A considerable number of independent case studies have shown the explicit role of rainfall and soil moisture as drivers of surface vegetation dynamics. However, the weak relationship exhibited by rainfall and soil moisture with vegetation productivity in some semi-arid and humid ecosystems of Africa due to a complex combination of social and environmental factors warrants further assessment of hydrological controls on surface vegetation changes. In this study, hydrological control on surface vegetation in West and Central Africa (WCA) using monthly Normalized Difference Vegetation Index (NDVI) (2002–2013) as a vegetation proxy, is explored by using terrestrial water storage (TWS) inverted from time-variable satellite gravity observations. Statistical relationships indicated that the temporal patterns of NDVI with TWS over West Africa, and in catchment-specific cases, were generally slightly stronger than the widely reported association between rainfall and NDVI. The strongest temporal association between TWS and NDVI was found in Lake Chad basin (R 2 =69% at α=0.05) and southern Mali (R 2 =62% at α=0.05). However, when the spatial relations of TWS-NDVI and rainfall-NDVI were evaluated (monthly and seasonal scales), rainfall showed a considerable and wider spread of significant association (α=0.05) with surface vegetation greenness compared to TWS. But some locations in the semi-arid Sahel exist where spatial relationships between NDVI and TWS were relatively higher and consistent as opposed to the rainfall-NDVI relationship. A contemporary understanding of the water driven variability in surface vegetation in WCA indicates that (i) the preponderance of observed positive correlations of NDVI with TWS and rainfall are predominantly found in the Sudano-Sahelian ecosystems, where total NDVI variability is apparently the highest (51.4%) and (ii) some of the Sahelian vegetation also show considerable and significant association with model-derived groundwater. As a strong hydrological indicator in rain-fed semi-arid catchments of West Africa, TWS can be employed as a resourceful tool to provide early warning systems and improve the monitoring of climate impacts on groundwater dependent vegetation communities.