This paper proposes a lightweight LiDAR-Inertial SLAM (Simultaneous Localization and Mapping) system specifically tailored for UAV pose estimation, aiming to meet the requirements of small UAVs in terms of precision and real-time performance in satellite signal denied area. The system primarily comprises two essential components: (1) To enhance pose estimation accuracy in environments with few geometric features, a surfel-based LiDAR point cloud registration algorithm is proposed, this algorithm achieves point cloud registration and pose estimation by minimizing the distance between points and surfels, while reducing the algorithm"s computational complexity and ensuring lightweight operation by discarding un-stable surface elements. (2) The framework of integrating this algorithm into the Error-State Kalman filter (ESKF) based LiDAR-inertial system is designed. The proposed SLAM system is evaluated through experiments conducted on experimental datasets. The results demonstrate superior pose estimation accuracy compared to existing LiDAR-Inertial Navigation Systems. Furthermore, while maintaining the performance in terms of runtime, the proposed technique reduces the average position deviation by 37.63% and the average attitude deviation by 33.94% in outdoor satellite signal denied environments.