气候变化领域集成服务门户(CCPortal): A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion

CCPortal

DOI	10.1109/TGRS.2020.2973762
	A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion
	Chen, Yang; Cao, Ruyin; Chen, Jin; Zhu, Xiaolin; Zhou, Ji; Wang, Guangpeng; Shen, Miaogen; Chen, Xuehong; Yang, Wei
通讯作者	Cao, RY (通讯作者)
发表日期	2020
ISSN	0196-2892
EISSN	1558-0644
起始页码	5179
结束页码	5194
卷号	58 期号:7
英文摘要	Spatiotemporal data fusion is a methodology to generate images with both high spatial and temporal resolution. Most spatiotemporal data fusion methods generate the fused image at a prediction date based on pairs of input images from other dates. The performance of spatiotemporal data fusion is greatly affected by the selection of the input image pair. There are two criteria for selecting the input image pair: the similarity criterion, in which the image at the base date should be as similar as possible to that at the prediction date, and the consistency criterion, in which the coarse and fine images at the base date should be consistent in terms of their radiometric characteristics and imaging geometry. Unfortunately, the consistency criterion has not been quantitatively considered by previous selection strategies. We thus develop a novel method (called cross-fusion) to address the issue of the determination of the base image pair. The new method first chooses several candidate input image pairs according to the similarity criterion and then takes the consistency criterion into account by employing all of the candidate input image pairs to implement spatiotemporal data fusion between them. We applied the new method to MODIS-Landsat Normalized Difference Vegetation Index (NDVI) data fusion. The results show that the cross-fusion method performs better than four other selection strategies, with lower average absolute difference (AAD) values and higher correlation coefficients in various vegetated regions including a deciduous forest in Northeast China, an evergreen forest in South China, cropland in North China Plain, and grassland in the Tibetan Plateau. We simulated scenarios for the inconsistency between MODIS and Landsat data and found that the simulated inconsistency is successfully quantified by the new method. In addition, the cross-fusion method is less affected by cloud omission errors. The fused NDVI time-series data generated by the new method tracked various vegetation growth trajectories better than previous selection strategies. We expect that the cross-fusion method can advance practical applications of spatiotemporal data fusion technology.
关键词	TIME-SERIES DATA MULTITEMPORAL MODIS SURFACE-TEMPERATURE REFLECTANCE FUSION BLENDING LANDSAT CLOUD SHADOW ALGORITHM PRODUCTS FRAMEWORK DYNAMICS
英文关键词	Earth; Artificial satellites; Remote sensing; MODIS; Spatiotemporal phenomena; Data integration; Spatial resolution; Landsat normalized difference vegetation index (NDVI); MODIS-Landsat; NDVI time series; spatiotemporal fusion; VIIRS NDVI
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000543775800056
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259981
推荐引用方式 GB/T 7714	Chen, Yang,Cao, Ruyin,Chen, Jin,et al. A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion[J]. 中国科学院青藏高原研究所,2020,58(7).
APA	Chen, Yang.,Cao, Ruyin.,Chen, Jin.,Zhu, Xiaolin.,Zhou, Ji.,...&Yang, Wei.(2020).A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58(7).
MLA	Chen, Yang,et al."A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58.7(2020).