气候变化领域集成服务门户(CCPortal): High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data

CCPortal

DOI	10.1029/2023GL107956
	High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data
	Li, Peng; Zhang, Yun; Liang, Cunren; Wang, Houjie; Li, Zhenhong
发表日期	2024
ISSN	0094-8276
EISSN	1944-8007
起始页码	51
结束页码	6
卷号	51 期号:6
英文摘要	Characterizing and understanding the changes in the flow regimes of rivers have been challenging. Existing global river network data sets are not updated and can only identify rivers wider than 30 m. We propose a novel automated method to map river networks on a monthly basin scale for the first time at 10-m resolution using Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral images, and the AW3D30 Digital Surface Model. This method achieved an overall accuracy of 95.8%. The total length of the Yellow River network produced is 40,280 km, approximately 3.2 times that of the Global River Widths from Landsat (GRWL) database, more effectively covering small and medium rivers. The monthly river geometry revealed a positive correlation between river network area and precipitation. This study is expected to provide a cost-effective alternative to accurately mapping global river networks and advance our understanding of the changes and drivers of river systems. Understanding the impacts of climate change and human activities on water resources across different regions greatly depends on the knowledge of river networks with high spatial and temporal resolution. Small tributaries are important components in river network evolution and water transmission. To date, several studies have mapped interannual variations in rivers with widths >30 m; however, the distribution and variations in small rivers remain unclear. By integrating multispectral and radar satellite remote sensing images as well as topographic data, we created continuous monthly river network maps at the basin scale, allowing us to capture the details of dynamic changes in river networks with higher spatiotemporal resolution. As a result, the method used in this study provides detailed information on small and medium rivers, with the length of the connected rivers being thrice that of the existing data sets. We demonstrate the possibility of mapping global river networks monthly at a resolution of 10 m, providing valuable information for global surface water resource planning and management and improving our understanding of spatial links between land and water interfaces.
英文关键词	river networks mapping; multispectral remote sensing; radar remote sensing; digital elevation model; catchment scale; change detection
语种	英语
WOS研究方向	Geology
WOS类目	Geosciences, Multidisciplinary
WOS记录号	WOS:001188827900001
来源期刊	GEOPHYSICAL RESEARCH LETTERS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304829
作者单位	Ocean University of China; Laoshan Laboratory; Peking University; Chang'an University
推荐引用方式 GB/T 7714	Li, Peng,Zhang, Yun,Liang, Cunren,et al. High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data[J],2024,51(6).
APA	Li, Peng,Zhang, Yun,Liang, Cunren,Wang, Houjie,&Li, Zhenhong.(2024).High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data.GEOPHYSICAL RESEARCH LETTERS,51(6).
MLA	Li, Peng,et al."High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data".GEOPHYSICAL RESEARCH LETTERS 51.6(2024).