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An integrated method for validating long-term leaf area index products using global networks of site-based measurements
Xu, Baodong1,2,4; Li, Jing1,3; Park, Taejin2; Liu, Qinhuo1,3; Zeng, Yelu1,5; Yin, Gaofei6; Zhao, Jing1; Fan, Weiliang7; Yang, Le1; Knyazikhin, Yuri2; Myneni, Ranga B.2
2018-05-01
Source PublicationREMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
Volume209Pages:134-151
Contribution Rank6
AbstractLong-term ground LAI measurements from the global networks of sites (e.g. FLUXNET) have emerged as a promising data source to validate remotely sensed global LAI product time-series. However, the spatial scale-mismatch issue between site and satellite observations hampers the use of such invaluable ground measurements in validation practice. Here, we propose an approach (Grading and Upscaling of Ground Measurements, GUGM) that integrates a spatial representativeness grading criterion and a spatial upscaling strategy to resolve this scale-mismatch issue and maximize the utility of time-series of site-based LAI measurements. The performance of GUGM was carefully evaluated by comparing this method to both benchmark LAI and other widely used conventional approaches. The uncertainty of three global LAI products (i.e. MODIS, GLASS and GEOV1) was also assessed based on the LAI time-series validation dataset derived from GUGM. Considering all the evaluation results together, this study suggests that the proposed GUGM approach can significantly reduce the uncertainty from spatial scale mismatch and increase the size of the available validation dataset. In particular, the proposed approach outperformed other widely used approaches in these two respects. Furthermore, GUGM was successfully implemented to validate global LAI products in various ways with advantaging frequent time-series validation dataset. The validation results of the global LAI products show that GLASS has the lowest uncertainty, followed by GEOV1 and MODIS for the overall biome types. However, MODIS provides more consistent uncertainties across different years than GLASS and GEOV1. We believe that GUGM enables us to better understand the structure of LAI product uncertainties and their evolution across seasonal or annual contexts. In turn, this method can provide fundamental information for further LAI algorithm improvements and the broad application of LAI product time-series.
KeywordSpatial representativeness grading Spatial upscaling Time-series ground measurements Global networks Leaf Area Index (LAI) Validation
DOI10.1016/j.rse.2018.02.049
Indexed BySCI
WOS KeywordPHOTOSYNTHETICALLY ACTIVE RADIATION ; ESSENTIAL CLIMATE VARIABLES ; WIRELESS SENSOR NETWORK ; TIME-SERIES ; SPATIAL HETEROGENEITY ; SURFACE REFLECTANCE ; RESOLUTION LAI ; GEOV1 LAI ; PART 2 ; MODIS
Language英语
Funding ProjectNational Natural Science Foundation of China[41671374] ; National Basic Research Program of China (973 Program)[2013CB733401] ; National Natural Science Foundation of China[41271366] ; National Aeronautics and Space Administration Earth Science Division[NNX14AP80A] ; National Aeronautics and Space Administration Earth Science Division[NNX14AI71G] ; National Aeronautics and Space Administration Earth Science Division[NNX16AO34H]
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000430897300011
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:28[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/21286
Collection山区发展研究中心
数字山地与遥感应用中心
Corresponding AuthorLi, Jing; Park, Taejin; Liu, Qinhuo
Affiliation1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
2.Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA
3.JCGCS, Beijing 100875, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA
6.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
7.Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China
Recommended Citation
GB/T 7714
Xu, Baodong,Li, Jing,Park, Taejin,et al. An integrated method for validating long-term leaf area index products using global networks of site-based measurements[J]. REMOTE SENSING OF ENVIRONMENT,2018,209:134-151.
APA Xu, Baodong.,Li, Jing.,Park, Taejin.,Liu, Qinhuo.,Zeng, Yelu.,...&Myneni, Ranga B..(2018).An integrated method for validating long-term leaf area index products using global networks of site-based measurements.REMOTE SENSING OF ENVIRONMENT,209,134-151.
MLA Xu, Baodong,et al."An integrated method for validating long-term leaf area index products using global networks of site-based measurements".REMOTE SENSING OF ENVIRONMENT 209(2018):134-151.
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