Remote sensing provides both fine and coarse scale observations of Earth's complex environmental systems. For example, leaf-scale observations of Nitrogen and water content can be made with spectroscopy; whereas vegetation density and cover can be observed across landscape scales with optical imagery and/or lidar. Snow properties can be estimated with lidar and imaging spectroscopy, as can soil carbon. These remote sensing observations can help predict the responses of Earth's complex systems, such as the critical zone, to environmental forcings, as well as provide a level of uncertainty in the predictions.
In this study we present a number of cutting-edge remote sensing techniques to measure the critical zone, and to make measurements and estimate uncertainties across scales. We use 3-d point clouds to monitor seasonal vegetation growth, changes in biomass and leaf area index, and to measure micro-topography in a manipulated climate experiment. The results from this work demonstrate our capacity to measure vegetation growth at the individual level, as well as capture changes in microtopography across seasons. We also explore the applicability of using raster versus point cloud data and demonstrate that in almost all instances, point cloud estimates are far superior to raster-based metrics in determining vegetation parameters. In an environment where 3-d point clouds fail to capture the heterogeneity, we utilize full-waveform lidar data to derive a number of similar biophysical parameters across space. We also use a combination of hyperspectral and lidar observations to inform and spatially map organic soil carbon across landscape scales. In all of these studies, our models explain at least 60% of the variance in our site-level observations with RMSEs that support high-quality data models. The results from these studies are currently being used to inform ecosystem dynamics models, model the critical zone, and to determine priority areas for restoration across the landscape.