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Article: Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery
Title | Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery |
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Authors | |
Keywords | Drainage networks High-resolution Sentinel-2 Stream burning River networks |
Issue Date | 2021 |
Publisher | Elsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/rse |
Citation | Remote Sensing of Environment, 2021, v. 255, p. article no. 112281 How to Cite? |
Abstract | 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. |
Description | Hybrid open access |
Persistent Identifier | http://hdl.handle.net/10722/304498 |
ISSN | 2023 Impact Factor: 11.1 2023 SCImago Journal Rankings: 4.310 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | WANG, Z | - |
dc.contributor.author | Liu, J | - |
dc.contributor.author | Li, J | - |
dc.contributor.author | Meng, Y | - |
dc.contributor.author | Pokhrel, Y | - |
dc.contributor.author | Zhang, H | - |
dc.date.accessioned | 2021-09-23T09:00:52Z | - |
dc.date.available | 2021-09-23T09:00:52Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Remote Sensing of Environment, 2021, v. 255, p. article no. 112281 | - |
dc.identifier.issn | 0034-4257 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304498 | - |
dc.description | Hybrid open access | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.publisher | Elsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/rse | - |
dc.relation.ispartof | Remote Sensing of Environment | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Drainage networks | - |
dc.subject | High-resolution | - |
dc.subject | Sentinel-2 | - |
dc.subject | Stream burning | - |
dc.subject | River networks | - |
dc.title | Basin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery | - |
dc.type | Article | - |
dc.identifier.email | Li, J: jinbao@hku.hk | - |
dc.identifier.email | Zhang, H: zhanghs@hku.hk | - |
dc.identifier.authority | Li, J=rp01699 | - |
dc.identifier.authority | Zhang, H=rp02616 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1016/j.rse.2020.112281 | - |
dc.identifier.scopus | eid_2-s2.0-85099503357 | - |
dc.identifier.hkuros | 325006 | - |
dc.identifier.volume | 255 | - |
dc.identifier.spage | article no. 112281 | - |
dc.identifier.epage | article no. 112281 | - |
dc.identifier.isi | WOS:000619233100003 | - |
dc.publisher.place | United States | - |