ICESat-2 photon counting data are widely used in near-shore bathymetry. However, a large number of noisy photons unavoidably exist in the acquired photon data, and photon denoising is necessary to accurately extract the underwater signal photons. Based on the phenomenon that the number of photons increases at the target object, a methodology for detecting ICESat-2 bathymetry photons based on quasi full-waveform is proposed. First, the ICESat-2 photon data are split at certain intervals to extract the sea surface and seafloor height photons within each interval. Second, a double-peaked Gaussian function is utilized to fit the photon heights in each interval to determine the sea surface and seafloor heights. Third, sea surface and seafloor height photons for each interval are connected along the track direction to generate sea surface and seafloor datums. Finally, the optimal buffer distance is calculated and the buffer is created to filter the photons, and then the refraction correction is applied to the seafloor photons. ICESat-2 data from Vieques Island and Saint Croix Island are selected for the experiment and the results are compared with in situ bathymetry data. The results show that the MAE of the bathymetry results extracted by the proposed method ranged from 0.16m to 0.30m and the RMSE ranged from 0.24m to 0.32m in different areas. Compared with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Adaptive Elevation Difference Thresholding Algorithm (AEDTA), the proposed method accurately recognizes the signal photons for different densities of photon data and different complexities of seafloor topography, and the extracted signal photons are complete and continuous, showing excellent robustness. By selecting suitable split spacing along the track direction and histogram separation spacing, the proposed method in this study achieves excellent performance in bathymetry extraction.

Bathymetric surveys are of great importance for submarine topography mapping and coastal construction projects. They are also of great significance for terrain surveys of islands and coastal zones, maritime navigation and marine management planning. Traditional ship-borne water depth measurement methods are costly and time-consuming, therefore, in recent years, passive optical remote sensing technology has become an important means for shallow water depth measurements. In addition, multispectral water depth optical remote sensing has wide application values. Considering the relationship between water depth and the inherent optical characteristics of water column, an inherent optical parameters linear model (IOPLM) is developed to estimate shallow water bathymetry from high spatial resolution multispectral images. Experiments were carried out in the shallow waters (≤20 m) around Dongdao Island in China’s Paracel Islands and Saipan Island in the Northern Mariana Islands. Different accuracy evaluation indexes were used to verify the model. The comparisons with the traditional log-linear model and the Stumpf model show that in terms of overall accuracy and accuracy in different water depths, the IOPLM has slightly better results and stronger retrieval capabilities than the other models. The mean absolute error (MAE) of Dongdao Island and Saipan Island reached 1.17 m and 1.92 m, and the root mean square error (RMSE) was 1.49 m and 2.4 m, respectively.

Atmospheric correction (AC) is the basis for quantitative water remote sensing, and adjacency effects form an important part of AC for medium- and high-spatial-resolution optical satellite images. The 6S radiative transfer model is widely used, but its background reflectance function does not take the radiance changes at water–land boundaries into account. If the observed land possesses bright features, the radiance of the adjacent water will be affected, leading to deviations in the AC results and increasing the uncertainty of water depth-based optical quantitative remote sensing. In this paper, we propose a model named WL-AE (a correction model for water–land boundary adjacency effects), which is based on the obvious radiance differences at water–land boundaries. This model overcomes the problem by which the background reflectance calculation is not terminated due to the highlighting pixel. We consider the influences of different $R_{ns}$ (neighborhood space) on the target pixel. The effective calculation of the equivalent background reflectance of the target pixel is realized, and the influence of the land area anomaly highlighting the pixel on the adjacent water is avoided. The results show that WL-AE can effectively improve the entropy and contrast of the input image and that the water–land boundary is greatly affected by adjacency effects, especially in the green and near-infrared bands, where the $M_{rc}$ (mean rate of change) are as high as 14.2% and 20.1%, respectively. In the visible wavelength, the $Sd$ of $R_{rc}$ (the relative rate of change) is positively correlated with $R_{ns}$, and the $Sd$ reaches 16.9%. Although the adjacency effect is affected by ground object types, its influence area remains within 3 km offshore. Based on the WL-AE and 6S results, the comparative test regarding bathymetric inversion shows that the influence is significant in the 0–5 m depth section. In Penang, the MRE of the 0–4 m inversion results is 31.4%, which is 10.5% lower than that of the 6S model.

As fundamental geophysical information, the high-precision detection of shallow water bathymetry is critical data support for the utilization of island resources and coral reef protection delimitation. In recent years, the combination of active and passive remote sensing technologies has led to a revolutionary breakthrough in satellite-derived bathymetry (SDB). Optical SDB extracts bathymetry by quantifying light–water–bottom interactions. Therefore, the apparent differences in the reflectance of different bottom types in specific wavelength bands are a core component of SDB. In this study, refined classification was performed for complex seafloor sediment and geomorphic features in coral reef habitats. A multi-model synergistic SDB fusion approach constrained by coral reef habitat classification based on the deep learning framework Mamba was constructed. The dual error of the global single model was suppressed by exploiting sediment and geomorphic partitions, as well as the accuracy complementarity of different models. Based on multispectral remote sensing imagery Sentinel-2 and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) active spaceborne lidar bathymetry data, wide-range and high-accuracy coral reef habitat classification results and bathymetry information were obtained for the Yuya Shoal (0–23 m) and Niihau Island (0–40 m). The results showed that the overall Mean Absolute Errors (MAEs) in the two study areas were 0.2 m and 0.5 m and the Mean Absolute Percentage Errors (MAPEs) were 9.77% and 6.47%, respectively. And R² reached 0.98 in both areas. The estimated error of the SDB fusion strategy based on coral reef habitat classification was reduced by more than 90% compared with classical SDB models and a single machine learning method, thereby improving the capability of SDB in complex geomorphic ocean areas.

The 532nm laser pulses emitted by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) demonstrate significant potential for shallow water depth measurement. However, due to factors such as atmospheric scattering and absorption, and solar background noise, ICESat-2 data inevitably contains many noise photons. Based on the continuously changing characteristics of the seafloor topography, this paper proposes an ICESat-2 bathymetry extraction algorithm based on cubic function fitting prediction interval along track segments. The key step of this algorithm is that, the underwater photons are selected using the range of the mean plus or minus five times the standard deviation of the Gaussian function fitted of the photon height histogram, after which cubic function fitting is performed. When the coefficient of determination (R2) of the fitting is larger than a threshold, the 95% prediction interval is used for denoising; if it is smaller than the threshold, denoising is performed using the Gaussian function fitting of the histogram. Five research areas are selected to conduct relevant experiments. The denoising results are evaluated by in-situ bathymetry data and the manually extracted signal photons, respectively. The results show that ICESat-2 bathymetry data and in-situ bathymetry data exhibit a highly consistent trend, with both R2 greater than 0.97 and RMSE less than 0.5 m. The evaluation metrics calculated based on the manually selected signal photons all exceed 93%, with F1 scores above 96%. Compared with adaptive elevation difference thresholding algorithm (AEDTA), density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS), the proposed algorithm exhibits the best denoising effect, and the extracted seafloor photons show good continuity without obvious noise photons.