Soil salinity significantly constrains agricultural productivity and land sustainability, particularly in irrigated areas. While, remote sensing offers large-scale monitoring capacity, but its accuracy depends on how effectively spectral information is integrated with advanced modeling approaches. This study evaluates the performance of a combined approach based on machine learning (ML) algorithms and satellite-derived predictors for soil salinity mapping in the Béni Amir Sub-perimeter of Tadla plain, Morocco. A total of 43 topsoil samples (0–10 cm) were collected and analyzed for electrical conductivity (ECe) and resampled to 144 samples for model training and testing. Predictor Variables were derived from Landsat-8 OLI data, including salinity indices (OLI-SI, SI, SI1), intensity indices (Int1, Int2), brightness index (BI), land degradation index (LDI), and reflectance values of selected spectral bands (B2-B7) were standardized and transformed with PCA to address multicollinearity. Four ML algorithms, Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Regressor (SVR), and Multi-Layer Perceptron (MLP) were tested. The results show that the Ece ranges from 0.84 to 10.28 dS/m with a standard deviation of 2.29 dS/m, indicating substantial salinity variability across the Béni Amir sub-perimeter. Individual predictors exhibited moderate correlation with Ece (R = 0.34-0.72). Among the applied models, KNN achieved the highest accuracy (mean coefficient of determination (R²) = 0.75 [0.73-0.77]; Root Mean Square Error (RMSE) = 0.61 dS/m). The resulting maps revealed a consistent southwestward increase in salinity, following the regional hydraulic flow. KNN classified 49% of the area as moderately saline, 22% as slightly saline, and 20% as non-saline, while the strongly and extremely saline classes covered 8.4% and 0.6%, respectively. RF, SVR, and MLP showed comparable trends, with moderately saline areas ranging between 30-41% and strongly to extremely saline soils below 10%. These findings demonstrated that combining satellite-derived data with ML enables a reliable assessment of soil salinity, supporting management of irrigated agroecosystems.

Soil salinity is a worldwide problem that negatively affects soil quality and persistently spreading, especially in arid and semi-arid climate. The present study focuses on the mapping of soil salinity risk in Tadla Plain. To achieve this goal, the approach of Soil Salinity Risk Index (SSRI) has been adopted. The use of SSRI based approach reveals the occurrence of three risk classes: low, moderate and severe. The moderate risk class dominates with a coverage representing 80% of the total area. The results achieved showed the prospect of this approach to delineate areas of soils prone to salinization risk.

Soil salinity is a significant threat to crop sustainability and food security. This review aims to provide the basis for effective soil salinity management by examining the different solutions to develop scientifically sound guidelines for farmers, maintain profitable agricultural production in saline farmland irrigated possibly with saline groundwater, and alleviate agricultural land degradation. A lesson learned from the Moroccan experience in tackling salinity is needed for integrating soil and water management and appropriate salt-tolerant crops with innovative agricultural practices as a package of saline agriculture. Moreover, regional and global cooperation to exchange emerging challenges, successful rehabilitation studies, and innovative solutions should be considered. This review concludes that no single parameter could be suggested as the only possible way for soil salinity rehabilitation.