气候变化领域集成服务门户(CCPortal): Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model

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DOI	10.1016/j.rse.2021.112484
	Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model
	Gao X.; Gray J.M.; Reich B.J.
发表日期	2021
ISSN	00344257
卷号	261
英文摘要	Land surface phenology (LSP) is a consistent and sensitive indicator of climate change effects on Earth's vegetation. Existing methods of estimating LSP require time series densities that, until recently, have only been available from coarse spatial resolution imagery such as MODIS (500 m) and AVHRR (1 km). LSP products from these datasets have improved our understanding of phenological change at the global scale, especially over the MODIS era (2001-present). Nevertheless, these products may obscure important finer scale spatial patterns and longer-term changes. Therefore, we have developed a Bayesian hierarchical model to retrieve complete annual sequences of LSP from Landsat imagery (1984-present), which has medium spatial resolution (30 m) but relatively sparse temporal frequency. Our approach uses Markov Chain Monte Carlo (MCMC) sampling to quantify individual phenometric uncertainty, which is especially important when considering long time series with variable observation quality and density, but has rarely been demonstrated. The estimated spring LSP had strong agreement with ground phenology records at Harvard Forest (R2 = 0.87) and Hubbard Brook Experimental Forest (R2 = 0.67). The estimated LSP were consistent with the recently released 30 m LSP product, MSLSP30NA, in its time period of 2016 to 2018 (R2 of 0.86 and 0.73 for spring and autumn phenology, respectively). Our Bayesian hierarchical model is an important step forward in extending medium resolution LSP records back in time as it accomplishes both critical goals of retrieving annual LSP from sparse time series and accurately estimating uncertainty. © 2021 The Authors
英文关键词	Bayesian; Data sparsity; Land surface phenology; Landsat; Long-term; Medium resolution; Plant phenology; Remote sensing; Time series
语种	英语
scopus关键词	Biology; Budget control; Climate change; Earth (planet); Forestry; Hierarchical systems; Image enhancement; Image resolution; Radiometers; Remote sensing; Surface measurement; Uncertainty analysis; Bayesian; Bayesian hierarchical model; Data sparsity; Land surface phenology; LANDSAT; Long-term; Medium resolution; Plant phenology; Remote-sensing; Times series; Time series; Indicator indicator
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/178819
作者单位	Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, United States; Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, United States; Department of Statistics, North Carolina State University, Raleigh, NC 27695, United States
推荐引用方式 GB/T 7714	Gao X.,Gray J.M.,Reich B.J.. Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model[J],2021,261.
APA	Gao X.,Gray J.M.,&Reich B.J..(2021).Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model.Remote Sensing of Environment,261.
MLA	Gao X.,et al."Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model".Remote Sensing of Environment 261(2021).