On Leaf BRDF Estimates and Their Fit to Microfacet Models

Abstract

Remote sensing provides high accuracy/precision for quantifying forest biophysical parameters needed for ecological management. Although the significant impact of bidirectional scattering distribution functions (BSDFs) 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 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 spectral regions (350-2500 nm) and accurately modeled the BRDF for extension to any illumination angle, viewing zenith, or azimuthal angle. We measured biconical directional reflectance factor 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 signalto-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.

Comparison of Soil Moisture Content Retrieval Models Utilizing Hyperspectral Goniometer Data and Hyperspectral Imagery From an Unmanned Aerial System

“…The BRF data used in this segment of our analysis was measured in our laboratory using a hyperspectral goniometer, the Goniometer of the Rochester Institute of Technology‐Two (GRIT‐T) (Harms et al., 2017) covering a range of moisture content from dry to fully saturated (Eon & Bachmann, 2021a, 2021b). The GRIT‐T is a sophisticated goniometric system designed for use in field and laboratory settings and has seen widespread application in previous research (Bachmann, Eon, Ambeau, et al., 2018; G. Badura et al., 2019; G. Badura & Bachmann, 2019; G. P. Badura et al., 2019; Eon et al., 2018, 2020; Eon & Bachmann, 2021b; Roth et al., 2020, 2021; Shiltz & Bachmann, 2023). Users can create custom scan patterns to manage the system's arm, head, and carriage.…”

“…Badura et al, 2019;G. Badura & Bachmann, 2019; G. P. Badura et al, 2019;Eon et al, 2018Eon et al, , 2020Eon & Bachmann, 2021b;Roth et al, 2020Roth et al, , 2021Shiltz & Bachmann, 2023). Users can create custom scan patterns to manage the system's arm, head, and carriage.…”

Developmental Trends in the Application and Measurement of the Bidirectional Reflection Distribution Function

“…The surface reflectance of different vegetation is anisotropic due to its structural distribution. For example, BRDF distribution tests on wheat leaves [8], sorghum [9], maple leaves, and other leaves [10] can be found that different vegetation has different BRDF characteristic models. Therefore, based on the sensitivity of multi-angle BRDF data observation to vegetation structure, BRDF data can improve the accuracy of vegetation classification to 91% [11], which can improve the assessment of the ecological environment in different regions.…”