Generalization of LULC Classification in Arid Environments Using Machine Learning and Spectral, Texture, and Topographic Features: Spatial and Seasonal Analyses with Implications for Urban Environmental Monitoring

Accurate land use/land cover (LULC) mapping from remotely sensed data remains challenging in arid regions, particularly for spatial and seasonal generalization. This work proposes a novel exclude-one-city-out (EOCO) framework based on machine learning (ML) to achieve LULC generalization across summer and winter in arid environments. Four cities in Saudi Arabia witnessing rapid urban growth were selected: Riyadh, Madinah, Jeddah, and Dammam. The ML models were trained on three cities and tested on the unseen city. Sentinel-2 surface reflectance data for the visible (Blue, Green, and Red) and near-infrared bands (NIR, SWIR1, and SWIR2) were used. Spectral indices, texture features, and topographical data were used to form five feature sets, which were utilized as inputs for four ML algorithms: random forest, support vector machine, classification and regression trees, and K-nearest neighbors. Statistical tests (Friedman, Kendall’s W, and Wilcoxon signed rank) were conducted to assess differences across ML models, feature sets, and seasons. The random forest model consistently outperformed other models across the five feature sets, while the spectral texture and combined feature sets outperformed other feature combinations. Significant differences in feature importance were observed across cities and seasons for spectral texture during summer and winter (p-values: 1.25 × 10−4 and 9.2 × 10−5, respectively), with strong agreement (Kendall’s W = 0.9212 and 0.9424). The findings can support urban environmental monitoring in arid regions, contributing to sustainable urban development.