Abstract
Flood duration is a crucial parameter for disaster impact assessment as it can directly influence the degree of economic losses and damage to structures. It also provides an indication of the spatio-temporal persistence and the evolution of inundation events. Thus, it helps gain a better understanding of hydrological conditions and surface water availability and provides valuable insights for land-use planning. The objective of this work is to develop an automatic procedure to estimate flood duration and the uncertainty associated with the use of multi-temporal flood extent masks upon which the procedure is based. To ensure sufficiently high observation frequencies, data from multiple satellites, namely Sentinel-1, Sentinel-2, Landsat-8 and TerraSAR-X, are analyzed. Satellite image processing and analysis is carried out in near real-time with an integrated system of dedicated processing chains for the delineation of flood extents from the range of aforementioned sensors. The skill of the proposed method to support satellite-based emergency mapping activities is demonstrated on two cases, namely the 2019 flood in Sofala, Mozambique and the 2017 flood in Bihar, India.
Highlights
Floods can be considered the most frequent, disastrous and widespread natural hazard [1,2,3,4,5], accounting for more than 43% of all disaster events recorded globally between 1998 and 2017
Flood duration estimation generally refers to time-series analysis of binary flood extent masks to derive a spatially explicit map that illustrates the length of time for which each pixel has been flooded during a pre-defined time range
The Total Flood Duration (TFD) product was derived from Sentinel-1/2, TerraSAR-X and Landsat-8 data and computed with a pixel spacing of 10 m
Summary
Floods can be considered the most frequent, disastrous and widespread natural hazard [1,2,3,4,5], accounting for more than 43% of all disaster events recorded globally between 1998 and 2017. The recently launched Copernicus Sentinel satellites of the European Commission show great potential for flood monitoring This is attributed to the high temporal and spatial resolutions of cost-free Sentinel data and the systematic acquisition capabilities of the sensors. The time between successive valid observations (pixels that are not covered by clouds, cloud shadows or otherwise marked as no-data) in the time-series is a function of the satellite acquisition plan and the presence of clouds in optical data. These two sources introduce further uncertainties to any resultant flood duration product
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