Abstract
This study proposes a data-driven excimer LiDAR framework for joint surface reflectivity mapping and atmospheric pollutant profiling, integrating physics-based sensing with deep learning-based multi-source data fusion. The system utilizes ultraviolet excimer LiDAR measurements in combination with auxiliary data from UAV platforms, satellite observations, and ground-based sensors to construct a unified environmental monitoring pipeline. A structured signal processing approach is applied to extract physically meaningful features, including backscatter and extinction coefficients, as well as differential absorption parameters. These features are subsequently fused using a deep learning architecture designed to model complex nonlinear relationships across heterogeneous data sources. Experimental results demonstrate that the proposed method achieves high predictive accuracy, with improved correlation and reduced error compared to traditional LiDAR and baseline fusion approaches. The framework effectively captures both vertical atmospheric pollutant distributions and horizontal surface reflectivity patterns, enabling comprehensive environmental analysis. Validation against external datasets confirms the robustness and generalization capability of the model under varying conditions. The integration of data-driven modeling with excimer LiDAR sensing enhances system performance while maintaining real-time capability. Overall, the proposed approach provides a scalable and efficient solution for advanced environmental monitoring, contributing to the development of intelligent remote sensing systems for air quality assessment and land-cover analysis.