Synthetic Aperture Radar (SAR) intensity is used as an input to many flood-mapping algorithms. The appearance of floodwater tends to cause a substantial decrease of backscatter intensity over scarcely vegetated terrain. However, limitations exist in areas where the SAR backscatter is not sufficiently sensitive to surface changes, e.g. shadow areas due to topography or obstacles on the ground, densely forested areas, sand, etc. Thus, we argue that it is of paramount importance to complement any SAR-based flood extent map with an exclusion map (EX-map) indicating all areas where the presence of water cannot be derived from SAR intensity observations. In this study, we introduce a methodology for generating an EX-map based on the analysis of time-series of SAR backscatter data. In particular, the identification of the EX-map is based on the combined use of three temporal indicators based on backscatter statistics, i.e. temporal median, minimum and standard deviation. As a test case, EX-maps were derived from Sentinel-1 data acquired during the 2014–2019 time period from six representative study sites. Reference maps were generated using a global land cover map, Digital Elevation Model (DEM)-derived shadow/layover masks, global urban footprint (GUF) data and a Sand Exclusion Layer (SEL). The cross-comparison revealed that the EX-map was consistent with reference maps obtained from other data sources.

合成孔径雷达 (SAR) 强度被用作许多洪水映射算法的输入。在几乎没有植被的地形上，洪水的出现往往会导致反向散射强度显着降低。然而，在 SAR 反向散射对表面变化不够敏感的区域存在限制，例如由于地形或地面障碍物、茂密森林区域、沙地等造成的阴影区域。因此，我们认为补充是至关重要的任何基于 SAR 的洪水范围图，带有排除图（EX-map），指示无法从 SAR 强度观测中推导出水存在的所有区域。在本研究中，我们介绍了一种基于 SAR 反向散射数据时间序列分析生成 EX 地图的方法。特别是，EX-map 的识别基于三个基于反向散射统计的时间指标的组合使用，即时间中值、最小值和标准偏差。作为一个测试案例，EX 地图来自于 2014-2019 年期间从六个代表性研究站点获得的 Sentinel-1 数据。参考地图是使用全球土地覆盖图、数字高程模型 (DEM) 衍生的阴影/停留遮罩、全球城市足迹 (GUF) 数据和沙尘排除层 (SEL) 生成的。交叉比较显示 EX 地图与从其他数据源获得的参考地图一致。EX-maps 来自于 2014-2019 年期间从六个代表性研究站点获得的 Sentinel-1 数据。参考地图是使用全球土地覆盖图、数字高程模型 (DEM) 衍生的阴影/停留遮罩、全球城市足迹 (GUF) 数据和沙尘排除层 (SEL) 生成的。交叉比较显示 EX 地图与从其他数据源获得的参考地图一致。EX-maps 来自于 2014-2019 年期间从六个代表性研究站点获得的 Sentinel-1 数据。参考地图是使用全球土地覆盖图、数字高程模型 (DEM) 衍生的阴影/停留遮罩、全球城市足迹 (GUF) 数据和沙尘排除层 (SEL) 生成的。交叉比较显示 EX 地图与从其他数据源获得的参考地图一致。