## 2 Materials and Methods
### Methods  
#### 2.3.1 Data Processing  
To analyse the seasonal and inter-annual spatiotemporal variations of GPP and climate variables, all the acquired gridded datasets were firstly re-projected to Africa Albers Equal Area Conic coordinate system to uniformly match geographical coordinates. Therefore, the obtained datasets for 8 and 16-day temporal resolutions were averaged to monthly and annually to match the objectives of this study. This study applied different light-use efficiency (LUE)-based models to estimate GPP. The LUE models proved to be realistic with hyperspectral satellite remotely sensed data and field spectrometer instruments from vegetation phenology processes [15]. All of the mean aggregated gridded datasets were re-sampled to 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) to match the spatial resolutions and smoothly harmonize the raster pixels by using the aggregated and focal statistics techniques in spatial analyst tools of ArcGis version 10.6 [16; 37; 38].  
To consider the effects of different concepts of fraction photosynthetically active radiation absorbed by vegetation canopy (FPAR\({}_{\text{Canopy}}\)) and fraction photosynthetically active radiation absorbed by vegetation chlorophyll (FPAR\({}_{\text{Chl}}\)), the vegetation photosynthesis model (VPM) was selected because of its excellency in estimating the relatively accurate GPP values [39]. Therefore, the VPM model was calibrated at vegetation canopy and chlorophyll levels as GPP-VPMCanopy and GPP-VPMChl respectively.  
To ensure the significant comparison of GPP simulations at different LUE models, the Carnegie-Ames-Stanford Approach (CASA) model was also applied in this study to estimate GPP. The CASA model considers the individual biomass (plant function types or ecosystem types), maximum light-use efficiency (LUE\({}_{\text{max}}\) or \(\varepsilon_{\text{max}}\)) and NPP/GPP ratio of a variety of ecosystem types to simulate GPP [7]. To determine the CASA-based optimized ecosystem types (biomes) and related coefficients including \(\sigma\) ratio and maximum light-use efficiency (LUE, \((\varepsilon)_{\text{max}}\)), the MODIS land cover data (MCD12C1) provided in 16 land cover classes were reclassified into 8 classes according to the present dominated ecosystem types in the GLR-SSA (Figure 2).  
Moreover, the modelled GPP results were validated against the FLUXCOM-based upscaled GPP values (GPP-EC) and compared with MODIS-GPP (MOD17A2H). In addition, climate characteristics including mean precipitation (Prec) and mean land surface air temperature (LST) were used to assess the impacts of seasonal and inter-annual climate variations on GPP dynamics. The entire data processing was performed in ENVI software version 5.6 (Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation, Boulder, CO, USA) and ArcGIS software version 10.6 (Environment Systems Research Institute (Esri) Inc., Redlands, CA, USA); the two R packages, namely, package for auto-correlation robust testing-acrt version 1.0 suggested by [PERSON], and [PERSON], 2018 [37] and Adaptive Smoothing of Digital Images-adimpro version 0.9.2 suggested by [PERSON], and [PERSON], 2006 [38]; and Origin-Pro version 9.0 software provided by OriginLab.