
Lars Eklundh
Professor

Seasonality extraction by function fitting to time-series of satellite sensor data
Author
Summary, in English
A new method for extracting seasonality information from time-series of satellite sensor data is presented. The method is based on nonlinear least squares fits of asymmetric Gaussian model functions to the time-series. The smooth model functions are then used for defining key seasonality parameters, such as the number of growing seasons, the beginning and end of the seasons, and the rates of growth and decline. The method is implemented in a computer program TIMESAT and tested on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data over Africa. Ancillary cloud data [clouds from AVHRR (CLAVR)] are used as estimates of the uncertainty levels of the data values. Being general in nature, the proposed method can be applied also to new types of satellite-derived time-series data.
Department/s
- Dept of Physical Geography and Ecosystem Science
Publishing year
2002
Language
English
Pages
1824-1832
Publication/Series
IEEE Transactions on Geoscience and Remote Sensing
Volume
40
Issue
8
Full text
- Available as PDF - 605 kB
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Document type
Journal article
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Atom and Molecular Physics and Optics
- Physical Geography
Keywords
- satellite sensor data
- seasonality
- TIMESAT
- time-series
- phenology
- vegetation index (NDVI)
- normalized difference
- function fitting
- data smoothing
- (CLAVR)
- clouds from AVHRR
- Advanced Very High Resolution Radiometer
- (AVHRR)
Status
Published
Project
- TIMESAT - software to analyze time-series of satellite sensor data
ISBN/ISSN/Other
- ISSN: 0196-2892