Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability

dc.contributor.author Liu, Ronggao
dc.contributor.author Shang, Rong
dc.contributor.author Liu, Yang
dc.contributor.author Lu, Xiaoliang
dc.date.accessioned 2017-03-15T19:40:37Z
dc.date.available 2018-12-07T09:33:04Z
dc.date.issued 2016-11
dc.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. en_US
dc.description.abstract 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. en_US
dc.description.embargo 2018-12-07 en_US
dc.description.sponsorship 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). en_US
dc.identifier.uri https://hdl.handle.net/1912/8802
dc.language.iso en en_US
dc.relation.uri https://doi.org/10.1016/j.rse.2016.11.023
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject MODIS en_US
dc.subject NDVI time series en_US
dc.subject Gap filling en_US
dc.subject Seasonal patterns en_US
dc.subject Vegetation phenology en_US
dc.title Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability en_US
dc.type Preprint en_US
dspace.entity.type Publication
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relation.isAuthorOfPublication.latestForDiscovery 32a30e08-2b6a-4a20-9508-bbdd8b2a7b6c
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