Inverse synthetic aperture LiDAR (ISAL) is a kind of coherent imaging system. It acquires images with speckles that affect target recognition and judgment. In recent years, some scholars proposes a model-based iterative reconstruction (MBIR) algorithm to solve the problem. The algorithm directly estimates the real valued reflectance instead of the complex valued one commonly used by traditional reconstruction methods, making the reconstructed image closer to the optical image. However, the MBIR algorithm faces the problems of complex optimization model, low efficiency, and difficult convergence when the gradient-free line search version is used. To address these problems, this study presents two proposals. First, the Markov relation between the distributions of the complex reflectance and reflectivity, and the measurement signal is obtained from the viewpoint of information transfer. The complex reflectance is assumed as a complete dataset of the reflectivity estimation that simplifies the optimization. Second, the surrogate function of a prior model, whose gradient is easier to obtain, and the logarithm transformation are used to improve the algorithm efficiency in which the original problem is transformed into an unconstrained problem with gradient. The effectiveness and efficiency of the proposed method are verified by simulation and outdoor experimental data from a target 7 km away. The results show that the proposed method can obtain better images within five iterations for echo data with carrier-to-noise ratio of 5 dB, 0 dB, and -5 dB.