The paper presents a program called TIMESAT for analyzing time-series of satellite sensor data. The program uses three different least-squares methods to process satellite data. The first method uses local polynomial functions and is classified as an adaptive Savitzky–Golay filter. The other two methods use harmonic and asymmetric Gaussian functions for fitting data. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used to extract seasonal parameters related to the growing seasons. The program is implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index (NDVI) 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. The methods are tested with the 8 km × 8 km pixel resolution Pathfinder AVHRR Land (PAL) data set generated by NASA/NOAA. The program uses an adaptive Savitzky–Golay filtering method and, optionally, newly developed methods based on upper envelope weighted fits to harmonic and asymmetric Gaussian model functions. The program is tested with the 8 km × 8 km pixel resolution Pathfinder AVHRR Land (PAL) data set generated by NASA/NOAA. 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. The program is tested with the 8 km × 8 km pixel resolution Pathfinder AVHRR Land (PAL) data set generated by NASA/NOAA. 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.The paper presents a program called TIMESAT for analyzing time-series of satellite sensor data. The program uses three different least-squares methods to process satellite data. The first method uses local polynomial functions and is classified as an adaptive Savitzky–Golay filter. The other two methods use harmonic and asymmetric Gaussian functions for fitting data. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used to extract seasonal parameters related to the growing seasons. The program is implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index (NDVI) 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. The methods are tested with the 8 km × 8 km pixel resolution Pathfinder AVHRR Land (PAL) data set generated by NASA/NOAA. The program uses an adaptive Savitzky–Golay filtering method and, optionally, newly developed methods based on upper envelope weighted fits to harmonic and asymmetric Gaussian model functions. The program is tested with the 8 km × 8 km pixel resolution Pathfinder AVHRR Land (PAL) data set generated by NASA/NOAA. 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. The program is tested with the 8 km × 8 km pixel resolution Pathfinder AVHRR Land (PAL) data set generated by NASA/NOAA. 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.