Aside from sonar systems, Airborne LIDAR Bathymetry (ALB) has become the most reliable depthometer. With the commercialization of ALB, several companies have produced powerful ALB, and research institutions have obtained the full-waveform data of water. Meanwhile, several algorithms have been proposed to process the signal of ALB. In this work, we introduce the theoretical basis of ALB and then review algorithms of correction for pulse stretching and peak finding of full waveform. Then, we analyze the main influencing factors of accuracy, including water depth, water color, and reflectance of substrates. We also provide an overview of the new application of ALB in substrate classification. In specific, ALB can help retrieve information from full waveform to the maximum extent. We draw the following conclusions: (1) Pulse stretching is mainly caused by the topography of substrate, whereas algorithms for correcting pulse stretching have been developed under the consideration of general terrain slope or incident angle of pulse. The complex topography of substrate should be considered, especially for the application in coral reef, where substrates distribute inhomogeneity. (2) Algorithms of peak finding of full waveform can be separated into three kinds: echo detection, mathematical approximation, and deconvolution. Echo detection methods run fast but are influenced by environmental noise more easily. The object function of mathematical approximation methods is difficult to be solved, but environmental parameters such as water attenuation coefficient can be derived. Deconvolution methods are stable but need to take effective measures to suppress noise. (3) The proposed algorithm cannot work well for extremely shallow water, especially at depths within centimeter level, but polarization lidar may solve the problem in the future. Low water quality and low reflectance of substrate reduce signal and noise ratio. Thus, new algorithms need to be developed for these conditions in the future. (4) The new application of ALB in substrate classification and data fusion with hyperspectral image indicates that further information in full waveform of water should be retrieved in the future.