| 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
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| 2018 | |
| Source Publication | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
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| ISSN | 0924-2716 |
| EISSN | 1872-8235 |
| Volume | 144Pages:189-201 |
| Subtype | Article |
| Contribution Rank | 1 |
| Abstract | With 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. |
| Keyword | VIIRS Temporal compositing Adaptive Global SA-Comp Clear-sky |
| DOI | 10.1016/j.isprsjprs.2018.07.009 |
| Indexed By | SCI |
| WOS Keyword | REMOTE-SENSING DATA ; HJ-1A/B CONSTELLATION ; LANDSAT IMAGES ; CLOUD SHADOW ; PLUS IMAGES ; VEGETATION ; MODIS ; MODEL ; INTERPOLATION ; RESOLUTION |
| Language | 英语 |
| Quartile | 1区 |
| Funding Project | Strategic 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 | 是 |
| WOS Research Area | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000447109900014 |
| Funding Organization | Strategic 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 |
| Publisher | ELSEVIER SCIENCE BV |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imde.ac.cn/handle/131551/23465 |
| Collection | 数字山地与遥感应用中心 |
| Corresponding Author | Li, Ainong |
| Affiliation | 1.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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| A self-adaptive appr（7083KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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