One of the most useful survey methods in nearshore studies is airborne light detection and ranging (LIDAR), which is able to densely sample topographic and shallow bathymetric elevation data over large geographic regions. Airborne LIDAR bathymetry systems are dependent on water clarity, but in the surf zone sediment and air bubbles entrained in the water column by wave breaking attenuate the laser pulse and compromise the LIDAR's ability to retrieve accurate bottom elevations. Data assimilation techniques can improve the ability of LIDAR systems to estimate bathymetry inside the surf zone. The assimilation methods are based on comparing pixel intensity patterns (scaled by offshore wave energy flux) extracted from time-averaged airborne imagery with dissipation profiles produced by a simple wave-energy transformation model. The subaerial topography and the offshore bathymetry are assumed known and an initial featureless bathymetry is assumed in the surf zone (where the data are missing). Differences between modeled dissipation and observed image pixel intensity patterns can be minimized by incrementally modifying the bathymetry. Final assimilated bathymetry estimates are compared with surveyed bathymetric data collected at the U.S. Army Corps of Engineers Field Research Facility in Duck, NC using traditional surveying methods. Analysis of data from three aerial overflights produced average root mean square differences between assimilated and surveyed bathymetry of 25–35 cm, similar to results from land-based systems. This methodology can be used to improve LIDAR-derived profiles where large gaps exist because of surf that attenuates the laser pulses, and allow for more complete evaluation of large-scale coastal behavior that includes profile evolution within the surf zone.