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

Jönsson, Per and Eklundh, Lars LU (2002) In IEEE Transactions on Geoscience and Remote Sensing 40(8). p.1824-1832
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
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)
in
IEEE Transactions on Geoscience and Remote Sensing
volume
40
issue
8
pages
1824 - 1832
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • wos:000178334200015
  • scopus:0036699946
ISSN
0196-2892
DOI
10.1109/TGRS.2002.802519
language
English
LU publication?
yes
id
bd9045a5-a020-4969-b038-ae638f29dff8 (old id 326611)
date added to LUP
2007-10-31 13:54:42
date last changed
2017-12-10 04:25:21
@article{bd9045a5-a020-4969-b038-ae638f29dff8,
  abstract     = {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.},
  author       = {Jönsson, Per and Eklundh, Lars},
  issn         = {0196-2892},
  keyword      = {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)},
  language     = {eng},
  number       = {8},
  pages        = {1824--1832},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Geoscience and Remote Sensing},
  title        = {Seasonality extraction by function fitting to time-series of satellite sensor data},
  url          = {http://dx.doi.org/10.1109/TGRS.2002.802519},
  volume       = {40},
  year         = {2002},
}