Mapping Flooded Vegetation Using COSMO-SkyMed: Comparison With Polarimetric and Optical Data Over Rice Fields

The capability of COSMO-SkyMed (CSK) radar to remotely sense standing water beneath vegetation using an auto- matic algorithm working on a single image is investigated. The ob- jective is to contribute to tackle the problem of missed detection of inundated vegetation by near real-time flood mapping algorithms using SAR data. The focus is on CSK because its four-satellite con- stellation is very suitable for rapid mapping. A set of CSK observa- tions of an area in Northern Italy where many rice fields are present and recurrent artificial inundations occur were analyzed. Consid- ering that double-bounce is the key process to detect floodwater under vegetation and that polarimetry is potentially able to dis- criminate double-bounce among different scattering mechanisms, single polarization CSK observations were compared with ALOS-2 and RADARSAT-2 fully polarimetric data. Such a multifrequency and multiangle dataset helped understanding the multitemporal signature of CSK data. A set of Landsat-8 images collected un- der cloud free conditions were also used as reference. Satellite acquisitions were gathered in order to ensure both spatial over- lap among the images of the various sensors and temporal overlap along most of the rice growing season. The comparison between CSK and polarimetric data showed that at least for a slender leaf plant like rice, CSK can be able to detect the enhancement of double-bounce backscattering involving water and vertical plant stems. For some selected fields, it was found a good agreement be- tween CSK-derived floodwater maps and those produced using the normalized-difference water index derived from Landsat-8 images, as well as double-bounce detection from polarimetric data.