Spatio-temporal vegetation dynamics are important drivers to characterize seasonal to annual water and carbon budgets. Spatial adjustment and evolution of the ecosystem is closely related to the geomorphic, climatic, and hydrologic settings. In particular, lateral hydrologic redistribution along flowpaths control the long-term joint adjustments of vegetation and soil over successional and quasi-geological time scales. For this reason, it is complex and challenging to incorporate the many relevant processes and feedbacks between ecological and hydrological systems for the full simulation of water, carbon, and nutrient cycling. Recent developments in remote sensing technology provide the potential to link dynamic canopy measurements with integrated process descriptions within distributed ecohydrological modeling frameworks. In this dissertation, three research studies are presented concerning estimation of spatio-temporal vegetation dynamics in application into a distributed ecohydrological model at the Coweeta Long Term Ecological Research site. In Chapter 2, we test whether the simulated spatial pattern of vegetation corresponds to measured canopy patterns and an optimal state relative to a set of ecosystem processes, defined as maximizing ecosystem productivity and water use efficiency at the catchment scale. A distributed ecohydrological model is simulated at a small catchment scale with various field measurements to see if the evolved pattern of vegetation density along the flowpaths leads to system-wide emergent optimality for carbon uptake over and above the individual patch. Lateral hydrological connectivity determines the degree of dependency on productivity and resource use with other patches along flowpaths, resulting in different system-wide carbon and water uptake by vegetation. In Chapter 3, phenological signals are extracted from global satellite products to find the topography-mediated controls on vegetation phenology in the study site. It provides a basis to understand spatial variations of local vegetation phenology as a function of microclimate, vegetation community types, and hillslope positions. In Chapter 4, near real-time vegetation dynamics are estimated by fusing multi-temporal satellite images, and integrated into the catchment scale distributed ecohydrological simulation. Integration of spatiotemporal vegetation dynamics into a distributed ecohydrological model helps to simulate ecohydrological feedbacks between vegetation patterns and lateral hydrological redistribution by reducing uncertainty related to state and flux variables.