Abstract: Soil salinization can often result in the continuous accumulation of soluble salt in the surface layer of soil, due mainly to natural and human activities. Saline soil has then posed a serious threat to the soil structure and crop yield in sustainable agriculture. It is very urgent to effectively monitor and manage the soil salinization in saline-alkali areas. Fortunately, hyperspectral imaging can be expected to estimate the soil salinity in recent years, due to its rich spectral information and high resolution. Among them, soil salt content (SSC) is one of the most important indicators to evaluate the degree of soil salinization. This study aims to estimate the soil salinity in the tillage layer of the oasis during hyperspectral imaging. The salinized areas were also precisely identified to optimize farmland management. A total of 193 soil samples were collected from the field in the Weigan-Kuqa River Oasis in Xinjiang, China. The salt content was then measured in the laboratory. Additionally, the spectrum of each soil sample was measured ten times and then averaged to obtain the spectral reflectance. The noisy spectral bands were also removed at the ends of the curves and two water absorption bands, followed by smoothing preprocessing using the Savitzky-Golay method. The spectral data was obtained to further transform using mathematical transformations, continuous wavelet transformation (CWT), and discrete wavelet transformation (DWT). According to Pearson correlation analysis, the spectral bands passing the significance test (P<0.01) were extracted as the SSC characteristic bands. A random forest model was then constructed to quantitatively estimate the SSC. The results indicate that: 1) There was a general consistency in the spectral curve shapes of soils with different salinization levels. There was a great variation in the patterns of reflectance with the wavelength and SSC. As the wavelength increased from the short (350 nm) to the long (2 450 nm), the reflectance increased in the range of 350~800 nm and then remained relatively constant in the range of 800~2 130 nm. While an absorption peak appeared around 2 130 nm corresponding to Na2SO4. After that, the reflectance started to decrease. Furthermore, the reflectance increased with the SSC in the entire range of wavelengths. There was also a positive correlation between them. But there was a decreasing trend at extreme levels of high salinization. 2) First-order differential transformation, CWT, DWT, as well as first-order differential combined with CWT and first-order differential combined with DWT, all significantly enhanced the sensitivity of the spectra to the SSC. Specifically, the DWT was performed the best among them. The highest correlation coefficient between the processed spectra and SSC reached −0.621 (P<0.01), indicating a negative correlation. The performance of the rest was ranked in the descending order of the first-order differential combined with CWT, first-order differential combined with DWT, CWT, and first-order differential transformation. 3) The first-order differential transformation, CWT, DWT, first-order differential combined with CWT, and first-order differential combined with DWT all improved the accuracy and stability of the model. The combination of first-order differential with CWT significantly enhanced the accuracy of SSC estimation. The optimal model used the full-band spectral of (1/R)'_CWT_2⁸, with the coefficients of determination (R²) of 0.821 and 0.715 for the training and validation sets, respectively, the root mean square errors (RMSE) of 16.049 and 17.467 g/kg, respectively, and the relative predictive determinant (RPD) values of 1.65 and 1.48, respectively. As such, the effective SSC estimation was achieved in the study area. Therefore, the mathematical transformations with the wavelet transformation were better utilized to process the smoothed original spectral data. The sensitivity of spectra to SSC was significantly enhanced after processing. Compared with the mathematical or wavelet transformations alone, the combined approach was more effective in quantitatively monitoring the SSC. The finding can also provide a valuable reference to alleviate the soil structure deterioration for agricultural production and environmental protection