Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery

Wang, Zifeng1,2; Liu, Junguo1; Li, Jinbao2; Meng, Ying1; Pokhrel, Yadu3; Zhang, Hongsheng2

2021-03-15

10.1016/j.rse.2020.112281

REMOTE SENSING OF ENVIRONMENT   影响因子和分区

0034-4257

1879-0704

Extraction of drainage networks is an important element of river flow routing in hydrology and large-scale estimates of river behaviors in Earth sciences. Emerging studies with a focus on greenhouse gases reveal that small rivers can contribute to more than half of the global carbon emissions from inland waters (including lakes and wetlands). However, large-scale extraction of drainage networks is constrained by the coarse resolution of observational data and models, which hinders assessments of terrestrial hydrological and biogeochemical cycles. Recognizing that Sentinel-2 satellite can detect surface water up to a 10-m resolution over large scales, we propose a new method named Remote Sensing Stream Burning (RSSB) to integrate high-resolution observational flow location with coarse topography to improve the extraction of drainage network. In RSSB, satellite-derived input is integrated in a spatially continuous manner, producing a quasi-bathymetry map where relative relief is enforced, enabling a fine-grained, accurate, and multitemporal extraction of drainage network. RSSB was applied to the Lancang-Mekong River basin to derive a 10-m resolution drainage network, with a significant reduction in location errors as validated by the river centerline measurements. The high-resolution extraction resulted in a realistic representation of meanders and detailed network connections. Further, RSSB enabled a multitemporal extraction of river networks during wet/dry seasons and before/after the formation of new channels. The proposed method is fully automated, meaning that the network extraction preserves basin-wide connectivity without requiring any postprocessing, hence facilitating the construction of drainage networks data with openly accessible imagery. The RSSB method provides a basis for the accurate representation of drainage networks that maintains channel connectivity, allows a more realistic inclusion of small rivers and streams, and enables a greater understanding of complex but active exchange between inland water and other related Earth system components.

Drainage networks High-resolution Sentinel-2 Stream burning River networks Small rivers Remote sensing

SCI ; EI

英语

第一 ; 通讯

Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20060402] ; National Natural Science Foundation of China[41625001] ; High-level Special Funding of the Southern University of Science and Technology["G02296302","G02296402"] ; National Science Foundation[1752729]

Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000619233100003

ELSEVIER SCIENCE INC

20210409812450

Behavioral research ; Biogeochemistry ; Catchments ; Data mining ; Extraction ; Greenhouse gases ; Remote sensing ; Rivers ; Topography

Air Pollution Sources:451.1 ; Geochemistry:481.2 ; Data Processing and Image Processing:723.2 ; Chemical Operations:802.3 ; Materials Science:951 ; Social Sciences:971

GEOSCIENCES

Web of Science

1.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
2.Univ Hong Kong, Dept Geog, Hong Kong, Peoples R China
3.Michigan State Univ, Dept Civil & Environm Engn, E Lansing, MI 48824 USA

Wang, Zifeng,Liu, Junguo,Li, Jinbao,et al. Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery[J]. REMOTE SENSING OF ENVIRONMENT,2021,255.

Wang, Zifeng,Liu, Junguo,Li, Jinbao,Meng, Ying,Pokhrel, Yadu,&Zhang, Hongsheng.(2021).Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery.REMOTE SENSING OF ENVIRONMENT,255.

Wang, Zifeng,et al."Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery".REMOTE SENSING OF ENVIRONMENT 255(2021).