optimal spectral bands for eight spectral data sets, concentrated on 947.11～949.31, 1　340.27, 1　394.11, 1　457.81～1　461.31, 1　537.68～1　551.39 and 1　602.32 nm. Taking the coefficient of determination (R2 ), root mean squared error (RMSE) and akaike’s information criterion(AIC) as criteria to select the best model. For the PLSR predicting models of SSC based on optimal bands from two different ways, the SGSD (LogR) obtained more robust calibration and prediction accuracies than other pre-processing inversion models. Compared with optimal bands, the full bands using PLSR method could obtain better prediction accuracies on the whole. Among all of the eight spectral data sets in full bands, the prediction accuracy of SGSD was the best, the corresponding R2 and RMSEP of the predicted model were 0.673 and 1.256. For the inversion models based on optimal bands, although there was a slight gap in the prediction accuracy with that based on full bands, they also had their own merits: these models were much simpler and thus the reducing model computation and modelling speed were more important than improving prediction accuracy. The results of this study showed that the method had a great potential for diagnosing and monitoring soil salinization quickly and conveniently in researching the relation between SSC and soil reflectance spectra.