We present a multi-sensor Bayesian passive microwave retrieval algorithm for
flood inundation mapping at high spatial and temporal resolutions. The
algorithm takes advantage of observations from multiple sensors in optical,
short-infrared, and microwave bands, thereby allowing for detection and mapping
of the sub-pixel fraction of inundated areas under almost all-sky conditions.
The method relies on a nearest-neighbor search and a modern sparsity-promoting
inversion method that make use of an a priori dataset in the form of two joint
dictionaries. These dictionaries contain almost overlapping observations by the
Special Sensor Microwave Imager and Sounder (SSMIS) on board the Defense
Meteorological Satellite Program (DMSP) F17 satellite and the Moderate
Resolution Imaging Spectroradiometer (MODIS) on board the Aqua and Terra
satellites. Evaluation of the retrieval algorithm over the Mekong Delta shows
that it is capable of capturing to a good degree the inundation diurnal
variability due to localized convective precipitation. At longer timescales,
the results demonstrate consistency with the ground-based water level
observations, denoting that the method is properly capturing inundation
seasonal patterns in response to regional monsoonal rain. The calculated
Euclidean distance, rank-correlation, and also copula quantile analysis
demonstrate a good agreement between the outputs of the algorithm and the
observed water levels at monthly and daily timescales. The current inundation
products are at a resolution of 12.5 km and taken twice per day, but a higher
resolution (order of 5 km and every 3 h) can be achieved using the same
algorithm with the dictionary populated by the Global Precipitation Mission
(GPM) Microwave Imager (GMI) products.Comment: 12 pages, 9 Figure