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    基于Sentinel-1A的安徽省2020年梅雨期洪水淹没监测

    Sentinel-1A based flood inundation monitoring in Anhui Province during the plum rain period of 2020

    • 摘要: 2020年超长梅雨期内的持续强降雨,导致安徽省发生全域性洪涝灾害,为了快速、准确地提取洪涝淹没范围,为防汛救灾提供科学支撑,选取安徽境内巢湖流域和淮河流域的灾前和灾中Sentinel-1A数据,首先,在快速预处理基础上,采用双极化水体指数(Sentinel-1A dual-polarized water index,SDWI)法,并结合地形因子对平原和山区分别提取水体信息,建立一套洪水淹没区监测流程; 然后通过该流程利用灾前、灾中两期合成孔径雷达数据提取2020年7月27日巢湖流域、淮河流域行蓄洪区洪水淹没范围。结果显示: SDWI比直接用后向散射系数提取水体具有优势; 7月27日巢湖流域洪水淹没区面积为524.8 km2,其中受洪灾较重的是白石天河子流域,西河子流域次之; 淮河流域安徽境内行蓄洪区,沿淮的4个地市淹没面积从大到小依次为淮南市、阜阳市、六安市、蚌埠市。研究表明,基于Sentinel-1A数据,采用SDWI和地形因子建立的洪水淹没区监测流程对平原和山区都具有较好的准确性、适用性,且具有较高的时效性,便于及时开展洪水灾害监测。

       

      Abstract: In 2020, a flood disaster occurred throughout Anhui Province due to the persistent heavy rainfall during the super-long plum rain period. To quickly and accurately extract the flood inundation ranges and provide scientific support for flood prevention and disaster relief, this study selected the pre-disaster and mid-disaster Sentinel-1A/SAR data of the Chaohu Lake and Huaihe River basin in Anhui Province. After rapid data preprocessing, this study extracted information about water bodies in the plains and mountainous areas using the Sentinel-1 dual-polarized water index (SDWI) method and topographic factors. Then, it established a monitoring process for flooded areas. Using this process, this study extracted the flood inundation ranges of the Chaohu Lake and Huaihe River basins on July 27, 2020 using the pre-disaster and mid-disaster synthetic aperture Radar (SAR) data. The results are as follows. The SDWI was superior to the backscattering coefficient in the extraction of information about water bodies. The Chaohu basin had a flood inundation area of 524.8 km2 on July 27, and the Baishitian River subbasin was the most severely inundated, followed by the Xihe River subbasin. In the flood flowing and storage areas of the Huaihe River basin within Anhui Province, the flood inundation area of four cities along the Huaihe River basin decreased in the order of Huainan City, Fuyang City, Lu’an City, and Bengbu City. The results of this study show that the Sentinel-1A-based monitoring process of flood inundation areas established using SDWI and topographic factors has high accuracy, applicability, and timeliness for plains and mountainous areas and is convenient for the timely monitoring of flood disasters in these areas.

       

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