A Global Dynamic Long-Term Inundation Extent Dataset at High Spatial Resolution Derived through Downscaling of Satellite Observations

A new procedure is introduced to downscale low-spatial-resolution inundation extents from Global Inundation Extent from Multi-Satellites (GIEMS) to a 3-arc-s (90m) dataset (known as GIEMS-D3). The methodology is based on topography and hydrography information from the HydroSHEDS database. A new floo...

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Bibliographic Details
Published in:Journal of hydrometeorology 2017-05, Vol.18 (5), p.1305-1325
Main Authors: Aires, Filipe, Miolane, Léo, Prigent, Catherine, Pham, Binh, Fluet-Chouinard, Etienne, Lehner, Bernhard, Papa, Fabrice
Format: Article
Language:English
Subjects:
Astrophysics
Basins
Climate change
Datasets
Earth
Hydrography
Hydrologic cycle
Hydrology
Landsat
Landsat satellites
Limnology
Neural networks
Physics
Probability
Radar
Remote sensing
Resolution
SAR (radar)
Satellite observation
Satellites
Science
Spaceborne remote sensing
Spatial resolution
Surface water
Synthetic aperture radar
Temporal variability
Temporal variations
Topography
Topography (geology)
Wetlands
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Summary:A new procedure is introduced to downscale low-spatial-resolution inundation extents from Global Inundation Extent from Multi-Satellites (GIEMS) to a 3-arc-s (90m) dataset (known as GIEMS-D3). The methodology is based on topography and hydrography information from the HydroSHEDS database. A new floodability index is introduced and an innovative smoothing procedure is developed to ensure a smooth transition, in the high-resolution maps, between the low-resolution boxes from GIEMS. Topography information is pertinent for natural hydrology environments controlled by elevation but ismore limited in human-modified basins. However, the proposed downscaling approach is compatible with forthcoming fusion of other, more pertinent satellite information in these difficult regions. The resulting GIEMS-D3 database is the only high-spatial-resolution inundation database available globally at a monthly time scale over the 1993–2007 period. GIEMS-D3 is assessed by analyzing its spatial and temporal variability and evaluated by comparisons to other independent satellite observations from visible (Google Earth and Landsat), infrared (MODIS), and active microwave (synthetic aperture radar).
ISSN:1525-755X
1525-7541
DOI:10.1175/jhm-d-16-0155.1