低空遥感结合卫星影像的河道流量反演

doi:10.12118/j.issn.1000-6060.2022.357

干旱区地理 ›› 2023, Vol. 46 ›› Issue (3): 385-396.doi: 10.12118/j.issn.1000-6060.2022.357

低空遥感结合卫星影像的河道流量反演

姜磊鹏^1,^2,³(),丁建丽^1,^2,³(),包青岭^1,^2,³,葛翔宇^1,^2,³,刘景明^1,^2,³,王瑾杰^1,^2,³

1.新疆大学地理与遥感科学学院，新疆乌鲁木齐 830046
2.新疆大学绿洲生态重点实验室，新疆乌鲁木齐 830046
3.新疆智慧城市与环境建模自治区普通高校重点实验室，新疆乌鲁木齐 830046

收稿日期:2022-07-15 修回日期:2022-09-28 出版日期:2023-03-25 发布日期:2023-03-31
通讯作者: 丁建丽
作者简介:姜磊鹏（1997-），男，硕士研究生，主要从事生态水文方面研究. E-mail: 1786032533@qq.com
基金资助:
国家自然科学基金项目(42171269);新疆院士工作站项目(2020.B—001)

Runoff estimation with low altitude remote sensing and satellite images

JIANG Leipeng^1,^2,³(),DING Jianli^1,^2,³(),BAO Qingling^1,^2,³,GE Xiangyu^1,^2,³,LIU Jingming^1,^2,³,WANG Jinjie^1,^2,³

1. College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, Xinjiang, China
2. Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, Xinjiang, China
3. Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Urumqi 830046, Xinjiang, China

Received:2022-07-15 Revised:2022-09-28 Online:2023-03-25 Published:2023-03-31
Contact: Jianli DING

摘要/Abstract

摘要：

中小型河流的径流量精准监测对干旱区生态稳定具有重要意义。然而中小型河流流量遥感精准反演存在困难。以新疆尼勒克县境内的喀什河种峰场河段为例，基于关系拟合法，依据实测水文数据、无人机数据和卫星数据，构建河宽、水深与流量之间幂函数关系模型，并利用卫星数据的时序性，反演监测河段24次不同时期的径流量。反演结果表明：当径流量为0~50 m³·s^-1和50~100 m³·s^-1时，基于河宽的水力几何形态径流量反演效果最优，均方根误差（RMSE）分别为7.15 m³·s^-1和2.81 m³·s^-1；当径流量为100~200 m³·s^-1和>200 m³·s^-1时，基于水深和河宽的水力几何形态径流量反演效果最佳，RMSE分别为13.37 m³·s^-1和1.06 m³·s^-1。研究结果可为水文资料缺乏区的中小型河流径流精细化监测与管理提供一种新方法，也对洪流灾害预测、水能资源开发与水生态系统修复具有较高的参考价值。

关键词: 无人机遥感, Sentinel-2, 河道流量, 关系拟合法, 反演

Abstract:

Accurate monitoring of runoff from small and medium-sized rivers is of great significance for ecological stability in arid areas. However, it is difficult to accurately retrieve the flow of small and medium-sized rivers by remote sensing. Taking the Zhongfengchang river section of Kashi River in Nilka County, Xinjiang, China, as an example, this study constructed a power function relationship model between river width, water depth, and discharge based on the relationship fitting method and measured hydrological data, unmanned aerial vehicle data, and satellite data. Using the time series of satellite data, the runoff volume of the monitored river section was inferred 24 times in different periods. The results show that when the runoff rate is 0-50 m³·s^-1 and 50-100 m³·s^-1, the inversion of the runoff rate based on the hydraulic geometry of the river width is optimal, with root mean square errors (RMSEs) of 7.15 m³·s^-1and 2.81 m³·s^-1, respectively; when the runoff rate is 100-200 m³·s^-1 and >200 m³·s^-1, the inversion of the hydraulic geometry based on water depth and river width is the best, with RMSEs of 13.37 m³·s^-1 and 1.06 m³·s^-1, respectively. These findings provide a new method for the fine monitoring and management of runoff of small and medium-sized rivers in areas lacking hydrologic data and have high reference value for flood disaster prediction, hydropower resource development, and water ecosystem restoration.

Key words: unmanned remote sensing, Sentinel-2, river discharge, relationship fitting method, estimation

姜磊鹏,丁建丽,包青岭,葛翔宇,刘景明,王瑾杰. 低空遥感结合卫星影像的河道流量反演[J]. 干旱区地理, 2023, 46(3): 385-396.

JIANG Leipeng,DING Jianli,BAO Qingling,GE Xiangyu,LIU Jingming,WANG Jinjie. Runoff estimation with low altitude remote sensing and satellite images[J]. Arid Land Geography, 2023, 46(3): 385-396.

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函数	H=F(W)	W=G(H)
方程	y=B₀+B₁x+B₂x²+B₃x³+B₄x⁴	y=B₀+B₁x+B₂x²+B₃x³+B₄x⁴
参数值	B₀：37.5882；B₁：30.8797 B₂：-3.65321；B₃：0.36687 B₄：-0.0143	B₀：88.38032；B₁：3.59985 B₂：-0.05631；B₃：0.00429 B₄：-0.000002
相关系数	0.98	0.98
均方根误差	0.56	0.50
残差平方和	4.77	3.51

日期（年-月-日）	概化水深/m	概化河宽/m	面积/m²	日期（年-月-日）	概化水深/m	概化河宽/m	面积/m²
2016-09-11	0.91	51.32	10264	2017-10-03	0.81	47.27	9454
2016-10-09	0.80	47.08	9416	2018-03-08	0.71	43.34	8668
2016-10-15	0.78	46.06	9212	2018-03-18	0.73	44.03	8806
2017-03-21	0.70	43.56	8712	2018-04-17	0.79	46.80	9360
2017-04-10	0.78	46.12	9224	2018-04-25	0.84	48.80	9760
2017-05-20	0.92	51.99	10398	2018-07-19	1.78	79.83	15966
2017-06-09	1.70	77.78	15556	2018-08-18	1.83	81.24	16248
2017-07-06	1.90	83.45	16690	2018-08-28	1.74	78.75	15750
2017-08-08	2.09	88.41	17682	2018-09-04	1.75	78.78	15756
2017-08-26	1.71	77.89	15578	2018-10-07	0.75	45.30	9060
2017-09-10	1.55	73.11	14622	2019-03-23	0.73	44.16	8832
2017-09-17	1.08	57.34	11468	2019-04-05	0.72	43.83	8766

方法	径流量0~50 m³·s^-1			径流量50~100 m³·s^-1
方法	RA/%	RMSE/m³·s^-1	MPE	RA/%	RMSE/m³·s^-1	MPE
式（1）	4.35	7.15	-0.15	4.38	2.81	-0.01
式（2）	42.34	19.75	0.31	16.32	10.85	-0.01
式（3）	19.42	8.86	-0.14	8.89	5.96	-0.20
方法	径流量100~200 m³·s^-1			径流量>200 m³·s^-1
方法	RA/%	RMSE/m³·s^-1	MPE	RA/%	RMSE/m³·s^-1	MPE
式（1）	13.60	22.29	-0.05	8.53	28.21	-0.01
式（2）	8.71	13.60	-0.10	3.54	11.20	0.00
式（3）	8.99	13.37	-0.05	0.38	1.06	-0.01

低空遥感结合卫星影像的河道流量反演

Runoff estimation with low altitude remote sensing and satellite images

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