Towards a Standard Approach for UAS-based Multi-Angular Dataset Collection for BRDF Analysis

In this work, we address the bidirectional reflectance distribution function (BRDF) characterization of homogeneous surfaces by means of multiangular datasets acquired with an unmanned aerial system (UAS) carrying a multispectral sensor (MAIA) replicating the spectral characteristics of the multispectral instrument onboard Copernicus Sentinel-2 satellite. The UAS field campaign was performed in clear-sky conditions over two different test sites, a vegetation cover and an asphalted area, exhibiting different behaviors in terms of surface reflectance anisotropy. A dual angular approach for the processing of the reflectance measurements is examined: a conical configuration considering a cone angle of 10° (hemispherical conical-reflectance distribution) and a directional configuration (hemispherical directional-reflectance distribution) considering a cone angle of 3°. Afterward, the retrieval of the parameters of the Ross–Li–Maignan BRDF model was implemented by a least-squared fitting of the UAS reflectance measurements for each MAIA band. The accuracy of the modeled reflectances was evaluated and the overall relative root-mean-square error between the measured and modeled reflectances was less than 10% for both test sites. The outcomes of the present study go toward the definition of a standard approach for UAS-based measurements with high angular resolution features for BRDF modeling, avoiding the well-known issues related to the use of ground-based and satellite-based instruments, and proving the UAS effectiveness in supporting calibration and validation activities of satellite missions.