Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales.

中文翻译：

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通过 Sentinel 1 卫星图像绘制的 2015-2016 年爱尔兰洪水对农业的影响

摘要 演示了哨兵 1 C 波段（5 cm 波长）合成孔径无线电探测和测距（RADAR）（简称 SAR）的洪水测绘能力，并使用这种方法绘制了整个地区发生的大面积洪水的范围。 2015-2016 年冬季的爱尔兰共和国。33 张 Sentinel 1 图像用于绘制 2015 年 11 月至 2016 年 4 月 6 个月内洪水的面积和持续时间。 11 个不同日期的洪水图描绘了全国洪水的发展和持续时间。这一时期的最大洪水范围估计为~24,356 公顷。降雨深度对前 5 d 和更长时期内洪水的影响程度较小。与去年春天相比，在 Landsat 8 植被指数差异图像中观察到受洪水影响的农场的光合活动减少。洪水地图的准确性根据受影响农场的洪水报告以及哥白尼应急管理服务和哨兵 2 的其他卫星衍生地图进行评估。蒙特卡罗模拟高程数据（20 m 分辨率，2.5 m 均方根误差 [ RMSE]) 被用来估计洪水的深度和体积。尽管模拟的洪水高度与测量的河流高度显示出很强的相关性，但观察到了几米的差异。讨论了未来的绘图策略，其中包括高时间分辨率的土壤水分数据，作为在一系列空间尺度上进行洪水响应的综合多传感器方法的一部分。