## 4 Results and Discussion
### Eddy Covariance Gross Primary Productivity and MODIS-Derived Data  
EVI was strongly correlated with EC GPP across all sites (R\({}^{2}\) = 0.83, RMSE = 1.04 g C m\({}^{-2}\) d\({}^{-1}\)) (Table 5). The partial correlation of the MODIS data showed that the EVI was closely coupled with EC GPP during both greening and browning phases (Table 4). The decline in PAW during the browning phase leads to a reduction in transpiration and latent heat flux and an increase in sensible heat flux and LST (\(r_{s}=-0.34\)) (Table 4, Figure 5). MODIS NBAR Band 7 exhibits a positive response during the greening (\(r_{s}\) = 0.32) and browning phases (\(r_{s}\) = 0.22), probably due to the band's sensitivity to changes  
Figure 5: Scatterplots of the relationships between the field variables EC GPP, PAW and VPD (top row) and moderate-resolution imaging spectroradiometer (MODIS)-derived variables (bottom row) during the greening (green color) and browning (brown color) phases on a per-site basis. (DAH = Dahra, DEM = Demokeya, WAF = Wankama Fallow). Figure 6: A narrowed-down example of a typical Sahelian growing season exemplified here by the 2009 growing season in Demokeya. Relative humidity (RH) begins to increase and vapor pressure deficit (VPD) begins to decrease a full month before the onset of the rainy season. Once the rains fall, plant-available water (PAW) is replenished and GPP begins to increase commencing the greening phase. Although PAW has been gradually decreasing during the growing season, the decrease in rainfall prompts a decrease in RH and an increase in VPD, which sharply reduced GPP due to stomatal closure. All variables have been normalized to between zero and one for visual purposes. in soil moisture during the wetting and drying phases. The relationship between EVI, LST, and MODIS NBAR Band 7 is shown in Figure 7. The slope of the regression line during the dry season (\(\beta_{1}=-0.24\)) shows a rapid increase in MODIS NBAR Band 7 for each unit decrease in EVI, whereas the change is more gradual during the growing season (Figure 7). Both MODIS NBAR Band 7 (\(0.20<\text{R}^{2}<0.50\)) and LST (\(0.33<\text{R}^{2}<0.58\)) had generally moderate relationships with EC GPP. However, when LST and MODIS NBAR Band 7 were included in an MLR model together with EVI, the combination was able to consistently explain higher variance (\(\text{R}^{2}\geq 0.88\)) across all sites (Figure 8). Both EVI and MLR GPP follow the seasonal progression of EC GPP well, but the MLR GPP model tracks the dry season EC GPP better than EVI (Figure 9). The inclusion of LST and MODIS NBAR Band 7 improves the variance explained by 6.7% from 0.83 for EVI to 0.89 for the MLR model comprising EVI, LST and MODIS NBAR Band 7 while decreasing the BIC from 1221 to 1024 (Table 5). When the data is split into the individual sites, a similar pattern emerges with the explained variance of MLR model over EVI increasing between 4.3% in Demokeya to 15.9% in Wankama (Table 5). The MLR GPP model for all the sites is shown in Equation (9):  
\[GPP=-4.28+[(34.51\times EVI)+(-0.06\times LST)+(7.81\times BAND7)] \tag{9}\]Figure 8: Cross-site relationship between the multiple linear regression (MLR) GPP model (\(\sim\)EVI + Band 7 + LST) and 8-day EC GPP for both the growing and seasons. The solid black line is the regression line with 95% confidence interval in light gray shading. Figure 7: Comparison of EVI and MODIS NBAR Band 7, with coloring that indicates corresponding values of LST for each site during the growing (July–October) and dry season (November–June). The solid black line is the regression line with 95% confidence interval in light gray shading. The ability of the MLR model to explain cross-site variability in EC GPP was improved (from 85% to 89%) when data from both the growing and dry seasons were included in the model. Although, there is no green herbaceous vegetation during the dry season, the small C fluxes that were observed could be due to the sparse tree cover [36].