Maximum light use efficiency (εmax) represents a plant's capacity to convert light into carbon during photosynthesis. Although prior studies have explored εmax variations between sunlit and shaded leaves or its temporal ties to canopy structure, the spatial relationship between biome-level εmax (εbiome) and biome structure remains poorly understood. We analysed data from 320 eddy covariance sites ( 855 site-years) with satellite-derived near-infrared reflectance of vegetation (NIRv) and leaf area index (LAI). We introduced NIRvN (NIRv/LAI) to isolate architectural effects from leaf quantity. Site-level εmax was calculated and aggregated by biome to derive εbiome. Results show εbiome rises nonlinearly with NIRv and LAI, saturating at high LAI, with crops and tropical evergreen forests deviating from this trend. Conversely, εbiome decreases linearly with increasing NIRvN, indicating that biomes with greater NIR scattering efficiency exhibit lower εbiome. These results enhance understanding of structural influences on carbon uptake across global biomes.

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.