Bathymetry Estimation Research Articles

An Improved Physics-Based Dual-Band Model for Satellite-Derived Bathymetry Using SuperDove Imagery

Shallow water bathymetry is critical for environmental monitoring and maritime security. Current widely used statistical models based on passive optical satellite remote sensing often rely on prior bathymetric data, limiting their application to regions lacking such information. In contrast, the physics-based dual-band log-linear analytical model (P-DLA) can estimate shallow water bathymetry without in situ measurements, offering significant potential. However, the quasi-analytical algorithm (QAA) used in the P-DLA is sensitive to non-ideal pixels, resulting in unstable bathymetry estimation. To address this issue and evaluate the potential of SuperDove imagery for bathymetry estimation in regions without prior bathymetric data, this study proposes an improved physics-based dual-band model (IPDB). The IPDB replaces the QAA with a spectral optimization algorithm that integrates deep and shallow water sample pixels to estimate diffuse attenuation coefficients for the blue and green bands. This allows for more accurate estimation of shallow water bathymetry. The IPDB was tested on SuperDove images of Dongdao Island, Yongxing Island, and Yongle Atoll. The results showed that SuperDove images are capable of estimating shallow water bathymetry in regions without prior bathymetric data. The IPDB achieved Root Mean Square Error (RMSE) values below 1.7 m and R2 values above 0.89 in all three study areas, indicating strong performance in bathymetric estimation. Notably, the IPDB outperformed the standard P-DLA model in accuracy. Furthermore, this study outlines four sampling principles that, when followed, ensure that variations in the spatial distribution of sampling pixels do not significantly impact model performance. This study also showed that the blue–green band combination is optimal for the analytical expression of the physics-based dual-band model.

Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea

This study aims to improve the accuracy of bathymetry predicted by gravity-geologic method (GGM) using the optimal machine learning model selected from machine learning techniques. In this study, several machine learning techniques were utilized to determine the optimal model from the performance of depth and gravity anomalies. In addition, a tuning density contrast calculated from satellite altimetry-derived free-air gravity anomalies (FAGAs) was applied to estimate enhanced bathymetry. By comparison with shipborne depth, the accuracy of the bathymetry estimated by using satellite altimetry-derived FAGAs and machine learning was evaluated. The findings reveal that the bathymetry predicted by the optimal machine learning using the Gaussian process regression and the GGM with a tuning density contrast can enhance the accuracy of 82.64 m, showing an improvement of 67.40% in the RMSE at shipborne depth measurements. Although the tuning density is larger than 1.67 g/cm3, bathymetry using satellite altimetry-derived FAGAs and machine learning can be effectively improved with higher accuracy.

Heuristic estimation of river bathymetry in braided streams using digital image processing

AbstractMeasurement of river bathymetry has been revolutionized by high‐resolution remote sensing that combines UAV platforms with SfM‐MVS photogrammetry. Mapping inundated and exposed areas simultaneously are possible using either two‐media refraction correction or some form of the Beer–Lambert Law to estimate water depths. If, as in turbid glacially‐fed braided streams, the bed is not visible then traditional survey techniques (e.g. differential GPS systems) are required. As an alternative, here we test whether the spatial distribution of water depths in a shallow braided stream can be modelled from basic planimetric data and used to estimate inundated zone bathymetry. We develop heuristic rules using; (1) distance from the nearest river bank; (2) total inundated width along a line tangential to the local flow direction; (3) local curvature magnitude and direction; and distance from the nearest flow (4) divergence and (5) convergence regions. We parameterize them using a sample of measured water depths in stepwise multiple linear regressions and validate them using independent data. Resulting water depth distribution maps explain between 50% and 60% of the measured water depth spatial variability when compared to independent data. After incorporating modelled water depths into digital elevation models (DEMs) of exposed areas, we show that the developed method is suitable for volumetric change calculations in both dry and inundated areas.

Addressing Challenges in Port Depth Analysis: Integrating Machine Learning and Spatial Information for Accurate Remote Sensing of Turbid Waters.

Bathymetry estimation is essential for various applications in port management, navigation safety, marine engineering, and environmental monitoring. Satellite remote sensing data can rapidly acquire the bathymetry of the target shallow waters, and researchers have developed various models to invert the water depth from the satellite data. Geographically weighted regression (GWR) is a common method for satellite-based bathymetry estimation. However, in sediment-laden water environments, especially ports, the suspended materials significantly affect the performance of GWR for depth inversion. This study proposes a novel approach that integrates GWR with Random Forest (RF) techniques, using longitude, latitude, and multispectral remote sensing reflectance as input variables. This approach effectively addresses the challenge of estimating bathymetry in turbid waters by considering the strong correlation between water depth and geographical location. The proposed method not only overcomes the limitations of turbid waters but also improves the accuracy of depth inversion results in such complex aquatic settings. This breakthrough in modeling has significant implications for turbid waters, enhancing port management, navigational safety, and environmental monitoring in sediment-laden maritime zones.

Estimation of near-coastal bathymetry using AIS ship movements

In near coastal environments, nautical charts provide crucial information for navigation and routing both in real-time operations and during planning stages. The cost of data collection as well as capacity constraints in the processing pipeline make reliable bathymetric information in such areas sparse. Prioritization rules can help guide the efforts to where information is the most valuable. AIS data provide accounts of real ship movements, indicating both desirable paths and minimum depths. We propose a statistical model for combining sparse bathymetric soundings with AIS observations for improved prediction of depths for generation of feasible transportation corridors. The method relies on viewing AIS draughts as censored observations of the true depth. A case-study is performed for the southern archipelago of Gothenburg using the program R-INLA. The non-stationarity caused by having boundaries with known (zero) depth and holes (land) in the domain is handled through discretization. Varying amounts of AIS data, ranging from none to 1824 observations, are used in the experiments. Results show predicted depths within the range of data values, and that inclusion of AIS data serve to push the field down to ensure that traverseable areas are predicted as such revealing corridors in narrow passages where bathymetric soundings are lacking.

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

Satellite derived bathymetry for rapid investigation for possible navigational channel design for coastal wind farm installation: a case study at Silavathurai, Sri Lanka

A rapid bathymetric survey was required for the transportation of wind turbines and blades to the Silavathurai coastline, Sri Lanka via sea. This area is a shallow uncharted area which makes this task a challenge. To overcome this limitation, remote sensing techniques were used to derive the bathymetry of the area using Sentinel-2 satellite images because of its high-resolution capabilities. The empirical bathymetric method was used by incorporating band ratio techniques that involves comparing different bands of the satellite imagery in estimating water depths. Three band combinations (Green–Blue, Red–Blue and Red–Green) were used and evaluated for their effectiveness in estimating water depths. The findings showed varying degrees of correlations between the in-situ measurements and bathymetry values. Green–Blue band combination gave the strongest correlation (R2 = 0.91) among the band combinations, indicating that it is most suitable for bathymetry estimation in such situations. Further, various depth zones were also tested for correlation analysis, which reveals higher correlation values for shallower depths. Then, accuracy analysis was done based on the computed Root Mean Square Error (RMSE) values and Green–Blue combination gave the least overall RMSE value (1.06 m) with the measured depths. Finally, the derived bathymetry data from the satellite images played a vital role in designing the navigation channel, ensuring safe transport of wind turbines for the Silavathurai wind farm project. The study emphasises the effectiveness of the remote sensing approach in determining bathymetry for shallow areas, offering insightful information for coastal renewable energy projects.

Satellite bathymetry estimation in the optically complex northern Baltic Sea

Satellite bathymetry estimation (SBE) is a cost-effective method for producing depth information for shallow marine and aquatic areas. However, most SBE methods have been developed and are refined in clear water conditions and their feasibility in optically complex waters is poorly understood. This hinders the use of SBE in optically complex waters.To test the feasibility of SBE in optically complex waters, we used multi-temporal Sentinel-2 (S-2) imagery and in-situ measured depth information from dive computers to fit bathymetric models for 14 study areas in the northern Baltic Sea. We compared different methods of using multi-temporal S2-scenes, atmospheric correction, and statistical modeling, as well as different band combinations to define the optimal model for each study area. We tested the dependence of the depth estimation on 13 environmental variables with potential influence on the accuracy of the SBE.The optimal bathymetric models differed among the study sites, with the differences manifested in different requirements for atmospheric correction, band combination, statistical model, and the use of multiple S2-scenes. The coefficients of determination (R2) of the optimal models varied between 0.42 and 0.79, with mean (X) R2 equaling to 0.65. The coefficients of determination were positively related to the proportion of sea within the 5 km search kernel and to the exposure of the study site. At the predicted depth of 2 m, the differences between observed and predicted depths varied between 0.11 and 0.60 m (X = 0.35 m). At the predicted depth of 1 m, the differences varied between 0.04 and 0.34 m (X = 0.21 m).SBE provides accurate depth estimates in optically complex waters, even when in-situ calibration data are limited. However, the maximum detectable depth obtained with SBE in optically complex waters is significantly lower than in clear water conditions. The SBE process can be fine-tuned to the specific characteristics of an area, improving its performance in optically complex conditions. In particular, the identification of the optimal band combination is critical for successful SBE. The depth estimates produced in this study can be used in a variety of applications that require accurate information on the seafloor bathymetry.

Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data

Machine Learning Based Estimation of Coastal Bathymetry From ICESat-2 and Sentinel-2 Data

GRDL: A New Global Reservoir Area‐Storage‐Depth Data Set Derived Through Deep Learning‐Based Bathymetry Reconstruction

AbstractReservoirs play a critical role in the global water cycle by regulating the flow of water from the environment into human systems. Accurate estimation of the area‐storage‐depth relationships for global reservoirs is essential for effective hydrological modeling and reservoir storage monitoring. Bathymetry reconstruction presents a promising approach to derive this information. Current bathymetry methods either rely on simple approximations or are constrained by dependence on altimetry data or field survey data. To overcome these limitations, this study presents a pioneering approach involving training a deep learning model to reconstruct bathymetry and establish precise area‐storage‐depth relationships. We trained the deep learning model with approximately 160,000 simulated reservoirs derived from Shuttle Radar Topography Mission (SRTM) and fine‐tuned the model based on hundreds of reservoirs with bathymetry data. By employing the trained model and SRTM, the bathymetry of 7,250 reservoirs in the Global Reservoir and Dam Database were subsequently reconstructed. The method is validated against comprehensive reference data sets, including 54 test reservoirs with bathymetry data, 118 satellite altimetry‐based reservoirs, and 68 LiDAR‐based reservoirs. The reconstructed bathymetry achieves a mean absolute error of 7.87 m and a mean error of +2.05 m for the test reservoirs. The validation against satellite altimetry and LiDAR‐based references significantly outperforms previous geometric approximation techniques, with median normalized root mean square error (NRMSE) values of 20.6% for area‐storage and 22.1% for area‐level curves. Additionally, the reservoir storage variations are estimated with precision, outperforming previous methods. The proposed deep learning‐based approach presents a robust solution for accurate reservoir bathymetry estimation and establishes more reliable area‐storage‐depth relationships for reservoirs worldwide.

Nearshore bathymetry estimation through dual-time phase satellite imagery in the absence of in-situ data

ABSTRACT Accurate bathymetric information is an important foundation for marine resource development and nearshore ecological protection. Existing empirical algorithms can estimate water depth from high resolution images with the aid of ground truth bathymetry data. However, empirical models are not applicable in areas without measured bathymetric data. To overcome the difficulty of estimating bathymetry in areas without measured data, we developed a dual-time phase bispectrum optimization algorithm (DPBOA) that combines the empirical data for estimating bathymetry in Case-I water using the blue-green bands of dual-temporal multispectral imagery. The hyperspectral optimization process exemplar is first transformed into a dual-band model applicable to multi-band images, and the model is then applied separately to the same locations of different time-phase images, using least squares optimization to derive all unknown model parameters. The depth was estimated by the dual-band model after the unknown parameters were determined. To assess the performance of the algorithm, bathymetric estimation was performed using dual-time images from Sentinel-2, Gaofen-2, Gaofen-6, and Worldview-2 in Ganquan Island and Qilianyu Island, respectively. Validation was performed with the data from airborne LiDAR bathymetry (ALB) and ICESat-2. The results indicate that for the Ganquan Islands, the coefficient of determination ( R 2 ) was 0.92, and the root mean square deviation (RMSE) was 1.26 m; for the Qilianyu Islands, the R 2 was 0.94 and the RMSE was 1.00 m. The developed model obtains better results compared with the Lyzenga empirical model and the dual-band bathymetry estimation algorithm in terms of bathymetry estimation without measured data. Additionally, the impacts of the time interval between images on bathymetry estimation are analyzed.

Satellite computed bathymetry assessment – Developing satellite LiDAR methods to enhance coastal bathymetry coverage

The Satellite Computed Bathymetry Assessment (SCuBA) project implements a new photon classification approach to prepare lidar returns from the ICESat-2 satellite mission as a source of bathymetric data in suitable shallow ocean environments. SCuBA data enhances and compliments other space-borne bathymetry estimation methods for hydrographic applications and improves depth uncertainty where applicable. Incorporating SCuBA data into hydrographic projects expands and enhances data collection capabilities that can inform other empirical satellite bathymetry models and help improve navigational safety products in areas where ship-based sonar and aircraft-borne lidar surveys are not feasible.

UAV video-based estimates of nearshore bathymetry

UAV video-based estimates of nearshore bathymetry

Study of various machine learning approaches for Sentinel-2 derived bathymetry.

In recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it became possible to develop bathymetry estimation algorithms that can predict seabed depth and utilize them systematically. Since there are a number of theoretical approaches, physical models, and empirical techniques to use satellite observations in order to estimate depth in the coastal zone, the presented article compares the performance and precision of the most common one to modern machine learning algorithms. More specifically, the models based on shallow neural networks, decision trees and Random Forest algorithms have been proposed, investigated and confronted with the performance of pure analytical models. The particular proposed machine learning models differ also in a set of satellite data bands used as an input as well as in applying or not geographical weighting in the learning process. The obtained results point towards the best performance of the regression tree algorithm that incorporated as inputs information about data localization, raw reflectance data from four satellite data bands and a quotient of logarithms of B2 and B3 bands. The study for the paper was performed in relatively optically difficult and spatially variant conditions of the south Baltic coastline starting at Szczecin, Poland on the west (53°26'17'' N, 14°32'32'' E) to Hel peninsula (54°43'04,3774'' N 18°37'56,9175'' E). The reference bathymetry data was acquired from Polish Marine Administration. It was obtained through profile probing with single-beam sonar or direct in-situ probing.

Multivariate Data Assimilation at a Partially Mixed Estuary

Abstract In 2013 and 2014, multiple field excursions of varying scope were concentrated on the Columbia River, a highly energetic, partially mixed estuary. These experiments included surface drifter and synthetic aperture radar (SAR) measurements during the ONR RIVET-II experiment, and a novel animal tracking effort that samples oceanographic data by employing cormorants tagged with biologging devices. In the present work, several different data types from these experiments were combined in order to test an iterative, ensemble-based inversion methodology at the mouth of the Columbia River (MCR). Results show that, despite inherent limitations of observation and model accuracy, it is possible to detect dynamically relevant bathymetric features such as large shoals and channels while originating from a linear, featureless prior bathymetry in a partially mixed estuary by inverting surface current and gravity wave observations with a 3D hydrostatic ocean model. Bathymetry estimation skill depends on two factors: location (i.e., differing estimation quality inside versus outside the MCR) and observation type (e.g., surface currents versus significant wave height). Despite not being inverted directly, temperature and salinity outputs in the hydrodynamic model improved agreement with observations after bathymetry inversion.

Aplikasi Penginderaan Jauh Citra Landsat-8 Untuk Pembuatan Peta Batimetri Di Perairan Pantai Jumpai, Klungkung, Bali

Klungkung Regency is the smallest regency in the province of Bali, Indonesia. The waters of Jumpai Beach are one of the areas in Klungkung, which is one area that is used by the surrounding population for fishing activities for fishermen to obtain fishery products. Remote sensing technology has been applied because of its effectiveness and the significance of its use in compiling and revising resource maps. It is also helpful to support resource planning and management. A bathymetry map is a map that describes the depth of the sea and is presented using contour lines. Contour lines are abstract lines connecting several locations or areas with the same height or depth. Remote sensing techniques are available to determine the ocean’s depth because the air has signals from the very bottom of the ocean with strong solid wavelengths. However, the penetration of electromagnetic energy is limited. Therefore, remote sensing techniques are adopted to infer water depth and shallowness. One of the satellites that can be used for bathymetry mapping is Landsat-8. Landsat imagery has a spatial resolution of 30 meters, complemented by the visible channels required to extract bathymetric maps. Accurate estimation of shallow water area bathymetry for the safety of small boat navigation such as fishing and for benthic studies. Remote sensing technology can be considered one of the most desirable alternative tools for bathymetry development.

A Novel Approach for Bathymetry Estimation through Bayesian Gravity Inversion

The bathymetry is the most superficial layer of the Earth’s crust on which it is possible to perform direct measurements. However, it is also well known that water covers more than 70% of the Earth’s surface, so an enormous expenditure of acquisition campaigns should be performed to produce a high-resolution map of this layer. Currently exploiting mainly commercial navigation routes, the sea floor coverage with shipborne sounding is only at 11%, and the remaining part is currently modeled by classical interpolation techniques or satellite-based gravity inversion methods. In the present work, a new method to refine bathymetry modeling based on the exploitation of global gravity field models is presented. In the proposed solution, once modeled and removed from the observed gravity field, the gravitational signals related to the deepest structures, a 3D Bayesian inversion algorithm is used to improve the actual knowledge of bathymetry. The proposed inversion method also enables limiting the solution to shipborne sounding measurements in such a way as to improve the seafloor grid where no local, high-quality information is available. Two test cases are discussed in the Mediterranean Sea region. Promising results are presented, opening the possibility of applying an analogous method to refine the bathymetry modeling at larger scales up to the global one.

Complex tsunamigenic near-trench seafloor deformation during the 2011 Tohoku–Oki earthquake

The near-trench coseismic rupture behaviour of the 2011 Tohoku–Oki earthquake remains poorly understood due to the scarcity of near-field observations. Differential bathymetry offers a unique approach to studying offshore coseismic seafloor deformation but has a limited horizontal resolution. Here we use differential bathymetry estimates with improved horizontal resolutions to investigate near-trench coseismic slip behaviours in the 2011 Tohoku–Oki earthquake. In the main rupture region, a velocity-strengthening behaviour in the shallow fault is observed. By contrast, the seafloor uplift decreases towards the trench, but the trend inverts near the backstop interface outcrop, revealing significant off-fault deformation features. Amongst various competing off-fault effects observed, we suggest that inelastic deformation plays a predominant role in near-trench tsunami excitation. Large trench-bleaching rupture is also observed immediately north of 39°, delimiting the northern extent of the main rupture region. Overall, striking spatial heterogeneity of the shallow rupture behaviour is revealed for the region.

Estimation of shallow stream bathymetry under varying suspended sediment concentrations and compositions using hyperspectral imagery

Hyperspectral remote sensing has the proven ability to provide bathymetric estimates over relatively clear water. High turbidity caused by suspended sediments scatters light, decreasing light penetration to depth and is thus regarded as the most confounding factor in remote sensing-based bathymetry. Therefore, the effect of varying sediment-laden flow conditions on remote sensing-based bathymetry measurements was investigated in this study to assess the applicability of hyperspectral imagery and a robust method was proposed to account for such conditions. To acquire spectral data under various suspended sediment and bottom conditions, experiments involving the injection of sand and yellow loess were performed in outdoor field-scale channels with three bottom conditions (coarse sand, vegetation, and fine sand) under shallow stream conditions (< 1 m). Using the acquired data, the random forest with recursive feature elimination (RF-RFE) method was used to select and learn the optimal spectral band combination for each bottom type. The results were then compared with those of the optimal band ratio analysis (OBRA) method, which has been most widely used for bathymetry measurements via remote sensing imagery. The reflectance spectra differed depending on the bottom type although they were similar regardless of the bottom type when high reflectance was distributed at high concentrations. Given such optically complex data, OBRA exhibited very low accuracy because it was substantially affected by suspended sediment. However, RF-RFE showed an accuracy of R2 ≥ 0.95 and mapped a stable bathymetry distribution under sediment-laden flow conditions by utilizing 6–12 spectral bands as the optimal combination, depending on the bottom type. This result demonstrated that although the contribution of bottom reflectance was weak when the suspended sediment concentration was high, hyperspectral remote sensing is still applicable if spectral bands of various wavelengths can be incorporated to account for nonlinearity. While the effectiveness of this method could be limited to the specific conditions of suspended sediment and bottom properties, it is expected to be more robust to various confounding factors after being trained under diverse spectral conditions.

Interferometric synthetic aperture sonar large scale simulated data benchmark of a ship wreck

AbstractInterferometric synthetic aperture sonar (InSAS) systems produce high resolution seafloor imagery and 3‐D bathymetry by exploiting the phase differences between image pairs formed from data collected by vertically separated arrays. This process is non‐trivial due to the inherent ambiguity of interferometric phase estimates, and the effect of layover, where the response of multiple scatterers with different positions are mapped onto the same pixel during imaging. These effects are particularly problematic for complex scenes with strong bathymetry, such as ship wrecks. This is an unsolved problem and improved algorithms for InSAS bathymetry estimation are needed. However, developing and assessing such algorithms using data from fielded InSAS systems is difficult because of a lack of accurate ground truth for the platform motion and the true 3‐D shape of the scene. This paper presents an openly available synthetic data set as an alternative to field‐collected data for use by InSAS algorithm developers. The dataset is derived from a high‐resolution 3‐D photogrammetry scan of the SS Thistlegorm wreck. It was produced by a point‐based diffraction model using parameters similar to the Minehunting UUV for Shallow water Covert Littoral Expeditions (MUSCLE) InSAS.