Hybrid SAR-Optical Remote Sensing for Flood Inundation Mapping: Feature Contribution Analysis in a Wetland Environment

Abstract

Flooding is a recurrent problem in Banjar Regency due to its low-lying wetland topography, high rainfall, andriver sedimentation. Nonetheless, precise flood mapping continues to be a challenge due to the interference of cloud coveron optical imaging and spectral ambiguity in diverse wetland ecosystems. To address these limitations, this study proposesa hybrid SAR–optical method that combines Sentinel-1 backscatter with Sentinel-2 water indices (FWEI and AWEI) usingpixel-level feature stacking and feature contribution analysis. Four machine learning classifiers—Random Forest, LogisticRegression, Support Vector Machine (SVM), and XGBoost—were evaluated using a spatially independent validation schemeto ensure robust generalization. The results indicate that AWEI surpasses individual features in water discrimination, whereasthe combination of SAR and AWEI consistently enhances classification stability and spatial coherence. Among the assessedmodels, SVM and RF exhibit similar performance, with SVM attaining the optimal classification balance (OA = 0.98, Kappa= 0.97, F1 = 0.98) for water detection, while the difference lacks statistical significance. Flood inundation maps were derivedthrough change detection between independently classified pre- and post-flood water maps. The results indicate that multisensor integration enhances the delineation of flood-affected areas, particularly in complex wetland environments. However,the study also highlights that the reliability of flood inundation mapping is inherently dependent on the accuracy of pre- andpost-flood water classification as well as the temporal consistency of the input imagery. Furthermore, feature contributionanalysis combined with spatial validation reveals that AWEI provided the strongest standalone classification performance,while SAR contributed complementary structural information that improved classification robustness. These findingsdemonstrate that spatial validation effectively mitigates autocorrelation bias, leading to more reliable and generalizablewater classification performance for flood inundation analysis.