## 5 Discussion  
The use of the NDVI has been most widely used as approaches for EO-based monitoring of vegetation productivity. Researchers have derived a variety of relevant measures from NDVI time series as a proxy of vegetation production, such as the sum of positive NDVI values over a given period, the maximum value of the NDVI over a year, or the NDVI integral across growing seasons [17,38]. Researchers also use NDVI or NDVI-derived variables as inputs to process-based models to calculated NPP, such as the Carnegie-Ames-Stanford Approach (CASA), the Terrestrial Observation and Prediction System Model (TOPS) and other light use efficiency models [8,11]. Several studies have suggested the existence of a positive relationship between NDVI and either biomass or NPP. However, the relationship between NDVI and NPP is not always linear. For biomes with high NPP, this relationship becomes saturated, whereas for biomes with low NPP, the relationships are influenced by the spectral characteristics of the bare soil. Our study also shows that a slightly weaker relationship is found over tree savanna where NPP is higher, and the estimated accuracy of tree savanna is lower than that of bush and grassland savanna. The basic assumption to effectively derive NPP from NDVI here is the significant linear relationship. Our results show that MODIS NPP has a significant linear relationship with GIMMS3g NDVI across the entire study area. [PERSON] _et al._[39] in the central U.S grasslands, found that a positive and statistically significant relationship between NDVI and NPP for areas with MAP between 280 mm and 1,150 mm which overlaps well with our study area. [PERSON] _et al._[17] successfully converted the GIMMS NDVI into NPP based on per-pixel correlations between overlapping years of GIMMS NDVI and SPOT VGT NPP in the African Sahel. Here we are evaluating the performance of the new AVHRR dataset based on the MODIS NPP product using bootstrapping regression. The accuracy of MODIS NPP especially in the savanna ecosystems potentially has considerable influence on our model estimates. Several studies have highlighted limitations of the MOD17 model such as the uncertainties of coarse resolution DAO meteorological reanalysis data used in the model, and the global trends and regional patterns derived from MODIS NPP have been challenged [11,40,41,42]. Few studies have been conducted in Africa to evaluate the efficiency of the MOD17 model [43,44,45]. [PERSON] _et al._[43] found that the MOD17 model underestimated NPP in the semi-arid Senegal. [PERSON] _et al._[44] reported that MODIS GPP performed reasonably well in explaining the variability in eddy covariance GPP at a range of African ecosystem, but the model was observed to underestimate GPP, especially in dry savanna ecosystems. [PERSON] _et al._[45] found that the MODIS GPP can capture the seasonality but the MODIS NPP was underestimated again in dry savanna ecosystems. The possible reasons include the uncertainties of the meteorological data, the inappropriate set of maximum light efficiency, and the insufficiency of vapor pressure deficit (VPD)'s constraint on GPP. However, a comparison of monthly NPP derived from eddy covariance flux sites with monthly NPP from the MOD17 algorithm suggests a better estimate of MODIS NPP in our study area, although the sample number is relatively small. Although there is some consensus that terrestrial photosynthetic activity has increased over the past two or three decades in the middle and high latitudes in the NH [4,9], no such agreement has been reached on tropical regions and specifically, across the most recent decades some studies have suggested a switch in trends globally [5]. Beyond just this basic interpretation of the trend and patterns over the last thirty years there is also additional confusion regarding interannual variations in NPP at global or regional scales as a result of the selection of model used to derive NPP, the research period selected, or/and the spatial resolution used.