河流水情要素遥感研究进展

doi:10.18306/dlkxjz.2020.04.014

Abstract

Abstract:

River is one of the most significant factors in driving the formation and evolution of landforms as well as one of the most important freshwater resources on the Earth. River characteristics, including water extent, water level/water depth, river discharge, water quality, and ice coverage, are vital to the dynamic monitoring of water resources and protection of eco-hydrological systems. Traditional methods of acquiring river characteristics are to use in situ data that were collected on hydrological gauges, which costs large amount of financial and material resources. It is urgent to develop a way of supplementing in situ data of rivers quickly and accurately for the increasing demand of river information. With the ability of omnidirectional and multi-temporal Earth observation, remote sensing has greatly improved the efficiency of acquiring river characteristics. It has been applied broadly in multi-scale river monitoring and hydrological simulation in ungauged basins. Therefore, it is necessary to summarize recent progresses in the field of remote sensing based river characteristics inversion, in order to further promote the application of remote sensing data and methods in this field. This article, therefore, focusing on remote sensing of river characteristics, summarizes recent progresses systematically on the extraction of water extent, inversion of water level/water depth, estimation of river discharge, and monitoring of water quality and ice coverage. Advantages and disadvantages of applying optical and microwave remote sensors for obtaining water extent and water level/depth are discussed in detail. The advanced data, specific methods, and related emerging technologies in this field are discussed and the following conclusions are made: 1) Newly available remotely sensed data have been making creative breakthroughs in spatial resolution, temporal resolution, and spectral resolution, which dramatically enrich data sources for river studies. 2) Most optical images still face the challenge of mixed pixels, while the application of SAR images is suffering from difficulties in developing complex processing algorithms. Meanwhile, accurate extractions of narrow and non-open waters need further research in the future. 3) The development of big data and cloud computing technologies provide excellent means for monitoring river characteristics at large spatial scales and long temporal scales, with both high spatial and high temporal resolutions.

Key words: water extent, water level/water depth, river discharge, water quality, ice coverage, optical/microwave remote sensors

SHI Zhuolin, HUANG Chang. Recent advances in remote sensing of river characteristics[J].PROGRESS IN GEOGRAPHY, 2020, 39(4): 670-684.

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卫星/传感器名称	数据起始年份	空间分辨率/m	时间分辨率/d
NOAA/AVHRR	1979	1100	0.5
Terra/Aqua MODIS	1999	250~1000	0.5
Suomi NPP-VIIRS	2012	375~750	0.5
Landsat MSS/TM/ETM+/OLI	1972	15~80	16
Sentinel-2 MSI	2015	10~60	5
ZY-3	2012	2.1~5.8	5
Planet CubeSat	2013	3	<1

水体指数	计算公式	局限性
TCW^[35]	TCW=0.0315×BLUE+0.2021×GREEN+0.3102×RED+0.1594×NIR- 0.6806×SWIR1-0.6109×SWIR2	易受建筑及其阴影影响
NDWI^[36]	NDWI=(GREEN-NIR)/(GREEN+NIR)	受建筑和山体阴影影响
MNDWI^[37]	MNDWI=(GREEN-SWIR1)/(GREEN+SWIR1)	建筑物、雪有时被错提为水体
AWEI^[38]	AWEI_nsh=4×(GREEN-SWIR1)-(0.25×NIR+2.75×SWIR2) AWEI_sh=BLUE+2.5×GREEN-1.5×(NIR+SWIR1)-0.25×SWIR2	高反射率地表可能被错提为水体,易受建筑噪声影响
WI₂₀₁₅^[32]	WI₂₀₁₅=1.7204+171×GREEN+3×RED-70×NIR-45×SWIR1-71×SWIR2	有时不能有效增强水体信息

卫星/传感器	数据起(止)年份	极化方式	空间分辨率/m	卫星重访周期/d
ERS-1/SAR	1991—2000	VV	30	35
ERS-2/SAR	1995—2010	VV	30	35
RadarSat-1/SAR	1995—2013	HH	8~100	1~3
RadarSat-2/ASAR	2007—	HH、VV、HV、VH	1~100	1~3
Sentinel-1/SAR	2014—	HH、VV、HH-HV、VH-VV	5×20	6

卫星/激光雷达	数据起(止)年份	脉冲次数/s	地面采样间隔/m	测量特点
ICESat/GLAS	2003—2009	40	约170	分周期工作,采集秋季、冬季、春季水位数据
ICESat-2	2018—	10000	约0.7	数据量显著增大,精度更高,更适用于近期内陆河流水位数据的获取

水文要素	光学遥感	微波遥感
水体范围	优势：易得多种时空分辨率影像,可用于不同尺度的河流研究,数据处理及提取方法较简便。光谱信息丰富,结果可视化程度较高不足：不适用于夜间观测、天气状况差及云层遮蔽的河流区域	优势：可以克服光学传感器易受光线、天气条件影响的不足不足：SAR影像来源相对较少,处理难度较高,面临地形及建筑阻隔下河流水体提取的精度问题
河流水位	优势：激光测高应用广泛,数据密度较大,内陆河流水位数据的可用性较强不足：无法获得较早年份的水位信息	优势：可以获得较早年份的水位信息不足：雷达波束宽度限制观测河宽,仅适用于大型河流水位观测,水位精确反演算法难度较大
河流水深	Lidar及光学遥感反演方法可用于河流水深提取,但需要大量训练数据,且不确定性较高,普适性仍有待研究	无法直接获得水深信息,可通过结合水位与水底地形计算水深,但水底地形信息获取难度较大

Recent advances in remote sensing of river characteristics

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