Spatio-temporal statistical methods for modelling land surface phenology

Kirsten M. De Beurs*, Geoffrey M. Henebry

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

247 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationPhenological Research
Subtitle of host publicationMethods for Environmental and Climate Change Analysis
EditorsIrene L. Hudson, Marie R. Keatley
PublisherSpringer
Chapter9
Pages177-208
Number of pages32
ISBN (Electronic)9789048133352
ISBN (Print)9789048133345
DOIs
Publication statusPublished - 2010
Externally publishedYes

Keywords

  • Derivatives
  • Model fit
  • Smoothing functions
  • Thermal time
  • Thresholds

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