## 2 Materials and Methods
### Methods  
Phenological variables were retrieved from the FAPAR time series using the model-fit approach described in [PERSON] _et al._[9]. Briefly, the seasonality (being uni- or bi-modal) is detected analyzing the autocorrelation of the FAPAR time series. Then, the seasonal FAPAR trajectory is fitted with a double hyperbolic tangent model. Finally, the start and end of season (SOS and EOS) are deemed to occur whenever the value of the modeled time series exceeds the initial base value by 20% of the growing amplitude and whenever the modeled value drops below the final base value plus 20% of the decay amplitude, respectively. The length of the growing season (GSL), the maximum FAPAR value (Peak), as well as the cumulative FAPAR value during the growing season (CFAPAR) are computed on the fitted model. When required, SOS is expressed in terms of anomaly (\(\Delta\)SOS, the actual SOS minus its mean value). The retrieval of phenology in the Sahel is challenging because satellite observations may be hampered by the presence of scattered clouds, especially during the wet season, and this can interfere with the detection of specific events, such as the SOS. Additionally, rain-fed arid ecosystem can exhibit \"false starts\", or complete season failures [44]. All of these factors may affect the correct phenology retrieval and introduce bias in the analysis. Relying on a model of the growing period, rather than the raw measurements themselves, is expected to deliver reasonable phenology dates, even if these occur on cloudy days, as the model functional form is fitted to the available observations.  
CFAPAR is defined as the integral of FAPAR during the growing period. Its magnitude is therefore a function of the shape of the FAPAR seasonal trajectory (including the maximum amplitude attained) and of the integration limits (start and end of season) defining the growing season. As these phenological parameters interact in a complex way in the determination of CFAPAR, their effect cannot be evaluated theoretically. For instance, a longer than usual growing season may be characterized by lower vegetation growth, resulting in lower CFAPAR. On the contrary, a shorter season may experience more rapid growth and result in a higher CFAPAR. In addition, no _a priori_ assumptions based on the functional form of the trajectory can be made when the season is shifted in time (delayed or earlier than usual start and end of the season), as frequently happens in terrestrial ecosystems.  
In this study, we aim at visualizing the relative importance of such phenological features in determining the seasonal CFAPAR over different geographical settings in the Sahel region. For this purpose, we analyze the correlations of the seasonal biomass production, represented here by CFAPAR, with the retrieved key phenology parameters (GSL and \(\Delta\)SOS) and the maximum productivity indicator (Peak). This analysis is intended to provide a first assessment of the relative importance of GSL, \(\Delta\)SOS and Peak rather than to define a statistical framework for estimating the biomass production, as the various parameters are not statistically independent.  
The plausibility of using CFAPAR as a proxy of biomass production was checked using field measurements. In addition, the observed correlation of the phenological parameters with CFAPAR was compared to the correlation of phenological parameters with measured biomass.