Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications.

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People, livelihoods, and infrastructure in Myanmar suffer from devastating monsoonal flooding on a frequent basis. Quick and effective management of flood risk relies on planning and preparedness to ensure the availability of supplies, shelters and emergency response personnel. The mandated government agency Department of Disaster Management (DDM) as well as local and international organizations play roles in producing, disseminating, and using accurate and timely information on flood risk. Currently, systematic flood risk maps are lacking, which leaves DDM to rely on inconsistent historic reports and local knowledge to inform their emergency planning. Although these types of knowledge are critical, they can be complemented to reduce bias and human error to planning processes and decisions. As such, the present situation has led to ineffective distribution of emergency response resources prior to flooding, leaving vulnerable populations less-than-prepared for inevitable flood events. Given these issues, we have developed a flood risk decision-support tool in collaboration with DDM. The tool uses surface water maps developed by the Joint Research Center (JRC), which were derived from more than 30 years of Landsat imagery. We have also incorporated population data, land cover data, and other information on flood exposure and vulnerability to create the first scalable and replicable Flood Risk Index (FRI) for flood risk reduction in Myanmar.