TIMESAT for Processing Time-Series Data from Satellite Sensors for Land Surface Monitoring

  • Lars EklundhEmail author
  • Per Jönsson
Part of the Remote Sensing and Digital Image Processing book series (RDIP, volume 20)


The TIMESAT software package has been developed to enable monitoring of dynamic land surface processes using remotely sensed data. The monitoring capability is based on processing of time-series for each image pixel using either of three smoothing methods included in TIMESAT: asymmetric Gaussian fits, double-logistic fits, and Savitzky-Golay filtering. The methods have different properties and are suitable for a wide range of data with different character and noise properties. The fitting methods can be upper-envelope weighted and can take quality data into account. Based on the fitted functions, growing season parameters are then extracted (beginning, end, amplitude, slope, integral, etc.), and can be merged into images. TIMESAT has been used in a number of application fields: mapping of phenology and phenological variations; ecological disturbances; vegetation classification and characterization; agriculture applications; climate applications; and for improving remote sensing signal quality. Future developments of TIMESAT will include new methods to better handle long gaps in time-series, handling of irregular time sampling, improved smoothing methods, and incorporation of the spatial domain. These modifications will enable use of TIMESAT also for high-resolution data, e.g. data from the planned ESA Sentinel-2 satellite.


Normalize Difference Vegetation Index Leaf Area Index Gross Primary Productivity Land Cover Class Smoothing Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors want to thank the many TIMESAT users, colleagues, and students who have contributed with constructive suggestions and ideas for improvement. We also thank the Swedish National Space Board, the Crafoord foundation, and the Krapperup foundation for financial support.


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Physical Geography and Ecosystem ScienceLund UniversityLundSweden
  2. 2.Group for Materials Science and Applied MathematicsMalmö UniversityMalmöSweden

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