## 3 Results and Discussion
### Correlation of CFAPAR with Phenological Variables  
The analysis of the spatial patterns of mean phenology of Section 3.1 highlights the presence of a strong spatial correlation among the phenological variables. In this section, we are interested in testing whether the correlation between CFAPAR and the phenological variables holds in time, as well, _i.e._, where and to what extent the seasonal CFAPAR at a given location can be explained by a variation in one or more key phenological parameters. Figure 3 shows the coefficient of determination of the linear regression of CFAPAR against GSL, Peak value and \(\Delta\)SOS as an RGB color composite. The dominance of red and magenta colors, especially in the southern agriculture band, highlights the importance of GSL and SOS. However, in large areas in the northern grassland band, Peak, _i.e._, the maximum biomass development attained during the season, is the most important factor (as shown by the dominance of greenish colors). Figure 4 shows separately the correlation coefficients of the three variables considered with CFAPAR (only pixels with significant regression at the 0.05 \(p\)-level are mapped). A positive correlation with GSL holds for most of the Sahel (Figure 4a). This finding is in agreement with the general expectation that longer growing seasons correspond to higher biomass production levels [12], and with previous studies relating crop production [62] and rangeland production [63] to GSL in the region. Figure 3: Visualization of the correlation between CFAPAR _vs._ GSL, Peak value and \(\Delta\)SOS in the RGB color space. The composite is based upon the coefficients of determination (\(R^{2}\)) of the linear regression CFAPAR _vs._ GSL (Red), Peak (Green) and \(\Delta\)SOS (Blue); no stretching is applied. Main land cover types are represented by the thick black lines (two simplified polygons, herbaceous cover in the north and crop cover in the south). The correlation with GSL is weaker in the northern grassland band where Peak also plays a major role (Figure 4b) and is positively correlated with CFAPAR. This result is in agreement with [PERSON] and [PERSON] [64], [PERSON] _et al._[35] and [PERSON] _et al._[38], who found that the re-greening trend observed in northern Sahel (a positive NDVI temporal trend in these studies) could be mainly explained by an increase in NDVI seasonal amplitude. The widespread absence of a significant correlation between the vegetation production proxy and Peak in the agricultural band raises questions about the general reliability of using the seasonal maximum of a remote sensing indicator as a suitable proxy of final crop yield, as is done in many studies [8]. The anomaly in SOS is negatively correlated with CFAPAR (Figure 4c) over large areas of the region, meaning that a delay in SOS is significantly associated with a reduction in CFAPAR. This correlation is more widespread and stronger in the southern agricultural band rather than in the northern grasslands: the percentage of pixels showing a significant relationship (at the 0.05 \(p\)-level) between \(\Delta\)SOS and CFAPAR is 66% in the agricultural band and 50% in the grassland band. In addition, taking into account only significant pixels, the mean absolute correlation is significantly higher (0.01 \(p\)-level) in the agricultural band (\(r=-0.71\)) than in the grassland band (\(r=-0.67\)). On the  
Figure 4: Correlation coefficient between CFAPAR and GSL (**a**); Peak (**b**); \(\Delta\)SOS (**c**). Land cover polygons (thick black lines) are as in Figure 3. Pixels where the linear regression is not significant (n.s.) at \(p=0.05\) are masked in grey. contrary, Peak is more important in the grassland band (76% of pixels with significant correlation, mean \(r=0.75\)) than in cropland band (42% of pixels with significant correlation, mean \(r=0.66\)).