%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Mangiarotti, Sylvain
%A Mazzega, P.
%A Hiernaux, P.
%A Mougin, Eric
%T The vegetation cycle in West Africa from AVHRR-NDVI data : horizons of predictability versus spatial scales
%D 2010
%L fdi:010049638
%G ENG
%J Remote Sensing of Environment
%@ 0034-4257
%K Vegetation cycle ; West Africa ; Horizon of predictability ; Spatial scale ; NDVI satellite data ; Nonlinear data analysis
%M ISI:000279495200013
%N 9
%P 2036-2047
%R 10.1016/j.rse.2010.04.010
%U https://www.documentation.ird.fr/hor/fdi:010049638
%> https://www.documentation.ird.fr/intranet/publi/2010/07-2/010049638.pdf
%V 114
%W Horizon (IRD)
%X The predictability of the vegetation cycle is analyzed as a function of the spatial scale over West Africa during the period 1982-2004 The NDVI-AVHRR satellite data time series are spatially aggregated over windows covering a range of sizes from 8 x 8 km(2) to 1024 x 1024 km(2). The times series are then embedded in a low-dimensional pseudo-phase space using a system of time delayed coordinates. The correlation dimension (D-c) and entropy of the underlying vegetation dynamics, as well as the noise level (sigma) are extracted from a nonlinear analysis of the time series The horizon of predictability (H-P) of the vegetation cycle defined as the time interval required for an n% RMS error on the vegetation state to double (i e reach 2n% RMS) is estimated from the entropy production. Compared to full resolution, the intermediate scales of aggregation (in the range of 64 x 64 km(2) to 256 x 256 km(2)) provide times series with a slightly improved signal to noise ratio, longer horizon of predictability (about 2 to 5 decades) and preserve the most salient spatial patterns of the vegetation cycle Insights on the best aggregation scale and on the expected vegetation cycle predictability over West Africa are provided by a set of maps of the correlation dimension (D-c), the horizon of predictability (H-P) and the level of noise (sigma).
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