Improving leaf area index retrieval over heterogeneous surface by integrating textural and contextual information: A case study in the heihe river basin

Yin, G; Li, J; Liu, Q; Li, L; Zeng, Y; Xu, B; Yang, L; Zhao, J

Improving leaf area index retrieval over heterogeneous surface by integrating textural and contextual information: A case study in the heihe river basin

Yin, G Li, J Liu, Q Li, L

Zeng, Y Xu, B Yang, L Zhao, J

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Publication Type:: Journal Article
Citation:: IEEE Geoscience and Remote Sensing Letters, 2015, 12 (2), pp. 359 - 363
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Yin, G	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Liu, Q	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-9983-6681	en_US
dc.contributor.author	Zeng, Y	en_US
dc.contributor.author	Xu, B	en_US
dc.contributor.author	Yang, L	en_US
dc.contributor.author	Zhao, J	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	IEEE Geoscience and Remote Sensing Letters, 2015, 12 (2), pp. 359 - 363	en_US
dc.identifier.issn	1545-598X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32645
dc.description.abstract	Spatial heterogeneity of land surface induces scaling bias in leaf area index (LAI) products. In optical remote sensing of vegetation, spatial heterogeneity arises both by textural and contextual effects. A case study made in the middle reach of the Heihe River Basin shows that the scaling bias in LAI retrieval is large up to 26% if the spatial heterogeneity within low-resolution pixels is ignored. To reduce the influence of spatial heterogeneity on LAI products, a correcting method combining both textural and contextual information is adopted, and the scaling bias may decrease to less than 2% in producing resolution-invariant LAI products. © 2004-2012 IEEE.	en_US
dc.relation.ispartof	IEEE Geoscience and Remote Sensing Letters	en_US
dc.relation.isbasedon	10.1109/LGRS.2014.2341925	en_US
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	Improving leaf area index retrieval over heterogeneous surface by integrating textural and contextual information: A case study in the heihe river basin	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	12	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

Spatial heterogeneity of land surface induces scaling bias in leaf area index (LAI) products. In optical remote sensing of vegetation, spatial heterogeneity arises both by textural and contextual effects. A case study made in the middle reach of the Heihe River Basin shows that the scaling bias in LAI retrieval is large up to 26% if the spatial heterogeneity within low-resolution pixels is ignored. To reduce the influence of spatial heterogeneity on LAI products, a correcting method combining both textural and contextual information is adopted, and the scaling bias may decrease to less than 2% in producing resolution-invariant LAI products. © 2004-2012 IEEE.

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http://hdl.handle.net/10453/32645