Understanding the linkages between between climatic and surface properties that influence water uptake and loss by vegetation is essential for understanding the impact of drought on dryland regions. The Normalized Difference Vegetation Index (NDVI) is a common metric used to identify vegetation condition across LULC types. Here we employ empirical dynamic modeling (EDM) to forecast NDVI changes for savannas, grasslands, and croplands across East Africa at a dekadal (10-day) time scale using satellite-derived environmental forcing variables. The model relies on state space reconstruction with lagged coordinate embedding of multiple time series observations to recover the dynamic environmental system that links vegetation dynamics to environmental forcing. We apply convergent cross mapping based on Takens’ Theorem to detect the impact of landcover on directional causal interactions and time delays between driving (e.g. LST, rainfall) and response variables (NDVI). The model brings to light that certain regions are highly consistent in their trajectories and therefore easier to project while other regions are more dispersive and thus more difficult to determine anomalies. In terms of land cover, we are able to make projections with high accuracy for grasslands out to 6 months ahead while croplands and savannas show reduced forecast skill overall and prove less useful after 3 months. The use of historical seasonal NDVI patterns to diagnose the manner by which landcover and land use determine climate-land surface couplings provides a means for defining critical areas of inquiry related to the impacts of future change, particularly the expansion of agricultural areas. In addition, the EDM approach provides a robust means for creating short term vegetation forecasts across LULC types in East Africa. These predictions can assist relief organizations in advising drought management, declaring food security classifications and providing early response to famine.