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Comparative Analysis and Comprehensive Trade-Off of Four Spatiotemporal Fusion Models for NDVI Generation

文献类型：期刊论文

作者Hu, Yunfeng3,4; Wang, Hao3,4; Niu, Xiaoyu1,3,4; Shao, Wei2,3,4; Yang, Yichen3,4
刊名REMOTE SENSING
出版日期2022-12-01
卷号14期号:23页码:18
关键词accuracy assessment comparison image spatiotemporal fusion vegetation index
DOI10.3390/rs14235996
通讯作者Hu, Yunfeng(huyf@lreis.ac.cn)
英文摘要It is still difficult to obtain high-resolution and fast-updated NDVI data, and spatiotemporal fusion is an effective means to solve this problem. The purpose of this study is to carry out the comparative analysis and comprehensive trade-off of spatiotemporal fusion models for NDVI generation and to provide references for scholars in this field. In this study, four spatiotemporal fusion models (STARFM, ESTARFM, FSDAF, and GF-SG) were selected to carry out NDVI image fusion in grassland, forest, and farmland test areas, and three indicators of root mean square error (RMSE), average difference (AD), and edge feature richness difference (EFRD) were used. A detailed evaluation and analysis of the fusion results and comprehensive trade-off were carried out. The results show that: (1) all four models can predict fine-resolution NDVI images well, but the phenomenon of over-smoothing generally exists, which is more serious in high-heterogeneity areas; (2) GF-SG performed well in the evaluation of the three indicators, with the highest comprehensive trade-off score (CTS) of 0.9658. Followed by ESTARFM (0.9050), FSDAF (0.8901), and STARFM (0.8789); (3) considering the comparative analysis and comprehensive trade-off results of the three test areas and the three indicators, among the four models, GF-SG has the best accuracy in generating NDVI images. GF-SG is capable of constructing NDVI time series data with high spatial and temporal resolution.
WOS关键词LAND-SURFACE PHENOLOGY ; REFLECTANCE FUSION
资助项目National Key Research and Development Plan Program of China[2021YFD1300501] ; National Natural Science Foundation of China[41977421] ; Network Security and Information Program of the Chinese Academy of Sciences[CAS-WX2021SF-0106] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20010202]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000897327000001
出版者MDPI
资助机构National Key Research and Development Plan Program of China ; National Natural Science Foundation of China ; Network Security and Information Program of the Chinese Academy of Sciences ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/188139]  
专题中国科学院地理科学与资源研究所
通讯作者Hu, Yunfeng
作者单位1.Yangtze Univ, Sch Geosci, Wuhan 430100, Peoples R China
2.Fuzhou Univ, Acad Digital China Fujian, Fuzhou 350116, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Yunfeng,Wang, Hao,Niu, Xiaoyu,et al. Comparative Analysis and Comprehensive Trade-Off of Four Spatiotemporal Fusion Models for NDVI Generation[J]. REMOTE SENSING,2022,14(23):18.
APA Hu, Yunfeng,Wang, Hao,Niu, Xiaoyu,Shao, Wei,&Yang, Yichen.(2022).Comparative Analysis and Comprehensive Trade-Off of Four Spatiotemporal Fusion Models for NDVI Generation.REMOTE SENSING,14(23),18.
MLA Hu, Yunfeng,et al."Comparative Analysis and Comprehensive Trade-Off of Four Spatiotemporal Fusion Models for NDVI Generation".REMOTE SENSING 14.23(2022):18.

入库方式： OAI收割

来源：地理科学与资源研究所

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