Using Sentinel-1 and Google Earth Engine cloud computing for detecting historical flood hazards in tropical urban regions: a case of Dar es Salaam

Abstract

This study investigates the potential of freely available Sentinel-1 imagery coupled with Google Earth Engine (GEE) for mapping and monitoring flooding in Dar es Salaam. Sentinel-1 images (n = 55) available during the rainy season (March–May) since 2016 were used and processed in GEE. For separating water and land surfaces, we used a histogram-based automatic thresholding method. The binarization accuracy was assessed using confusion matrix based on 1064 randomly generated points in GEE. Overall accuracy of 95% (Kappa = 0.90) were achieved. Dar es Salaam has experienced flood inundation per flood event on average over an area of 50 km² in March 2019 and 2021. Territories located along the Ocean and inland water shores, built and bare ground were subject to flooding compared to other land cover types. Flooding inundations have been difficult to detect in the city center. With the current temporal and spatial resolution of Sentinel-1, flood detection in city centers remains a challenge yet. However, Sentinel-1 images, coupled with GEE cloud computing simplified flood mapping and monitoring in a large urban region and this approach can be applied in other large cities and their surroundings for countries where data gap and lack of processing tools are critical challenges.

Keywords:

• Google Earth Engine
• urban flooding
• open-access data
• flood monitoring
• SAR

Acknowledgements

The authors pass our gratitude to Universite Libre De Bruxelles (ULB), Brussels, Belgium for the postdoctoral grant support given to the first author. The authors are grateful for the team in Geospatial Analysis (ANAGEO) of ULB for the good working environment and discussions related to this paper and other issues. From this team, the authors would like to acknowledge Nicholus Mboga and Stefanos Georganos for their contributions as part of the discussions.

Authors’ contributions

All authors contributed to the study conception and design. Material preparation, data extraction and analysis were performed by Biadgilgn Demissie. The first draft of the manuscript was written by Biadgilgn and all co-authors have revised the draft critically by adding new inputs. All authors read and approved the final version to be published and to be accountable for all aspects of the work.

Disclosure statement

On behalf of all co-authors, the corresponding author states that there is no conflict of interest

Availability of data and materials

Data are available and can be provided when required.

Code availability

Data were processed using the GEE cloud computing. The GEE codes can be available after accepted for publication.