Satellite-derived NDVI time series are fundamental to the remote sensing of vegetation phenology, but their application is hindered by prevalent noise resulting chiefly from varying atmospheric conditions and sun-sensor-surface viewing geometries. A model-based empirical comparison of six selected NDVI time series noise reduction techniques revealed the general superiority of the Double Logistic and Asymmetric Gaussian function-fitting methods over four alternative filtering techniques. However, further analysis demonstrated a strong influence by noise level, strength and bias, and the extraction of phenological variables on technique performance. Users are therefore strongly cautioned to consider both their ultimate objectives and the nature of the noise present in an NDVI data set when selecting an approach to noise reduction, particularly when the derivation of phenological variables is a final goal. This work is crucial to improving current understandings of NDVI time series noise reduction and its role in the remote sensing of vegetation phenology.