Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology

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A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, methodology): “The choice of the smoothing gap filling or compositing method may have a large impact on the accuracy of the phenology extracted from the reconstructed time series (Hird and McDermid, 2009b; Atkinson et al., 2012).”
An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data (2016, background): “Both the asymmetric Gaussian algorithm and the double logistic function are modifications of local model functions, which have been proven to be effective in capturing phenological cycle events defined by NDVI curves (Atkinson et al., 2012).”
Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs (2013, background): “• principle component analysis [113]; • Fourier analysis [114,99]; • harmonic analysis [115]; • wavelet decomposition [12,116]; • and curve fitting [98,105,117].”