Remote sensing provides high accuracy/precision for quantifying forest biophysical parameters needed for ecological management. Although the significant impact of Bidirectional Scattering Distribution Functions (BSDF) on remote sensing of vegetation is well-known, current forest metrics derived from sensor data seldom take leaf BSDF into account, and despite the importance of BSDF effects, leaf directional scattering measurements are almost nonexistent. Previous studies have been limited in the spectral coverage and resolution of observed electromagnetic radiation (EMR) and lacked models to interpolate all source-sensor angles beyond measurements. This study captured deciduous broadleaf Bidirectional Reflectance Distribution Functions (BRDFs) from the visible through shortwave-infrared (SWIR) spectral regions (350-2500 nm) and accurately modeled the BRDF for extension to any illumination angle, viewing zenith, or azimuthal angle. We measured bi-conical directional reflectance factor (BCRF) of leaves from three species of large trees, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra). We then fit the data through nonlinear regression to physical, microfacet BRDF models, resulting in normalized root mean square errors of less than 8%, averaged across all wavelengths (excluding low signal-to-noise spectral regions). We extracted leaf physical parameters, including the index of refraction and a relative physical roughness from the microfacet models delineating the three species. The implications for forestry remote sensing are important, as rigorous models to represent leaves allow for the creation of more accurate forest scenes for radiative transfer modeling. Such accuracy enables higher fidelity sensor evaluations and data processing algorithms.

Remote sensing increasingly has become an important tool for forest management. In the development of forest metrics from remote-sensing data, currently many models omit the individual leaf bidirectional scattering distribution function (BSDF). Past studies, and the currently available data, often do not adequately incorporate transmission, cover the broader reflective domain, and/or incorporate models to extend to any illumination and view angle combination. We estimated broadleaf bidirectional transmittance distribution functions (BTDFs) in this study using the goniometer of the Rochester Institute of Technology-Two (GRIT-T), which records spectral data in the UV-A through shortwave infrared (SWIR) spectral regions (350-2500 nm). We measured three species of large tree leaves, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra). We accurately modeled leaf BTDF with extension to any illumination angle, viewing zenith, and azimuthal angle through nonlinear regression to a physically-based microfacet BTDF. The model fit showed a mean of less than 7% normalized root-mean-squared error (NRMSE) spectrally from 450 to 2300 nm (lower and upper wavelength range omitted due to detector noise). The microfacet models provide highly useful physical quantities such as a relative roughness, index of refraction, and absorption, which are all directly related to leaf optical properties. These physical quantities have implications for plant physiology, vegetation remote sensing, and physics-based image generation. Specifically, the accuracy of radiative transfer modeling in forest canopies depends on rigorous representations of leaves, and this increase in accuracy can lead to the development of higher fidelity data processing algorithms for remote sensing. Data and programing scripts are available at- http://dx.doi.org/10.21227/yjek-2059

Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45° interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data.

The Citizen CATE 2024 next-generation experiment placed 43 identical telescope and camera setups along the path of totality during the total solar eclipse (TSE) on 8 April 2024 to capture a 60-minute movie of the inner and middle solar corona in polarized visible light. The 2024 TSE path covered a large geographic swath of North America and we recruited and trained 36 teams of community participants (“citizen scientists”) representative of the various communities along the path of totality. Afterwards, these teams retained the equipment in their communities for on-going education and public engagement activities. Participants ranged from students (K12, undergraduate, and graduate), educators, and adult learners to amateur and professional astronomers. In addition to equipment for their communities, CATE 2024 teams received hands-on telescope training, educational and learning materials, and instruction on data analysis techniques. CATE 2024 used high-cadence, high-dynamic-range (HDR) polarimetric observations of the solar corona to characterize the physical processes that shape its heating, structure, and evolution at scales and sensitivities that cannot be studied outside of a TSE. Conventional eclipse observations do not span sufficient time to capture changing coronal topology, but the extended observation from CATE 2024 does. Analysis of the fully calibrated dataset will provide deeper insight and understanding into these critical physical processes. We present an overview of the CATE 2024 project, including how we engaged local communities along the path of totality, and the first look at CATE 2024 data products from the 2024 TSE.