The accurate estimation of nearshore bathymetry is necessary for multiple aspects of coastal research and practices. The traditional shipborne single-beam/multi-beam echo sounders and Airborne Lidar bathymetry (ALB) have a high cost, are inefficient, and have sparse coverage. The Satellite-derived bathymetry (SDB) method has been proven to be a promising tool in obtaining bathymetric data in shallow water. However, current empirical SDB methods for multispectral imagery data usually rely on in situ depths as control points, severely limiting their spatial application. This study proposed a satellite-derived bathymetry method without requiring a priori in situ data by merging active and passive remote sensing (SDB-AP). It realizes rapid bathymetric mapping with only satellite remotely sensed data, which greatly extends the spatial coverage and temporal scale. First, seafloor photons were detected from the ICESat-2 raw photons based on an improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which could calculate the optimal detection parameters for seafloor photons by adaptive iteration. Then, the bathymetry of the detected seafloor photons was corrected because of the refraction that occurs at the air–water interface. Afterward, the outlier photons were removed by an outlier-removal algorithm to improve the retrieval accuracy. Subsequently, the high spatial resolution (0.7 m) ICESat-2 derived bathymetry data were gridded to match the Sentinel-2 data with a lower spatial resolution (10 m). All of the ICESate-2 gridded data were randomly separated into two parts: 80% were employed to train the empirical bathymetric model, and the remaining 20% were used to quantify the inversion accuracy. Finally, after merging the ICESat-2 data and Sentinel-2 multispectral images, the bathymetric maps over St. Thomas of the United States Virgin Islands, Acklins Island in the Bahamas, and Huaguang Reef in the South China Sea were produced. The ICESat-2-derived results were compared against in situ data over the St. Thomas area. The results showed that the estimated bathymetry reached excellent inversion accuracy and the corresponding RMSE was 0.68 m. In addition, the RMSEs between the SDB-AP estimated depths and the ICESat-2 bathymetry results of St. Thomas, Acklins Island, and Huaguang Reef were 0.96 m, 0.91 m, and 0.94 m, respectively. Overall, the above results indicate that the SDB-AP method is effective and feasible for different shallow water regions. It has great potential for large-scale and long-term nearshore bathymetry in the future.

The recently developed Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), furnished with the Advanced Terrain Laser Altimeter System (ATLAS), delivers considerable benefits in providing accurate bathymetric data across extensive geographical regions. By integrating active lidar-derived reference seawater depth data with passive optical remote sensing imagery, efficient bathymetry mapping is facilitated. In recent times, machine learning models are frequently used to define the nonlinear connection between remote sensing spectral data and water depths, which consequently results in the creation of bathymetric maps. A salient model among these is the convolutional neural network (CNN), which effectively integrates contextual information concerning bathymetric points. However, current CNN models and other machine learning approaches mainly concentrate on recognizing mathematical relationships within the data to determine a water depth function and remote sensing spectral data, while oftentimes disregarding the physical light propagation process in seawater before reaching the seafloor. This study presents a physics-informed CNN (PI-CNN) model which incorporates radiative transfer-based data into the CNN structure. By including the shallow water double-band radiative transfer physical term (swdrtt), this model enhances seawater spectral features and also considers the context surroundings of bathymetric pixels. The effectiveness and reliability of our proposed PI-CNN model are verified using in situ data from St. Croix and St. Thomas, validating its correctness in generating bathymetric maps with a broad experimental R² accuracy exceeding 95% and remaining errors below 1.6 m. Preliminary results suggest that our PI-CNN model surpasses conventional methodologies.

Satellite-derived bathymetry (SDB) methods have been traditionally hindered by the need for in situ reference bathymetric points. However, the light detection and ranging (LiDAR) instruments on the new ICESat-2 satellite have revolutionized SDB by providing high-precision reference bathymetric point cloud datasets (RBPCDs) in shallow water. While the density-based spatial clustering of applications with noise (DBSCAN) has been effective in photon cloud processing, it has been challenging to determine key parameters due to the complexity of terrain changes. Furthermore, ICESat-2 is unable to measure deep water depths greater than 50 m, which would be less efficient if it has to process the entire track data. To overcome these challenges, we have developed an adaptive ellipse denoising algorithm with adjustable key parameters and a shallow-water feature photon (SWFP) extraction method. These innovative techniques were applied to the Caribbean Sea and the South China Sea, resulting in impressive datasets consisting of 848 395 and 438 643 RBPCDs, respectively. The mean absolute error (MAE) of RBPCDs was found to be within 0.6 m, and the RBPCDs were consistent with in situ data. By combining RBPCDs with Sentinel-2 data using a neural network (NN)-based SDB method, we have created detailed bathymetry maps over 15 islands in the Caribbean Sea. Our adaptive method has great potential for large-scale nearshore RBPCD construction, and these RBPCDs will undoubtedly enhance SDB implementations in the future.

Rotation of rotor blades is typically micro-motion and would induce micro-Doppler modulation. However, accurate detection will become extremely difficult when the echo signal is corrupted by strong noise. This paper proposes a method to realize extraction of micro-Doppler feature from the rotating rotor blade echo signals with low signal-to-noise ratio (SNR). The proposed method is characterized by applying low-rank decomposition for saliency detection of spectrogram. MATLAB simulation results verify that the proposed method is effective in saliency detection of the spectrogram from rotor blade echo signals and is robust in estimating the maximum micro-Doppler shift under low SNR.