Mapping soil salinity using machine learning and remote sensing data in semi-arid croplands

Abstract

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.