Spatio-temporal statistical methods for modelling land surface phenology
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 fitt...
| Main Authors: | , |
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| Format: | Book Part |
| Language: | English |
| Published: |
Springer
2010
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| Subjects: | |
| Online Access: | https://research.wur.nl/en/publications/spatio-temporal-statistical-methods-for-modelling-land-surface-ph https://doi.org/10.1007/978-90-481-3335-2_9 |
| Summary: | 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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