REMOTE SENSING OF ENVIRONMENT | 卷:255 |
Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery | |
Article | |
Wang, Zifeng1,2  Liu, Junguo1  Li, Jinbao2  Meng, Ying1  Pokhrel, Yadu3  Zhang, Hongsheng2  | |
[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 | |
关键词: Drainage networks; High-resolution; Sentinel-2; Stream burning; River networks; Small rivers; Remote sensing; | |
DOI : 10.1016/j.rse.2020.112281 | |
来源: Elsevier | |
【 摘 要 】
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.
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