基于Sentinel-1A SAR影像的上下游水位响应分析及其在洪涝预警中的应用  

Downstream response to the upstream water level variation and its application in flood early warning based on Sentinel-1A SAR images

作　　者：高龙 阎福礼 GAO Long;YAN Fuli(Aerospace Information Research Institute, Chinese Academy of Sciences,Beijing 100094, China;School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 100049, China)

机构地区：[1]中国科学院空天信息创新研究院,北京100094 [2]中国科学院大学电子电气与通信工程学院,北京100049

出　　处：《中国科学院大学学报（中英文）》2022年第1期91-101,共11页Journal of University of Chinese Academy of Sciences

基　　金：国家重点研发计划项目(2016YFB0501505)资助。

摘　　要：在水文实测数据匮乏地区,尝试完全利用遥感技术建立一种基于上下游水位响应规律的洪涝预测方法,对当地洪涝预警工作具有重要意义。以斯里兰卡南部Nilwara河为例,利用14期Sentinel-1A SAR影像(2015—2017年)提取的上下游洪峰水位数据,建立下游洪峰水位预测模型,预测下游最大洪水淹没范围,并在模型精度评价的基础上,进一步利用2018年的4期Sentinel-1A SAR数据进行真实性检验。结果表明:1)遥感技术反演出的洪峰水位数据能有效反映上下游水位消涨变化的情况;2)建立的4种下游水位预测模型中,ASTER GDEMV2数据下建立的指数型模型拟合效果最好,R^(2)为0.79,RMSE为0.45,说明上下游洪峰水位消涨一致,且有良好相关性;3)真实性检验表明,预测的最大淹没范围总体精度不低于0.71,效果稳定、良好。该方法可为水文实测数据匮乏地区的遥感洪涝预警技术提供一种新的视角。For under-developed regions where the rivers have no or scarce hydrological gauging datasets,it is significant to explore the remote sensing techniques to determine the dynamic variation of up-/downstream water levels and to alert the potential flood inundation.In this work,the Nilwara Ganga in southern Sri Lanka,which is prone to floods,was taken as an example.A total of 14 scene Sentinel-1A SAR images from 2015 to 2017 were chosen to determine the up-/downstream flood peak levels.Based on the derived datasets,the prediction models of downstream flood peak levels were established,as well as the forecasting model on the maximum flood extent of the Nilwara Ganga.Consequently,the accuracy of the prediction model was evaluated,and an experiment of the predicted flood inundation was validated using 4 scene Sentinel-1A SAR images in 2018.The primary conclusions are summarized as follows:1)The fluctuation of the up-/downstream flood peak levels can be accurately and efficiently extracted by remote sensing technique;2)Among the established models,including quadratic polynomial,liner,power function,and exponential regression models,the exponential regression model under the ASTER GDEMV2 data is the optimal one,with R^(2) of 0.79 and RMSE of 0.4,which means a consistent fluctuation between the upstream and downstream flood peak levels;3)The validation results indicated that the overall accuracy of the predicted maximum flood extent is not less than 0.71.The method proposed in this paper aims to provide a new perspective for the flood early warning methodology using remote sensing techniques in the drainage area with less or no gauging datasets.

关 键 词：遥感技术 洪涝预警 洪峰水位 最大淹没范围 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]