Flood Extent Mapping of the 2022 Sindh Flood: A Remote Sensing Analysis Using SAR, Optical Data, and Google Engine Platform

Abstract Submission: Floods are the most frequently occurring natural disasters worldwide caused by extreme rainfall, river overflow, high runoff, glacier melting, and flood infrastructure failure. Climate change has further increased the frequency and severity of floods. While traditional methods are limited over large areas, remote sensing provides a viable solution to analyze floods. However, challenges such as cloudy weather conditions can hinder accurate flood detection. To address these problems, this study used Optical and SAR remote sensing indices in the cloud computing platform of Google Earth Engine (GEE). This paper analyzes impacts of the 2022 flood in Sindh province, Pakistan, on population and cropland area. SAR and optical remote sensing have been used to delineate flood extent. Population data has been collected from JRC Global Human Settlement Population Layer. To estimate the cropland affected, the MODIS Land Cover Type product has been used. GEE is used to analyze spatial distribution and overlaps of the flooding, population, and cropland. The overlap area values are tabulated and analyzed to the extent of impact of flooding. The resulting maps showed cropland areas inundated, population affected, and total flooded areas in Sindh. GEE platform provided fast flood extent maps enabling the timely provision of critical information to authorities which enhances the emergency response efforts. Though this study used the 2022 flood as a case study, the findings and the methodology can be adopted and applied by other researchers to analyze any flood in different regions to get near real-time information for emergencies during the flood.

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