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

  • Margaret JohnsonEmail author
  • Petruţa C. Caragea
  • Wendy Meiring
  • C. Jeganathan
  • Peter M. Atkinson
Article
  • 106 Downloads

Abstract

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.

Keywords

Land surface phenology Time series Uncertainty quantification 

Notes

Acknowledgements

We are grateful to the NERC Earth Observation Data Centre for providing the MTCI data. We thank the Editor, Associate Editor, and Referee for valuable comments that led to substantial improvements in the paper. This work was partially supported by the National Science Foundation (DMS-1638521).

Supplementary material

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Copyright information

© International Biometric Society 2018

Authors and Affiliations

  1. 1.Department of StatisticsNorth Carolina State UniversityRaleighUSA
  2. 2.The Statistical and Applied Mathematical Sciences InstituteDurhamUSA
  3. 3.Department of StatisticsIowa State UniversityAmesUSA
  4. 4.Department of Statistics and Applied ProbabilityUniversity of California, Santa BarbaraSanta BarbaraUSA
  5. 5.Department of Remote SensingBirla Institute of Technology (BIT), MesraRanchiIndia
  6. 6.Faculty of Science and TechnologyLancaster UniversityLancasterUK
  7. 7.School of Natural and Built EnvironmentQueen’s University BelfastBelfastNorthern Ireland, UK
  8. 8.Geography and Environmental ScienceUniversity of Southampton, HighfieldSouthamptonUK
  9. 9.Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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