Next to precipitation, secondary water sources emerging from shallow groundwater and lateral redistribution of soil moisture, together with soil properties modulating their accessibility are highly important in water-limited ecosystems. However, effects of these land-associated secondary inputs are not well known over large domains given the mismatch of spatial scales of processes. Here, we quantify the role of land properties on the spatial variations of seasonal decay rate of vegetation cover over water-limited regions of Africa, using machine learning. Over the study domain, 17 % of these variations are directly attributed to land properties, and 16 % are attributed to interaction effects of land properties with climate and vegetation. Locally, total land attributed variations account for more than 60 % in hotspots with different land properties like shallow groundwater, complex topography, and favourable soil properties. Our findings lend empirical evidence for the importance of local-scale secondary water inputs over large domains.