Summary: A new proliferation of optical instruments that can be attached to towers over or within ecosystems, or ‘proximal’ remote sensing, enables a comprehensive characterization of terrestrial ecosystem structure, function, and fluxes of energy, water, and carbon. Proximal remote sensing can bridge the gap between individual plants, site‐level eddy‐covariance fluxes, and airborne and spaceborne remote sensing by providing continuous data at a high‐spatiotemporal resolution. Here, we review recent advances in proximal remote sensing for improving our mechanistic understanding of plant and ecosystem processes, model development, and validation of current and upcoming satellite missions. We provide current best practices for data availability and metadata for proximal remote sensing: spectral reflectance, solar‐induced fluorescence, thermal infrared radiation, microwave backscatter, and LiDAR. Our paper outlines the steps necessary for making these data streams more widespread, accessible, interoperable, and information‐rich, enabling us to address key ecological questions unanswerable from space‐based observations alone and, ultimately, to demonstrate the feasibility of these technologies to address critical questions in local and global ecology.

Ephemeral rivers in arid regions act as linear oases, where corridors of vegetation supported by accessible groundwater and intermittent surface flows provide biological refugia in water-limited landscapes. The ecological and hydrological dynamics of these systems are poorly understood compared to perennial systems and subject to wide variation over space and time. This study used imagery obtained from an unmanned aerial vehicle (UAV) to enhance satellite data, which were then used to quantify change in woody vegetation cover along the ephemeral Kuiseb River in the Namib Desert over a 35-year period. Ultra-high resolution UAV imagery collected in 2016 was used to derive a model of fractional vegetation cover from five spectral vegetation indices, calculated from a contemporaneous Landsat 8 Operational Land Imager (OLI) image. The Normalized Difference Vegetation Index (NDVI) provided the linear best-fit relationship for calculating fractional cover; the model derived from the two 2016 datasets was subsequently applied to 24 intercalibrated Landsat images to calculate fractional vegetation cover for the Kuiseb extending back to 1984. Overall vegetation cover increased by 33% between 1984 and 2019, with the most highly vegetated reach of the river exhibiting the greatest positive change. This reach corresponds with the terminal alluvial zone, where most flood deposition occurs. The spatial and temporal trends discovered highlight the need for long-term monitoring of ephemeral ecosystems and demonstrate the efficacy of a multi-sensor approach to time series analysis using a UAV platform.

Shifts in rainfall frequency and intensity can lead to heavy crop loss in rainfed agricultural systems. Small‐scale farmers who plant with limited resources need to carefully select management strategies that are well suited for their environment. Farmers must choose between planting higher‐yielding varieties that take longer to mature and lower‐yielding varieties that can be harvested sooner. To better understand the interactions between rainfall variability, cultivar choice, and cropping success, we implement an ecohydrological model that accounts for variation in daily soil moisture and converts water stress to crop yield. We apply the model to growing conditions of dryland farmers in central Kenya, which is a drought‐prone and semiarid region with spatially heterogeneous rainfall. To simulate stochastic daily rainfall, we derive parameters in 10‐day increments from a 30+ year daily rainfall data set. We use these properties to model the stochastic seasonal water availability for cultivars with different maturation lengths. In agreement with past studies, our analysis shows that storms are becoming more intense and less frequent. We show that maize crops are prone to water deficit in the part of the growing season when crop water requirements are highest. Despite the potential for higher‐yielding, late maturing varieties to improve total harvest, we find that early maturing varieties that are drought‐avoidant have the lowest likelihood of failure. In light of reduced rainfall totals, we show that the historical probability of crop failure was lowest in the past and is now increasing.

Accurate and timely observations of individual‐scale transpiration are critical for predicting ecosystem responses to climate change. Existing remote sensing methods for measuring transpiration lack the spatial resolution needed to resolve individual plants, and their sources of uncertainty are not well‐constrained. We present two novel approaches for independently quantifying fine‐scale transpiration using thermal imagery and a suite of environmental sensors mounted on an unmanned aerial vehicle (UAV) platform. The first is a surface energy balance (SEB) approach designed for fine‐scale thermal imagery; the second uses profiles of air temperature (T_a) and humidity (h_r) to calculate transpiration from the Bowen Ratio. Both approaches derive the energy equivalent of transpiration, latent heat flux (λE), solely using data acquired from the UAV. We compare the two approaches and their sources of uncertainty using data from several flights at a grassland eddy covariance site in 2021 and 2022 and using typical diurnal conditions to evaluate the uncertainty of λE estimates for each approach. The SEB approach generated independent, UAV‐based estimates of λE within ∼20% of eddy covariance measurements and was most sensitive to surface temperature and resistance to heat transfer. λE calculated from the Bowen Ratio approach was ∼30% higher than tower values due to inaccuracies in T_a and h_r, the main sources of uncertainty in this approach. The Bowen Ratio approach has a lower overall potential uncertainty, indicating its potential for improvement over the SEB approach. Our results are the first physically‐based observations of transpiration derived solely from a UAV platform, with no ancillary data inputs.

Soil moisture drydown patterns encode signatures of vegetation water‐use. Previous characterizations of the drydown patterns assume a static linear relationship between water‐limited transpiration and available moisture. However, ecohydrological studies show that vegetation exhibits a spectrum of responses to water availability, suggesting that soil moisture loss functions may be nonlinear. To represent these dynamics, we introduce a nonlinearity parameter to the loss function. Our analysis shows that the nonlinear loss model improves the characterization of the satellite‐observed soil moisture drydowns. Globally, functional responses of drydowns are dominated by convex nonlinearity, showing less ecosystem water loss in dry soils than the linear loss function predicts. We find distinct degrees of nonlinearity among different vegetation types; areas with non‐woody vegetation more frequently exhibit a concave nonlinearity, the signature of aggressive water‐use strategies. We propose the nonlinear loss function as a continuous and dynamic framework to represent vegetation water‐use under changing water availability.