Detecting the ocean surface from the raw data of the MABEL photon-counting lidar

With much smaller footprints (approximately a few tens of meters), the data of a laser altimeter are promising for obtaining the sea level near offshore areas, where radar altimeters with larger footprints cannot operate. However, the current ocean surface detection methods for a photon-counting lidar cannot effectively eliminate the noise photons when measuring the sea surface, thereby introducing a ranging bias. In this paper, a new ocean surface detection method is derived based on the JONSWAP (Joint North Sea Wave Project) wave spectrum and LM (Levenberg-Marquardt) nonlinear least-squares fitting. Using the data photons that are captured by the NASA MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar, the new method is tested and compared to the MABEL standard result. The new method achieved better profile detection of sea surfaces and successfully discarded the noise photons in a sub-layer below the sea surface from the MABEL standard result. By reconstructing the "accumulated waveform", we found that the noise photons in the sub-layer produce small tails after the main waveform, which introduces an overestimated ranging bias of 9 cm. This difference of 9 cm is similar to the sea level bias of 10 cm that was obtained from the ICESat/GLAS laser altimeter data and the TOPEX/Poseidon radar altimeter data in an earlier study, which limited the use of laser altimeter data. According to the analysis in this paper, we can partially interpret what occurred for the ICESat/GLAS waveform tails when ICESat was measuring sea surfaces. The newly derived method can protect the MABEL and incoming ICESat-2 data photons from noise photon interference and ranging bias when measuring the sea surface.