Yin, Gaofei等:Improving Leaf Area Index Retrieval Over Heterogeneous Surface by Integrating Textural and Contextual Information: A Case Study in the Heihe River Basin
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2015-02-02

Improving Leaf Area Index Retrieval Over Heterogeneous Surface by Integrating Textural and Contextual Information: A Case Study in the Heihe River Basin
作者:Yin, GF (Yin, Gaofei)[ 1,2 ] ; Li, J (Li, Jing)[ 1,3 ] ; Liu, QH (Liu, Qinhuo)[ 1 ] ; Li, LH (Li, Longhui)[ 1,3 ] ; Zeng, YL (Zeng, Yelu)[ 1,2 ] ; Xu, BD (Xu, Baodong)[ 1,2 ] ; Yang, L (Yang, Le); Zhao, J (Zhao, Jing)[ 1 ]
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷: 12  期: 2  页: 359-363
DOI: 10.1109/LGRS.2014.2341925
出版年: FEB 2015
摘要
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.
通讯作者地址: Yin, GF (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[ 3 ] Univ Technol Sydney, Plant Funct Biol & Climate Change Cluster, Ultimo, NSW 2007, Australia