IMHE OpenIR  > 数字山地与遥感应用中心
Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products
Xu, Baodong1,2,3; Li, Jing2,4; Liu, Qinhuo2,4; Huete, Alfredo R.5; Yu, Qiang6,7; Zeng, Yelu1,2,3; Yin, Gaofei8; Zhao, Jing2,4; Yang, Le2,4
Corresponding AuthorLi, Jing ; Liu, Qinhuo
2016
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
EISSN2151-1535
Volume9Issue:7Pages:3267-3282
SubtypeArticle
AbstractContinuous leaf area index (LAI) observations from global ground stations are an important reference dataset for the validation of remotely sensed LAI products. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of station-observed LAI dataset in the product pixel grid. Three evaluation indicators, including dominant vegetation type percent (DVTP), relative absolute error (RAE) and coefficient of sill (CS), were established to quantify different levels of spatial representativeness. The DVTP was used to evaluate whether the station-observed vegetation type was the dominant one in the pixel grid, and the RAE and CS were applied to quantify the point-to-area consistency for a given station observation and the spatial heterogeneity caused by the different density of vegetation within the pixel, respectively. The proposed approach was applied to 25 stations from the Chinese Ecosystem Research Network, and results show significant differences of representativeness errors at different levels. The spatial representativeness for different stations varied seasonally with different vegetation growth stages due to temporal changes in heterogeneity, but the spatial representativeness remained consistent at interannual timeframes due to the relatively stable vegetation structure and pattern between adjacent years. A large error can occur in MOD15A2 product validation when the representativeness level of station LAI observations is low. This approach can effectively distinguish various levels of spatial representativeness for the station-observed LAI dataset at the pixel grid scale, which can consequently improve the reliability of LAI product validation by selecting LAI observations with a high degree of representativeness.
KeywordChinese Ecosystem Research Network (Cern) Continuous Leaf Area Index (Lai) Observations Heterogeneity Spatial Representativeness Validation
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
DOI10.1109/JSTARS.2016.2560878
WOS Subject ExtendedEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
Indexed BySCI
WOS KeywordIN-SITU MEASUREMENTS ; CYCLOPES GLOBAL PRODUCTS ; SYSTEM DATA RECORD ; MODIS-LAI PRODUCT ; PROSAIL MODEL ; TIME-SERIES ; LAND-COVER ; PART 2 ; MONITORING STATIONS ; CHLOROPHYLL CONTENT
Language英语
Quartile2区
TOP
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000384905500038
Funding OrganizationNational Natural Science Foundation of China(41271366) ; National Basic Research Program of China(2013CB733401) ; National High Technology Research and Development Program of China(2012AA12A304 ; 2012AA12A305)
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/18216
Collection数字山地与遥感应用中心
Affiliation1.State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
2.Joint Ctr Global Change Studies, Beijing 100875, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
4.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
5.Univ Technol, Plant Funct Biol & Climate Change Cluster, Sydney, NSW 2007, Australia
6.Univ Technol, Sch Life Sci, Sydney, NSW 2007, Australia
7.Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Peoples R China
8.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
Recommended Citation
GB/T 7714
Xu, Baodong,Li, Jing,Liu, Qinhuo,et al. Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2016,9(7):3267-3282.
APA Xu, Baodong.,Li, Jing.,Liu, Qinhuo.,Huete, Alfredo R..,Yu, Qiang.,...&Yang, Le.(2016).Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,9(7),3267-3282.
MLA Xu, Baodong,et al."Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 9.7(2016):3267-3282.
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