ABSTRACT
Time series provide the possibility to monitor interannual and intra-annual processes of the Earth’s surface. Annual cycles of vegetative activity are used for phenological analysis (Asner et al., 2000), crop monitoring (Tottrup and Rasmussen, 2004), or estimating net primary productivity (Running et al., 2000). Changes or modifications of these cycles due to droughts (Tucker et al., 1994), El Niño events (Anyamba et al., 2002), or human impacts (de Beurs and Henebry, 2004) are observed with multi annual time series mostly using vegetation indices such as NDVI from the AVHRR sensor. Climate modeling, change detection studies, and other applications in the framework of global change require high-quality time series with a standardized, consistent, and reliable time series generation process (Sellers et al., 1996; Justice et al., 2002). Therefore, the quality of the time series determines its usability for long-term analysis.
