Airborne-based Light Detection and Ranging (LiDAR) offers the potential to measure snow depth and vegetation structure at high spatial resolution over large extents and thereby increase our ability to quantify snow water resources. Here we present airborne LiDAR data products at four Critical Zone Observatories (CZO) in the Western United States: Jemez River Basin, NM, Boulder Creek Watershed, CO, Kings River Experimental Watershed, CA, and Wolverton Basin, CA. We make publicly available snow depth data products (1 m 2 resolution) derived from LiDAR with an estimated accuracy of <30 cm compared to limited in situ snow depth observations.

Soil‐mantled pole‐facing hillslopes on Earth tend to be steeper, wetter, and have more vegetation cover compared with adjacent equator‐facing hillslopes. These and other slope aspect controls are often the consequence of feedbacks among hydrologic, ecologic, pedogenic, and geomorphic processes triggered by spatial variations in mean annual insolation. In this paper we review the state of knowledge on slope aspect controls of Critical Zone (CZ) processes using the latitudinal and elevational dependence of topographic asymmetry as a motivating observation. At relatively low latitudes and elevations, pole‐facing hillslopes tend to be steeper. At higher latitudes and elevations this pattern reverses. We reproduce this pattern using an empirical model based on parsimonious functions of latitude, an aridity index, mean‐annual temperature, and slope gradient. Using this empirical model and the literature as guides, we present a conceptual model for the slope‐aspect‐driven CZ feedbacks that generate asymmetry in water‐limited and temperature‐limited end‐member cases. In this conceptual model the dominant factor driving slope aspect differences at relatively low latitudes and elevations is the difference in mean‐annual soil moisture. The dominant factor at higher latitudes and elevations is temperature limitation on vegetation growth. In water‐limited cases, we propose that higher mean‐annual soil moisture on pole‐facing hillslopes drives higher soil production rates, higher water storage potential, more vegetation cover, faster dust deposition, and lower erosional efficiency in a positive feedback. At higher latitudes and elevations, pole‐facing hillslopes tend to have less vegetation cover, greater erosional efficiency, and gentler slopes, thus reversing the pattern of asymmetry found at lower latitudes and elevations. Our conceptual model emphasizes the linkages among short‐ and long‐timescale processes and across CZ sub‐disciplines; it also points to opportunities to further understand how CZ processes interact. We also demonstrate the importance of paleoclimatic conditions and non‐climatic factors in influencing slope aspect variations. Copyright © 2017 John Wiley & Sons, Ltd.

[1] Feedbacks among vegetation dynamics, pedogenesis, and topographic development affect the "critical zone"-the living filter for Earth's hydrologic, biogeochemical, and rock/ sediment cycles. Assessing the importance of such feedbacks, which may be particularly pronounced in water-limited systems, remains a fundamental interdisciplinary challenge. The sky islands of southern Arizona offer an unusually well-defined natural experiment involving such feedbacks because mean annual precipitation varies by a factor of five over distances of approximately 10 km in areas of similar rock type (granite) and tectonic history. Here we compile high-resolution, spatially distributed data for Effective Energy and Mass Transfer (EEMT: the energy available to drive bedrock weathering), above-ground biomass, soil thickness, hillslope-scale topographic relief, and drainage density in two such mountain ranges (Santa Catalina: SCM; Pinaleño: PM). Strong correlations exist among vegetation-soil-topography variables, which vary nonlinearly with elevation, such that warm, dry, low-elevation portions of these ranges are characterized by relatively low above-ground biomass, thin soils, minimal soil organic matter, steep slopes, and high drainage densities; conversely, cooler, wetter, higher elevations have systematically higher biomass, thicker organic-rich soils, gentler slopes, and lower drainage densities. To test if eco-pedo-geomorphic feedbacks drive this pattern, we developed a landscape evolution model that couples pedogenesis and topographic development over geologic time scales, with rates explicitly dependent on vegetation density. The model self-organizes into states similar to those observed in SCM and PM. Our results highlight the potential importance of eco-pedo-geomorphic feedbacks, mediated by soil thickness, in water-limited systems.

Unmanned aerial vehicles (UAVs) provide a new research tool to obtain high spatial and temporal resolution imagery at a reduced cost. Rapid advances in miniature sensor technology are leading to greater potentials for ecological research. We demonstrate one of the first applications of UAV lidar and hyperspectral imagery and a fusion method for individual plant species identification and 3D characterization at submeter scales in south-eastern Arizona, USA. The UAV lidar scanner characterized the individual vegetation canopy structure and bare ground elevation, whereas the hyperspectral sensor provided species-specific spectral signatures for the dominant and target species at our study area in leaf-on condition. We hypothesized that the fusion of the two different data sources would perform better than either data type alone in the arid and semiarid ecosystems with sparse vegetation. The fusion approach provides 84-89% overall accuracy (kappa values of 0.80-0.86) in target species classification at the canopy scale, leveraging a wide range of target spectral responses in the hyperspectral data and a high point density (50 points/m 2 ) in the lidar data. In comparison, the hyperspectral image classification alone produced 72-76% overall accuracies (kappa values of 0.70 and 0.71). The UAV lidar-derived digital elevation model (DEM) is also strongly correlated with manned airborne lidar-derived DEM (R 2 = 0.98 and 0.96), but was obtained at a lower cost. The lidar and hyperspectral data as well as the fusion method demonstrated here can be widely applied across a gradient of vegetation and topography to monitor and detect ecological changes at a local scale.

Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.