Currently the science of the remote sensing community does not meet the stringent requirements of sustainable agriculture and forestry practices, where quasi-real-time decision making of irrigation management, fertilizer application and disease detection is needed. The two highlighted obstacles that the remote sensing community needs to overcome are: a) the high cost and operational limitations of airborne remote sensing for short turnaround time needed for agricultural applications, and b) the lack of detailed vegetation canopy structural information that can be used to bridge the gap between spectroscopic methods with 3D radiative transfer models. Recent remote sensing advancements have addressed these challenges using two recently-available, high-potential technological developments: a) Unmanned Aerial Vehicles (UAV) coupled with microsensors, and b) Ground-based Light Detection and Ranging (LiDAR) systems. The introduction of cost-effective UAV-based systems allows us to characterize individual trees within forested and agricultural ecosystems thereby highlighting novel scientific issues at new spatial scales. Such issues can potentially be addressed with terrestrial LiDAR systems that allow the characterization of spatial organization of tree crown elements from a ground-level perspective. As such, this project aims to couple the use of UAV-based imagery with ground-level LiDAR data to characterize important biophysical processes at unprecedented spatial and temporal resolutions, suitable for precision, and sustainable forestry/agricultural monitoring. The project will investigate the impact of vegetation architectural parameters, retrieved using a ground-based LiDAR scanner, on the quantitative estimation of physiological indicators of stress (i.e. evapotranspiration, and leaf chlorophyll content) using UAV-based spectral imagery and 3D radiative transfer modeling.