Abstract
Abstract. 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.
Highlights
Capturing the diurnal spatiotemporal dynamics of inundation over coastal regions, deltaic surfaces, and river floodplains requires high-resolution observations in both time and space, which are not available from the typical sparse ground-based sensors
We develop a method to retrieve sub-pixel inundation fraction (“inundation” referring to regions where water covers the land surface, excluding permanent water bodies) only from passive microwave observations based on a set of paired visible to near-infrared (VNIR) and passive microwave training samples
The microwave data are obtained from the Defense Meteorological Satellite Program (DMSP) SSM/ISSMIS Pathfinder Daily Equal-Area Scalable Earth Grid (EASE-Grid; see Armstrong and Brodzik, 1995) brightness temperatures distributed by the National Snow and Ice Data Center (NSIDC)
Summary
Capturing the diurnal spatiotemporal dynamics of inundation over coastal regions, deltaic surfaces, and river floodplains requires high-resolution observations in both time and space, which are not available from the typical sparse ground-based sensors. Brakenridge and Anderson (2006) showed that the visible red band 1 (0.62–0.67 μm) and near-infrared (NIR) band 2 (0.84–0.87 μm) from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites can be used to detect water over land surfaces They mapped several hundreds of flood events at different sites all over the world by classification of water via thresholding over the NIR band and the normalized difference vegetation index, NDVI = (NIR − red)/(NIR + red) introduced by Rouse et al (1974). Several years of observations (2000–present) by these two sensors allow us to collect adequate overlapping data to link coarse-scale SSMIS passive microwave data to highresolution MODIS VNIR data in the form of an organized dataset This collection of almost coincident observations does not contain direct information about surface inundation in a cloudy sky, as the radiative signals in VNIR wavelengths cannot penetrate clouds.
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