Flood Mapping from Satellite Imagery: A Response-Based Framework for Quantifying Flood Hazard and Uncertainty

Document Type

Article

Publication Date

8-1-2026

Abstract

Flood hazard mapping is crucial for planning flood protection and mitigation measures, minimizing flood losses, and developing effective flood response plans. Typically, flood hazard mapping adopts an event-based deterministic approach to compute flood hazard for a specific design event (e.g., a 100-year flood), assuming that the probability of flood drivers (e.g., discharge) approximates the probability of flood extent and inundation. In contrast, this study develops a framework that utilizes satellite images for response-based probabilistic flood hazard mapping, where flood hazard maps are generated from the time series of flood maps. Employing a calibrated flood detection algorithm and Sentinel-1 imagery, we first derive surface water extent maps for each half of the monsoon month (May to October) from 2015 to 2023 across the northeastern region of Bangladesh (approximately 21,000 km²). We derive flood depths using two static depth estimation techniques and five digital elevation models (DEMs). Flood depths are calculated as the difference in the elevations of a selected pixel and the highest elevation of the watered (flooded) pixels within a defined boundary. Two approaches are adopted to define the boundary: (1) dividing the study area into 600 m x 600 m grids, and (2) dividing the area into polygons of connected watered pixels. Finally, flood hazard maps and their associated uncertainties are computed at each pixel for a 20-year return period (i.e., a 20-year flood). Additionally, the probability of flood occurrence exceeding an inundation depth (e.g., 1 m) is also investigated. The study reveals significant differences (i.e., uncertainty) in the flood depth estimates originating from the DEMs and the depth estimation methods. The proposed approach provides a rapid and computationally inexpensive alternative to numerical hydrodynamic models for response-based probabilistic flood mapping, particularly for data-scarce and resource-limited regions.

Publication Title

Earth Systems and Environment

Volume

10

Issue

4

First Page

4161

Last Page

4176

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