In precision agriculture, vegetation and soil are monitored by multispectral sensors that can observe outside the visible bands. In contrast to satellites and manned aircraft, unmanned aerial vehicles (UAVs) allow anyone to easily acquire near-real time data at a reasonable price. However, UAV images do not account for the anisotropic reflectance and solar irradiance from the ground surface, so extracting the reflectance of vegetation is difficult. To solve this problem, this study developed a bidirectional reflectance distribution function (BRDF) that expresses the anisotropic reflectance of the Earth’s surface as a function of the geometric relationship with the UAV sensor and the Sun. To compensate for the effect of changes in solar incident energy due to clouds and solar irradiance, the solar irradiance was measured and corrected on the ground rather than in the air to avoid errors due to the flight attitude. Before processing by the BRDF and correcting for the solar irradiance, the UAV obtained striated orthomosaic images for which the vegetation indices were affected by the position and attitude of the Sun and the UAV sensor. After the correction, consistent values were calculated for the vegetation indices throughout the images. The accuracy of the UAV data was analyzed by comparison with Sentinel 2A. Reflectance differences are 0.02% to 6.37% from the image without correction. After applying the correction, it reduced to 0.27%, 0.61%, 0.16%, and 0.65% from the blue, green, red, and near-infrared bands, respectively. This study is valuable for obtaining accurate values for vegetation indices under a wide range of weather and geometric conditions at different sites because UAVs to collect images are a rare case under optimal conditions.