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

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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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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