## 3 Results and Discussion
### Consistency Check with Ground Measurements  
Figure 6a shows the correlation between field measurements of herbaceous dry biomass in Senegal and CFAPAR (\(r=0.58\)). The relationship is significant (\(p<0.001\)) and corroborates our assumption that the CFAPAR can be used as proxy of biomass production. However, a relevant scatter is present, and the total biomass variance (_i.e._, temporal and spatial variability together) explained by the linear regression is limited to 34%.  
The average correlation between measured biomass and CFAPAR at each site (Figure 6b) represents the temporal component of the overall correlation shown in Figure 6a, where data points of different years and locations are pooled together. This average temporal correlation is increased (\(r=0.74\), Figure 6b) with respect to the overall one, indicating that the relationship between measured biomass and CFAPAR holds at the pixel level and it is degraded when different locations are pooled together. This fact does not represent a major concern with respect to the correlations presented in Section 3.2, as the analysis was performed at the pixel level as well.  
The reduction in sample size available for the regression analyses shown in Figure 6a,b (\(n=167\) for the global regression and \(n=3\) to \(7\) for the site-level regressions, respectively) contributes toward explaining the increase of the average \(p\)-value of the site-level regressions of measured biomass _vs._ CFAPAR, as compared to the global regression. Nevertheless, Figure 6b shows that the correlation sign and strength between the variables and CFAPAR agrees with that of the same variables and measured biomass. Even if geographically limited to the Matam region in Senegal, this consistency check adds some confidence to our phenological analysis at the Sahel level.  
The lower correlation found when comparing the RS-derived variables to measured biomass production rather than to the RS-derived CFAPAR may be explained by the fact that CFAPAR is computed over the full growing season (_i.e._, EOS is determined for each season), whereas field  
Figure 6: **(a)** Linear regression statistics and scatterplot between measured biomass (years 2005–2011) and CFAPAR; (b) average of the site-level correlation coefficients and statistical significance: the green bar refers to the correlation between measured biomass and CFAPAR; the blue and yellow bars refer to the correlations of GSL, \(\Delta\)SOS and Peak _vs._ measured biomass (blue) or CFAPAR (yellow), respectively. Error bars refer to \(\pm 1\) standard deviation.  
measurements are performed in the proximity of the actual end of the season, at some arbitrary day in October (the exact date may change site by site, and it is not available). The reduced statistical significance of the relationship between RS-derived variables and measured biomass can be partially explained by the smaller sample size available for the two type of regressions (\(n=3\) to 7 when using measured biomass and \(n=15\) when using CFAPAR). Other causes of disagreement between CFAPAR and field measured biomass may include: spatial mismatch between the RS footprint (3 \(\times\) 3 pixels) and the field transect; and possible biomass removal by animal grazing (undetected by single end-of-season biomass field measurements).