## 1 Introduction  
The Sahel, a semi-arid region on the southern border of the Sahara desert, is characterized by a marked inter-annual climatic variability. The region has witnessed a number of food security crises in recent decades (e.g., [1, 2]) following the more severe droughts during the 1970s and the 1980s [3]. Early warning monitoring systems aim at detecting unfavorable crop and pasture conditions as early as possible during the growing season to inform governments, regional and international stakeholders about impending risks [4, 5]. For this purpose, the analysis of optical remote sensing (RS) data relies on high temporal frequency observations provided by moderate to coarse spatial resolution satellite instruments (e.g., Satellite Pour l'Observation de la Terre, SPOT-VEGFATION; and Moderate-resolution Imaging Spectroradiometer, MODIS) to evaluate vegetation biomass production and crop yield (e.g., [6]). Vegetation conditions are commonly assessed on the basis of anomalies of the current value of the RS indicator (typically the normalized difference vegetation index, NDVI [7]), with respect to a value extracted, for the same period of the year, from a reference temporal profile. This reference is often represented by what is assumed to be the \"normal\" condition [8], _i.e._, the \"long-term\" average computed on the entire RS time series. The main disadvantage of this approach is that the comparison is made at predefined dates within the year regardless of the actual plant growth stage. In this way, a precocious or delayed start of the predefined growing season is implicitly assumed to have an effect on plant condition. For instance, an earlier (or later) than usual development will in any case generate a positive (or negative) NDVI anomaly in the initial part of the season, even if the overall production is not actually affected. Approaches accounting for vegetation phenology in the analysis of overall growing season productivity have recently been proposed to overcome this possible limitation [9, 10, 11]. The study of phenology is important, because the timing and the temporal characteristics of vegetation growth directly control vegetation productivity (for a review, see [PERSON] _et al._[12]). In particular, the length of the growing season was found to be positively correlated with carbon uptake in different ecosystems, ranging from deciduous forests, savannahs, grasslands and crops [13, 14, 15, 16]. These findings mainly reflect the fact that plants manage to make optimal use of energy, water and other resources when these are available. Phenology information could be used for crop and pasture monitoring, as the start of the growing season can be derived early in the season from satellite data [17, 18]. Furthermore, the start of the growing season can also be forecasted using climatic indices [19]. Despite this attractive feature, current knowledge about the relationship between phenology and gross primary production (GPP) in the Sahel is limited. [PERSON] and [PERSON] [20] analyzed NDVI data from Meteosat Second Generation and rainfall estimates from the Tropical Rainfall Monitoring Mission and showed that a delay or false start (_i.e._, initial green-up followed by a setback) of the rainy season had a significant negative effect on the growth of vegetation. Anomalies in the timing of the start of the growing season have recently been considered as explanatory variables in designing empirical drought indices using data mining techniques [21, 22]. However, the link between the timing of the start of the season, as derived from remote sensing indicators, and vegetation production at the end of the growing season has not been clearly identified. Water availability is the main limiting factor for plant growth in warm semi-arid and arid regions. Ecosystems are highly sensitive not only to the annual rainfall amount, but also to the rainfall distribution (timing, continuity and duration of the wet season), which, in turn, controls the vegetation development patterns.