TIMESAT - a program for analyzing time-series of satellite sensor data. Three different least-squares methods for processing time-series of satellite sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky–Golay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI and seasonal amplitudes. Based on general principles, the TIMESAT program can be used also for other types of satellite-derived time-series data.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Johnson, Margaret; Caragea, Petruţa C.; Meiring, Wendy; Jeganathan, C.; Atkinson, Peter M.: Bayesian dynamic linear models for estimation of phenological events from remote sensing data (2019)
- Victor Maus and Gilberto Câmara and Marius Appel and Edzer Pebesma: dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R (2019) not zbMATH
- Zhou, Zeng-Guang; Hu, Chang-Miao; Tang, Ping; Zhang, Zheng: Monitoring abrupt changes in satellite time series by seasonal confidence interval of regression residuals (2016)
- Bolin, David; Lindström, Johan; Eklundh, Lars; Lindgren, Finn: Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields (2009)