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Automatic monitoring of surface water dynamics using Sentinel-1 and Sentinel-2 data with Google Earth Engine
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-08 , DOI: 10.1016/j.jag.2022.103010
Zhiheng Chen, Shuhe Zhao

Dynamic monitoring of floods is important for water resource management and disaster prevention. Obtaining multitemporal surface water distribution maps using remote sensing technology can help in elucidating the trends in water expansion so that measures can be quickly formulated. Sentinel-1 synthetic aperture radar (SAR) observation data are particularly suitable for this task because of their high spatial resolution and short revisit cycle, as well as its cloud-penetration ability. However, quickly and accurately mapping floods from a large number of SAR images remains challenging because of the enormous pressure on data acquisition and processing. Hence, in this study, we designed a new automatic SAR image flood mapping method based on the Google Earth Engine (GEE) cloud platform, which is an improvement over the Otsu method, and solves the problem of a higher segmentation threshold caused by images that do not meet the bimodal distribution hypothesis. In addition, to eliminate the omissions caused by salt-and-pepper noise and the misclassification caused mainly by low-backscattering-intensity vegetation and mountain shadows, we constructed an algorithm based on topological relationships and a DSM (Digital Surface Model) local search algorithm. The proposed method achieved an accuracy of 96.213% and 98.611% and F1 scores of 0.87254 and 0.89298 for plains and mountainous terrain, respectively. This method uses powerful computing resources and abundant datasets provided by the GEE cloud platform, and can be used for large-scale, long-term, and dynamic flood monitoring.



中文翻译:

使用 Sentinel-1 和 Sentinel-2 数据和 Google Earth Engine 自动监测地表水动态

洪水动态监测对于水资源管理和灾害预防具有重要意义。利用遥感技术获取多时相地表水分布图有助于阐明水膨胀趋势,从而快速制定措施。Sentinel-1 合成孔径雷达 (SAR) 观测数据特别适合这项任务,因为它们具有高空间分辨率、短重访周期以及穿透云的能力。然而,由于数据采集和处理的巨大压力,从大量 SAR 图像中快速准确地绘制洪水图仍然具有挑战性。因此,在本研究中,我们设计了一种新的基于谷歌地球引擎(GEE)云平台的自动SAR图像洪水映射方法,它是对Otsu方法的改进,解决了不满足双峰分布假设的图像导致的分割阈值较高的问题。此外,为了消除椒盐噪声造成的遗漏和主要由低后向散射强度植被和山影造成的误分类,我们构建了基于拓扑关系的算法和DSM(数字表面模型)局部搜索算法. 所提出的方法在平原和山区地形上的准确率分别为 96.213% 和 98.611%,F1 分数分别为 0.87254 和 0.89298。该方法利用GEE云平台提供的强大计算资源和丰富的数据集,可用于大规模、长期、动态的洪水监测。

更新日期:2022-09-08
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