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

Jonsson, P., Eklundh, L.·2002·IEEE Transactions on Geoscience and Remote Sensing

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.

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Metadata

Authors
Jonsson, P., & Eklundh, L.
Venue
IEEE Transactions on Geoscience and Remote Sensing
Published
2002-08
Volume
40
Issue
8
Pages
1824-1832
ISSN
0196-2892 (ISSN-L)
OpenAlex
W2113503197
Cited by
1,272
References
0
FWCI
4.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Seasonality extraction by function fitting to time-series of satellite sensor data. https://app.cochat.ai/papers/10.1109/tgrs.2002.802519
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Jonsson, P., & Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2002.802519

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.