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Seasonality extraction by function fitting to time-series of satellite sensor data

Author:
  • Per Jönsson
  • Lars Eklundh
Publishing year: 2002
Language: English
Pages: 1824-1832
Publication/Series: IEEE Transactions on Geoscience and Remote Sensing
Volume: 40
Issue: 8
Document type: Journal article
Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.

Abstract 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.

Keywords

  • Atom and Molecular Physics and Optics
  • Physical Geography
  • 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)

Other

Published
  • TIMESAT - software to analyze time-series of satellite sensor data
  • ISSN: 0196-2892
E-mail: lars [dot] eklundh [at] nateko [dot] lu [dot] se

Professor

Dept of Physical Geography and Ecosystem Science

+46 46 222 96 55

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Teaching staff

Dept of Physical Geography and Ecosystem Science

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Department of Physical Geography and Ecosystem Science
Lund University
Sölvegatan 12
S-223 62 Lund
Sweden

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