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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/259913
- author
- Jönsson, Per
LU
and Eklundh, Lars
LU
- organization
- publishing date
- 2004
- 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 Press Ltd.
- external identifiers
-
- wos:000225367100004
- scopus:5144233854
- ISSN
- 1873-7803
- DOI
- 10.1016/j.cageo.2004.05.006
- project
- TIMESAT - software to analyze time-series of satellite sensor data
- language
- English
- LU publication?
- yes
- id
- 6c6391c2-1d30-4311-8cbc-5092ae9a369c (old id 259913)
- date added to LUP
- 2016-04-01 11:40:46
- date last changed
- 2022-04-20 19:57:46
@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}},
keywords = {{NOAA; TIMESAT; phenology; seasonality; function fitting; data smoothing; CLAVR; NDVI; AVHRR}},
language = {{eng}},
number = {{8}},
pages = {{833--845}},
publisher = {{Pergamon Press Ltd.}},
series = {{Computers & Geosciences}},
title = {{TIMESAT - a program for analyzing time-series of satellite sensor data}},
url = {{https://lup.lub.lu.se/search/files/2591482/2374986.pdf}},
doi = {{10.1016/j.cageo.2004.05.006}},
volume = {{30}},
year = {{2004}},
}