Under the dual pressures of climate change and intensified human activities, the distribution and migration of soil moisture, heat, and salinity in arid regions have become increasingly intense, directly affecting regional land use and ecological security. Accurately mapping soil salinity and understanding the coupled dynamics of soil water, heat, and salt are essential for sustainable agriculture and ecosystem conservation. However, the performance of different remote sensors varies significantly under complex surface conditions. Soil moisture, a critical factor influencing electromagnetic signal responses, has not yet been assessed for its contribution across sensor types. Furthermore, the fine-scale interactions among moisture, temperature, and salinity remain poorly understood. Taking Aksu and Weiku oases as case study areas, this study proposed a synergistic multi-source remote sensing framework for soil salinity estimation, incorporating gravimetric soil water content as a collaborative factor. The coupling coordination degree (CCD) of surface soil water-heat-salt interactions was further evaluated based on land surface temperature derived from Landsat-8. Results indicated that: (1) Integrating the temperature vegetation drought index constructed from both linear and nonlinear methods with a Random Forest model significantly improved soil water content retrieval accuracy, increasing R2 by 0.25-0.43. (2) Introducing soil water content as a synergistic variable notably enhanced soil salinity estimation based on the fusion of Sentinel-1 and Landsat-8, with R2 increasing by 0.08-0.39 and RMSE decreasing by 7.88-12.17 dS/m. The salinization risk was notably higher at oasis fringes than in core agricultural zones. (3) The CCD characteristics of surface soil water-heat-salt exhibited significant spatial heterogeneity, with an average CCD of 0.51, indicating a reluctant coordination state. Forest (0.57) and cropland (0.51) showed relatively higher coordination, while grassland had the lowest level (0.47). The proposed framework enhances salinity monitoring accuracy in arid oases. It provides technical support for irrigation planning, land-use optimization, and salinization control, and contributes to sustainable land management and ecological security in arid regions.