TIMESAT - a program for analyzing time-series of satellite sensor data

By Per Jönsson, Lars Eklundh. Computational Geosciences, 2004

1,851 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/27defb1bc27e64c6c6db48693b073579b28bb21c

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Citations in the corpus

Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, methodology): “Concerning the phenological parameters, Jönsson & Eklundh (2002, 2004„) developed the TIMESAT software, which extracts nine phenological parameters from NDVI profile, such as the beginning, the peak, or the length of the season.”
An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data (2016, methodology): “The first three algorithms have been widely used for remote sensing time-series data analysis (Jönsson & Eklundh, 2002, 2004).”
Mapping cropland-use intensity across Europe using MODIS NDVI time series (2016, methodology): “To identify double-cropping, we counted the number vegetation peaks per growing season using TIMESAT (Jönsson and Eklundh 2004, Li et al 2014), which detects double peaks based on the amplitude ratio between the primary and the secondary peak (Jönsson and Eklundh 2004).”
Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness (2007, methodology): “Their approach has been refined and recently applied to significant advantage by Jonsson and Eklundh (2002, 2004).”
Noise reduction of NDVI time series: An empirical comparison of selected techniques (2009, methodology): “These findings support the conclusions drawn by Jönsson and Eklundh (2002, 2004) and Beck et al. (2006).”
Applications of Remote Sensing to Alien Invasive Plant Studies (2009, background): “algorithm may be required to filter out noise [81].”
Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data (2016, background): “…and minimize spurious signals (e.g., cloud and snow): (1) implementing the Savitzky–Golay filter to smooth the NDVI3g time series (Chen et al 2004, Jönsson and Eklundh 2004); (2) identifying background NDVI and replacing NDVI that varied irregularly during the winter period (e.g., Beck et al…”
Self-Attention for Raw Optical Satellite Time Series Classification (2019, background): “Similarly, [16, 17] fit non-symmetric Gaussian curves to satellite time series.”
Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends (2015, methodology): “…information, used to mask the monthly ET between 1982 and 2010, was first determined from the global inventory modeling andmapping studies normalized difference vegetation index (NDVI3g) dataset (Pinzon and Tucker 2014) using a Savitzky–Golay filter (Chen et al 2004, Jonsson and Eklundh 2004).”
Mapping the extent of abandoned farmland in Central and Eastern Europe using MODIS time series satellite data (2013, methodology): “We applied a Savitsky–Golay filter to the time series (2003–2009) and used TIMESAT 2.3 (Jönsson and Eklundh 2004) to calculate the (1) start of the growing cycle, (2) end of the growing cycle, (3) base NDVI, (4) maximum NDVI, (5) length of the growing cycle, and (6) center of the growing cycle for…”
Timing of seasonal migration in mule deer: effects of climate, plant phenology, and life‐history characteristics (2011, methodology): “To describe annual deviations in patterns of green-up and senescence, we used program TIMESAT (Jönsson and Eklundh 2004) to calculate variables derived from NDVI data including: Julian date of onset of spring and onset of autumn, rate of increase in NDVI at the beginning of the season, rate of…”