Remote Sensing and GIS-Based Flood Hazard and Susceptibility Mapping: A Review of Methods, Datasets and Applications
DOI:
https://doi.org/10.66132/ngce20260204Keywords:
Flood hazard, Hazard Mapping, Geographic Information System (GIS), Synthetic aperture radar (SAR), Disaster risk, Machine learningAbstract
Flood hazard and susceptibility mapping has become a core component of disaster risk reduction because it converts hydrological, geomorphological, meteorological, land-cover, and exposure information into spatial products that can guide preparedness, land-use planning, infrastructure siting, agricultural risk management, and emergency response. Remote sensing and geographic information systems (GIS) have transformed this field by providing synoptic Earth observation, repeat monitoring, terrain derivatives, rainfall products, land-use dynamics, flood inventories, and computational environments for multi-criteria, statistical, machine-learning, and cloud-based modeling. This review synthesizes recent and foundational literature on remote sensing and GIS applications in flood hazard and susceptibility mapping, with emphasis on data sources, conditioning factors, model families, validation practices, operational limitations, and future research needs. The review distinguishes flood hazard, susceptibility, vulnerability, exposure, and risk; evaluates optical, synthetic aperture radar (SAR), digital elevation model, rainfall, soil, land-cover, and ancillary datasets; and compares weighted overlay, analytical hierarchy process, frequency ratio, weights of evidence, logistic regression, support vector machine, random forest, XGBoost, convolutional neural network, deep-learning, and explainable-AI approaches. The evidence shows that GIS-based multi-criteria analysis remains useful in data-poor contexts, while SAR-derived flood inventories and machine-learning models are increasingly important for empirical, repeatable, and event-based susceptibility mapping. However, model reliability is constrained by inventory quality, spatial sampling design, class imbalance, scale dependence, DEM uncertainty, transferability limits, and insufficient validation against independent flood events. The review concludes that the strongest future direction is not simply adopting more complex algorithms, but building transparent, validated, multi-source, reproducible, and decision-oriented flood-mapping workflows that combine physical knowledge, Earth observation, local evidence, and explainable geospatial artificial intelligence.
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