Floods are one of the most frequent and catastrophic events among weather-related disasters. Between 2009 and 2018, about 800 million people were affected by floods globally, and incurred $400 billion of economic losses. Despite of these losses, as well as the need to understand the potential impacts of climate and land-use/cover changes to floods, the knowledge about accurate, cost-and-time efficient methods for improving situation awareness during flood emergency is still insufficient. This study aims to address these gaps by using Synthetic Aperture Radar (SAR)-derived measurements and innovative machine learning techniques 1) to compare the performance of radar imagery of various bandwidths, 2) to assess the use of SAR intensity and interferometric coherence, and 3) to investigate the relationship between land-use/cover changes and flood occurrence.
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