Leveraging PolSAR Features and Machine Learning for Improved Land Cover Discrimination with ALOS-2 PALSAR-2: A Comprehensive Evaluation over the Istanbul Metropolitan Region

Abstract. Accurate and timely land cover mapping in heterogeneous metropolitan environments remains a fundamental challenge in Earth observation, particularly under conditions where optical imagery is compromised by cloud cover or seasonal atmospheric interference. This study presents a systematic evaluation of four state-of-the-art machine learning algorithms Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and a shallow Artificial Neural Network (ANN) for pixel-based land cover classification over the Istanbul metropolitan region using single-date ALOS-2 PALSAR-2 L-band Synthetic Aperture Radar (SAR) imagery. The methodological framework integrates dual-polarimetric backscatter coefficients (HH and HV) with Grey-Level Co-occurrence Matrix (GLCM) texture features, Land Parcel Identification System (LPIS) boundaries for reference data delineation, Bayesian hyperparameter optimization, and LightGBM-guided Recursive Feature Elimination (RFE) to establish a reproducible and computationally efficient classification pipeline. Among all tested configurations, LightGBM achieved the highest overall accuracy (OA = 85.1%, κ = 0.81) with a 10-feature subset identified through RFE, while XGBoost demonstrated the strongest performance for urban class discrimination. Bayesian optimization yielded statistically meaningful improvements over default configurations for all gradient-boosting models. The optimal feature count was found to be ten, with HV-derived texture features particularly Entropy and Contrast identified as the most discriminative predictors. These results confirm that systematic feature engineering and algorithm tuning are as critical as classifier selection in SAR-based land cover mapping and lay the foundation for scalable operational workflows applicable to rapidly urbanizing regions.