Variation of Bi-Directional Reflectance at Multiple Spatial Resolutions Over Different Land Cover Types Using UAV-Based Multiangle Images

In recent years, the UAV has become a convenient platform to obtain multi-angle reflectance observations and study bi-directional reflectance distribution function (BRDF) characteristics at a higher spatial resolution than satellite. However, only a few vegetation types were concerned in previous studies, leading to a lack of BRDF knowledge for various objects and preventing further recognition application. In this study, UAV-based multi-angle observations were collected for six typical natural and artificial targets including larch forest, grass, artificial turf, asphalt road, cement hut, and model plane. Firstly, fitting accuracy of directional reflectance was calculated, and then we analyzed the variance patterns of spectral and anisotropic indices along with spatial resolutions (i.e., 1-100 m). The results show that: (1) The RTLSR_C kernel-driven model is still applicable for UAV with fitting RMSEs of reflectance less than 0.05, showing multi-scale adaptability for both UAV and satellite; (2) For grass and artificial turf, the normalized difference vegetation index (NDVI) decreases as spatial resolution increases, and a significant change with view zenith angle can be observed with the minimum at the hotspot; (3) The anisotropic flat index (AFX) varies with ground types, light, shadows, and sample spatial homogeneity. Notably, there is a sudden change in AFX for larch forest at 5 m near canopy width. Similar NDVI and AFX values are found between grass and artificial turf. This study further reveals BRDF patterns for new target types at varying spatial scales, providing evidence for the applicability of the kernel-driven model at high spatial resolution and target camouflage.