ABSTRACT
Floods are the most recurring and devastating natural hazards that impact human lives and cause severe economic loss throughout the world. This chapter presents an all-inclusive methodology to calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas based entirely on satellite remote sensing data. It aims to combine remotely sensed multispectral estimates that include optical and microwave data sets within a hydrologic modeling framework to characterize the spatial extent of flooding over scarcely gauged basins. There are several methods of identifying flooded versus nonflooded areas using optical remote sensing imagery. A distributed hydrologic model (coupled routing and excess storage [CREST]) developed by Wang et al. was used to generate modeled flood areal extents for comparison with the satellite-based flood inundation maps. The CREST model was calibrated using available daily observed discharge data for the period between 1998 and 2004. A 1-year period was used for warming up the model states.
