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Automatic flood detection using sentinel-1 images on the google earth engine

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Abstract

Flood is considered to be one of the most destructive natural disasters. It is important to detect the flood-affected area in a reasonable time. In March 2019, a severe flood occurred in the north of Iran and lasted for 2 months. In the present paper, this flood event has been monitored by Sentinel-1 images. The Otsu thresholding algorithm has been applied to separate flooded areas from remaining land covers. The threshold value of −14.9 dB was derived and applied to each scene to delineate flooded areas. There was high variability of the inundated area; however, the presented threshold correctly represented the variation of the flood. The resultant maps were further verified by independent datasets. The overall accuracies were higher than 90%, confirming the applicability of the Otsu automatic thresholding method in flood mapping. The automatic approach is efficient in rapid fold mapping across complex landscapes.

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Data availability

Data are available upon reasonable request.

Code availability

The code is available online at https://code.earthengine.google.com/6dcd097df2c9e9b821858e5046a8b21d

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Acknowledgements

The authors acknowledge the Google Earth Engine for providing Sentinel-1 images and computation capabilities.

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Authors and Affiliations

  1. Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

    Meysam Moharrami, Mohammad Javanbakht & Sara Attarchi

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  1. Meysam Moharrami
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  2. Mohammad Javanbakht
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  3. Sara Attarchi
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Correspondence to Sara Attarchi.

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Highlights

• The findings of this research provide insights into the exploitation of SAR images in flood mapping based on an automatic thresholding method.

• The automatic thresholding approach is efficient in rapid flood mapping across complex landscapes.

• The exploitation of freely available Sentinel-1 images highlights the application of the presented research.

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Moharrami, M., Javanbakht, M. & Attarchi, S. Automatic flood detection using sentinel-1 images on the google earth engine. Environ Monit Assess 193, 248 (2021). https://doi.org/10.1007/s10661-021-09037-7

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