Two methods for processing time-series of satellite sensor data are presented. The first method is based on an adaptive Savitzky-Golay filter, and the second on non-linear least-squares fits to asymmetric Gaussian model functions. Both methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting phenological parameters related to the growing seasons. The methods are applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index (NDVI) data over Africa giving spatially coherent images of phenological parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI, seasonal amplitudes etc. The results indicate that the two methods complement each other and that they may be suitable in different areas depending on the behavior of the NDVI signal.