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Consistent and scalable estimation of vegetation structural parameters from imaging spectroscopy is essential to remote sensing for ecosystem studies, with applications to a wide range of biophysical assessments. To support global vegetation assessment, NASA has proposed the Hyperspectral Infrared Imager (HyspIRI) imaging spectrometer, which measures the randiance 380-2500nm in 10nm contiguous bands with 60m ground sample distance (GSD). However, because of the large pixel size on the ground, there is uncertainty as to the effects of vegetation structure on observed radiance. This research evaluates linkages between vegetation structure and imaging spectroscopy. Specifically, we assess the impact of within-pixel vegetation density and position on large-footprint spectral radiances. To achieve this objective, three virtual forest scenes were constructed, which correspond to the actual veg- etation structure of the National Ecological Observatory Network (NEON) Pacific Southwest domain (PSW; D17; Fresno, CA). These were used to simulate anticipated HyspIRI data (60m GSD) using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, a first-principles synthetic image generation model de- veloped by the Rochester Institute of Technology. Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) and NEON's high-resolution imaging spectrometer (NIS) data were used to verify the geometric parameters and physical models. Multiple simulated HyspIRI data sets were generated by varying within-pixel structural variables, such as forest density, position, and distribution of trees, in order to assess the impact of sub-pixel structural variation on observed HyspIRI data. Results indicate that HyspIRI is sensitive to sub-pixel vegetation density variation in the visible to short- wavelength infrared spectrum due to vegetation structural changes, and associated pigment and water content variation. This has implications for improving the system's suitability for consistent global vegetation structural assessments by adapting calibration strategies to account for this sub-pixel variation.
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