Soil salinization hinders sustainable agricultural development in coastal regions. Developing a multi-layer soil salinity inversion model and accurately predicting water demand for salt regulation are essential for improving soil salinity management. Wudi County in Shandong Province was selected as the research area, with 79 sampling sites chosen. Soil salinity was measured at the surface (0-20 cm), middle (20-40 cm), and bottom (40-60 cm) layers. Vegetation and salinity indices were extracted from Landsat 8 remote sensing imagery to estimate surface soil salinity. A correlation-based inversion method was developed to obtain multi-layer soil salinity data by leveraging the strong correlation between adjacent soil layers. The water requirement for salt regulation was optimized and predicted by integrating the results from multi-layer soil salinity estimation with Groundwater Management System (GMS) software. The results indicated that the surface layer soil salinity inversion model performed well, with an R-2 >0.75 and an RMSE < 0.43 g/kg for both the training and validation sets. Additionally, the prediction accuracy of the correlation-based inversion method exceeded that of the direct modeling approach, with the middle and bottom layer soil salinity models achieving an R-2> 0.6 and an RMSE < 1 g/kg. Soil salinization in the study area was more severe in the northeast than in the southwest, with both measured and estimated data showing similar spatial distributions. Over the past decade, the overall trend of soil salinization has shown a general decline with localized intensification. The salt distribution patterns in saline soil profiles were predominantly homogeneous and bottom-accumulated. The projected water demand for salt regulation calculated from the estimated data was slightly lower than the actual measurements, yet their spatial distribution was nearly identical. This study provides a scientific foundation for the dynamic monitoring and precise management of soil salinity in coastal regions.
