Abstract: Soil salinization has become a major limiting factor for land productivity in coastal areas, a key agricultural ecological zone in China. In recent years, fractional-order differentiation (FOD) data transformation has gained attention for its applications in hyperspectral remote sensing-based salinization monitoring. However, the effectiveness of FOD varies across spatial and temporal scales due to differences in soil formation environments, and its applicability in humid to semi-humid coastal salinized areas remains unexplored. To address this gap, this study focuses on Huanghua City in Hebei Province, a representative coastal salinized region in northern China, to evaluate the potential of FOD for monitoring soil salinization. Using hyperspectral imagery from the HJ-2B satellite, the study employed the Grünwald-Letnikov (G-L) fractional-order differentiation method to transform spectral data across fractional orders from 0 to 2.0, with a step size of 0.1. This method aimed to reduce baseline drift and noise while enhancing spectral feature variations. Spectral characteristics of three soil types (non-salinized, mildly salinized, and heavily salinized) were analyzed, along with the correlation between spectral reflectance and soil salinity. Data from 60 field-collected soil samples were used to calculate Pearson correlation coefficients. Key spectral bands most sensitive to soil salinity were identified and utilized as input variables to develop a soil salinity inversion model based on a gradient boosting machine (GBM). The results demonstrated the following: FOD Enhances Spectral Correlation with Soil Salinity. FOD significantly improved the correlation between spectral data and soil salinity. The spectral differences among the three soil types were most pronounced under 0.9 order differentiation, achieving the highest correlation with soil salinity (maximum correlation coefficient of 0.58). This represents improvements of 25%, 2.5%, and 50% compared to the original spectrum, first-order differentiation, and second-order differentiation, respectively. The findings highlight FOD’s ability to enhance spectral reflectance and soil salinity relationships by mining deeper spectral information. Lower Order FOD is Optimal for Coastal Salinized Soils. Lower order FOD better captured salinity-induced spectral changes compared to higher order transformations. The 0.9 order differentiation was optimal, as it yielded the most significant spectral differences among non-salinized, mildly salinized, and heavily salinized soils. Further Pearson correlation analysis identified five key spectral bands—960 nm, 975 nm, 1,630 nm, 1,975 nm, and 2,140 nm—that were highly correlated with soil salinity. Among these, the 960 nm, 1,630 nm, and 1,975 nm bands showed high sensitivity to salinity changes, influenced by soil moisture and mineral properties. Improved Inversion Accuracy with Optimized FOD. Based on FOD transformations and correlation analysis, six fractional orders (0, 0.5, 0.9, 1.0, 1.1, and 1.5) were selected for soil salinity inversion modeling. The 0.9 order transformation achieved the best inversion accuracy, with a coefficient of determination (R²) of 0.78 and a root mean square error (RMSE) of 1.0 g/kg. In contrast, the original spectrum and high order transformations performed poorly. For example, the 1.5 order spectrum yielded an R² of just 0.07, worse than the original spectrum’s R² of 0.36, indicating that higher order transformations amplified noise and reduced prediction accuracy. In conclusion, FOD data transformation effectively uncovers nonlinear relationships between soil salinity and spectral information, significantly improving the prediction capability of soil salinity models. These findings provide a scientific basis for hyperspectral remote sensing-based salinization monitoring in coastal areas and offer valuable insights for improving the ecosystem of salinized soils in northern China.