Abstract: Salinized wasteland can be served as the temporary salt reservoir with the much higher salt content beyond the average level. Therefore, the high-precision inversion of soil salinity can be realized to explore the difference in the spectral response of soil salinity in different land use types and its influence on the remote sensing model. In this study, a typical salinization region (Yongji of Hetao irrigation district in China) was chosen as the study region. The salinized wasteland was relatively scattered and mostly concentrated around the agricultural land. The salt content in the salinized wasteland was much higher than that in the agricultural land. Firstly, in-situ hyperspectral measurement (FieldSpec 4 Hi-Res, and ASD) was carried out for the agricultural land and salinized wasteland in April from 2018 to 2020. Secondly, the spectral data was subjected to the various spectral transformations, including the fundamental transformation (original, reciprocal, logarithm, and radical transformation), derivative transformation (the first and the second derivative), and spectral index (normalized differential soil index, difference soil index, and simple ratio soil indices), respectively. Thirdly, the multiple stepwise regressions were used to acquire the characteristic bands and spectral indices. Lastly, the single land type salt inversion model (Agricultural Land (AL), Salinized Wasteland (SW)), and the overall salt inversion model (Agricultural Land + Salinized Wasteland (AL+SW)) were constructed using the characteristic wavelength and characteristic spectral index, respectively. The model accuracy under different modeling was evaluated using the coefficient of determination (R2), and Root Mean Square Error (RMSE). As such, the best modeling was proposed for the regional soil salinization. The results showed that the average content of soil salinity in the samples of AL, SW, and AL+SW model was 5.09, 13.42, and 7.09 g/kg, respectively. Specifically, the SW spectral reflectance was greater than that of the AL in each wavelength range of different grades of salt zone. Among them, the average differences were 0.040, 0.020, and 0.034 in the slightly, moderately, and strongly saline soil, respectively. Spectral transformation and spectral index were effectively improved the correlation between the soil salt and spectrum in the different land types. Compared with the fundamental transformations (reciprocal, logarithm, and root), the derivative transformations significantly increased the range of sensitive wavelengths for the high correlation coefficient at specific wavelengths. The accuracy of models with the characteristic spectral index was much higher than that with the characteristic wavelength in different land types. After the first derivative transformation, the average R2 of AL, SW, and AL+SW regression models increased compared with the wavelength regression model. The average R2 of AL, SW, and AL+SW regression models also increased after the second derivative transformation. The salinization inversion model of single land type significantly improved the inversion accuracy of regional soil salt. A significant increase from 0.50 to 0.61 was found in the average R2 of the spectral index model under each transformation in the single-land type salinization inversion model (AL, and SW), compared with the overall model (AL+SW model ). The average R2 values of the fundamental transformation, the first, and the second derivative models were 0.06, 0.11, and 0.17 higher than that of the overall model, respectively. At the same time, there was the increase from 0.74 to 0.92 in the average R2 of the single land type salinization inversion model using the optimal spectral index, compared with the overall model. Therefore, the inversion models of soil salt for the different land use types can be expected to ensure the inversion closer to the actual situation, particularly for the various land use types with the large differences in salinity.