The escalating environmental concern of salinity has far-reaching impacts on the global community. The detrimental effects of salinity significantly degrade soil fertility, resulting in economic losses in agriculture and posing a threat to food security. Monitoring and analysing changes in soil salinity over time are essential tasks for effectively devising strategies in natural resource management for the future. Numerous models based on multispectral and dielectric techniques have been formulated to estimate soil salinity utilizing data from Landsat and Sentinel satellites. Palacode Taluk, located in the Dharmapuri district of Tamil Nadu, stands out as an area severely affected by natural salinity-related factors. Consequently, there is an urgent necessity to pinpoint the most accurate assessment method for salinity in this region, thereby improving the well-being of farmers, their livelihoods, and the overall ecosystem. This study aims to investigate and compare the effectiveness of different salinity models derived from Sentinel-1, Sentinel-2, and Landsat 8 OLI satellites. Various soil salinity indices, acquired through different combinations of visible and infrared bands from Sentinel 2A, along with a modified salinity index developed from Landsat data, were scrutinized to explore the potential of multispectral models. Additionally, the study evaluated the performance of dielectric models by examining the DSDM and Hallikainen model. Statistical analysis and validation were conducted through linear regression analyses to establish correlations between on-site measurements and satellite-based models. The results suggest that dielectric models produce better results for the specified study area in comparison to multispectral models. Specifically, the Hallikainen model performed the best, demonstrating a stronger correlation with on-site measurements. Within the multispectral models, the modified salinity index generated from Landsat data exhibited a greater correlation with actual soil salinity measurements than the models utilizing various combinations of bands from sentinel 2A data.