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Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology

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Phenological Research

Abstract

This chapter surveys 12 different spatio-temporal statistical methods to determine the start and end of the growing season using a time series of satellite images. In the first section of the chapter, we divided the methods into four categories: thresholds, derivatives, smoothing functions, and fitted models. The general use, advantages, and potential limitations of each method are discussed. In the second section of the chapter, a case study is presented to highlight one method from each category. The four study areas range from the Northwest Territories in Canada to the winter wheat areas in south-central Kansas. We concluded the case study with a discussion of the differences in results for the four methods. The chapter is finished with a synopsis discussing the use of nomenclature, the problems with a lack of statistical error structure from most methods, and the perennial issue of oversmoothing.

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Acknowledgments

The GIMMS data were provided by Tucker, C.J., J.E. Pinzon, and M.E. Brown (2004), Global Inventory Modeling and Mapping Studies, Global Land Cover Facility, University of Maryland, College Park, Maryland. NCEP Reanalysis data were provided by the NOAA-CIRES Climate Diagnostics Center, Boulder, CO from their website at http://www.cdc.noaa.gov/. We would like to thank P. de Beurs for the application development that allowed us to estimate the land surface phenology model parameters more efficiently. This research was supported in part by a NASA LCLUC grant to GMH.

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Authors and Affiliations

  1. Department of Geography, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

    Kirsten M. de Beurs

  2. Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD, USA

    Geoffrey M. Henebry

Authors
  1. Kirsten M. de Beurs
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  2. Geoffrey M. Henebry
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Corresponding author

Correspondence to Kirsten M. de Beurs .

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Editors and Affiliations

  1. School of Mathematics & Statistics, University of South Australia, Adelaide, 5001, Australia

    Irene L. Hudson

  2. Melbourne School of Land & Environment, University of Melbourne, Water St., Creswick, 3363, Australia

    Marie R. Keatley

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de Beurs, K.M., Henebry, G.M. (2010). Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology. In: Hudson, I., Keatley, M. (eds) Phenological Research. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3335-2_9

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