基于多源雷達(dá)遙感技術(shù)的黃河徑流反演研究

閔林; 王寧; 毋琳; 李寧; 趙建輝

doi:10.11999/JEIT190494

基于多源雷達(dá)遙感技術(shù)的黃河徑流反演研究

doi: 10.11999/JEIT190494 cstr: 32379.14.JEIT190494

閔林^{1, 2, 3},
王寧^{1, 2, 4},
毋琳^{1, 2, 4},
李寧^{1, 2, 4},
趙建輝^{1, 2, 4, ,}

1.
河南大學(xué)河南省智能技術(shù)與應(yīng)用工程技術(shù)研究中心開(kāi)封 475004
2.
河南大學(xué)河南省大數(shù)據(jù)分析與處理重點(diǎn)實(shí)驗(yàn)室開(kāi)封 475004
3.
河南大學(xué)信息化管理辦公室開(kāi)封 475004
4.
河南大學(xué)計(jì)算機(jī)與信息工程學(xué)院開(kāi)封 475004

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(U1604145, 61871175, 61601437)，河南省高等學(xué)校重點(diǎn)科研項(xiàng)目(18B520010, 19A420005)，河南省科技攻關(guān)計(jì)劃項(xiàng)目(182102210233, 192102210082)，河南省青年人才托舉工程(2019HYTP006)

詳細(xì)信息

作者簡(jiǎn)介:
閔林：男，1963年生，教授，研究方向?yàn)镾AR圖像處理

王寧：男，1995年生，碩士生，研究方向?yàn)镾AR圖像處理

毋琳：女，1978年生，副教授，研究方向?yàn)镾AR圖像處理

李寧：男，1987年生，教授，研究方向?yàn)槎嗄Ｊ胶铣煽讖嚼走_(dá)成像及其應(yīng)用技術(shù)方面的研究

趙建輝：男，1980年生，副教授，研究方向?yàn)镾AR圖像處理

通訊作者:
趙建輝　jhzhao@henu.edu.cn

中圖分類(lèi)號(hào): TN958
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- 文章訪問(wèn)數(shù): 2407
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-07-03
- 修回日期: 2020-01-22
- 網(wǎng)絡(luò)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:
黃河是我國(guó)華北地區(qū)重要的水資源，采用雷達(dá)遙感方式對(duì)其徑流進(jìn)行監(jiān)測(cè)可以便捷地反映出黃河的旱澇變化趨勢(shì)，具有重要的現(xiàn)實(shí)意義。目前，雷達(dá)遙感徑流反演常用雷達(dá)高度計(jì)(RA)獲取水位信息用以構(gòu)建水深-徑流模型，這種方法忽略了河面變化對(duì)徑流波動(dòng)的影響，具有一定的局限性。該文提出一種基于多源雷達(dá)遙感技術(shù)的徑流計(jì)算模型(MRRS-RCM)，綜合應(yīng)用RA測(cè)高技術(shù)與合成孔徑雷達(dá)(SAR)信息提取技術(shù)，以曼寧公式為基礎(chǔ)，構(gòu)建MRRS-RCM模型實(shí)現(xiàn)徑流反演。該文選取黃河下游3個(gè)研究站點(diǎn)進(jìn)行徑流反演實(shí)驗(yàn)，結(jié)果證明MRRS-RCM模型徑流反演結(jié)果的相對(duì)均方根誤差(RRMSE)達(dá)到13.969%，優(yōu)于傳統(tǒng)徑流監(jiān)測(cè)15%～20%的精度要求。
- 合成孔徑雷達(dá) /
- 徑流計(jì)算模型 /
- 雷達(dá)高度計(jì) /
- 關(guān)系擬合 /
- 黃河
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

HTML全文

圖 1 基于MRRS-RCM模型的徑流反演方法處理流程

下載: 全尺寸圖片幻燈片

圖 2 研究區(qū)域

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圖 3 研究站點(diǎn)衛(wèi)星提取水位與實(shí)測(cè)水位對(duì)比

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圖 4 河面提取結(jié)果

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圖 5 研究站點(diǎn)河寬擬合結(jié)果

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圖 6 研究站點(diǎn)水深-徑流模型擬合結(jié)果

下載: 全尺寸圖片幻燈片

圖 7 研究站點(diǎn)MRRS-RCM模型擬合結(jié)果

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圖 8 研究站點(diǎn)模型反演徑流與實(shí)測(cè)徑流對(duì)比

下載: 全尺寸圖片幻燈片

表 1 研究站點(diǎn)空間信息表

站點(diǎn)	地理坐標(biāo)	所屬河段	水文站距離(km)
A	113.667, 34.915	花園口	2.9
B	114.764, 34.893	夾河灘	2.2
C	115.158, 35.419	高村	9.5

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表 2 RA數(shù)據(jù)信息表

站點(diǎn)	數(shù)據(jù)名稱(chēng)	重訪周期(d)	衛(wèi)星軌道	波段	最小目標(biāo)寬度(m)	數(shù)據(jù)級(jí)別	數(shù)據(jù)來(lái)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 遙感數(shù)據(jù)時(shí)間表

數(shù)據(jù)名稱(chēng)	數(shù)據(jù)類(lèi)型	建模數(shù)據(jù)時(shí)間	測(cè)試數(shù)據(jù)時(shí)間
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 水位提取結(jié)果精度評(píng)價(jià)表

站點(diǎn)	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 河寬擬合結(jié)果精度評(píng)價(jià)表

站點(diǎn)	河寬-水位R-square	河寬-徑流R-square
A	0.834	0.906
B	0.879	0.921
C	0.908	0.917

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表 6 徑流結(jié)果精度評(píng)價(jià)表

站點(diǎn)	R-square		RMSE(m³)		RRMSE(%)
站點(diǎn)	MRRS-RCM模型	水深-徑流模型	MRRS-RCM模型	水深-徑流模型	MRRS-RCM模型	水深-徑流模型
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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