Reconstructing cloudfree NDVI composites using Fourier analysis of time series

By G. Roerink, M. Menenti, W. Verhoef. 2000

642 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/6b00562c390f1f22e11fd1c8f68eac6de40b8c2c

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Citations in the corpus

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling (2007, methodology): “To derive the state variable at model time step, different interpolation techniques, such as linear interpolation, fast Fourier transformations (Roerink et al., 2000) and wavelet approaches, are used to fill the gaps between two observations.”
Seasonality extraction by function fitting to time-series of satellite sensor data (2002, neutral): “lines, the HANTS method was developed [24].”
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter (2004, methodology): “, 1992); (2) Fourier-based fitting methods (Cihlar, 1996; Roerink et al., 2000; Sellers et al., 1994); and (3) asymmetric function fitting methods such as the asymmetric Gaussian function fitting approach (Jonsson & Eklundh, 2002) and the weighted least-squares linear regression approach (Swets et a”
Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology (2012, background): “…curve fitting (Bradley et al., 2007), moving average (White et al., 2009), Fourier decomposition (Jakubauskas et al., 2001; Moody & Johnson, 2001; Roerink et al., 2000; Verhoef et al., 1996; Wagenseil & Samimi, 2006), logistic function (Beck et al., 2006; Zhang et al., 2003), asymmetric Gaussian…”
Noise reduction of NDVI time series: An empirical comparison of selected techniques (2009, background): “The literature contains reference to a broad variety of strategies designed to reduce the impacts of these issues (e.g., Beck et al., 2006, 2007; Bradley et al., 2007; Holben, 1986; Jönsson & Eklundh, 2002, 2004; Ma & Veroustraete, 2006; Moody & Johnson, 2001; Roerink et al., 2000; Sellers et al., 1”
Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness (2007, background): “Roerink et al. (2000) adapted a variation of harmonic analysis to identify and reject cloud-affected NDVI data for a year long sequence of European observations, concluding that only the mean value and two annual harmonics (T51 year and T56 months) had significant amplitudes in their data.”
Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015, methodology): “Similarly, the harmonic analysis of time series (HANTS) method was proposed in [70], and has been successfully used in reconstructing cloud-free NDVI composites [50].”
Remote Sensing and Cropping Practices: A Review (2018, background): “However, these data raise new issues, and techniques have to be developed to handle irregularly-spaced time series and to identify and remove cloud contaminated observations, as found in the Harmonic Analysis of Time-Series (HANTS) [213], or the temporal gap-filling and resampling method in the iota”
Harmonic regression of Landsat time series for modeling attributes from national forest inventory data (2018, methodology): “Other harmonic regression methods have also been developed to produce cloud-free AVHRR images (Roerink et al., 2000).”
Copernicus Global Land Cover Layers - Collection 2 (2020, methodology): “Temporal descriptors were derived through a harmonic model, fitted through the time steps of each of the VI’s based on a Fourier transformation [30,32].”