Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry contributes to formulating reliable bathymetric strategies, particularly for areas such as the Spratly Islands, where in situ observations are exceptionally scarce. In this study, we took Anda Reef as a case study and evaluated the performance of eight common SDB approaches by integrating Sentinel-2 images with Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The bathymetric maps were generated using two classical and six machine-learning algorithms, which were then validated with measured sonar data. The results illustrated that all models accurately estimated the depth of coral reefs in the 0–20 m range. The classical algorithms (Lyzenga and Stumpf) exhibited a mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of less than 0.990 m, 1.386 m, and 11.173%, respectively. The machine learning algorithms generally outperformed the classical algorithms in accuracy and bathymetric detail, with a coefficient of determination (R²) ranging from 0.94 to 0.96 and an RMSE ranging from 1.034 m to 1.202 m. The multilayer perceptron (MLP) achieved the highest accuracy and consistency with an RMSE of as low as 1.034 m, followed by the k-nearest neighbor (KNN) (1.070 m). Our results provide a practical reference for selecting SDB algorithms to accurately obtain shallow water bathymetry in subsequent studies.

Significant range differences were identified between shallow and deep channels of the Mapper5000 bathymetry light detection and ranging (LiDAR) system with segmented field-of-view (FOV) receivers. Range difference varied with depth and water optical properties. The main feature was the maximum value in range difference curves, which ranged from 0.3 to 0.6 m and usually exceeded the International Hydrographic Organization (IHO) accuracy standards. Sensitivity analyses based on a semianalytical Monte Carlo simulation model revealed that the scattering phase function and laser beam divergence angle played more important roles in causing pulse dispersion and determining the amplitude and position of maximum range difference than absorption and scattering coefficients. A range difference correction method by fitting existing shallow and deep channel data in the overlapping range with a cubic polynomial was proposed to correct the deep channel data in the entire depth range that LiDAR can detect. Depth discontinuity at the junction of the shallow channel and deep channel measurements was successfully removed, and the mean and standard deviation of corrected range differences were within 0.01 and 0.1 m, respectively. A combination of range difference correction and mean bias corrector can be an alternative method for depth bias correction of segmented-FOV LiDAR when referenced sonar data is not available.

Determination of the correct water surface height (WSH) from green laser (532 nm) echoes alone in bathymetry LiDAR is challenging, as the green laser return near the water surface involves both specular reflection from the air–water interface and backscattered return from the water volume. In this paper, a low-complexity method based on linear approximation of the leading edge (LLE) is proposed. The results of this LLE method were compared with those of three common algorithms of peak detection, half peak power, and surface-volume-bottom implemented on airborne datasets with various surface roughness conditions. In addition, the method was evaluated in waters with a wide range of optical properties through a controllable tank experiment. The uncertainties in the WSHs of all algorithms were greater when the water volume backscattering dominated the surface return; they were sensitive to variations in the optical properties of water, and increased exponentially with decreasing LiDAR attenuation coefficient (K_LiDAR). Comparatively, the LLE algorithm had the fastest computational speed and demonstrated the best performance in situations where specular reflection or volume backscatter return was dominant, with average and maximum errors of less than 0.06 and 0.13 m, respectively.

Airborne bathymetry LiDAR, such as Mapper5000, experienced varied water-bottom return intensities. Under the high bottom return signal condition, owing to the clear water body, high bottom reflectance, low depth, or high gain setting of the detector, the peak power of the received signal from the bottom often exceeds the linear dynamic range of the 532 nm detector assembly. The resulting signal saturation distorted the recorded bottom return waveforms and resulted in undesired depth bias exceeding 2.0 m in certain extreme cases. In this study, we developed a low-complexity peak position restoration method for this “saturation in-water range bias” correction based on an intersection point of double linear approximations of the leading and trailing edges of the saturated bottom waveform (DLI). The validation of corrected results with consistent sonar depth measurements indicated that the DLI method demonstrated considerable robustness to the effect of pulse stretching under saturation conditions and reduced the mean absolute depth bias from 1.46 m to 0.47 m. Compared with Gaussian and exponentially modified Gaussian fitting algorithms, the DLI was faster and the most effective at reducing depth bias under strong saturation conditions. The success of DLI benefited from the small width of the transmitted laser pulse and the sharp slope of the leading edge of the saturated bottom waveform. However, the interference of volume backscattering signals at shallow depths could affect the performance of DLI, which should be carefully removed.

Accurate land-sea classification of ATL03 datasets is the prerequisite for signal photons identification and higher-level data production. Current photon classification methods are mainly based on physical models and machine learning methods. Physical models necessitate a priori knowledge of the background noise rate, while machine learning methods require extensive manual intervention, which can be time-consuming. This work introduced a fast feature detection model (FFD model) for land-sea classification that eliminates the need for prior knowledge and is not constrained by time. The land-sea boundary usually experiences significant photon height changes and reflective intensity differences on both sides. The FFD model was developed to efficiently detect the inflection point of photon reflective intensity for preliminary land-sea classification. Minor classification errors were carefully corrected based on topographic geometric distribution in further refinement. The FFD model was applied to three typical land-sea boundary types to evaluate its classification accuracy. Validation of classification results with consistent Sentinel-2 images and manually tagging photons indicated that the FFD method exhibited substantial robustness to various land-sea boundary landforms. The FFD model can process nearly 10⁵ raw photon classifications per second, with an average overall accuracy of 99.42 % for preliminary classification results. Coastal slope and surface reflection in land-sea boundary may have impact on classification accuracy. These effects were carefully eliminated in the refinement process, resulting in an average overall accuracy improvement to 99.86 %. Consequently, the FFD model represented a robust and efficient solution for automatic land-sea classification in coastal shallow water, coral reefs, and estuary areas.

The continuous demand for coastal zone resource exploitation and marine ecosystem protection challenges satellite remote sensing observation of shallow sea apparent optical properties. The existing satellite retrieval method of the light detection and ranging (LiDAR) attenuation coefficient ( $K_{\mathrm {lidar}}$ ) relies on water column photons, so it cannot achieve accurate water quality measurements in shallow water depths. In this study, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data were innovatively fused with Sentinel-3 image to retrieve comprehensive $K_{\mathrm {lidar}}$ . First, $K_{\mathrm {lidar}}$ is calculated in offshore areas using ICESat-2 water column photons and in nearshore areas using seafloor photons. ICESat-2-derived $K_{\mathrm {lidar}}$ was then integrated with Sentinel-3 image, employing Bayesian-optimized CatBoost (BO-CatBoost) to invert the spatial distribution of both offshore and nearshore areas. Finally, the retrieval results’ weights were assigned based on Sentinel-3’s band ratio, yielding comprehensive $K_{\mathrm {lidar}}$ results for the shallow seas. The proposed method successfully obtained an accurate spatial distribution of $K_{\mathrm {lidar}}$ in Passu Keah, Culebra, and Marquesas Keys. The results show that the coefficient of determination ( $R^{2}$ ), mean relative error (MRE), and mean absolute error (MAE) for the three study areas range from 0.7402 to 0.8557, 0.0405 to $0.0885~\mathrm {m}^{-1}$ , and 0.0033 to $0.0055~\mathrm {m}^{-1}$ , respectively. The root mean square error (RMSE) ranges from 0.0043 to $0.0077~\mathrm {m}^{-1}$ . It has higher reliability than Sentinel-3’s $K_{d}{(}532{)}$ . As satellite remote sensing improves, the method will efficiently map the LiDAR attenuation coefficient across vast areas, offering reliable data for resource development, water quality, and ecosystem conservation.