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

作者：A. Chaaou, Hamza Ait-Ichou, Said El Hachemy, M. Chikhaoui, M. Naimi, M. Hssaisoune, Mohammed El Hafyani, Y. Ait Brahim, L. Bouchaou · 发表于：Frontiers in Soil Science · 年份：2025 · DOI：10.3389/fsoil.2025.1653400 · 被引用次数：10

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 (...