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Bayesian dynamic linear models for estimation of phenological events from remote sensing data

Bayesian dynamic linear models for estimation of phenological events from remote sensing data
Bayesian dynamic linear models for estimation of phenological events from remote sensing data

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.

Land surface phenology, Time series, Uncertainty quantification
1085-7117
1-25
Johnson, Margaret
722ce22b-1571-4753-bec0-9829e8ce7e92
Caragea, Petruţa C.
9c6dbf72-5d76-4b68-84c5-2fb582aa638d
Meiring, Wendy
5e476b82-cdaf-4df4-af04-36285612dbe5
Jeganathan, C.
f859ef31-fe01-4623-9ebf-d78466c974ca
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Johnson, Margaret
722ce22b-1571-4753-bec0-9829e8ce7e92
Caragea, Petruţa C.
9c6dbf72-5d76-4b68-84c5-2fb582aa638d
Meiring, Wendy
5e476b82-cdaf-4df4-af04-36285612dbe5
Jeganathan, C.
f859ef31-fe01-4623-9ebf-d78466c974ca
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Johnson, Margaret, Caragea, Petruţa C., Meiring, Wendy, Jeganathan, C. and 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. (doi:10.1007/s13253-018-00338-y).

Record type: Article

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.

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More information

Accepted/In Press date: 17 October 2018
e-pub ahead of print date: 5 November 2018
Published date: March 2019
Keywords: Land surface phenology, Time series, Uncertainty quantification

Identifiers

Local EPrints ID: 428144
URI: https://eprints.soton.ac.uk/id/eprint/428144
ISSN: 1085-7117
PURE UUID: 05c35d9c-61ba-4eeb-86e0-13097eb5b518

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Date deposited: 12 Feb 2019 17:30
Last modified: 12 Feb 2019 17:30

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Contributors

Author: Margaret Johnson
Author: Petruţa C. Caragea
Author: Wendy Meiring
Author: C. Jeganathan
Author: Peter M. Atkinson

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