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.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. Beck, P.S.A., Jönsson, P., Hogda, K.-A., Karlsen, S.R., Eklundh, L.TIMESAT 4 is undergoing final checks before we release it. 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. To achieve robustness when fitting to data with gaps we changed the way logistic functions are fitted, including a fixed baseline (Jönsson et al. For more information please contact Stefano Testa (e-mail: have developed TIMESAT 4 to handled irregularly spaced data. They also developed a technique based on interpolation for correcting the error. The magnitude of this error was quantified by Testa et al. 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. TIMESAT 3 is based on data values that are equally spaced in time. 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. 2009).Ī 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. due to insect infestations (Eklundh et al. We also use TIMESAT with Terra/MODIS data in the development of systems for detection of forest disturbances, e.g. 2009), and for analyzing relationships between NDVI of nemoboreal and boreal coniferous forests and models of conifer cold hardiness, budburst and photosynthetic efficiency. 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. 2012) and to evaluate satellite and climate data-derived indices of fire risk in savanna ecosystems (Verbesselt et al. 2007), for spatio-temporal patterns of growing seasons on Ireland (O’Connor et al. ![]() 2007), for mapping high-latitude forest phenology (Beck et al. 2007), for use with MSG SEVIRI data (Stisen et al. 2009), for improving data in ecosystem classification (Tottrup et al. for mapping environmental and phenological changes in Africa from 1982 till today (Eklundh and Olsson 2003, Hickler et al. ![]() TIMESAT has been used in a number of applications, e.g. 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. 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).
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