River discharge estimation based on remote sensing
LI Hemou1,2,3(), BAI Juan3, GAN Fuping3(), LI Xianqing1,2, WANG Zekun1,2,3
1. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology(Beijing), Beijing 100083, China 2. College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China 3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Since the availability of global runoff data decrease year by year, the inversion algorithms, as substitutes for the river discharge measured at hydrological stations, have become increasingly important. With the continuous development of satellite remote sensing technology, the methods for estimating river discharge have increased in number. This study systematically summarized the remote sensing-based inversion methods for river discharge, as well as the inversion methods for hydraulic remote sensing elements that are closely related to the estimation of river discharge and the progress made in them. Moreover, this study reviewed the methods, principles, and application status of two types of algorithms based on hydrological models and empirical regression equations and summarized the applicable conditions and shortcomings of different methods. Finally, this study predicted the worldwide development trends of the river discharge inversion based on the satellite remote sensing technology, including ① actively developing the advanced data assimilation technology for satellite remote sensing data; ② integrating new sensor products; ③ optimizing and innovating algorithms.
李和谋, 白娟, 甘甫平, 李贤庆, 王泽坤. 遥感估算河道流量研究进展[J]. 自然资源遥感, 2023, 35(2): 16-24.
LI Hemou, BAI Juan, GAN Fuping, LI Xianqing, WANG Zekun. River discharge estimation based on remote sensing. Remote Sensing for Natural Resources, 2023, 35(2): 16-24.
QuickBird/0.6 m/4~6 d IKONOS/0.58 m/3 d WorldView-1/0.81 m/1.7 d
中国雅砻江
仅基于高精度遥感河宽数据校准的水文模型能够估算河道流量
基于经验回归方程
水位-流量经验曲线法
Kouraev等[27] (2004年)
TOPEX-Poseidon/600 m/10 d
北极鄂毕河
卫星测高数据可以估算大型流域的部分河道流量演算
Birkinshaw等[30] (2010年)
ERS-2/30 m/35 d ENVISAT/350 m/35 d
亚洲湄公河
NSE介于0.823~0.935之间
Papa等[31] (2012年)
Jason-2/12.5 m/10 d
亚洲恒河和雅鲁藏布江
平均误差为13%和6.5%
河宽-流量经验曲线法
Smith等[36](2008年)
MODIS/250 m/8 d
俄罗斯勒拿河
在河流长度足够长时,可以将建立的河宽-流量关系曲线延用到河流其他位置
Pavelsky等[37] (2014年)
RapidEye/5 m/1 d
北美塔纳诺河
相对误差为6.7%
Elmi等[38] (2015年)
MODIS/250 m/8 d
非洲尼日尔河
改进河宽-流量经验曲线算法不需要流量数据与卫星图像同步观测
C/M信号法
Brakenridge等[39] (2007年)
AMSR-E/25 km/16 d
全球57 条河流
基于被动微波遥感亮度温度的C/M信号法能够估算河流流量
Tarpanelli等[40] (2013年)
MODIS/250 m/8 d
欧洲波河
基于光学遥感数据的C/M信号法可以估算中型流域流量
Li等[46](2019年)
Landsat/30 m/16 d
中国黑河
基于C/M信号法发展出MPR法,能够估算小河流流量
AMHG法
Gleason等[48] (2014年)
Landsat/30 m/16 d
全球34 条河流
相对均方根误差介于26%~41%之间
Rao等[49](2020年)
ResourceSat/23 m/24 d Landsat/30 m/16 d
印度4 条河流
NSE介于0.8~0.89之间
Mengen等[50] (2020年)
Sentinel-1/10 m/6,12 d
亚洲湄公河
采用SAR卫星遥感数据,相对均方根误差为19.5%
多水力特征参数经验法
Birkinshaw等[53] (2012年)
ERS-2/30 m/35 d ENVISAT/350 m/35 d Landsat/30 m/16 d
亚洲湄公河和北极鄂毕河
联合水位、河宽和河道坡度估算流量,NSE介于0.86~0.9之间
Sichangi等[55] (2016年)
MODIS/250 m/8 d 10 个测高卫星数据
全球8 条河流
使用卫星反演水位和有效河宽估算流量,NSE介于0.60~0.97之间
Bjerklie等[54] (2018年)
Jason-2/12.5 m/10 d ICESat/70 m/91 d Landsat/30 m/16 d
北美育空河
采用曼宁公式和普朗特卡门公式2种物理流阻方程估算流量
Yang等[4] (2019年)
航空遥感(无人机)
中国新疆10 条河流
坡度-面积法与无人机遥感技术结合,能够估算无资料地区河流流量
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2023, Vol. 35 
), 白娟3, 甘甫平3(
