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Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data
Johnson, Margaret1,2; Caragea, Petruta C.3; Meiring, Wendy4; Jeganathan, C.5; Atkinson, Peter M.6,7,8,9
2019-03-01
Source PublicationJOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
ISSN1085-7117
Volume24Issue:1Pages:1-25
Corresponding AuthorJohnson, Margaret(maggie.johnson510@gmail.com)
AbstractEstimating 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.
KeywordLand surface phenology Time series Uncertainty quantification
DOI10.1007/s13253-018-00338-y
WOS KeywordTIME-SERIES ; VEGETATION
Indexed BySCI
Language英语
Funding ProjectNational Science Foundation[DMS-1638521]
Funding OrganizationNational Science Foundation
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiology ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000458540200001
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/49354
Collection中国科学院地理科学与资源研究所
Corresponding AuthorJohnson, Margaret
Affiliation1.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
Recommended Citation
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.
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