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Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series
Yin, Gaofei; Li, Ainong; Jin, Huaan; Bian, Jinhu
2018
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
EISSN1366-5901
Volume39Issue:10Pages:3287-3305
SubtypeArticle
Contribution Rank1
AbstractSpatiotemporal fusion (STF) technologies are commonly used to acquire high spatiotemporal resolution remote sensing observations. However, most STF technologies fail to consider the nonlinear variation in vegetation in the time domain. Based on the Best Linear Unbiased Estimator (BLUE), this paper proposed a novel STF algorithm (referred to BLUE) which accounts for the phenological characteristics of vegetation. First, annual time series of normalized difference vegetation index (NDVI) data with high spatial resolution but low temporal resolution is fitted using a double logistic function and used as the background field. Then, NDVI data with low spatial resolution but high temporal resolution is used as the observation field. The information in the background and observation fields is fused using the BLUE to obtain high spatiotemporal resolution NDVI data. The proposed algorithm was used to produce dense time series of 30m resolution NDVI data for a 10km x 10km experimental area in 2014. The experimental results demonstrate that the accuracy of fusion results from the proposed BLUE method are higher than those from the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Linear Mixing Growth Model (LMGM), especially when the temporal component of surface heterogeneity is dominant. The proposed algorithm has broad prospects in vegetation monitoring at high spatiotemporal resolution.
KeywordSpatiotemporal fusion NDVI
DOI10.1080/01431161.2018.1439202
Indexed BySCI
WOS KeywordLEAF-AREA INDEX ; MODIS DATA FUSION ; REFLECTANCE FUSION ; BLENDING LANDSAT ; SURFACE ; ALGORITHM ; RETRIEVAL ; FRAMEWORK ; DYNAMICS ; IMAGES
Language英语
Quartile3区
Funding ProjectNational Natural Science Foundation of China[41631180] ; National Natural Science Foundation of China[41601403] ; National Natural Science Foundation of China[41571373] ; National Natural Science Foundation of China[41531174] ; National Key Research and Development Program of China[2016YFA0600103] ; CAS 'Light of West China' Program ; Youth Talent Team Program of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences[SDSQB-2015-02]
TOP
WOS Research AreaRemote Sensing ; Imaging Science & Photographic Technology
WOS SubjectRemote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000427181200011
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China ; CAS 'Light of West China' Program ; Youth Talent Team Program of Institute of Mountain Hazards and Environment, Chinese Academy of Sciences
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/22854
Collection数字山地与遥感应用中心
Corresponding AuthorLi, Ainong
AffiliationChinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Sichuan, Peoples R China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Corresponding Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Yin, Gaofei,Li, Ainong,Jin, Huaan,et al. Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2018,39(10):3287-3305.
APA Yin, Gaofei,Li, Ainong,Jin, Huaan,&Bian, Jinhu.(2018).Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series.INTERNATIONAL JOURNAL OF REMOTE SENSING,39(10),3287-3305.
MLA Yin, Gaofei,et al."Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series".INTERNATIONAL JOURNAL OF REMOTE SENSING 39.10(2018):3287-3305.
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