Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data

doi:10.1007/s13253-018-00338-y

	Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data
	Johnson, Margaret 1,2; Caragea, Petruta C.3; Meiring, Wendy 4; Jeganathan, C.5; Atkinson, Peter M.6,7,8,9
刊名	JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
	2019-03-01
卷号	24 期号:1 页码:1-25
关键词	Land surface phenology Time series Uncertainty quantification
ISSN号	1085-7117
DOI	10.1007/s13253-018-00338-y
通讯作者	Johnson, Margaret(maggie.johnson510@gmail.com)
英文摘要	Estimating the timing of the occurrence of events that characterize growth cycles in vegetation from time series of remote sensing data is desirable for a wide area of applications. For example, the timings of plant life cycle events are very sensitive to weather conditions and are often used to assess the impacts of changes in weather and climate. Likewise, understanding crop phenology can have a large impact on agricultural strategies. To study phenology using remote sensing data, the timings of annual phenological events must be estimated from noisy time series that may have many missing values. Many current state-of-the-art methods consist of smoothing time series and estimating events as features of smoothed curves. A shortcoming of many of these methods is that they do not easily handle missing values and require imputation as a preprocessing step. In addition, while some currently used methods may be extendable to allow for temporal uncertainty quantification, uncertainty intervals are not usually provided with phenological event estimates. We propose methodology utilizing Bayesian dynamic linear models to estimate the timing of key phenological events from remote sensing data with uncertainty intervals. We illustrate the methodology on weekly vegetation index data from 2003 to 2007 over a region of southern India, focusing on estimating the timing of start of season and peak of greenness. Additionally, we present methods utilizing the Bayesian formulation and MCMC simulation of the model to estimate the probability that more than one growing season occurred in a given year. Supplementary materials accompanying this paper appear online.
资助项目	National Science Foundation[DMS-1638521]
WOS关键词	TIME-SERIES ; VEGETATION
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000458540200001
资助机构	National Science Foundation
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/49353]
专题	中国科学院地理科学与资源研究所
通讯作者	Johnson, Margaret
作者单位	1.North Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA 2.Stat & Appl Math Sci Inst, Durham, NC 27709 USA 3.Iowa State Univ, Dept Stat, Ames, IA USA 4.Univ Calif Santa Barbara, Dept Stat & Appl Probabil, Santa Barbara, CA 93106 USA 5.Birla Inst Technol, Dept Remote Sensing, Ranchi 835215, Jharkhand, India 6.Univ Lancaster, Fac Sci & Technol, Lancaster LA1 4YR, England 7.Queens Univ Belfast, Sch Nat & Built Environm, Belfast BT7 1NN, Antrim, North Ireland 8.Univ Southampton, Geog & Environm Sci, Southampton SO17 1BJ, Hants, England 9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Johnson, Margaret,Caragea, Petruta C.,Meiring, Wendy,et al. Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data[J]. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS,2019,24(1):1-25.
APA	Johnson, Margaret,Caragea, Petruta C.,Meiring, Wendy,Jeganathan, C.,&Atkinson, Peter M..(2019).Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data.JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS,24(1),1-25.
MLA	Johnson, Margaret,et al."Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data".JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 24.1(2019):1-25.