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遥感技术与应用  2022, Vol. 37 Issue (1): 186-195    DOI: 10.11873/j.issn.1004-0323.2022.1.0186
青促会十周年专栏     
基于GEE云平台的黄河源区河流径流量遥感反演研究
史宜梦1,3(),刘希胜2,朱文彬3(),宋宏利1
1.河北工程大学 地球科学与工程学院,河北 邯郸 056038
2.青海省水文水资源测报中心,青海 西宁 810001
3.中国科学院地理科学与资源研究所,北京 100101
Research on Inversion of River Discharge in High Mountain Region based on GEE Platform
Yimeng Shi1,3(),Xisheng Liu2,Wenbin Zhu3(),Hongli Song1
1.School of Earth Science and Engineering,Hebei University of Engineering,Handan 056038,China
2.Hydrology and Water Resources Forecast Center of Qinghai Province,Xining 810001,China
3.Institute of Geographic Sciences and Natural Resources Research,Beijing 100101,China
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摘要:

河流径流量是陆地上最重要的水文要素之一,准确获取径流信息对于区域的水资源评价和生态修复方面都具有重要作用。研究基于Google Earth Engine(GEE)云平台提供的Sentinel-1、Sentinel-2影像数据,结合数字高程模型(DEM)对河长、河宽、糙率、比降、河深和流速等水力学参数进行遥感估算,进而采用关系拟合法与改进的曼宁公式法对黄河源区唐乃亥站点附近河段进行径流量反演研究,探讨了河段长度差异对径流量反演精度的影响,并通过建立站点河段与上下游河段之间的河宽关系,实现了对站点河段径流量监测时间序列的扩展补充。结果表明,两种模型均能有效进行径流量的模拟估算,其纳什效率系数(NSE)均在0.80以上;关系拟合法与改进的曼宁公式法径流反演的均方根误差(RMSE)分别为233.431 m3s-1和271.704 m3s-1,相对均方根误差(RRMSE)分别为16%和24%,关系拟合法反演精度总体优于改进的曼宁公式法。通过对不同长度河段径流量的反演结果对比分析发现,辫状河心滩的河宽估算在汛期存在较大的不确定性,进而影响径流反演精度,在河段选择时应予以规避;站点河段与上下游河段的平均河宽之间具有强烈的相关性,相关系数均在0.96以上,因此上下游河段的影像数据可为站点河段径流量反演提供重要补充,实现目标河段径流量的加密监测。

关键词: 径流量反演GEE云平台水体指数Sentinel?1Sentinel?2    
Abstract:

River runoff is one of the most important hydrological elements on land. Accurate access to runoff information plays an important role in regional water resources evaluation and ecological restoration. Based on the Sentinel-1 data and Sentinel-2 data provided by the Google Earth Engine cloud platform, combined with digital elevation model, the hydraulic parameters such as river length, river width, roughness, slope, river depth and velocity were estimated by remote sensing. Then, the relationship fitting method and improved Manning formula method were used to inverse the runoff of the reach near Tangnaihai station in the source area of the Yellow River. The influence of the length difference of the reach on the runoff inversion accuracy is discussed. By establishing the river width relationship between the station reach and the upper and lower reaches, the runoff monitoring time series of the station reach can be extended and supplemented. The results show that the two models can effectively simulate and estimate the runoff, and the Nash efficiency coefficient is above 0.80; the root mean square error of the relationship fitting method and the improved Manning formula method are 233.431 m3s-1 and 271.704 m3s-1 respectively, and the relative root mean square error are 16% and 24% respectively. The inversion accuracy of relationship fitting method is better than that of the improved Manning formula method. Through the comparative analysis of runoff inversion results of different lengths of river reaches, it is found that the river width estimation of braided river core beach has great uncertainty in flood season, which affects the accuracy of runoff inversion, and should be avoided in the selection of river reach; there is a strong correlation between the average river width of the station reach and the upstream and downstream reaches, and the correlation coefficient is above 0.96. The data can provide an important supplement for the runoff inversion of the station reach and realize the intensive monitoring of the runoff of the target river section.

Key words: Discharge retrieval    GEE cloud platform    Water index    Sentinel-1    Sentinel-2
收稿日期: 2021-01-25 出版日期: 2022-04-08
ZTFLH:  P332  
基金资助: 青海三江源生态保护和建设二期工程科研和推广项目(2018-S-3);中国科学院青年创新促进会资助项目(2020056)
通讯作者: 朱文彬     E-mail: 657880480@qq.com;zhuwb@igsnrr.ac.cn
作者简介: 史宜梦(1995-),女,河北石家庄人,硕士研究生,主要从事水文遥感研究。E?mail:657880480@qq.com
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引用本文:

史宜梦,刘希胜,朱文彬,宋宏利. 基于GEE云平台的黄河源区河流径流量遥感反演研究[J]. 遥感技术与应用, 2022, 37(1): 186-195.

Yimeng Shi,Xisheng Liu,Wenbin Zhu,Hongli Song. Research on Inversion of River Discharge in High Mountain Region based on GEE Platform. Remote Sensing Technology and Application, 2022, 37(1): 186-195.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0186        http://www.rsta.ac.cn/CN/Y2022/V37/I1/186

图1  研究区概况(a)站点地理位置图 (b)唐乃亥站河段位置概况审图号:GS(2016)2556
图2  技术路线图
图3  河流断面示意图
图4  唐乃亥站点高程-距离散点图(黑色点为利用DEM提取的河流中心线上点的高程值)
图5  唐乃亥站点河段与“S1”河段平均河宽相关关系散点图
图6  Sentinel-1/2数据平均河宽与实测径流量相关关系散点图
NSERMSE/m3s-1RRMSE/%
模型1(验证期)0.855233.43116
模型20.809271.70424
表1  关系拟合法精度评价
图7  不同长度河段的重复日期的归一化河宽计算结果
河段长度/km5678910111213141520
数据量/幅494846464644444241413935
模型一NSE/-0.9150.9310.9470.9400.9060.9500.9580.9130.8770.8840.8810.842
模型二NSE/-0.8920.8430.8850.8380.9280.6220.8540.9520.9550.9100.8540.774
表2  Sentinel-2数据不同长度河段的数据量与重复日期下径流量模型反演精度结果
图8  波动区平均河宽与模型二精度相关关系图
图9  唐乃亥站点河段与上下游河段归一化平均河宽对比图
上游10 km上游20 km上游30 km上游40 km上游50 km上游60 km上游70 km下游10 km下游20 km
相关系数(-)0.9960.9810.9850.9840.9880.9800.9650.9690.988
模型一NSE(-)0.9130.8690.8940.8400.8220.8010.8080.9060.847
模型二NSE(-)0.8460.8770.8600.8590.8210.7620.7930.8080.808
补充数据量/幅364442373636402217
去除重复数据量/幅3693243452
表3  上下游河段径流量反演精度评价表
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