Equipped with an Advanced Topographic Laser Altimeter System (ATLAS), ICESat-2 (Ice, Cloud and land Elevation Satellite-2) is a photon-counting laser altimetry mission with strong potential for nearshore bathymetry. In this study, a novel filtering and bathymetric method termed a segmented adaptive filtering bathymetry has been proposed. Sea-surface photons are identified from peaks in the elevation-density histogram, enabling separation of surface and seafloor photons. The seafloor photons are then partitioned into along-track segments, where seafloor signal photons are extracted using an adaptive elliptical kernel whose parameters and orientation are determined from local density patterns and seafloor slope. The seafloor profile is obtained by polynomial fitting, and nearshore depth is estimated from the elevations of the surface and seafloor signal photons. To ensure and improve the accuracy and reliability of the proposed method, ICESat-2 data from Qilianyu Islands at the South China Sea and West Island at the Florida Keys of the United States were adopted to perform experiments. Furthermore, the bathymetric results obtained by ICESat-2 datasets at different experimental areas were compared with the reference bathymetry obtained by the airborne light detection and ranging (LiDAR) bathymetry (ALB) system. Finally, the bathymetric accuracy validation and assessment were performed. The highest accuracy of root mean square error (RMSE) and coefficient of determination (R2) has reached 0.37 m and 98%, respectively. The accuracy validation of bathymetric results at different study areas demonstrated that the method proposed in this study can automatically and effectively achieve high-precision nearshore bathymetry and topographic surveys.

Airborne full-waveform LiDAR is crucial for precise shallow-water bathymetry and terrain mapping, providing high-resolution spatial data but generating vast amounts of data that need efficient processing. This paper introduces a data processing method for airborne full-waveform LiDAR based on dual buffering and CPU-GPU heterogeneous collaborative computing. Furthermore, a four-module desktop system is developed. Data from Qingge Port is used in the demo to test the proposed method’s effectiveness and the software’s features.

The Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), equipped with the Advanced Topographic Laser Altimeter System (ATLAS), uses a 532-nm laser to measure global surface elevations and enable spaceborne nearshore bathymetry. The main challenge in ATLAS-based bathymetry is accurately distinguishing water-surface and seafloor photons from diverse environmental datasets. This is particularly difficult due to the strong attenuation of laser signals in water, contamination from sun glint and subsurface backscattering, and the sparse distribution of bottom-return photons in turbid coastal waters. To address this issue, this study proposes a novel bathymetric method based on the adaptive-bandwidth mean shift filtering method (AMFM). This method adaptively determines bandwidth parameters via k-nearest neighbors to identify surface/seafloor photon clustering centers, reconstructs wave and seabed topography using B-splines for automatic photon extraction, and performs refraction correction to ensure bathymetric accuracy. The bathymetric results were highly accurate, with slope, coeffi-cient of determination (R-2), and root mean square error (RMSE) values reaching 0.98, 0.98, and 0.47 m, respectively. AMFM achieved high accuracy and full adaptivity in photon filtering for the ATLAS dataset. The shift bandwidth can adaptively change based on the data distribution to adjust the cluster shape and quantity, which is robust for detecting noise and outliers. In addition, numerous experiments were performed with various ATLAS datasets covering areas around the South China Sea, Atlantic Ocean, and Pacific Ocean. This method, which includes curve fitting in postprocessing, is suitable for photon datasets with various spatial distributions and densities. Finally, based on the methods' ability to search for the cluster center and detect local density maxima, photon datasets can be separated into several intervals in the along-track direction to achieve segment handling. Using multithreading technology, AMFM achieved high calculation efficiency and adaptively detected signal photons, thereby providing a powerful solution for processing massive amounts of ATLAS data.

Accurate shallow-water bathymetry is critical for coastal management, navigation, and environmental monitoring. Traditional methods, such as shipborne single/multibeam sonar and passive optical (satellite or aerial imagery) techniques, suffer from limitations in very shallow zones and turbid waters. Airborne lidar bathymetry (ALB) uses a green (532-nm) laser to penetrate water and directly measure the seabed depth, overcoming many of these constraints. Modern ALB platforms—from NASA’s early Airborne Oceanographic Lidar (AOL) to NOAA’s Scanning Hydrographic Operational Airborne Lidar Survey (SHOALS) and Coastal Zone Mapping and Imaging Lidar (CZMIL) as well as commercial systems, like RIEGL’s VQ series and Leica/Airborne Hydrography AB (AHAB)’s Chiroptera—integrate high-energy pulsed lasers, inertial/GNSS navigation, and full-waveform digitizers to capture detailed subsurface profiles. Full-waveform recording retains the entire echo pulse shape, enabling advanced processing: it improves weak bottom echo detection, profiles water-column attenuation, and separates overlapping signals. Key challenges remain in full-waveform ALB (FW-ALB): bottom returns can be faint or overlapped by surface scattering, water-wave refraction errors must be corrected, and turbidity drastically reduces the signal-to-noise ratio (SNR). Emerging research trends address these issues via sophisticated waveform decomposition, stacking methods, multiwavelength scanning, and machine learning (ML) for signal classification. This review surveys the latest FW-ALB technology and algorithms and typical applications and identifies future directions, aiming to synthesize advances in sensor design, processing techniques, and practical bathymetric outcomes to guide ongoing development in shallow-water lidar bathymetry.

This article provides a comprehensive review of photon-counting lidar systems. With the rapid advancement of photon-counting detectors and their supporting technologies, these systems—characterized by their sensitivity to individual photons—have emerged as a promising solution for next-generation active remote sensing. Photon-counting lidar systems facilitate high-resolution, large-scale 3D Earth observation with extremely low energy consumption, making them a crucial technical foundation for future remote sensing applications.

Abstract Airborne photon‐counting LiDAR has emerged as a promising tool for shallow‐water bathymetry, offering high spatial resolution and penetration depth for coastal and nearshore applications. This study presents an integrated workflow for processing airborne photon‐counting LiDAR data to estimate shallow‐water bathymetry. The workflow combines voxel‐level false discovery rate gating, layer‐wise adaptive clustering, and wave‐driven refraction correction to improve the reliability of depth estimates. The approach was validated using two coastal data sets, representing real‐world conditions with varying water clarity and background noise. Results show that the workflow effectively separates surface, water‐column, and bottom layers, achieving high accuracy in clear‐water conditions at North Reef (RMSE 0.214 and 0.309 m) and operationally useful retrieval in noisier class‐II waters at Weizhou Island (RMSE 1.342 and 1.558 m), where spatial gradients are still preserved within the effective penetration range. This workflow is suitable for operational coastal monitoring and environmental assessment.

Photon counting LiDAR systems are an efficient method for nearshore bathymetry and topographic mapping. Current single-photon LiDAR systems predominantly utilize lasers with wavelengths of 1550 nm or 532 nm and typically have a repetition rate of less than 100 kHz, which fails to meet the integrated marine and terrestrial detection needs of remote sensing. Addressing these limitations, this study has developed a novel dual-frequency photon counting LiDAR system that incorporates a miniaturized high-repetition-rate blue-green laser, a wide dynamic range highly sensitive photon-resolvable detector, and an embedded real-time photon data high-speed sampling and storage mechanism. This system achieves high-repetition-rate laser pulses of 1000 kHz and energy detection ranging from pico-watts to micro-watts. The bathymetric capabilities are improved to 2.5 times the Secchi disk depth, with bathymetric accuracy better than 0.2 m, ranging accuracy better than 0.1 m, and horizontal positioning accuracy better than 0.5 m. Moreover, weighing less than 8 kg, it is compatible with mainstream airborne platforms, significantly enhancing the integrated marine and terrestrial detection capabilities of remote sensing.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), featuring the advanced topographic laser altimeter system (ATLAS), pioneered spaceborne photon-counting LiDAR technology. The first spaceborne laser system in Earth’s orbit with water detection capabilities, offers a more direct approach for charting the bathymetry and underwater topography in coastal waters. However, the refraction effect of water column on light is not taken into account by ATLAS products, which will cause the position change of signal photons on the seafloor, consequently reducing the precision of nearshore bathymetry and underwater topography mapping. In the previous studies, the fluctuation water surface has been assumed as the plane to achieve the water refraction correction. In this process, the water incident angle, refraction angle and water refraction direction are same for all seafloor photons, which decreases the accuracy of the photon position and the nearshore bathymetry. Therefore, we present an innovative method for addressing refraction correction by tracking the trajectory of individual photons on the seafloor and reconstructing sea-wave profiles to achieve high-accuracy refraction correction for ATLAS data. In this method, the instantaneous sea wave has been modeled using the extracted signal photon of water surface and the proposed weight cubic polynomial model. Further, the corresponding various incident and refraction angles of each seafloor photon were accurately obtained to calculate various the displacement quantity and direction. Moreover, a coordinate correction model was introduced to aim at enhancing the accuracy of photon coordinates on the seafloor and mapping of underwater topography. Validation results demonstrate that the proposed method for refraction correction effectively enhances the bathymetric precision. The maximum depth displacement corrected in the study area reached 5.46 m, occurring at a water depth of 16.01 m. In the along-track direction, there was a range of maximum displacements from -0.54 to 0.47 m, while the maximum relative displacement reached 1.01 m, significantly exceeding the displacement observed in the cross-track direction.

China launched its first hyperspectral remote sensing satellite for geological resource and environmental survey and monitoring, Geology-1, on 17 May 2025. Weighing just 83 kg, the satellite features a compact and integrated design with 26 spectral bands covering visible to shortwave infrared wavelengths. Cutting-edge optical technologies minimize interference and enhance image clarity. Geology-1 represents a major breakthrough in creating lightweight, specialized, and cost-effective satellites for the geological industry.

Shallow water depth information plays a critical role in coastal and marine applications, and satellite-derived bathymetry (SDB) has emerged as a valuable approach due to its extensive coverage and capability to generate comprehensive bathymetric maps. This study integrates ICESat-2 photon-counting LiDAR data with WorldView-2 multispectral imagery, employing two empirical models, the Band Ratio (BR) and the Linear Band (LB), and two machine learning models, Support Vector Regression (SVR) and Random Forest (RF), to assess the accuracy and reliability of water depth estimation in three regions of the South China Sea: Wuzhizhou Island, Qilianyu Island, and Gongshi Reef. The results show that all four models can effectively achieve high-precision shallow water bathymetry, with the Random Forest (RF) model exhibiting the highest overall accuracy (RMSE = 1.13 m, R² = 0.90). Additionally, although the two machine learning models outperform the empirical models in terms of accuracy and robustness, they exhibit higher sensitivity to environmental variations, particularly in shallow-water areas with reef platforms and whitecaps.

The airborne light detection and ranging (LiDAR) bathymetry (ALB) system is a promising and effective approach for surveying nearshore bathymetry and underwater terrain. Deep-learning techniques have been developed to reduce waveform superposition in shallow and deep areas. These methods avoid the complex transmission process of laser pulses in the water column and the intricate determination of various parameters and thresholds in traditional ALB methods. However, studies on ALB bathymetry using deep-learning techniques remain insufficient. To improve the accuracy and reliability of nearshore bathymetry, this study proposes deep-learning bathymetry fusing waveform features and spatial-angular field features (DBWSF). This method utilizes the waveform curvature to construct an energy curve, enhancing the waveform's features. Additionally, it employs a Gram angle difference field to convert the temporal waveform into a two-dimensional Gram angle difference field image, increasing the dimensions and quantity of waveform features. Finally, this method constructs a dual- path neural network with an attention mechanism to extract the water surface and bottom waveform signals precisely to achieve nearshore bathymetry. In comparison to sample data, DBWSF exhibited high bathymetric accuracy across three study areas (Ganquan Island, Lingyang Reef, and Dong Island), achieving a root mean squared error of 0.21 m. The R2 in these regions were 99.6 %, 95.1 %, and 99.8 %, respectively. In the above three study areas, compared with the bathymetric results obtained using the waveform decomposition method, DBWSF was more accurate, with improvements in RMSE of 0.33, 0.27, and 0.04 m. Compared with multilayer perceptron (MLP), the corresponding accurate improvements in RMSE with DBWSF were 0.47, 0.40, and 0.25 m. Compared with other two methods, the R2 value for DBWSF in the three study areas exceeded 95 % and reached a highest value of 99.8 %. The results demonstrated the bathymetric capability, reliability, and transferability of DBWSF for determining nearshore bathymetry in different water environments. The novel LiDAR bathymetric deep-learning technique can effectively and intelligently produce precise nearshore bathymetry and seafloor topography maps.

Shallow-water bathymetric maps provide vital geographic information for various coastal and marine applications such as environmental management, engineering construction, oil and gas resource exploration, and ocean fisheries. Recently, satellite-derived bathymetry (SDB) has emerged as an alternative approach to shallow-water bathymetry, particularly in hard-to-reach areas. In this research, an innovative approach to bathymetry was introduced. This method provides a reliable approach for generating high-accuracy and high-reliability shallow water bathymetry results. By using Sentinel-2 time series imagery combined with ICESat-2 data, four bathymetry results at different time points are produced based on four traditional bathymetry methods. For the results at each location, a statistical method is applied to evaluate the bathymetry results, remove erroneous data, and generate high-confidence bathymetry results. The validation results indicated that the accuracy of the proposed bathymetric method achieved an R² range of 0.96 to 0.99 and an RMSE between 0.42 and 1.18 meters. When contrasted with traditional methods that utilize a single temporal image, a notable enhancement in bathymetric accuracy was observed.

Airborne LiDAR bathymetry (ALB) system is an attractive and efficient method for nearshore bathymetry and underwater topography mapping. To ensure and improve the measurement accuracy and reliability in various water environments, different receivers using a segmented field of view (FOV) have been designed and are implemented in ALB. These are used to obtain various echo data of multiple channels and perform bathymetry at various water depths. However, in echo waveform processing, detailed information about the water body described by the different echo waveforms is lacking, making it difficult to overcome severe waveform superposition in extremely shallow areas or to separate the weak echo of the water bottom from the noise in extremely deep areas. Therefore, we employed a novel method of multi-channel waveform fusion bathymetry (MWFB) to achieve information complementarity, gain multi-channel waveforms, and detect the robustness of water and bottom echo signals. This method uses an adaptive signal waveform extraction of a multichannel fusion mechanism to eliminate systematic and random noise, and the waveform curvature is used to reconstruct the energy–waveform curves. Iterative decomposition based on waveform curvature and energy curves was conducted to improve the correctness of waveform decomposition and the reliability of waveform components. Furthermore, the water surface and bottom peaks were detected based on the multichannel waveform curvature, which enabled acquisition of the accurate temporal positions of the peaks and achieve high-accuracy bathymetry. According to the experimental results and a comparison with reference datasets, the highest bathymetric accuracy of the MWFB method reached an RMSE of 0.22. For the different study areas, the bathymetric accuracy of the slope reached 95%, 92%, and 97% at Ganquan Island, Lingyang Reef, and Bei Island, respectively. Furthermore, the bathymetric point numbers were effectively increased and bathymetric accuracy was also improved in shallow and deep water, which illustrates the superior bathymetric performance of the MWFB method and its ability to provide a high-efficiency waveform dataset through multi-channel data fusion. The novel LiDAR bathymetric method proposed in this study can effectively achieve high-accuracy nearshore bathymetry and seafloor topographical mapping.

The matching of remote sensing images is a critical and necessary procedure that directly impacts the correctness and accuracy of underwater topography, change detection, digital elevation model (DEM) generation, and object detection. The texture of images becomes weaker with increasing water depth, and this results in matching-extraction failure. To address this issue, a novel method, homography-based motion statistics with an epipolar constraint (HMSEC), is proposed to improve the number, reliability, and robustness of matching points for weak-textured seafloor images. In the matching process of HMSEC, a large number of reliable matching points can be identified from the preliminary matching points based on the motion smoothness assumption and motion statistics. Homography and epipolar geometry are also used to estimate the scale and rotation influences of each matching point in image pairs. The results show that the matching-point numbers for the seafloor and land regions can be significantly improved. In this study, we evaluated this method for the areas of Zhaoshu Island, Ganquan Island, and Lingyang Reef and compared the results to those of the grid-based motion statistics (GMS) method. The increment of matching points reached 2672, 2767, and 1346, respectively. In addition, the seafloor matching points had a wider distribution and reached greater water depths of −11.66, −14.06, and −9.61 m. These results indicate that the proposed method could significantly improve the number and reliability of matching points for seafloor images.

Improving the accuracy of nearshore bathymetric measurements is essential for understanding coastal environments, resource management, and navigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the first laser satellite that uses the photon-counting technique. The ICESat-2 is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which enables higher-accuracy measurements of water, ice, and land elevation on Earth. Two-media photogrammetric bathymetry is a type of nearshore bathymetric technology that uses the geometrical characteristics of light rays. With this technique, the accuracy and reliability mainly depend on eliminating systematic errors and ensuring accurate spatial photogrammetric positioning relative to the object being measured. To improve the bathymetric accuracy of two-media photogrammetry, we integrated high-accuracy elevation data from photon datasets as constraining and control parameters. The improved method effectively eliminated systematic errors in two-media photogrammetry during the established joint-block adjustment model. To improve its accuracy and reliability, we employed multispectral WorldView-2 stereo images in our experiments. Furthermore, the bathymetric results were validated and assessed using in situ and photon data. The experimental results show that the highest accuracy achieved with the bathymetric measurements in our study area was a root mean square error (RMSE) of 0.96 m and a mean absolute error of 0.57 m. Using the proposed fusion method, the bathymetric accuracy (as measured using the RMSE) was 1 m higher than that of two-media photogrammetry without the photon datasets.

As one of the most critical features on the earth's surface, coastal zone mandates high-quality extraction of its representative feature, the coastline. Prior methodologies primarily emphasize on edge and small-scale information. However, during large-scale image processing, misclassification might occur due to the difficulty in determining whether a local area belongs to the land or sea. To address this, we propose a deep learning-based multiscale coastline extraction algorithm in this study. It comprises a multiscale coastal zone dataset built upon a tile map service structure and a scene classification-based multiscale coastal zone classifier, employing quadtree decomposition to identify coastal zones from low to high levels. Contrasting with conventional semantic segmentation, the scene classification network, owing to its larger receptive field, can accurately discern land and sea. This accuracy is further enhanced by using quadtree decomposition to process images with lower resolution and larger coverage. The results suggest that our proposed method effectively eliminates confusing features, with the overall experimental classification accuracy attesting to the effectiveness of our approach, yielding a 6% improvement. Moreover, the screening process in this study significantly reduces the number of input samples for the segmentation network, thus boosting computational speed.

Nearshore bathymetry plays an essential role in various applications, and satellite-derived bathymetry (SDB) presents a promising approach due to its extensive coverage and comprehensive bathymetric map production capabilities. Nevertheless, existing retrieval techniques, encompassing physics-based and pixel-based statistical methodologies such as support vector regression (SVR), band ratio, and Kriging regression, exhibit limitations stemming from the intricate water reflectance process and the under-exploitation of the spatial component inherent in SDB. To surmount these obstacles, we introduce employment of deep convolutional networks (DCNs) for SDB in this study. We assembled multiple scenes utilizing networks with varying scale emphasis and an assortment of satellite datasets characterized by distinct spatial and spectral resolutions. Our findings reveal that these deep learning models yield high-caliber bathymetry outcomes, with nonlinear normalization further mitigating residuals in shallow water regions and substantially enhancing retrieval performance. A comparative analysis with the prevalent SVR technique substantiates the efficacy of the proposed methodology.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a new measurement strategy thanks to an advanced topographic laser altimeter system that uses photon counting technology for highly accurate nearshore bathymetry and underwater topographic mapping. In this technique, however, the refraction of water leads to a displacement of the position of the photons on the seafloor, which reduces the accuracy of the measurement. To overcome this problem, a novel refraction correction approach constructed on the ray tracing and JONSWAP spectrum is proposed in this study for ICESat-2. In this method, the coordinate compensation of each seafloor photon was applied in the WGS84 coordinate system to improve the positioning precision of seafloor photons. Experimental results showed that the proposed refraction correction method could precisely reduce the displacement of each photon on the seafloor. For the displacement elimination and coordinate compensation, the highest accuracy of photon counting measurements for the seafloor profile could reach-0.29 m of bias and 0.42 m of root mean square error in our study area. Finally, the influence of the Earth's curvature on the refraction correction was analyzed in detail, revealing that curvature displacement ranged in value between-5 and-6 power. Therefore, it was considered negligible in terms of its effects on underwater topography and nearshore bathymetry mapping.

在浅水测深技术中,星载激光测量系统可以覆盖一些机载/舰载系统难以到达的偏远水域,具有比被动光学影像水深测量精度更高、可全天时工作等独特的优势.以稀疏而少量的主动星载激光测量值为水深标定点,融合被动星载遥感影像,主被动融合的浅水测深是当前的趋势.本文首先介绍了星载单光子激光雷达的工作范围、物理参数和数据产品,概述了测量原理,综述了现有的星载单光子激光雷达测深的理论传输模型,对比了不同的点云数据去噪处理算法的优劣,归纳了星载融合测深反演技术在不同环境中的应用,总结了当前存在的问题,并对该技术未来的前景和发展方向进行了展望.