A multi-angle reflectance dataset of wheat and peach trees with unmanned aerial vehicle imagery

Medium- to high-resolution satellite data, such as Sentinel-2 and Landsat-8, have significantly enhanced the accuracy of vegetation monitoring. However, canopy reflectance and vegetation indices are affected by the bidirectional reflectance distribution function (BRDF) effect, introducing uncertainties in vegetation phenology monitoring and parameter retrieval. Herein, a multi-angle reflectance dataset was developed using unmanned aerial vehicles (UAVs) to investigate the angular effect over the crop canopy across various growth stages. Multi-angle measurements were acquired over two 10 m × 10 m plots of wheat and peach trees in Yukou Town, Pinggu District, Beijing, China, during their respective key growth stages (29 Feb to 21 May in 2024 for wheat; 11 Apr to 12 Jun in 2024 for peach trees). Wheat measurements were obtained using a DJI Matrice 350 RTK equipped with a Cubert ULTRIS X20 Plus hyperspectral imager, while peach tree data were captured with an Agrowing 61MP Sextuple multispectral imager. UAV flights used a spherical-helical trajectory to maximize solar-view geometry coverage, yielding over 1,800 angular observations under clear-sky conditions. All UAV imagery was post-processed and stored in text and Excel formats. The dataset provides high-quality, multi-angle spectral measurements for two representative crops during multiple growth stages, enabling in-depth investigations into BRDF characteristics, angular sensitivity of spectral metrics, and temporal spectral dynamics. Furthermore, the data support the validation of physical models for vegetation remote sensing, vegetation parameter inversion, and the training of machine-learning models for more remote sensing applications.