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High-quality time series of remote sensing data are needed for long-term global change studies. Since newer sensors such as MODIS provide pixel-level data quality indicators, these datasets can be employed to filter time series and interpolate invalid data with statistical or contextual methodologies. This study presents a novel automated technique for time-series generation using ranked data quality indicators and stepwise temporal interpolation of short data gaps. The methodology focuses exclusively on the temporal characteristics of each pixel as they would have been observed with good observations. The methodology is exemplarily applied to MODIS NDVI data of the entire country of Germany. Multiple time series, also those generated with other techniques, were compared with a reference set to evaluate the performance of selected parameters. The automated time-series generation approach is less time consuming, and, if parameters are specified with care, the quality is comparable to other approaches.