Publication Date:
2022-05-26
Description:
© The Author(s), 2016. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing of Environment 189 (2017): 164-179, doi:10.1016/j.rse.2016.11.023.
Description:
A variety of approaches are available to fill the gaps in the time series of
vegetation parameters estimated from satellite observations. In this paper, a scheme
considering vegetation growth trajectory, protection of key point, noise resistance and
curve stability was proposed to evaluate the gap-filling approaches. Six approaches
for gap filling were globally evaluated pixel-by-pixel based on a reference NDVI
generated from MODIS observations during the past 15 years. The evaluated
approaches include the Fourier-based approach (Fourier), the double logistic model
(DL), the iterative interpolation for data reconstruction (IDR), the Whittaker smoother
(Whit), the Savitzky-Golay filter (SG) and the locally adjusted cubic spline capping
approach (LACC). Considering the five aspects, the ranks of the overall performance
are LACC 〉 Fourier 〉 IDR 〉 DL 〉 SG 〉 Whit. The six approaches are similar in
filling the gaps and remaining the curve stability but there are large difference in
protection of key points and noise resistance. The SG is sensitive to noises and the
Whit is poor in protection of key points. In the monsoon regions of India, all
evaluated approaches don’t work well. This paper provides some new views for
evaluating the gap filling approaches that will be helpful in selecting the optimal
approach to reconstruct the time series of parameters for data applications.
Description:
This research was funded by the key research and development programs for
global change and adaptation (2016YFA0600201), the National Natural Science
Foundation from China (41171285) and the carbon project of the Chinese Academy
of Sciences (XDA05090303).
Description:
2018-12-07
Keywords:
MODIS
;
NDVI time series
;
Gap filling
;
Seasonal patterns
;
Vegetation phenology
Repository Name:
Woods Hole Open Access Server
Type:
Preprint
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