Rapid Flash Flood Mapping Using Sentinel-1 Sar Data on the Google Earth Engine Platform

Year 2025, Volume: 12 Issue: 4, 408 - 414, 12.01.2026

Hakan Uyguçgil

https://doi.org/10.26650/ijegeo.1650297

https://izlik.org/JA47GG57DL

Abstract

Climate change significantly increases the frequency and intensity of extreme weather events, thereby elevating disaster risks in urban areas. Sudden heavy rainfall events result in severe damage and necessitate rapid emergency responses. In this context, remote sensing technologies—particularly synthetic aperture radar (SAR)—are critical for post-disaster assessment. Unlike optical sensors, SAR systems operate independently of atmospheric conditions such as cloud cover, making them ideal for rapid flood detection. This study presents a case analysis of the flash flood that occurred on 18 July 2017 in the Silivri district of Istanbul using Sentinel-1 SAR data processed on the Google Earth Engine (GEE) platform. A change detection approach was employed, comparing pre- and post-flood SAR images to delineate the flood extent. The resulting flood map indicates that approximately 23.84 km² was inundated. The spatial distribution of the flood zones was further validated by comparing the SAR-derived extent with independent rainfall data from the Global Precipitation Measurement (GPM) dataset, which revealed a strong correlation between peak rainfall and flooded areas. Key methodological steps included speckle noise reduction via low-pass filtering, masking of permanent water bodies, and the application of a threshold value—set at four (4) based on preliminary tests—to effectively enhance the detection of flood-affected regions. Although this threshold yielded satisfactory results, the study acknowledges that dynamic thresholding methods and multi-temporal analysis could further improve delineation accuracy and capture the flood’s temporal evolution. A formal uncertainty analysis is recommended to quantify mapping errors, thereby enhancing the robustness of the results. The findings underscore the potential of Sentinel-1 SAR data for rapid flood mapping and highlight its value in informing urban planning, disaster risk management, and emergency response strategies. By facilitating the swift identification of affected areas, this approach can significantly improve post-disaster interventions and contribute to the development of more resilient urban infrastructures.

Keywords

Sentinel-1 SAR , flash flood , remote sensing , flood mapping , Google Earth Engine

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