Abstract
In this study, an analysis of multi-temporal and multi-frequency Synthetic Aperture Radar data is performed to investigate the backscatter behavior of various semantic classes in the context of flood mapping in central Europe. The focus is mainly on partially submerged vegetation such as forests and agricultural fields. The test area is located at River Saale, Saxony-Anhalt, Germany, which is covered by a time series of 39 TerraSAR-X data acquired within the time interval December 2009 to June 2013. The data set is supplemented by ALOS PALSAR L-band and RADARSAT-2 C-band data. The time series covers two inundations in January 2011 and June 2013 which allows evaluating backscatter variations between flood periods and normal water level conditions using different radar wavelengths. According to the results, there is potential in detecting flooding beneath vegetation in all microwave wavelengths, even in X-band for sparse vegetation or leaf-off forests.
Highlights
Synthetic Aperture Radar (SAR) is the preferred tool for flood mapping from space
The study was conducted along the River Saale located in Saxony-Anhalt, Germany, which was recently affected by floods in January 2011 and June 2013
The ability of TerraSAR-X to detect flooding under vegetation will be compared with C- and L-band sensors and discussed in the context of the findings of other studies
Summary
Synthetic Aperture Radar (SAR) is the preferred tool for flood mapping from space. This is due to the following reasons: On the one hand, a SAR sensor provides its own source of illumination in the microwave range. It is characterized by near all-weather/day-night imaging capabilities independent of atmospheric conditions. A flat water surface acts as a specular reflector which scatters the radar energy away from the sensor. This causes relatively dark pixels in radar data which contrast with non-water areas. In comparison to optical sensors, SAR offers the unique opportunity to detect—to a certain extent—standing water beneath vegetation
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