Accuracy Evaluation of Fine-Scale BRDF Archetype Inversion Considering Vegetation Structure Clustering Based on the LESS 3-D Simulations at Forest Scenes

The anisotropic reflectance characteristic (i.e., bidirectional reflectance distribution function (BRDF) effect) lays the foundation of quantitative remote sensing, while it is difficult to reconstruct at high spatial resolution due to near-nadir small-angle observations. Based on the traditional archetype inversion algorithm, this study explores and evaluates a two-step method considering vegetation structure clustering to retrieve fine-scale BRDF, using LESS simulated sufficient multiangle reflectances in forest scenes at multiple scales. First, full inversion of the kernel-driven model RTLSR_C was performed to investigate the BRDF feature at 1–50 m. Subsequently, the sensitivity of normalized difference vegetation index (NDVI) and anisotropic flat index (AFX) to forest structure was comprehensively analyzed. For reflectances at small view zenith angles (VZAs) of 0° and 15° close to Sentinel multispectral instrument (MSI) observations at 5, 10, and 25 m, forest structure cluster-based BRDF parameters were first retrieved based on accumulated intraclass reflectances, where prior BRDF information from 50-m LESS data and 500-m MODIS global representative sites were compared. Finally, cluster-based BRDF parameters were used as prior archetypes to retrieve pixel BRDF. Results show good fitting root mean square errors (RMSEs) for full inversion, with most average RMSEs below 0.05, and spectral and angular reflectance are sensitive to the fraction of vegetation cover (FVC), tree height, and crown length at 5 m and larger scales. In addition, the two-step method based on structure clustering yields a slightly higher BRDF inversion accuracy than that of the one-step method, and the traditional one-step archetype inversion algorithm demonstrates its simplicity and accuracy. This study provides new insights for fine-scale BRDF inversion.