EGU25-6094, updated on 14 Mar 2025
https://doi.org/10.5194/egusphere-egu25-6094
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Oral | Thursday, 01 May, 16:40–16:50 (CEST)
 
Room 2.31
Enhanced Flood Hazard Assessment and Mapping Using SAR Data: A Case Study of Afghanistan’s Flood Events (2018–2024)
M. Sulaiman Fayez Hotaki1, Mahdi Motagh1,2, and Mahmud Haghshenas Haghighi1
M. Sulaiman Fayez Hotaki et al.
  • 1Institute of Photogrammetry and Geoinformation, Leibniz University Hannover, Hannover, Germany (hotaki@ipi.uni-hannover.de)
  • 2GFZ German Research Center for Geosciences, Potsdam, Germany (motagh@gfz-potsdam.de)

Afghanistan faces severe flood risks, but challenges such as limited flood data, cloud cover, and difficulties in on-ground data collection hinder traditional flood mapping methods. This study introduces an automated flood mapping approach using Synthetic Aperture Radar (SAR) data to overcome these limitations. Combining SAR intensity and interferometric coherence analyses, the method improves flood detection accuracy, particularly in complex terrains and rapid-onset events. The study spans the period from 2018 to 2024, covering 17 flood events across the country.

Processed on the Google Earth Engine (GEE), the method enables near-real-time monitoring by analyzing dense Sentinel-1 SAR time series data. SAR intensity identifies floodwaters, while coherence detects subtle changes in vegetated and urban areas, where intensity alone may fall short. Interferometric coherence was derived using the Hybrid Pluggable Processing Pipeline (HyP3), a cloud-based SAR processing platform accessed via the Alaska Satellite Facility (ASF) Data portal.

Validated against high-resolution PlanetScope imagery, the approach achieved F1 scores exceeding 82% in key provinces like Faryab and Baghlan. Land cover analysis revealed irrigated agriculture as the most affected type (709 hectares), while coherence mapping highlighted vulnerable urban areas, such as Baghlan-e-Markazi and Charkiar cities.

Compared to the Global Flood Monitoring (GFM) system, this method significantly improves detection accuracy, capturing up to 83% more flood extent in certain areas. For example, in Baghlan Province, it detected 709 hectares of flooding versus GFM’s 114 hectares.

By leveraging SAR data, HyP3, and GEE’s processing capabilities, this method provides a scalable, rapid-onset, and efficient solution for flood monitoring in data-scarce regions. Covering seven years of flood events, it offers a valuable tool for disaster management in Afghanistan and other regions vulnerable to climate change-induced flooding.

How to cite: Hotaki, M. S. F., Motagh, M., and Haghshenas Haghighi, M.: Enhanced Flood Hazard Assessment and Mapping Using SAR Data: A Case Study of Afghanistan’s Flood Events (2018–2024), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6094, https://doi.org/10.5194/egusphere-egu25-6094, 2025.