Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.936 IF 4.936
  • IF 5-year value: 5.615 IF 5-year
    5.615
  • CiteScore value: 4.94 CiteScore
    4.94
  • SNIP value: 1.612 SNIP 1.612
  • IPP value: 4.70 IPP 4.70
  • SJR value: 2.134 SJR 2.134
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 107 Scimago H
    index 107
  • h5-index value: 63 h5-index 63
Volume 21, issue 6
Hydrol. Earth Syst. Sci., 21, 2685–2700, 2017
https://doi.org/10.5194/hess-21-2685-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Observations and modeling of land surface water and energy...

Hydrol. Earth Syst. Sci., 21, 2685–2700, 2017
https://doi.org/10.5194/hess-21-2685-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Jun 2017

Research article | 08 Jun 2017

A multi-sensor data-driven methodology for all-sky passive microwave inundation retrieval

Zeinab Takbiri1, Ardeshir M. Ebtehaj1, and Efi Foufoula-Georgiou2,1 Zeinab Takbiri et al.
  • 1Department of Civil, Environmental and Geo- Engineering and St. Anthony Falls Laboratory, University of Minnesota, Twin Cities, Minneapolis, Minnesota, USA
  • 2Department of Civil and Environmental Engineering, University of California, Irvine, California, USA

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.

Publications Copernicus
Download
Short summary
We present a multi-sensor retrieval algorithm for flood extent mapping at high spatial and temporal resolution. While visible bands provide flood mapping at fine spatial resolution, their capability is very limited in a cloudy sky. Passive microwaves can penetrate through clouds but cannot detect small-scale flooded surfaces due to their coarse resolution. The proposed method takes advantage of these two observations to retrieve sub-pixel flooded surfaces in all-sky conditions.
We present a multi-sensor retrieval algorithm for flood extent mapping at high spatial and...
Citation