A Multi-sensor Data-driven methodology for all-sky Passive Microwave Inundation Retrieval
Zeinab Takbiri1,2, Ardeshir M. Ebtehaj1, and Efi Foufoula-Georgiou1,31Department of Civil, Environmental and Geo-Engineering and St. Anthony Falls Laboratory, University of Minnesota, Twin Cities, Minneapolis, USA 2Department of Electrical and Computer Engineering, University of Minnesota, Twin Cities, Minneapolis, USA 3Department of Civil and Environmental Engineering, University of California, Irvine, USA
Received: 26 Oct 2016 – Accepted for review: 30 Oct 2016 – Discussion started: 01 Nov 2016
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 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 database 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 diurnal variability (i.e., morning and evening) of inundation due to localized convective precipitation. At longer time-scales, 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 time scales.
Takbiri, Z., Ebtehaj, A. M., and Foufoula-Georgiou, E.: A Multi-sensor Data-driven methodology for all-sky Passive Microwave Inundation Retrieval, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-560, in review, 2016.
Zeinab Takbiri et al.
Zeinab Takbiri et al.
An earth-gridded SSM/I data set for cryospheric studies and global change monitoring