Savannas are widespread global biomes covering ~20% of terrestrial ecosystems on all continents except Antarctica. These ecosystems play a critical role in regulating terrestrial carbon cycle, ecosystem productivity, and the hydrological cycle and contribute to human livelihoods and biodiversity conservation. Despite the importance of savannas in ecosystem processes and human well-being, the presence of mixed woody and herbaceous components at scales much fin-er than most medium and coarse resolution satellite imagery poses significant challenges to their effective representation in remote sensing and modeling of vegetation dynamics. Although pre-vious studies have attempted to separate woody and herbaceous components, the focus on greenness indices and fractional cover provides little insight into spatio-temporal variability in woody and herbaceous vegetation structure, in particular, leaf area index (LAI). This paper pre-sents a method to partition 1km spatial resolution Moderate Resolution Imaging Spectroradiome-ter (MODIS) aggregate green leaf area index (LAIA) from 2003-2015, into separate woody (LAIW) and herbaceous (LAIH) constituents in both drought seasonal savannas and moist tropical forests of Sub-Saharan Africa (SSA). In our analysis, we use an allometric relationship describing the variation in peak within-canopy woody LAI of dominant tree species (LAIWpinc) across gradi-ents in mean annual precipitation (MAP), coupled with independent estimates of woody canopy cover (τw), to constrain seasonally changing LAIW. We present the LAI partitioning approach and highlight the broad spatial and temporal patterns of woody and herbaceous LAI across SSA. The long-term average 8-day phenologies of woody and herbaceous LAI (averaged across 2003-2015) are available for evaluation, research and application purposes.