Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

A comprehensive understanding of soil salinity distribution in arid regions is essential for making informed decisions regarding agricultural suitability, water resource management, and land use planning. methodology was developed to identify Sudan by utilizing optical radar-based satellite data as well variables obtained from digital elevation models that are known indicate variations salinity. The includes the transfer areas where similar conditions prevail. geographically coordinated database established, incorporating a variety environmental based on Google Earth Engine (GEE) Electrical Conductivity (EC) measurements saturation extract samples collected at three different depths (0–30, 30–60, 60–90 cm). Thereafter, Multinomial Logistic Regression (MNLR) Gradient Boosting Algorithm (GBM), were utilized spatially classify levels region. To determine applicability model trained reference site target area, Multivariate Environmental Similarity Surface (MESS) analysis conducted. producer’s accuracy, user’s Tau index parameters used evaluate model’s spatial confusion indices computed assess uncertainty. At depths, values area ranged 0.38 0.77, whereas 0.66 0.88, decreasing depth increased. Clay normalized ratio (CLNR), Salinity Index 1, SAR important modeling. It found subsoils middle northwest both had higher level compared topsoil. This study highlighted effectiveness means identifying evaluating management facing significant salinity-related challenges. approach can be instrumental alternative suitable activities regional level.