气候变化领域集成服务门户(CCPortal): Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction

CCPortal

DOI	10.1016/j.rse.2020.112130
	Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction
	Zhou J.; Chen J.; Chen X.; Zhu X.; Qiu Y.; Song H.; Rao Y.; Zhang C.; Cao X.; Cui X.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	Dozens of spatiotemporal fusion methods have been developed to reconstruct vegetation index time-series data with both high spatial resolution and frequent coverage for monitoring land surface dynamics. Although several studies comparing the different fusion methods have been conducted, selecting the suitable fusion methods is still challenging, as inevitable influential factors tend to be neglected. To address this problem, this study compared six typical spatiotemporal fusion methods, including the Unmixing-Based Data Fusion (UBDF), Linear Mixing Growth Model (LMGM), Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), One Pair Dictionary-Learning method (OPDL), and Flexible Spatiotemporal DAta Fusion (FSDAF), based on simulation experiments and theoretical analysis considering three influential factors between sensors: geometric misregistration, radiometric inconsistency, and spatial resolution ratio. The results indicate that Fit-FC achieved the best performance with the strongest tolerance to geometric misregistration when radiometric inconsistency was negligible; thus, it is the first recommended algorithm for blending normalized difference vegetation index (NDVI) imagery. Instead, the FSDAF could generate the best results if radiometric inconsistency was non-negligible. These findings could help users determine the method that is appropriate for different remote sensing datasets, and provide guidelines for developers in the future development of novel methods. © 2020 Elsevier Inc.
英文关键词	Geometric misregistration; Normalized difference vegetation index (NDVI); Radiometric inconsistency; Spatial resolution ratio; Spatiotemporal fusion
语种	英语
scopus关键词	Blending; Data fusion; Image resolution; Radiometry; Regression analysis; Remote sensing; Time series; Vegetation; Comparative studies; Dictionary learning; High spatial resolution; Influential factors; Normalized difference vegetation index; Normalized difference vegetation index time series; Spatio-temporal data; Spatio-temporal fusions; Learning systems; comparative study; land surface; NDVI; reconstruction; remote sensing; satellite imagery; sensitivity analysis; spatial resolution; spatiotemporal analysis; time series analysis
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179066
作者单位	State Key Laboratory of Earth Surface Processes and Resource Ecology, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong; Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China; North Carolina State University, North Carolina Institute for Climate Studies, Asheville, NC 28805, United States
推荐引用方式 GB/T 7714	Zhou J.,Chen J.,Chen X.,et al. Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction[J],2021,252.
APA	Zhou J..,Chen J..,Chen X..,Zhu X..,Qiu Y..,...&Cui X..(2021).Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction.Remote Sensing of Environment,252.
MLA	Zhou J.,et al."Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction".Remote Sensing of Environment 252(2021).