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Data-model fusion for improving LAI mapping: a case study over China's land mass
Huang, Mei1,2; Chen, Jing M.1; Deng, Feng1
2011
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
Volume32Issue:22Pages:7279-7296
AbstractA simple data-model fusion method is developed to improve leaf area index (LAI) mapping using satellite data. The objective is to overcome two issues with satellite-derived LAI maps: (1) optical remote sensing data are often seriously affected by the atmosphere due to clouds, and in some areas no reliable data are obtained in the whole growing season, and (2) seasonal variations in conifer LAI derived from satellite data are often distorted by the seasonal variations in leaf greenness (pigments), the background vegetation and snow cover, etc., and the derived LAI reflects the overall greenness rather than the actual forest leaf area present in a pixel. These shortcomings of satellite measurements can be greatly alleviated when an ecological model is used to simulate the LAI in the absence of reliable remote sensing data and to estimate the seasonal variation of LAI according to ecological principles. The usefulness of this fusion method is demonstrated through improving a China-wide LAI map series in 10-day intervals at 1 km resolution using Satellite Pour l'Observation de la Terre (SPOT) VEGETATION (VGT) data.
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
WOS Subject ExtendedRemote Sensing ; Imaging Science & Photographic Technology
WOS KeywordLEAF-AREA INDEX ; NET PRIMARY PRODUCTIVITY ; TERRESTRIAL ECOSYSTEMS ; SATELLITE IMAGERY ; CLIMATE ; SURFACE ; SEASONALITY ; FORESTS ; TERRA ; WATER
Indexed BySCI
Language英语
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000298372800020
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/67998
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHuang, Mei
Affiliation1.Univ Toronto, Dept Geog, Toronto, ON M5S 3G3, Canada
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Huang, Mei,Chen, Jing M.,Deng, Feng. Data-model fusion for improving LAI mapping: a case study over China's land mass[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2011,32(22):7279-7296.
APA Huang, Mei,Chen, Jing M.,&Deng, Feng.(2011).Data-model fusion for improving LAI mapping: a case study over China's land mass.INTERNATIONAL JOURNAL OF REMOTE SENSING,32(22),7279-7296.
MLA Huang, Mei,et al."Data-model fusion for improving LAI mapping: a case study over China's land mass".INTERNATIONAL JOURNAL OF REMOTE SENSING 32.22(2011):7279-7296.
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