The vegetation cycle in West Africa from AVHRR-NDVI data : horizons of predictability versus spatial scales

S. Mangiarotti 1, * P. Mazzega 2 P. Hiernaux 2 Eric Mougin 2
* Corresponding author
1 CESBIO - Centre d'études spatiales de la biosphère
2 GET - Géosciences Environnement Toulouse
Abstract : 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).
Keywords : Vegetation cycle West Africa Horizon of predictability Spatial scale NDVI satellite data Nonlinear data analysis
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S. Mangiarotti, P. Mazzega, P. Hiernaux, Eric Mougin. The vegetation cycle in West Africa from AVHRR-NDVI data : horizons of predictability versus spatial scales. Remote Sensing of Environment, Elsevier, 2010, 114 (9), pp.2036-2047. ⟨10.1016/j.rse.2010.04.010⟩. ⟨ird-01062699⟩

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