The paper introduces TIMESAT, a computer program designed for analyzing time-series data from satellite sensors, specifically focusing on Normalized Difference Vegetation Index (NDVI) data. The program employs three least-squares methods to process and smooth NDVI data, including an adaptive Savitzky-Golay filter and fits to harmonic and asymmetric Gaussian functions. These methods incorporate qualitative information from cloud contamination datasets to enhance the accuracy of seasonal parameter extraction. The program is tested using NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, successfully extracting spatially coherent images of seasonal parameters such as the beginnings and ends of growing seasons, seasonally integrated NDVI, and seasonal amplitudes. The methods are implemented in a FORTRAN90 program, TIMESAT, which can be used for various types of satellite-derived time-series data. The paper also discusses the methodology, implementation, and results of the program, highlighting its effectiveness in handling noisy data and extracting meaningful seasonal information.The paper introduces TIMESAT, a computer program designed for analyzing time-series data from satellite sensors, specifically focusing on Normalized Difference Vegetation Index (NDVI) data. The program employs three least-squares methods to process and smooth NDVI data, including an adaptive Savitzky-Golay filter and fits to harmonic and asymmetric Gaussian functions. These methods incorporate qualitative information from cloud contamination datasets to enhance the accuracy of seasonal parameter extraction. The program is tested using NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, successfully extracting spatially coherent images of seasonal parameters such as the beginnings and ends of growing seasons, seasonally integrated NDVI, and seasonal amplitudes. The methods are implemented in a FORTRAN90 program, TIMESAT, which can be used for various types of satellite-derived time-series data. The paper also discusses the methodology, implementation, and results of the program, highlighting its effectiveness in handling noisy data and extracting meaningful seasonal information.