Synthetic Aperture Radars (SAR) are adequate sensors for mapping water bodies from space as they can be used to acquire data of equal quality both day and night and practically regardless of weather conditions. Furthermore, the global coverage of SAR data provides an opportunity to generate global scale flood records that are essential for improving our understanding of flood risks worldwide and of how these risks are changing over time. In this study, we introduce an automatic change-detection based method that allows global-scale flood records to be generated using the readily and freely available ENVISAT-ASAR data collection. It consists of the following three steps: (i) flood image identification; (ii) reference image selection; (iii) floodwater detection. As a test case, this study uses all available ENVISAT-ASAR images from eight different orbital tracks that were acquired over the United Kingdom over the period 2005–2012. Due to a lack of large-scale ground truth data, the evaluation of the results is carried out using different data sources. First, subsets of the flood maps over the Severn River basin are evaluated using a flood extent map that was manually digitized from very high-resolution aerial imagery. According to our results, the overall accuracy of both flood maps' subsets is higher than 85% while the user accuracy of the flood class is above 88%. Next, for the regions and images without available ground truth data, a visual inspection is carried out using simulations generated by the hydraulic model LISFLOOD-FP, as well as LANDSAT 7 ETM+ images obtained with a 30 m spatial resolution. Meanwhile, by comparing the acquisition dates of identified flood SAR images, the LISFLOOD-FP model results and optical data, a good agreement has been found. The experimental results over the United Kingdom indicate that the proposed method has strong potential for the generation of a global flood data record from the ENVISAT-ASAR archive.

合成孔径雷达（SAR）是用于从太空测绘水体的合适传感器，因为它们可用于获取昼夜质量相同的数据，几乎不受天气条件的影响。此外，SAR数据的全球覆盖范围提供了生成全球规模洪水记录的机会，这对于增进我们对全球洪水风险以及这些风险如何随时间变化的理解至关重要。在这项研究中，我们介绍了一种基于自动变化检测的方法，该方法允许使用易于免费获得的ENVISAT-ASAR数据收集来生成全球范围的洪水记录。它包括以下三个步骤：（i）洪水图像识别；（ii）参考图像选择；（iii）洪水检测。作为测试用例，这项研究使用了从2005年至2012年期间在英国获得的八个不同轨道的所有可用ENVISAT-ASAR图像。由于缺乏大规模的地面真实数据，因此使用不同的数据源对结果进行评估。首先，使用从非常高分辨率的航空影像手动数字化的洪水范围图来评估塞文河流域上的洪水图的子集。根据我们的结果，两个洪水地图的子集的整体准确度都高于85％，而洪水类别的用户准确度则高于88％。接下来，对于没有可用的地面真实数据的区域和图像，使用由水力模型LISFLOOD-FP生成的模拟以及以30 m空间分辨率获得的LANDSAT 7 ETM +图像进行视觉检查。与此同时，通过比较识别的洪水SAR图像的采集日期，LISFLOOD-FP模型结果和光学数据，已经找到了很好的协议。英国的实验结果表明，该方法具有很大的潜力，可以从ENVISAT-ASAR档案中生成全球洪水数据记录。