气候变化领域集成服务门户(CCPortal): Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data

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DOI	10.1016/j.rse.2020.112190
	Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data
	Yang B.; Liu H.; Kang E.L.; Shu S.; Xu M.; Wu B.; Beck R.A.; Hinkel K.M.; Yu B.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	No single satellite remote sensing system is able to provide the observations on the Earth's surface at both high spatial and high temporal resolution due to the general trade-off between orbit revisit frequency and satellite sensor's spatial resolution. This paper presents a spatio-temporal Cokriging (ST-Cokriging) method for assimilating remote sensing data sets acquired by multiple remote sensing systems with different temporal sampling frequencies and different spatial resolutions. By extending the traditional Cokriging technique from a sole spatial domain to a spatio-temporal domain, we derived and implemented ST-Cokriging algorithm that explicitly takes the spatial covariance, temporal covariance and spatio-temporal covariance structures within and between different data sets into account. Compared with previous downscaling methods, such as, STARFM and FSDAF, our ST-Cokriging method produces more accurate and reliable assimilation results for the heterogeneous region, with associated uncertainty estimates. This method has been implemented into a software package using Python language within ArcGIS environment. The advantages and effectiveness of our ST-Cokriging method have been demonstrated through an application example, in which MODIS images (daily, 250 m and 500 m spatial resolution) and Landsat TM/ETM+ images (16 days revisit cycle, 30 m) are assimilated to generate daily spectral bands and NDVI images at 30 m spatial resolution. Our validation and accuracy assessments indicate that our ST-Cokriging method can effectively fill in data gaps due to clouds and generate reliable assimilation results and uncertainty estimates at both high spatial resolution and high temporal frequency © 2020 Elsevier Inc.
英文关键词	Cokriging; Data assimilation; Landsat; MODIS; Multi-spectral; Spatio-temporal modeling
语种	英语
scopus关键词	Computer software; Economic and social effects; Image resolution; Orbits; Reactor cores; Uncertainty analysis; Covariance structures; High spatial resolution; High temporal frequency; High temporal resolution; Remote sensing system; Satellite remote sensing systems; Spatio-temporal domains; Uncertainty estimates; Remote sensing; accuracy assessment; algorithm; ArcGIS; data assimilation; detection method; downscaling; kriging; Landsat thematic mapper; NDVI; remote sensing; satellite data; satellite imagery; spatial resolution; spatiotemporal 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/178975
作者单位	Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH 45221, United States; Department of Sociology, University of Central Florida, Orlando, FL 32816, United States; Department of Geography, University of Alabama, Tuscaloosa, AL 35487, United States; Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221, United States; Department of Geography and Planning, Appalachian State University, Boone, NC 28608, United States; Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China; School of Geographic Sciences, East China Normal University, Shanghai, 200241, China; College of Marine Science University of South Florida St. PetersburgFL 33701, United States
推荐引用方式 GB/T 7714	Yang B.,Liu H.,Kang E.L.,et al. Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data[J],2021,255.
APA	Yang B..,Liu H..,Kang E.L..,Shu S..,Xu M..,...&Yu B..(2021).Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data.Remote Sensing of Environment,255.
MLA	Yang B.,et al."Spatio-temporal Cokriging method for assimilating and downscaling multi-scale remote sensing data".Remote Sensing of Environment 255(2021).