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

Summary, in 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.

• Author: Per Jönsson; Lars Eklundh
• Department/s: Dept of Physical Geography and Ecosystem Science
• Publishing year: 2002
• Language: English
• Pages: 1824-1832
• Publication/Series: IEEE Transactions on Geoscience and Remote Sensing
• Volume: 40
• Issue: 8
• Full text: - 605 kB; Download statistics
• Links: http://dx.doi.org/10.1109/TGRS.2002.802519
• Document type: Journal article
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: Atom and Molecular Physics and Optics; Physical Geography
• 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)
• Status: Published
• Project: TIMESAT - software to analyze time-series of satellite sensor data
• ISBN/ISSN/Other: ISSN: 0196-2892