Salinity stress is a major constraint to crop productivity in arid and semi-arid regions, highlighting the need for innovative, data-driven methods to evaluate genotype performance under such conditions. This study, conducted at the Sahline experimental station (Tunisia) in collaboration with the Italian agtech firm aSpace, assesses the salinity tolerance among onion (Allium cepa L.) genotypes cultivated in two field plots—one irrigated with saline water and the other with non-saline water. The methodology integrates AI-predicted soil parameters (organic matter, electrical conductivity, pH, and N-P-K contents) with multi-spectral, multi-temporal satellite imagery (Sentinel-2, PROBA-V, Landsat 8, and MODIS) collected from March to June 2025. Key vegetation and salinity indices—including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Bare Soil Index (BSI), and the Normalized Difference Salinity Index (NDSI)—were computed and cluster analysis was performed to map healthy vegetation patches within the trial. Preliminary results at the plot level reveal clear physiological differences due to salinity. NDVI values were consistently lower in the saline plot, starting as early as March (0.0971 vs. 0.2296 in the non-saline plot) and averaging 0.0984 versus 0.2308 across the entire monitoring period. The NDSI, a salinity-specific index, remained consistently higher in the saline plot (mean: 0.1184 vs. 0.0885), confirming persistent salt stress and aligning with the observed spectral vegetation decline. In parallel, the BSI — which reflects bare soil exposure and indirectly indicates poor canopy development — peaked in April in both plots, reaching 0.2266 in the non-saline and 0.2141 in the saline plot. The slightly higher BSI in the saline plot may reflect areas where salinity stress prevented full canopy development. Interestingly, the MSAVI and EVI were slightly higher in the saline plot across months, possibly due to surface reflectance effects or early-stage physiological responses under stress. These trends were consistent over the three-month monitoring period and are dynamically visualized through an ArcGIS web map interface. aSpace’s AI platform enabled rapid, field-scale estimation of soil properties, overcoming the limitations of traditional sampling and providing scalable, high-resolution coverage. The current results demonstrate the value of integrating AI and remote sensing for rapid, non-destructive phenotyping of salinity response. This integrated approach offers a replicable and scalable framework to support smarter, faster, and more precise crop selection strategies and can be extended to assess salinity resilience and responses to other abiotic stresses in marginal environments.