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TIMESAT - a program for analyzing time-series of satellite sensor data

Jönsson, Per LU and Eklundh, Lars LU (2004) In Computers & Geosciences 30(8). p.833-845
Abstract
Three different least-squares methods for processing time-series of satellite sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky-Golay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving... (More)
Three different least-squares methods for processing time-series of satellite sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky-Golay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI and seasonal amplitudes. Based on general principles, the TIMESAT program can be used also for other 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
NOAA, TIMESAT, phenology, seasonality, function fitting, data smoothing, CLAVR, NDVI, AVHRR
in
Computers & Geosciences
volume
30
issue
8
pages
833 - 845
publisher
Pergamon
external identifiers
  • wos:000225367100004
  • scopus:5144233854
ISSN
1873-7803
DOI
10.1016/j.cageo.2004.05.006
language
English
LU publication?
yes
id
6c6391c2-1d30-4311-8cbc-5092ae9a369c (old id 259913)
date added to LUP
2007-10-23 13:50:29
date last changed
2017-12-17 03:20:17
@article{6c6391c2-1d30-4311-8cbc-5092ae9a369c,
  abstract     = {Three different least-squares methods for processing time-series of satellite sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky-Golay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI and seasonal amplitudes. Based on general principles, the TIMESAT program can be used also for other types of satellite-derived time-series data.},
  author       = {Jönsson, Per and Eklundh, Lars},
  issn         = {1873-7803},
  keyword      = {NOAA,TIMESAT,phenology,seasonality,function fitting,data smoothing,CLAVR,NDVI,AVHRR},
  language     = {eng},
  number       = {8},
  pages        = {833--845},
  publisher    = {Pergamon},
  series       = {Computers & Geosciences},
  title        = {TIMESAT - a program for analyzing time-series of satellite sensor data},
  url          = {http://dx.doi.org/10.1016/j.cageo.2004.05.006},
  volume       = {30},
  year         = {2004},
}