A Solar Trajectory Model for Multi-Spectral Image Correction of DOM from Long-Endurance UAV in Clear Sky

Abstract

Extracting accurate surface reflectance from multispectral UAV (unmanned aerial vehicle) imagery is a fundamental task in remote sensing. However, most studies have focused on short-endurance UAVs, with limited attention given to long-endurance UAVs due to the challenges posed by dynamically changing incident radiative energy. This study addresses this gap by employing a solar trajectory model (STM) to accurately estimate incident radiative energy, thereby improving reflectance calculation precision. The STM method addresses the following key issues: The experimental results demonstrated that the root mean square error (RMSE) of the STM method in Shanghai was 15.80% compared to the standard reflectance, which is 51% lower than the downwelling light sensor (DLS) method and 37% lower than the traditional method. This indicates that the STM method provides results that are more accurate, aligning closely with standard values. In Tianjin, the RMSE was 24% lower than the DLS method and 65% lower than the traditional method. The STM effectively mitigates inconsistencies in incident radiative energy across different image strips captured by long-endurance UAVs, ensuring uniform reflectance accuracy in digital orthophoto maps (DOMs). The proportion of corrected reflectance errors within the ideal range (±10%) increased by 24% compared to the histogram matching method. Furthermore, the optimal flight duration for long-endurance UAVs launched at noon was extended from 50 min to 150 min. In conclusion, this study demonstrates that applying the STM to correct multispectral imagery obtained from long-endurance UAVs significantly enhances reflectance calculation accuracy for DOMs, offering a practical solution for improving reflectance imagery quality under clear-sky conditions.