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Abstract Amazônia is a species-rich region of immense importance to Earth’s water and carbon cycling. Photosynthesis drives the global carbon cycle, so understanding photosynthetic differences across diverse landscapes is a key task of ecophysiology and ecosystem science. Unfortunately, due to physiological and logistical constraints, ground-based photosynthesis data in Amazônia remain scarce and the ‘traditional’ steady-state (SS) method of gas exchange is slow and inefficient. The Dynamic Assimilation™ Technique (DAT) promises a new way to perform A/Ci curves rapidly without requiring SS conditions. Thus far, this technique has only been validated in greenhouse or agricultural-field-grown species and has yet to be tested in forest trees of diverse physiology morphology and environmental adaptation. To test the utility of the DAT in a complex tropical forest ecosystem, we compared the DAT with the SS method in 13 Amazonian trees in situ. We found strong agreement between Vcmax from DAT curves and SS curves, while Jmax was underestimated in DAT curves. We conclude that the DAT provides a robust and rapid estimation of Vcmax. We also identified diverse and unexpected DAT curve shapes among some trees, including the presence of an ‘overshoot’ in assimilation beyond model-derived ribulose-1,5-bisphosphate (RuBP) regeneration limitations. The presence of overshoot may elucidate microclimate and species differences in RuBP regeneration rates and emphasizes the considerable importance of DAT curve protocol specifications, such as the effect of ramp rate and direction on Jmax and TPU. Overall, the DAT saved time relative to the SS method and proved to be an effective and rapid method for quantifying Vcmax in tropical trees. 

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.