The terrestrial carbon (C) cycle has a great role in influencing the climate. Hence, it is critical that we fully understand the scale-specific (spatial and temporal) complexities of the terrestrial C cycle. Remote sensing, in combination with modeling and precise ground measurements, is the only means to better understand the C cycle across a wide range of scales and help us arrive at global-scale conclusions. This chapter introduces various remote sensing approaches that are currently used in C cycle research. Various empirical and process-based strategies to map C indicators are discussed at the outset. Further, we examine the application of remote sensing techniques to deriving spatial datasets that aid in mapping C indicators. Here we emphasize different global-scale datasets that are currently available to remote sensing scientists and ecological modelers. Subsequently, we discuss a scheme to reconstruct the historical C-balance of an ecosystem using the InTEC model that primarily relies on remote sensing datasets. Here, we demonstrate how the current biomass and soil C pools result from the integrated effects of plant growth, stand age, climate change, atmospheric CO2 concentration, Ndeposition and disturbance. Since validation is essential in gaining confidence in remote sensing-based estimates, we discuss various approaches that measure the fluxes of C between the biosphere and the atmosphere. Because remote sensing alone is insufficient in explaining the complexity and non-linearity in the C cycle processes, it is essential to understand how different environmental factors influence plant physiology and biogeochemistry. To this end, we discuss the usefulness of a spatially explicit process model (BEPS-Terrainlab V2.0) that has a tight coupling between hydrological, ecophysiological and biogeochemical processes that runs within a remote sensing-driven modeling framework. © 2009 Nova Science Publishers, Inc. All rights reserved.