Inversion of Yellow River Runoff Based on Multi-source Radar Remote Sensing Technology
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摘要:
黄河是我国华北地区重要的水资源,采用雷达遥感方式对其径流进行监测可以便捷地反映出黄河的旱涝变化趋势,具有重要的现实意义。目前,雷达遥感径流反演常用雷达高度计(RA)获取水位信息用以构建水深-径流模型,这种方法忽略了河面变化对径流波动的影响,具有一定的局限性。该文提出一种基于多源雷达遥感技术的径流计算模型(MRRS-RCM),综合应用RA测高技术与合成孔径雷达(SAR)信息提取技术,以曼宁公式为基础,构建MRRS-RCM模型实现径流反演。该文选取黄河下游3个研究站点进行径流反演实验,结果证明MRRS-RCM模型径流反演结果的相对均方根误差(RRMSE)达到13.969%,优于传统径流监测15%~20%的精度要求。
Abstract:The Yellow River is an important water resource in China. Using radar remote sensing to monitor the runoff of the Yellow River can conveniently reflect the changing trend of drought and flood, which has important practical significance. At present, Radar Altimeter (RA) commonly is used to construct a water depth-runoff model in runoff inversion. This method ignores the influence of river surface change on runoff fluctuation and has certain limitations. A Multi-source Radar Remote Sensing Runoff Calculation Model (MRRS-RCM) is proposed. In this study, RA technology and Synthetic Aperture Radar (SAR) technology are used to construct MRRS-RCM model on the basis of the Manning’s equation to realize runoff inversion. Three stations are selected for experiments in the lower reaches of the Yellow River. The results show that the Relative Root Mean Square Error (RRMSE) of MRRS-RCM runoff inversion reaches 13.969%, which is better than the accuracy requirement of traditional runoff monitoring of 15%~20%.
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表 1 研究站点空间信息表
站点 地理坐标 所属河段 水文站距离(km) A 113.667, 34.915 花园口 2.9 B 114.764, 34.893 夹河滩 2.2 C 115.158, 35.419 高村 9.5 表 2 RA数据信息表
站点 数据名称 重访周期(d) 卫星轨道 波段 最小目标宽度(m) 数据级别 数据来源 A Sentinel-3A 27 095 Ku 300 Level-2 ESA B Jason-3 10 164 Ku 500 Level-2 CNES C Jason-3 10 001 Ku 500 Level-2 CNES 表 3 遥感数据时间表
数据名称 数据类型 建模数据时间 测试数据时间 Sentinel-3A RA 2017.01-2018.12 2019.01-2019.08 Jason-3 RA 2018.01-2018.12 2019.01-2019.08 Sentinel-1A SAR 2018.01-2018.12 2019.01-2019.08 表 4 水位提取结果精度评价表
站点 R-square RMSE(m) RRMSE(%) A 0.9474 0.1639 0.182 B 0.9507 0.1244 0.170 C 0.9481 0.1532 0.258 表 5 河宽拟合结果精度评价表
站点 河宽-水位R-square 河宽-径流R-square A 0.834 0.906 B 0.879 0.921 C 0.908 0.917 表 6 径流结果精度评价表
站点 R-square RMSE(m3) RRMSE(%) MRRS-RCM模型 水深-径流模型 MRRS-RCM模型 水深-径流模型 MRRS-RCM模型 水深-径流模型 A 0.9694 0.8647 201.2 287.9 12.622 20.773 B 0.9564 0.8902 218.6 291.6 14.546 19.801 C 0.9537 0.8874 193.8 296.2 14.739 20.393 -
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