气候变化领域集成服务门户(CCPortal): Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud

CCPortal

DOI	10.1016/j.rse.2020.111901
	Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud
	Moreno-Martínez Á.; Izquierdo-Verdiguier E.; Maneta M.P.; Camps-Valls G.; Robinson N.; Muñoz-Marí J.; Sedano F.; Clinton N.; Running S.W.
发表日期	2020
ISSN	00344257
卷号	247
英文摘要	Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution (30 m) observations over land. Our approach uses images from the Landsat (30 m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500 m spatial resolution and daily revisit cycle). We implement a bias-aware Kalman filter method in the Google Earth Engine (GEE) platform to obtain fused images at the Landsat spatial-resolution. The added bias correction in the Kalman filter estimates accounts for the fact that both model and observation errors are temporally auto-correlated and may have a non-zero mean. This approach also enables reliable estimation of the uncertainty associated with the final reflectance estimates, allowing for error propagation analyses in higher level remote sensing products. Quantitative and qualitative evaluations of the generated products through comparison with other state-of-the-art methods confirm the validity of the approach, and open the door to operational applications at enhanced spatio-temporal resolutions at broad continental scales. © 2020 The Authors
英文关键词	Data fusion; Gap filling; Kalman filter; Landsat; MODIS; Smoothing
语种	英语
scopus关键词	Image resolution; Kalman filters; Reflection; Uncertainty analysis; Error propagation analysis; High resolution sensors; Model and observation; Operational applications; Qualitative evaluations; Spatial and temporal resolutions; Spatio-temporal resolution; State-of-the-art methods; Remote sensing; aerosol; algorithm; Aqua (satellite); cloud; Kalman filter; MODIS; remote sensing; satellite sensor; smoothing; spatial resolution; spectral analysis; spectral resolution; Terra (satellite); uncertainty 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/179222
作者单位	Image Processing Laboratory (IPL), Universitat de València, València, Spain; Numerical Terradynamic Simulation Group (NTSG), WA Franke College of Forestry and Conservation, University of Montana, Missoula, United States; Institute of Geomatics, University of Natural Resources and Life Sciences, Wien, Austria; Department of Geosciences, University of Montana, United States; Department of Ecosystem and Conservation Sciences, WA Franke College of Forestry and Conservation, University of Montana, United States; Panthera, New York, NY, United States; Department of Geographical Sciences, University of Maryland, College Park, United States; Google, Inc., Mountain View, CA, United States
推荐引用方式 GB/T 7714	Moreno-Martínez Á.,Izquierdo-Verdiguier E.,Maneta M.P.,et al. Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud[J],2020,247.
APA	Moreno-Martínez Á..,Izquierdo-Verdiguier E..,Maneta M.P..,Camps-Valls G..,Robinson N..,...&Running S.W..(2020).Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud.Remote Sensing of Environment,247.
MLA	Moreno-Martínez Á.,et al."Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud".Remote Sensing of Environment 247(2020).