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A self-adaptive approach for producing clear-sky composites from VIIRS surface reflectance datasets
Bian, Jinhu1,2,3; Li, Ainong1; Huang, Chengquan3; Zhang, Rui3; Zhan, Xiwu4
2018
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
EISSN1872-8235
Volume144Pages:189-201
SubtypeArticle
Contribution Rank1
AbstractWith the launch of the Joint Polar Satellite System (JPSS)/Soumi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, the need for the operational monitoring of terrestrial processes at the regional and global scales led to the expansion of terrestrial remote sensing products (e.g., the clear-sky composited surface reflectance products) generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite (VIIRS) data. Seamless cloud composites are usually generated using a single criterion without an explicit consideration of phenological variations among different surface types. However, because the spectral signals of many surface types change dramatically due to seasonal variations, the single-criterion compositing methods are only effective for specific surface cover conditions. This study proposed a new self-adaptive compositing approach (SA-Comp) to produce global terrestrial clear-sky VIIRS surface reflectance composites. The proposed approach employs contextual spectral and temporal information to determine the surface cover conditions within a pre-defined temporal window, and adaptively selects the most suitable criterion. A comprehensive evaluation of the SA-Comp approach was conducted by comparing it with the maximum NDVI (MaxNDVI), minimum Red (MinRed) and maximum ratio (MaxRatio) compositing schemes, and with the MODIS and VIIRS composited surface reflectance products. The results, including visual representations and temporal profiles, revealed that the SA-Comp approach outperformed all of the other methods. The results also highlighted that the SA-Comp approach is more feasible and effective at compositing global VIIRS data and has great potential for regional, national and even global terrestrial monitoring.
KeywordVIIRS Temporal compositing Adaptive Global SA-Comp Clear-sky
DOI10.1016/j.isprsjprs.2018.07.009
Indexed BySCI
WOS KeywordREMOTE-SENSING DATA ; HJ-1A/B CONSTELLATION ; LANDSAT IMAGES ; CLOUD SHADOW ; PLUS IMAGES ; VEGETATION ; MODIS ; MODEL ; INTERPOLATION ; RESOLUTION
Language英语
Quartile1区
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19030303] ; National Natural Science Foundation project of China[41701432] ; National Natural Science Foundation project of China[41631180] ; National Natural Science Foundation project of China[41571373] ; National Key Research and Development Program of China[2016YFA0600103] ; National Key Research and Development Program of China[2016YFC0500201-06] ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS[SDS-135-1708] ; CAS
TOP
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000447109900014
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences (CAS) ; National Natural Science Foundation project of China ; National Key Research and Development Program of China ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS ; CAS
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/23465
Collection数字山地与遥感应用中心
Corresponding AuthorLi, Ainong
Affiliation1.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
4.NOAA, Ctr Satellite Applicat & Res, NESDIS, College Pk, MD USA
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
Bian, Jinhu,Li, Ainong,Huang, Chengquan,et al. A self-adaptive approach for producing clear-sky composites from VIIRS surface reflectance datasets[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2018,144:189-201.
APA Bian, Jinhu,Li, Ainong,Huang, Chengquan,Zhang, Rui,&Zhan, Xiwu.(2018).A self-adaptive approach for producing clear-sky composites from VIIRS surface reflectance datasets.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,144,189-201.
MLA Bian, Jinhu,et al."A self-adaptive approach for producing clear-sky composites from VIIRS surface reflectance datasets".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 144(2018):189-201.
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