Noise reduction of NDVI time series: An empirical comparison of selected techniques

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 the strong influence of noise level, strength, and bias, and the extraction of phenological variables on technique performance. Users are 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 deriving phenological variables.