Many ecological systems follow a seasonal cycle affecting primary production, carbon flux, and vegetative gas emissions. The seasonal variation of ecological systems are both affected by and have effects upon climatic factors. A quantitative estimate of the seasonal variation of vegetation is required to characterize ecological systems and their interaction with climate. Monitoring the spaliotemporal variation of foliar biomass density (FBD) over one year will provide a quantitative estimate of the annual cycle and regional variation of photosynthetic activity. FBD is a quantitative measure of leafy material per unit of area produ\:ed by photosynthetically active vegetation. This seasonal variation in FBD is an important parameter for global and other large scale investigations of ecological, hydrological, and biogeochemical systems which require data and expertise from a variety of sources and disciplines. Therefore, FBD is potentially of great utility for ecologists, hydrologists, climatologists, and atmospheric scientists. Recent regional scale investigations of ecological systems concluded that the repetitive coverage and synoptic view of remotely sensed measurements provide data to monitor the seasonal variation of biomass. A method to estimate the seasonal variation of FBD at global scales has not been developed. The objective of this research is to develop a methodology that could be used to estimate the seasonal variation of FBD for the entire terrestrial biosphere. By coupling global satellite data, measured field data, and a vegetation classification, a model was developed to estimate the global spatiotemporal variation of FBD. Comparisons between literature estimates of FBD and estimated FBD from this model were made as a means of validation. A more specific comparison was conducted between grasslands based on work conducted in the Senegalese Sahel region in Africa. Finally, a sensitivity analysis was performed to characterize the potential propagation of error associated with the literature FBD estimates used to drive this model. / Graduation date: 1992