基于多源雷达遥感技术的黄河径流反演研究

闵林; 王宁; 毋琳; 李宁; 赵建辉

doi:10.11999/JEIT190494

基于多源雷达遥感技术的黄河径流反演研究

doi: 10.11999/JEIT190494

闵林^{1, 2, 3},
王宁^{1, 2, 4},
毋琳^{1, 2, 4},
李宁^{1, 2, 4},
赵建辉^{1, 2, 4, ,}

1.
河南大学河南省智能技术与应用工程技术研究中心开封 475004
2.
河南大学河南省大数据分析与处理重点实验室开封 475004
3.
河南大学信息化管理办公室开封 475004
4.
河南大学计算机与信息工程学院开封 475004

基金项目: 国家自然科学基金(U1604145, 61871175, 61601437)，河南省高等学校重点科研项目(18B520010, 19A420005)，河南省科技攻关计划项目(182102210233, 192102210082)，河南省青年人才托举工程(2019HYTP006)

详细信息

作者简介:
闵林：男，1963年生，教授，研究方向为SAR图像处理

王宁：男，1995年生，硕士生，研究方向为SAR图像处理

毋琳：女，1978年生，副教授，研究方向为SAR图像处理

李宁：男，1987年生，教授，研究方向为多模式合成孔径雷达成像及其应用技术方面的研究

赵建辉：男，1980年生，副教授，研究方向为SAR图像处理

通讯作者:
赵建辉　jhzhao@henu.edu.cn

中图分类号: TN958
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出版历程
- 收稿日期: 2019-07-03
- 修回日期: 2020-01-22
- 网络出版日期: 2020-03-27
- 刊出日期: 2020-07-23

Inversion of Yellow River Runoff Based on Multi-source Radar Remote Sensing Technology

Lin MIN^{1, 2, 3},
Ning WANG^{1, 2, 4},
Lin WU^{1, 2, 4},
Ning LI^{1, 2, 4},
Jianhui ZHAO^{1, 2, 4
, ,}

1.
Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China
2.
Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China
3.
Network Information Center Office, Henan University, Kaifeng 475004, China
4.
College of Computer and Information Engineering, Henan University, Kaifeng 475004, China

Funds: The National Natural Science Foundation of China (U1604145, 61871175, 61601437), The College Key Research Project of Henan Province (18B520010, 19A420005), The Plan of Science and Technology of Henan Province (182102210233, 192102210082), The Youth Talent Lifting Project of Henan Province (2019HYTP006)

摘要

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。
- 合成孔径雷达 /
- 径流计算模型 /
- 雷达高度计 /
- 关系拟合 /
- 黄河
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%.
- Synthetic Aperture Radar (SAR) /
- Runoff calculation model /
- Radar Altimetry (RA) /
- Relationship fitting /
- Yellow River

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图 1 基于MRRS-RCM模型的径流反演方法处理流程

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图 2 研究区域

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图 3 研究站点卫星提取水位与实测水位对比

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图 4 河面提取结果

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图 5 研究站点河宽拟合结果

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图 6 研究站点水深-径流模型拟合结果

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图 7 研究站点MRRS-RCM模型拟合结果

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图 8 研究站点模型反演径流与实测径流对比

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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

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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

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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

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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

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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

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表 6 径流结果精度评价表

站点	R-square		RMSE(m³)		RRMSE(%)
站点	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

下载: 导出CSV

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