The software package TIMESAT was developed for estimating growing seasons from satellite time-series, as well as for computing phenological metrics from the data (Jönsson and Eklundh 2002, 2003, 2004, Eklundh and Jönsson 2003). TIMESAT iteratively fits smooth mathematical functions to time-series of noisy satellite data, and key phenological metrics (beginning and end of the growing season, length of the season, amplitude, integrated value, asymmetry of the season etc.) are extracted for each image pixel.

TIMESAT has been used in a number of applications, e.g. for mapping environmental and phenological changes in Africa from 1982 till today (Eklundh and Olsson 2003, Hickler et al. 2005, Olsson et al. 2005, Seaquist et al. 2006, Heumann et al. 2007, Seaquist et al. 2009), for improving data in ecosystem classification (Tottrup et al. 2007), for use with MSG SEVIRI data (Stisen et al. 2007), for mapping high-latitude forest phenology (Beck et al. 2007), for spatio-temporal patterns of growing seasons on Ireland (O’Connor et al. 2012) and to evaluate satellite and climate data-derived indices of fire risk in savanna ecosystems (Verbesselt et al. 2006).

We use TIMESAT as an integrated part in our development of carbon models based on data from Terra/MODIS (Olofsson and Eklundh 2007, Olofsson et al. 2007, 2008, Sjöström et al. 2009), and for analyzing relationships between NDVI of nemoboreal and boreal coniferous forests and models of conifer cold hardiness, budburst and photosynthetic efficiency. We also use TIMESAT with Terra/MODIS data in the development of systems for detection of forest disturbances, e.g. due to insect infestations (Eklundh et al. 2009).

A modified version of TIMESAT 2.3 is integrated in the processing of MODIS data into a phenology product (MOD09PHN and MOD15PHN) by the North American Carbon Program (Gao et al. 2008).

Hird and McDermid (2009) showed that the methods in TIMESAT have good performance, balancing the ability to reduce noise and the maintenance of signal integrity.

TIMESAT 3 is based on data values that are equally spaced in time. However, since many data products, such as MODIS NDVI or EVI composites, are composed of data from irregularly spaced observation dates, this may lead to errors in the timing of seasonal profiles and phenological parameters. The magnitude of this error was quantified by Testa et al. (2014). They also developed a technique based on interpolation for correcting the error. For more information please contact Stefano Testa (e-mail: laricetum.deciduae@gmail.com).

We have developed TIMESAT 4 to handled irregularly spaced data. To achieve robustness when fitting to data with gaps we changed the way logistic functions are fitted, including a fixed baseline (Jönsson et al. 2018). Several other improvements have been implemented and the new TIMESAT was applied to the generation of European high-resolution vegetation phenology and productivity data for Copernicus (HR-VPP), see Tian et al. (2021). TIMESAT 4 is undergoing final checks before we release it.

References

• Beck, P.S.A., Jönsson, P., Hogda, K.-A., Karlsen, S.R., Eklundh, L. and Skidmore, A.K., 2007, A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula, International Journal of Remote Sensing, 28, 4311-4330.
  
• Boyd D.S., Almond S., Dash J., Curran P.J., and Hill R.A., 2011, Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index (MTCI) data, International Journal of Remote Sensing, 32, 8421-8447.
  
• Eklundh L., Johansson, T., and Solberg, S., 2009, Mapping insect defoliation in Scots pine with MODIS time-series data . Remote sensing of Environment, 113, 1566-1573.
• Eklundh, L. and Jönsson, P., 2003, Extracting Information about Vegetation Seasons in Africa from Pathfinder AVHRR NDVI Imagery using Temporal Filtering and Least-Squares Fits to Asymmetric Gaussian Functions. In Image and Signal Processing for Remote Sensing VIII. Proceedings of SPIE Vol 4885, edited by Serpico. S.S. Society of Photo-Optical Instrumentation Engineers, pp. 215-225.
• Eklundh, L. and Olsson, L., 2003, Vegetation index trends for the African Sahel 1982-1999, Geophysical Research Letters, 30, 1430-1433.
• Gao, F., Morisette, J.T., Wolfe, R.E., Ederer, G., Pedelty, J., Masuoka, E., Myneni, R., Tan, B. and Nightingale, J., 2008, An algorithm to produce temporally and spatially continuous MODIS-LAI time series, IEEE Geoscience and Remote Sensing Letters, 5.
• Heumann, B.W., Seaquist, J.W., Eklundh, L. and Jönsson, P., 2007, AVHRR Derived Phenological Change in the Sahel and Soudan, Africa, 1982 - 2005, Remote Sensing of Environment, 108, 385-392.
• Hickler, T., Eklundh, L., Seaquist, J., Smith, B., Ardö, J., Olsson, L., Sykes, M. and Sjöström, M., 2005, Precipitation controls Sahel greening trend. Geophysical Research Letters, 32, L21415.
• Hird J.N., McDermid G.J., 2009, Noise reduction of NDVI time series: An empirical comparison of selected techniques, Remote Sensing of Environment, vol. 113, 248-258.
• Jönsson, P. and Eklundh, L., 2002, Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Transactions on Geoscience and Remote Sensing, 40, 1824-1832.
• Jönsson, P. and Eklundh, L., 2003, Seasonality extraction from time-series of satellite sensor data. In Frontiers of Remote Sensing Information Processing, edited by Chen. C.H. World Scientific Publishing, pp. 487-500.
• Jönsson, P. and Eklundh, L., 2004, TIMESAT - a program for analysing time-series of satellite sensor data, Computers and Geosciences, 30, 833-845.
• Jönsson, P., Cai, Z., Melaas, E., Friedl, M., & Eklundh, L., 2018, A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data. Remote Sensing, 10(4), 635. doi:10.3390/rs10040635
• O’Connor B., Dwyer E., Cawkwell F., Eklundh L., 2012, Spatio-temporal patterns in vegetation start of season across the island of Ireland using the MERIS Global Vegetation Index, ISPRS Journal of Photogrammetry and Remote Sensing, 68, 79-94.
• Olofsson, P. and Eklundh, L., 2007, Estimation of absorbed PAR across Scandinavia from satellite measurements. Part II: modeling and evaluating the fractional absorption, Remote Sensing of Environment, 110, 240- 251.
• Olofsson, P., Eklundh, L., Lagergren, F., Jönsson, P. and Lindroth, A., 2007, Estimating Net Primary Production for Scandinavian forests using data from Terra/MODIS, Advances in Space Research, 39, 125-130.
• Olofsson, P., Lagergren, F., Lindroth, A., Lindström, J., Klemedtsson, L., Kutsch, W., and Eklundh, L., 2008, Towards Operational Remote Sensing of Forest Carbon Balance across Northern Europe, Biogeosciences, 5, 817-832.
• Olsson, L., Eklundh, L. and Ardö, J., 2005, A recent greening of the Sahel—trends, patterns and potential causes, Journal of Arid Environments, 63, 556-566.
• Seaquist, J. W., Hickler, T., Eklundh, L., Ardö, J. and Heumann, B., 2009, Disentangling the effects of climate and people on sahel vegetation dynamics. Biogeosciences, 6, 469-477.
• Seaquist, J.W., Olsson, L., Ardö, J. and Eklundh, L., 2006, Broad-scale increase in NPP Quantified for the African Sahel, 1982-1999, International Journal of Remote Sensing, 27, 5115-5122.
• Sjöström M., Ardö J., Eklundh L., El-Tahir B.A., El-Khidir H.A.M., Pilesjö P. and Seaquist J., 2009, Evaluation of satellite based indices for primary production estimates in a sparse savanna in the Sudan. Biogeosciences, 6, 129-138.
• Stisen, S., Sandholt, I., Norgaard, A., Fensholt, R. and Eklundh, L., 2007, Estimation of diurnal air temperature using MSG SEVIRI data in West Africa, Remote Sensing of Environment, 110, 262- 274.
• Testa S., Borgogno Mondino E.C. and Pedroli C., 2014, Correcting MODIS 16-day composite NDVI time-series with actual acquisition dates. European Journal of Remote Sensing 47, 285-305.
Tian, F., Cai, Z., Jin, H., Hufkens, K., Scheifinger, H., Tagesson, T., . . . Eklundh, L. (2021). Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sensing of Environment, 260, 112456. doi:https://doi.org/10.1016/j.rse.2021.112456
• Tottrup, C., Schultz Rasmussen, M., Eklundh, L. and Jönsson, P., 2007, Using 250-meter spatial resolution MODIS data and regression tree modeling to map fractional land cover across the highlands of mainland Southeast Asia, International Journal of Remote Sensing, 28, 23-46.
• Verbesselt, J., Jönsson, P., Lhermitte, S., van Aardt, J. and Coppin, P., 2006, Evaluating satellite and climate data derived indices as fire risk indicators in savanna ecosystems. IEEE transactions of Geoscience and Remote Sensing, 44, 1622.