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

TitleBasin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery
Authors
KeywordsDrainage networks
High-resolution
Sentinel-2
Stream burning
River networks
Issue Date2021
PublisherElsevier 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?
AbstractExtraction 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.
DescriptionHybrid open access
Persistent Identifierhttp://hdl.handle.net/10722/304498
ISSN
2021 Impact Factor: 13.850
2020 SCImago Journal Rankings: 3.611
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWANG, Z-
dc.contributor.authorLiu, J-
dc.contributor.authorLi, J-
dc.contributor.authorMeng, Y-
dc.contributor.authorPokhrel, Y-
dc.contributor.authorZhang, H-
dc.date.accessioned2021-09-23T09:00:52Z-
dc.date.available2021-09-23T09:00:52Z-
dc.date.issued2021-
dc.identifier.citationRemote Sensing of Environment, 2021, v. 255, p. article no. 112281-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/304498-
dc.descriptionHybrid open access-
dc.description.abstractExtraction 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.languageeng-
dc.publisherElsevier Inc. The Journal's web site is located at http://www.elsevier.com/locate/rse-
dc.relation.ispartofRemote Sensing of Environment-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectDrainage networks-
dc.subjectHigh-resolution-
dc.subjectSentinel-2-
dc.subjectStream burning-
dc.subjectRiver networks-
dc.titleBasin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery-
dc.typeArticle-
dc.identifier.emailLi, J: jinbao@hku.hk-
dc.identifier.emailZhang, H: zhanghs@hku.hk-
dc.identifier.authorityLi, J=rp01699-
dc.identifier.authorityZhang, H=rp02616-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.rse.2020.112281-
dc.identifier.scopuseid_2-s2.0-85099503357-
dc.identifier.hkuros325006-
dc.identifier.volume255-
dc.identifier.spagearticle no. 112281-
dc.identifier.epagearticle no. 112281-
dc.identifier.isiWOS:000619233100003-
dc.publisher.placeUnited States-

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