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| Titel |
A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products |
| VerfasserIn |
S. Kandasamy, F. Baret, A. Verger, P. Neveux, M. Weiss |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 10, no. 6 ; Nr. 10, no. 6 (2013-06-20), S.4055-4071 |
| Datensatznummer |
250018300
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| Publikation (Nr.) |
 copernicus.org/bg-10-4055-2013.pdf |
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| Zusammenfassung |
| Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more
than 10 yr of observations well suited to describe and understand the
dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete
because of cloud cover. This
study compares eight methods designed to improve the continuity by filling
gaps and consistency by smoothing the time course. It includes methods
exploiting the time series as a whole (iterative caterpillar singular
spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass
filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on
limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay
filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric
Gaussian function (AGF)), in addition to the simple climatological LAI yearly
profile (Clim). Methods were applied to the MODIS leaf area index product for
the period 2000–2008 and over 25 sites showed a large range of seasonal
patterns. Performances were discussed with emphasis on the balance achieved
by each method between accuracy and roughness depending on the fraction of
missing observations and the length of the gaps. Results demonstrate that
the EMD, LPF and AGF methods were failing because of a significant fraction of
gaps (more than 20%), while ICSSA, Whit and SGF were always providing
estimates for dates with missing data. TSGF (Clim) was able to
fill more than 50% of the gaps for sites with more than 60%
(80%) fraction of gaps. However, investigation of the accuracy of the
reconstructed values shows that it degrades rapidly for sites with more than
20% missing data, particularly for ICSSA, Whit and SGF. In these
conditions, TSGF provides the best performances that are significantly better than
the simple Clim for gaps shorter than about 100 days. The roughness of the
reconstructed temporal profiles shows large differences between the various
methods, with a decrease of the roughness with the fraction of missing data,
except for ICSSA. TSGF provides the smoothest temporal profiles for sites
with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim
provide smoother profiles than TSGF for sites with a % gap < 30%.
Impact of the accuracy and smoothness of the reconstructed time series were
evaluated on the timing of phenological stages. The dates of start, maximum
and end of the season are estimated with an accuracy of about 10 days for
the sites with a % gap < 10% and increases rapidly with the % gap.
TSGF provides more accurate estimates of phenological timing up to a
% gap < 60%. |
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