Satellite-based soil texture mapping using GLOBAL–LOCAL spectral libraries for flood hazard assessment in arid regions

Abstract

Flooding in arid and semi-arid regions is a growing concern due to rapid urban expansion and more frequent extreme rainfall events, yet accurate flood hazard mapping remains difficult because of scarce in situ data and limited high-resolution soil information. To the best of our knowledge, this study represents one of the first demonstrations of satellite-based soil texture mapping via GLOBAL–LOCAL soil spectral library fusion for operational flood hazard assessment in an arid country. The fused model showed strong performance for sand (R² = 0.72, RMSE = 8%) and moderate performance for silt (R² = 0.44, RMSE = 2.8%) and clay (R² = 0.55, RMSE = 5.4%), while the Sentinel-2 land-cover classification used to mask non-soil surfaces achieved 93.75% overall accuracy (κ = 0.90). Combining predicted USDA soil texture classes with elevation, slope, hydrological, and land-cover data yielded a structural flood hazard map that highlights areas of higher susceptibility in low-elevation coastal zones and urban areas underlain by finer-textured soils, rather than predicting event-specific flood extents or water depths. Validation against Landsat 9 observations from a single major flood event in April 2024 showed strong spatial correspondence between high and very-high hazard classes and observed inundation in coastal cities and along major stream networks. By demonstrating the feasibility of transferring laboratory-trained soil spectral models to satellite sensors and embedding them in flood hazard mapping, this study establishes a scalable and transferable methodology applicable to arid and semi-arid regions worldwide where soil data scarcity limits hazard assessment. The resulting flood hazard and road vulnerability maps translate the scientific modelling framework into decision-ready products that support urban zoning regulations, drainage and transport infrastructure design and upgrading, and emergency response planning in rapidly developing arid and semi-arid regions.