This work aimed to prospect future space-borne LiDAR sensor capacities for global bathymetry over inland and coastal waters. The sensor performances were assessed using a methodology based on waveform simulation. A global representative simulated waveform database is first built from the Wa-LiD (Water LiDAR) waveform simulator and from distributions of water parameters assumed to be representative at the global scale. A bathymetry detection and estimation process is thus applied to each waveform to determine the bathymetric measurement probabilities in coastal waters, shallow lakes, deep lakes and rivers for a range of water depths. Finally, with a sensitivity analysis of waveforms, the accuracy and some limiting factors of the bathymetry are identified for the dominant water parameters. Two future space-borne LiDAR sensors were explored: an ultraviolet (UV) LiDAR and a green LiDAR. The results show that the bathymetric measurement probabilities at a 1 m depth is 63%, 54%, 24% and 19% with the green LiDAR for deep lakes, coastal waters, rivers and shallow lakes, respectively, and 10%, 22%, 1% and 0% with the UV LiDAR, respectively. The threshold values of dominant water parameters (sediment, yellow substance and phytoplankton concentrations) above which bathymetry detection fails were identiήed and mapped. The accuracy on the bathymetry estimates for both LiDAR sensors is 2.8 cm for one standard deviation with negligible bias (approximately 0.5 cm). However, these accuracy statistics only include the errors coming from the digitizing resolution and the inversion algorithm.