Full-waveform airborne LiDAR bathymetry (ALB), which provides waveforms and point clouds, has become an essential technology for shallow water surveys. However, weak seafloor echoes are challenging to detect accurately because of waveform feature confusion caused by the complex measurement environments. To address this issue, waveform feature importances, feature histograms, and feature spaces of 14-dimensional waveform features are conducted to analyze the waveform feature confusion. Then, a random forest with optimized thresholds (RFOT) is proposed to detect normal seafloor echoes and weak seafloor echoes. Finally, waveform sharpening and condition screening are used to extract the seafloor echoes for overlapping waveforms in very shallow waters. The proposed method was verified with 14 swaths obtained by the Optech Aquarius system around Wuzhizhou Island. The results show that the energy features (area under curve, amplitude, etc.) can better discriminate the difference between weak seafloor echoes and noise than the shape features (RL area ratio, kurtosis, etc.). The number of seafloor echoes detected by the proposed method increased by 148.86% compared with the Aquarius system. The reference data prove that seafloor points detected by the proposed method are accurate and effective. Thus, this contribution effectively improves the bathymetric performance of the ALB system.

Space-borne photon-counting light detection and ranging (LiDAR) can obtain high-precision bathymetry information for nearshore waters. However, the signals obtained by the space-borne photon-counting LiDAR contain a large amount of noise due to atmospheric scattering, solar radiation and instrumental noise. Therefore, the denoising of the space-borne photon-counting LiDAR is a key component in the acquisition of bathymetric information. With respect to the difficulty of accurately denoise the density heterogeneity photons in different water depths, this paper proposes a hierarchical multiscale denoising method for space-borne photon-counting LiDAR based on minimum spanning tree. First, sea surface photons and water-column photons are obtained separately based on the kernel density estimation. Second, the minimum spanning tree is constructed and split based on the spatial distribution of water-column photons. In the process of splitting the minimum spanning tree, the length threshold of the interrupted branches is adaptively calculated. Then, water-column photons are denoised based on the hierarchical multiscale subtree node judgment strategy. Finally, water-column photon denoising results are refined using a box plot outlier detection method. The proposed method is compared with some state-of-the-art photon denoising algorithms using six trajectory data under various seafloor terrains. The experimental results demonstrate that the proposed method is better than other photon denoising algorithms, and can obtain accurately denoising results for different density distributions of photons under different seafloor terrains.

The Chinese Haiyang-2B (HY-2B) satellite is equipped with a scanning microwave radiometer (SMR) for global marine atmospheric water vapor detection. To calibrate precipitable water vapor (PWV) derived from the SMR accurately, a novel calibration method, referred to as Spatial Correction for Water Vapor (SC-WV), is proposed. This method integrates PWV data obtained from the Global Navigation Satellite System (GNSS) with the fifth generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis products (ERA5). By leveraging the ERA5 data, the spatial variations in water vapor surrounding the GNSS station are calculated, and the GNSS-derived PWV is subsequently corrected to obtain precise measurements at the SMR grid nodes. The calibration of SMR PWV within a 200 km radius around GNSS stations is conducted using GNSS PWV data observed at 47 island stations and 44 coastal stations from the International GNSS Service (IGS) in 2021. Both the proposed SC-WV method and the traditional inverse distance weighting (IDW) method are employed for comparison. The results demonstrate the applicability of the SC-WV method for both GNSS island and coastal stations, enabling the accurate calibration of the SMR PWV at each grid node surrounding the GNSS stations. On the basis of the GNSS PWV measurements obtained from island and coastal stations, the average bias values for the SMR PWV were 0.5 and 0.8 mm, respectively, with corresponding root mean square errors (RMSEs) of 2.6 and 2.8 mm respectively. The consistent accuracy indices observed provide evidence of the robustness and validity of the SC-WV method.

Based on Precipitable Water Vapor (PWV) obtained from five coastal Global Navigation Satellite System (GNSS) stations, this study investigates the characteristics and interrelationships of sea surface waves and atmospheric parameters during seawater encroachment in the Bohai and Yellow Seas of China in mid-October 2024. Results indicate that the correlation coefficient between GNSS PWV and ERA5 PWV data is 0.97 (p< 0.01), with a mean bias of -3.28 mm and a standard deviation of 2.70 mm. During the seawater encroachment, three notable increases in PWV were observed, with peaks ranging from 30 to 55 mm, all accompanied by precipitation (less than 10 mm). The PWV change rates varied spatially, with the Yellow Sea coast experiencing faster changes than the Bohai Sea coast. Under strong wind conditions, sea surface wave characteristics were manifested by increased wave heights and longer wave periods, alongside an increased whitecap coverage of breaking waves (3%–5%). The temporal variation curves reveal an inverse relationship between PWV and wave variables, with PWV lagging both breaking and non-breaking waves by approximately 3–12 h. Meanwhile, the process involves enhanced water vapor evaporation and intensified air-sea heat exchange. These factors affected the weighted mean temperature and caused a continuous decrease in PWV. Consequently, zonal differences in the spatial distribution of PWV were observed. By linking GNSS PWV with wave variables, this research introduces a novel insight for the study of air-sea interaction processes and coastal hazard monitoring under extreme weather events.

Complex terrains in coastal zones and shallow water areas around islands and reefs can be quickly detected via airborne LiDAR bathymetry (ALB) technology. Due to equipment placement deviations and measurement uncertainty, measurement deviations can occur in the overlapping areas of adjacent strips, so it is particularly necessary to register point clouds. To address areas with few seafloor structures and to overcome the challenges of extracting features in water areas with small terrain changes, a registration method for ALB seafloor point clouds based on the adaptive matching of corresponding points is proposed. First, the normal vector zenith angle, curvature change, and omnidirectional variance in the seafloor points are calculated. Then, the corresponding points are adaptively matched according to the terrain feature similarity and distance constraints. Finally, the random sample consensus (RANSAC) algorithm and iterative closest point (ICP) algorithm are used for coarse registration and fine registration, respectively. The experimental results show that the method provides high accuracy and a uniform distribution of corresponding points. The root mean square error (RMSE) values when the registration method is applied to a flat area and coral reef area are 0.102 and 0.041 m, respectively. Compared with that of the ICP alignment algorithm, the accuracy of the proposed method is improved by 0.114 and 0.227 m, respectively, and compared with that of the normal distributions transform (NDT) algorithm, the accuracy is improved by 0.316 and 0.452 m, respectively; hence, the proposed method provides an effective solution for the registration of ALB data.

The avalanche photodiode equipped in LiDAR heavily relies on the reliability of the gain amplifier to consistently output a sufficient voltage signal, enabling the weak return signal to be captured. However, in certain situations, an increase in voltage will lead to saturation, which distorts the full waveform and results in inaccurate coordinate and intensity readings, causing issues in various applications. To minimize data voids and eliminate artifacts related to the analysis of clipped waveforms, this study focuses on developing an automated framework [Detection and Restoration of Saturated Waveforms without Prior Information (DRSW)] for detecting and recovering saturated waveforms associated with high reflectivity or near-field targets without any prior knowledge. The framework designs four feature descriptors based on topographic, intensity, and waveform characteristics to differentiate between saturated and unsaturated signals, enabling automatic segmentation into two categories. To address the challenges posed by signal saturation-induced peak flattening and broadening, a combined halves-Gaussian model (CHGM) is crucially presented to describe the rising and falling edges of saturated waveform with different halves of normal Gaussian functions (halves-Gaussian functions). In the CHGM, background noise is segmented rather than as a whole to improve modeling accuracy. The effectiveness of CHGM in reconstructing saturated waveforms is evaluated through rigorous comparative experiments with cubic spline, Kriging, and Gaussian mixture model (GMM) methods. The results indicate that the maximum correction error of the CHGM is limited to within 7 DN, and the peak position remains unaltered without any shift.

Water vapor over oceans is integral to climate research, weather prediction, and various scientific disciplines. Owing to challenges in deploying in situ instruments, water vapor over oceans is predominantly measured using satellite-borne sensors such as the satellite-borne scanning microwave radiometer (SMWR) and satellite-borne optical imager (SOI). Nevertheless, evaluations of satellite-based remote sensing of atmospheric water vapor over oceans using alternative independent techniques remain limited. In this study, we examine the performance of satellite-borne sensors in measuring water vapor over oceans using shipborne global navigation satellite system (GNSS) precipitable water vapor (PWV) from 2014 to 2021. The comprehensive evaluation of satellite-based PWV over oceans reveals that SMWR PWV outperforms SOI PWV by approximately 2 mm in root-mean-square (rms). Among all satellite-borne SMWRs, Fengyun (FY)-3 C Microwave Radiation Imager-1 exhibits superior agreement with a mean value of 0.26 mm and an rms of 2.20 mm. Both SMWRs and SOIs exhibit diminished agreement in wetter areas, especially for SOIs, attributable to heightened sensitivity to cloudy and moist weather conditions. Temporally, most satellite-borne sensors demonstrate stable performance in PWV retrieval over oceans, with no apparent observation drift. In addition, MODIS exhibits slightly better stability performance compared to SMWRs. The inter-technique validations affirm the elevated accuracy and stability of satellite-based PWV over oceans. Nonetheless, long-term calibration and refinement of algorithms remain imperative, particularly in tropical regions.

Enteromorpha prolifera is as a frequent marine ecological environment disaster. How to quickly and accurately monitor Enteromorpha prolifera is of great significance to its management and protection of the marine ecological environment. The detection of Enteromorpha prolifera from high spatial resolution remote sensing images (HSRIs) is an important technical means for monitoring Enteromorpha prolifera disasters. With respect to the difficulty of accurate detection of Enteromorpha prolifera area boundary in HSRIs, this paper proposes an Enteromorpha prolifera detection method for HSRIs based on boundary-assisted dual-path convolutional neural networks (CNN). First, a large-scale HSRIs Enteromorpha prolifera detection dataset, FIO-EP, is created and made publication to facilitate the field of HSRIs Enteromorpha prolifera detection. Then, a boundary-assisted dual-path CNN framework is designed to detect Enteromorpha prolifera in HSRIs based on the shape distribution characteristics of Enteromorpha prolifera. In the CNN framework, accurate detection of Enteromorpha prolifera areas in HSRIs is achieved by fusing initial detection and boundary detection results of Enteromorpha prolifera. The proposed method is compared with some state-of-the-art Enteromorpha prolifera detection algorithms using the FIO-EP dataset. The experimental findings demonstrate that the proposed method can obtain 88.28% F1-score and 79.02% intersection-over-union (IOU), and is superior to other state-of-the-art Enteromorpha prolifera detection algorithms.

Global Navigation Satellite System-Acoustic (GNSS-A) technology has been widely used in ocean engineering and ocean environmental science. Accurate sound travel time modeling is essential for GNSS-A seafloor positioning. Currently, half of the two-way travel time (TWTT) has been used as an approximation for the one-way travel time (OWTT). In this work, the time error of the approximate OWTT is investigated under different survey configurations, and a sequential GNSS-A seafloor positioning method using the extended Kalman filter (EKF) is developed to investigate the impact of sound travel time modeling. Simulations show that the time error induced under the static survey configuration is less than 0.6 ms; the time error induced under the circle survey configuration with a stable inclination angle is stable, but the time error of the line survey configuration can reach 28 ms. As confirmed through field experiments, sequential GNSS-A seafloor positioning using TWTT modeling is more stable than OWTT modeling. The positioning residuals of TWTT modeling are similar to those of OWTT modeling under the circle configuration but at least 2 times less than those of OWTT modeling under the line survey configuration. Furthermore, the average positioning residuals of OWTT and TWTT modeling can be greatly reduced for a survey configuration combining circular and linear tracks. These findings provide a feasible method for improving the precision and efficiency of GNSS-A seafloor positioning.

Airborne laser bathymetry (ALB) systems with digital full-waveform signal collection can obtain corresponding temporal positions from several backscattering surfaces by laser beam irradiation. This information can help describe the multi-elevation structures of the target and explore the echo signal attenuation response in different nonuniform mediums during laser propagation. Therefore, a full-waveform echo signal is quite practical for integrated water-land detection. However, the wavelength used in the ALB system is generally in the visible band range of 470~580 nm, and the received signal is constantly interfered with by many nontarget factors, such as imperfections in the receiving channel or the strong scattering from the transmission medium. The conventional processing method transforms nontarget interference into noise point cloud filtering or classification extraction, enabling the detection of a single surface or regular geometry. The accuracy of the identification and extraction for multi-elevation target surfaces echo signal is always reduced due to the significant noise signal intensity. We proposed a signal component detection method by constructing the echo signal feature functions and the conditional random field (CRF) model based on the full-waveform decomposition. The processing result for actual measurement data verified that the CRF strategy can effectively reduce the uncertainty of target surface detection. Compared with the single-beam echo sounder, the root mean square errors of the elevation deviation underwater were reduced by 3.2 cm and 4.9 cm respectively in the two different experimental areas.

Water vapor over oceans is significant for numerical weather prediction (NWP) and climate research. Ocean platform-based global navigation satellite system (GNSS) which can sense the atmospheric water vapor is becoming an important supplement for water vapor measurements over oceans. However, the application of ocean platform-based GNSS meteorology is normally based on geodetic GNSS receivers, which implies the high cost of hardware. In this contribution, we investigate the potential of retrieving real-time water vapor over oceans with a low-cost receiver (u-blox F9P), and a geodetic GNSS receiver (Trimble NetR9) is also equipped in the experiment vessel. The post-processed Trimble NetR9 zenith total delay (ZTD) estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 precipitable water vapor (PWV) products are used for the validation of real-time ZTDs and PWV values. The results show that the real-time ZTDs derived from the low-cost multi-GNSS (GPS + Galileo) observations obtain a difference of over 2.13 cm in root-mean-square (RMS) compared to the post-processed ZTDs with an averaged initialization time of approximately 40 mins. In addition, compared to ERA5 PWV, the real-time PWV derived from u-blox F9P multi-GNSS observations shows a difference in RMS of approximately 4 mm. Although u-blox F9P multi-GNSS performs relatively worse than Trimble NetR9 multi-GNSS in real-time ZTD/PWV estimates, the accuracy of low-cost GNSS receivers derived water vapor over oceans can still meet the requirements for NWP and nowcasting, which demonstrates promising prospects in supplementing the measurements of water vapor over oceans.

The wet tropospheric correction (WTC) retrieved from the onboard calibration microwave radiometer (CMR) of Haiyang-2A (HY-2A) is critical in monitoring the global sea level. However, the CMR WTC became significantly biased from June 2017 due to the failure of the 18.7-GHz band, which caused massive errors in the sea surface height (SSH) measurements. We investigate the accuracy of the CMR WTC derived from the two remaining bands to address this problem. A comprehensive evaluation using multisource data demonstrates that the dual-band + backscattering coefficient (BC) algorithm achieves comparable accuracy to the three-band algorithm, and it does not suffer from any large errors when the equipment works well. Hence, we calibrated the HY-2A CMR data with the dual-band + BC algorithm when the 18.7-GHz band failed, and the accuracy of the CMR WTC is improved from 2.34 to 1.39 cm compared with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 data. In addition, the SSH measurements are improved significantly by a maximum of 2 cm in mean value using the dual-band + BC WTC during the failure period of HY-2A CMR. Compared with Jason-3 SSH measurements, the HY-2A with dual-band + BC shows a slightly larger difference than HY-2A with three-band by 0.1 cm in rms. This method prolongs the operational lifetime of the HY-2A CMR and could be used in the reprocessing of HY-2A observations.

Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP).