Satellite-derived Bathymetry Research Articles

A satellite-derived bathymetry method combining depth invariant index and adaptive logarithmic ratio: A case study in the Xisha Islands without in-situ measurements

Accurate bathymetric data is crucial for various aspects such as marine resource exploitation and marine ecological conservation. Currently, satellite-derived bathymetry (SDB) based on empirical and physical models has been widely utilized in constructing underwater terrain in shallow seas. However, the application of such SDB models is limited in remote island reef areas lacking in-situ measurement data. To overcome this issue, the manuscript proposes an unconstrained SDB optimization method without in-situ measurement data, utilizing satellite multispectral imagery (Geoeye-1) and spaceborne LiDAR data (ICESat-2). By classifying the seafloor substrate in coral reef areas into sandy and coral, based on the depth invariant index (DII), we employ an adaptive logarithmic ratio model for unconstrained SDB. The ICESat-2 LiDAR data are then used to correct the SDB results, achieving bathymetry optimization in the coral reef area of the Xisha Islands. Additionally, the proposed method is applied to Yuanzhi Island of the Xisha Islands, and the accuracy of the bathymetric results is evaluated against ALB (Airborne LiDAR Bathymetry) data. The findings demonstrate that compared to conventional methods, our method can improve the accuracy of SDB results with good adaptability. In the Yuanzhi Island area, the proposed method yields SDB results with an R2 of 0.93, an MAE (Mean Absolute Error) of 0.94, and an RMSE (Root Mean Square Error) of 1.12 m, compared to ALB data. The average error is less than 10 % of the maximum depth, essentially meeting the requirements of the International Hydrographic Organization (IHO) standards for depth measurement error when depth is <20 m. This study can offer a novel approach for enhancing bathymetric accuracy around offshore and remote islands, where gathering underwater terrain data is challenging.

Satellite-derived bathymetry using Sentinel-2 in mesotidal coasts

Coastal zones are strategic environments of high socioeconomic, political, and ecological value, with over half of the world’s population residing within 200 km of the coast. This proximity highlights their vulnerability to extreme events, which are exacerbated by global changes, leading to significant coastal impacts such as erosion, flooding, and ecosystem services deterioration. Consequently, efficient and operational methodologies for continuous monitoring are urgently needed to face these challenges. Bathymetric data are essential for understanding coastal dynamics, yet traditional data collection methods are often constrained by logistical challenges and high costs. Spaceborne remote sensing techniques offer significant advantages over traditional ground-based methods, particularly in terms of cost-effectiveness and operational efficiency. Over the last half-century, different Satellite-derived bathymetry (SDB) methodologies have been developed; however, challenges still persist. In this research, we applied a robust SDB methodology to three different study sites: Cíes Islands, Baiona Bay, and Vao beach within the Ría de Vigo, Galicia (NW Spain). These areas offer diverse and complex mesotidal environments to test for the very first time the methodology's efficacy. SDB was retrieved with a median absolute error (MedAE) ranging from 0.35 m to 1.55 m for depths up to 14 m. Results with different data source were evaluated, obtaining MedAE for nautical charts ranging from 0.46 m to 1.55 m. The precision between the data sources were quite close. In addition, multi-image composite was generated using images coinciding with both low tide (LT) and high tide (HT) conditions across the three zones. The lowest MedAE values were consistently obtained in images classified as LT (0.46 m) corresponding to Vao area. The results highlight the potential of nautical charts as a reliable source of calibration data for SDB, confirm the effectiveness of multi-image and switching models to correct artifacts and turbidity, considering tidal effects, improving single image approaches, and leverage visible bands for precise depth retrieval under varying conditions.

An Empirical Evaluation of the Localised Accuracy of Satellite-Derived Bathymetry and SDB Depth Change

The weakness of global metrics for assessing the accuracy of satellite-derived bathymetry (SDB) is demonstrated and the magnitude of local accuracy variability quantified. SDB was developed for three small areas (500 m-by-1500 m) near Key West Florida (United States) that varied in depth and geomorphometric complexity. Data employed were 2016 and 2019 airborne lidar and three 2016 and four 2019 Landsat images. The modelling methodology was a widely used quasi-empirical linear regression-based approach. Local accuracy of SDB depth change – which was expected to be pronounced between 2016 and 2019 due to the occurrence of Category 5 Hurricane Irma in 2017 – was also calculated and examined. The global correlation (R 2) between lidar-based depth and SDB for 2016 and 2019 varied from about 0.1 to 0.9 which produced relative (to depth) root mean square errors (RMSEs) between 5% and 25%. No image was consistently best for all three areas nor for either 2016 and 2019. SDB-based depth change was poorly correlated to the reference lidar-based depth change for all three areas. Spatial depiction of SDB accuracy showed considerable variability even within the three relatively small areas employed for 2016 and 2019 SDB depth estimates and 2016–2019 SDB depth change estimates.

Application of gradient boosting machine in satellite-derived bathymetry using Sentinel-2 data for accurate water depth estimation in coastal environments

Accurate water depth estimation is crucial in coastal environmental management, resource exploration, and ecological protection. Traditional water depth measurement methods are often time-consuming and costly, especially in vast sea areas where their application is limited. However, with the rapid development of remote sensing technology, particularly the widespread use of high-resolution satellite imagery, water depth remote sensing has emerged as a more efficient, economical, and widely applicable solution. In this study, we utilized Sentinel-2 satellite data and applied various algorithms to accurately estimate water depth in the Nanshan Port area. The results showed that the Gradient Boosting Machine (GBM) model excelled in monitoring shallow water and coastal environments, effectively addressing challenges such as light attenuation and water scattering in turbid waters. Compared to traditional methods, GBM-generated predictions were smoother and more detailed. This study not only demonstrates the significant potential of satellite remote sensing for water depth measurement but also points to future directions for algorithm optimization and the integration of remote sensing technologies. It is expected to bring revolutionary progress to oceanic scientific research and coastal management.

Analysis of Bathymetry Accuracy Using Sentinel 2 Satellite on Different Characteristics Waters in Bali Island

Bathymetry surveys today are often carried out using the echo-sounding method, but this method has disadvantages, such as requiring a lot of time and being quite expensive. Along with the development of technology, some alternative methods can be used to visualize bathymetry, such as remote sensing. Remote Sensing uses satellite imagery in the operation, while the technique to acquire bathymetry is called Satellite-Derived Bathymetry (SDB). This method uses an optical satellite with several color bands or multispectral images. In this research, a satellite used to map ocean depth is Sentinel-2. The SDB technique used in this research is the Lyzenga Algorithm. The Lyzenga algorithm uses multilinear logarithms in its operation and can be used using three optical image channels (blue, green, and red channels). Supported by the SDB algorithm, an analysis of research locations was carried out at several points in the waters of Bali Island due to the diversity of water characteristics such as sea depth and wave height. From several analysis results of different characteristic waters in Bali Island, We can see that many parameters impact the result of the Satellite to visualize bathymetry. The Satellite's optimal result for reading the bathymetry depth is approximately 30 meters. But in reality, some cases can interfere with the accuracy of Satellite visualizing bathymetry within this depth. Breaking waves, high water sedimentation, and some objects that could guide the Satellite to misread them as elevation.

Accuracy of Bathymetric Depth Change Maps Using Multi-Temporal Images and Machine Learning

Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created by “stacking” the bands of the times t1 and t2 images, geographically coincident reference data for times t1 and t2 allow for “true” depth change to be calculated for the pixels of the multi-temporal image, and this information is used to fit a single model that estimates depth change directly rather than indirectly as in the model-differencing approach. The multi-temporal image approach reduced the depth change RMSE by about 30%. The machine learning modelling method (categorical boosting) outperformed linear regression. Overfitting of models was limited even for the CatBoost models having the maximum number of variables examined. The visible Sentinel-2 spectral bands contributed most to the model predictions. Though the multi-temporal stacked image approach produced clearly superior depth change estimates compared to the conventional approach, it is limited only to those areas for which geographically coincident multi-temporal reference/“true” depth data exist.

Shallow Water Depth Estimation of Inland Wetlands Using Landsat 8 Satellite Images

Water depth affects many aspects of wetland ecology, hydrology, and biogeochemistry. However, acquiring water depth data is often difficult due to inadequate monitoring or insufficient funds. Satellite-derived bathymetry (SBD) data provides cost-effective and rapid estimates of the water depth across large areas. However, the applicability and performance of these techniques for inland wetlands have not been thoroughly evaluated. Here, a time series of bathymetry data for inland wetlands in West Kentucky and Tennessee were derived from Landsat 8 images using two widely used empirical models, Stumpf and a modified Lyzenga model and three machine learning models, Random Forest, Support Vector regression, and k-Nearest Neighbor. We processed satellite images using Google Earth Engine and compared the performance of water depth estimation among the different models. The performance assessment at validation sites resulted in an RMSE in the range of 0.18–0.47 m and R2 in the range of 0.71–0.83 across all models for depths &lt; 3.5 m, while in depths &gt; 3.5 m, an RMSE = 1.43–1.78 m and R2 = 0.57–0.65 was obtained. Overall, the empirical models marginally outperformed the machine learning models, although statistical tests indicated the results from all the models were not significantly different. Testing of the models beyond the domain of the training and validation data suggested the potential for model transferability to other regions with similar hydrologic and environmental characteristics.

Satellite‐Derived Bathymetry in Dynamic Coastal Geomorphological Environments Through Machine Learning Algorithms

AbstractIn the field of coastal geomorphology, advancements in space technology have revolutionized coastal mapping and understanding. Satellite‐derived bathymetry (SDB) research has progressed, focusing on various estimation methods using high‐resolution satellite imagery and in‐situ data. Challenges arise when applying these methods to the Indian coastline due to its turbid waters and intricate features such as creeks and deltas, laden with sediment and submerged rocks. A study aims to assess multivariate machine learning (ML) regression techniques for estimating bathymetric data. The study employs ground‐truth data and imagery from Aster, Landsat‐8, and Sentinel‐2 at distinct sites known for complex underwater landscapes. Several algorithms including Multiple Linear Regression, Support Vector Regressor, Gaussian Process Regression (GPR), Decision Tree Regression, K‐Neighbors Regressor, k‐fold cross‐validation with Decision Tree Regression, and Random Forest (RF) are evaluated for SDB. Results from the Vengurla Site show that using the Landsat‐8 data set with the GPR algorithm achieves R2 0.94, root mean squared error (RMSE) 1.53 m, and MAE 1.14 m, utilizing visible spectrum bands. At Mormugao, the Sentinel‐2 data set with GPR and RF algorithms attains R2 0.97 and RMSE 1.23 m, with GPR outperforming RF, having an MAE of 1.05 m compared to RF's 1.22 m. This study underscores the potential of ML regression techniques in overcoming challenges with using SDB for mapping coastal geomorphology, particularly in intricate underwater terrains and turbid waters by assimilating sophisticated algorithms and their refined cartographic representation.

Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities

Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities

Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

Performance test of clean-coastal-water composite sentinel 2A image for shallow water bathymetry mapping

The “clean-coastal-water time-series composite” image preprocessing algorithm is one of the alternatives for obtaining minimal atmospheric disturbance satellite images for data inputs in Satellite-Derived Bathymetry (SDB). However, the performance of time-series composite images for shallow water bathymetric mapping needs to be assessed, considering the presence of pixel and band dispersion during the composite image construction process. This research aims to study the result of a clean-coastal-water time-series composite Sentinel-2 by using cloud computing preprocessing as input data for SDB in shallow waters, compare the results of SDB from a Sentinel-2 single date image and from the clean-coastal-water time-series composite image using a random forest regressor (RFR) algorithm in terms of accuracy and spatial distribution of the bathymetry, and evaluate the performance and quality of bathymetric maps obtained from a single date image and clean-coastal-water time-series composite Sentinel-2 images based on International Hydrographic Organization standards (IHO). The results show that not all time-series composite processes can produce clean images of interference from clouds, sunglints, bad waters, and pixel wave breaks. The acquisition time range affects the quality of the time-series composite image. The SDB RFR model can handle the complex relationship between the in situ water depth variable and the surface reflectance value of a clean-coastal-water time-series composite image that has pixel and band dispersion. Overall, the SDB RFR model was successfully used to predict bathymetry in shallow waters with accuracy that meets the IHO standard CATZOC requirements, with RMSE and MAE values of less than 0.5 m as well as TVU confidence level values of more than 95% at a depth of 0–5 m resulting from each - respectively clean-coastal-water time-series composite images in shallow waters around Morotai Island.

REVIEW OF MODERN BATHYMETRIC SURVEY TECHNIQUES AND THEIR IMPACT ON OFFSHORE ENERGY DEVELOPMENT

This paper presents a comprehensive review of modern bathymetric survey techniques and their impact on offshore energy development. Bathymetric surveys play a crucial role in mapping the seafloor and are essential for various offshore activities, including oil and gas exploration, renewable energy development, and submarine cable installation. The review synthesizes recent advancements in bathymetric surveying technologies, focusing on their role in improving the exploration and monitoring of offshore energy resources. The first section of the paper provides an overview of traditional bathymetric survey methods, such as single-beam and multi-beam echo sounders, which have been widely used for mapping the seafloor. The limitations of these methods, including limited coverage and resolution, are discussed, highlighting the need for more advanced techniques. The second section of the paper discusses the recent advancements in bathymetric survey technologies, such as airborne lidar, satellite-derived bathymetry, and unmanned surface vehicles (USVs). These technologies offer significant improvements in terms of coverage, resolution, and efficiency compared to traditional methods. For example, airborne lidar can provide high-resolution bathymetric data over large areas, making it ideal for rapid seafloor mapping in offshore energy exploration. The third section of the paper examines the impact of these advancements on offshore energy development. Improved bathymetric surveying techniques enable more accurate mapping of the seafloor, which is crucial for identifying suitable locations for offshore energy infrastructure, such as oil and gas platforms, wind farms, and wave energy converters. Furthermore, these techniques facilitate the monitoring of underwater structures and environmental changes, helping to ensure the safe and sustainable operation of offshore energy facilities. In conclusion, modern bathymetric survey techniques have revolutionized offshore energy development by providing more accurate, efficient, and cost-effective methods for mapping the seafloor. These advancements are essential for meeting the growing demand for offshore energy resources while minimizing environmental impact and ensuring operational safety.&#x0D; Keywords: Modern Bathymetric, Survey Techniques, Impact, Offshore Energy, Development.

On the use of satellite information to detect coastal change: Demonstration case on the coast of Spain

Recent developments in satellite processing tools allow low-cost, fast and automatic processing of a large amount of information from Earth observation, enhancing the capability of detecting coastal changes from space at different temporal scales. Some works have assessed the quality of these data and applied it to detect coastal evolution locally, most of them focusing on mid-term and long-term changes in the coastline. In this work, we evaluate the capability to monitor changes in coastal morphology at various temporal and spatial scales using 1D (coastlines) and 3D (bathymetry) satellite-derived data obtained from site-specific processing methods. Local characteristics were included in several phases of the development of the satellite products used here: i) geolocated very high resolution images from each pilot site were used in the coregistration process to enhance geolocation accuracy in images from different missions, ii) different spectral indices were tested at each pilot site to obtain more reliable detection of the coastline at all sites and iii) measured topobathymetry data were used to obtain datum-based satellite shorelines and bathymetry. The accuracy and skill of those satellite products were assessed at several pilot sites in Spain. The results indicated high horizontal accuracy (RMSE < pixel size), with errors on the order of half of the pixel size (RMSE = 5.0 m and for Sentinel-2 and 18.8 m for Landsat5). Furthermore, the coastlines used here presented errors comparable to those obtained from the widely used open-source tool CoastSat. Time-series analysis using satellite-derived shorelines showed that coastal change processes can be detected at several temporal and spatial scales, such as short-term erosion and accretion events on a small beach, seasonal beach rotation, and long-term trends at local and regional scales. However, the results from satellite-derived bathymetry indicated that the quantitative assessment of the coastal morphology with 3D products is still limited. Some in situ measurements are necessary to obtain satellite data that represent site-specific conditions. However, the quantity of this auxiliary in situ measurements required to obtain reliable time series of satellite derived shorelines and bathymetry is significantly lower than the quantity required by traditional monitoring methods. The results are discussed, highlighting the gaps that need to be filled in the future so that satellite-derived products can be used in usual coastal change monitoring practices.

Satellite-Derived Bathymetry in Support of Maritime Archaeological Research—VENμS Imagery of Caesarea Maritima, Israel, as a Case Study

Deriving bathymetry by means of multispectral satellite imagery proves to be a replicable method, offering high-resolution coverage over large areas while keeping costs low. Maritime archaeologists often require bathymetric mapping at a high resolution and with a large spatial coverage. In this paper, we demonstrate the implementation of SDB in maritime archaeology using high-resolution (5 m/pixel) data from Vegetation and Environment monitoring on a New Micro-Satellite (VENμS) imagery. We focus on the area of the Roman harbour of Sebastos, located at Caesarea Maritima along the Israeli coast of the Eastern Mediterranean. For extracting SDB, we take an empirical approach, which is based on the integration of satellite imagery and sonar depth measurements, resulting in a blue-green band ratio algorithm that provides reliable results up to a water depth of 17 m. Comparison with in situ depth measurements yielded an RMSE of 0.688 m. The SDB mapping is complemented by satellite-based identification of above- and below-water rocks. The presented approach can readily be replicated in other regions using various types of multispectral satellite imagery, particularly when only coarse bathymetric sonar data are available, thus substantially contributing to our ability to perform maritime archaeological research.

Automated classification of valid and invalid satellite derived bathymetry with random forest

Recent decades have seen rapid growth in algorithms and workflows for generating bathymetry from multispectral satellite imagery, with the output typically referred to as satellite derived bathymetry (SDB). An inherent challenge is that, while SDB algorithms generally output a value for every pixel in each input scene, the value of any particular raster cell may not represent a valid depth. Typically, large portions of a satellite scene will correspond to optically deep waters, where SDB depth retrieval is impossible. Boats, boat wakes, breaking waves, clouds, and land can also cause erroneous depth estimates. Compounding this challenge is the fact that SDB algorithms tend to be poor at self-diagnosing invalid retrievals. As a result, SDB grids often require substantial manual editing to generate reliable bathymetric digital elevation models (DEMs). This study investigates the ability to automatically classify valid and erroneous bathymetry from SDB grids using random forest and surface features generated from the SDB grids, spectral bands values, and band indices. The trained models achieved mean intersection over union accuracies of 73–91% at five geographically-diverse test sites of differing sizes, seafloor morphologies, substrates, and wind and wave climates. These methods can be used to rapidly assess satellite image-based bathymetry grids and are currently being adapted for operational use within the National Oceanic and Atmospheric Administration’s National Ocean Service.

An enhanced large-scale benthic reflectance retrieval model for the remote sensing of submerged ecosystems in optically shallow waters

Shallow water benthic habitats have been significantly degraded and seriously threatened by intensifying climate changes and anthropogenic stressors. Benthic reflectance (Rbλ) of optically shallow waters (OSWs) is a key parameter for remote sensing of benthic habitats’ composition and health status. The remote sensing reflectance (Rrsλ) just above the water surface contains the coupled spectral information of the water column and seabed in OSWs, which makes our effort challenging to separate the signals between seabed and water column properties. Due to too many unknowns of the water column and seabed optical properties, the current approaches are inaccurate or inadequate to retrieve benthic reflectance spectra over a large area from remotely sensed data. In this study, we proposed an enhanced large-scale benthic reflectance (LSBR) retrieval model from satellite data in OSWs with only input of Rrsλ. A new spectral library of benthic reflectance was built upon multi-source reflectance datasets obtained from several open-source field measurements. Then, the specific relationship between Rb443nm and Rb490nm was developed. By combining a semi-analytical benthic reflectance (SABR) model based on radiative transfer simulations and this relationship, water-column chlorophyll concentration (chl) was determined. Moreover, large-scale water depth was estimated by Rrsλ through satellite-derived bathymetry (SDB) technologies. Finally, benthic reflectance was retrieved with the inputs of the satellite-derived Rrsλ, chl, and water depth. In this way, the LSBR model fulfills the Rbλ retrieval with only input of satellite-derived Rrsλ. The new model was successfully applied to Sentinel-2/MSI data in the seagrass region of Xincun Bay and the coral region of Huaguang Reef and further verified by using field measurements and results of the along-track benthic reflectance (ATBR) retrieval model, which combined active lidar and passive high-resolution satellite images. Additionally, to gain insights into the long-term evolution of benthic communities, a typical application of the LSBR model for detecting benthic changes was conducted using eight years of Sentinel-2/MSI data in Heron Reef, depicting a similar consistency between the probable deterioration and restoration region and the sea surface temperature (SST) anomalies. The results demonstrated the robustness and accuracy of the LSBR model in retrieving benthic reflectance at large scales. The LSBR model should be helpful for further investigating benthic changes which can enhance the ability to monitor and assess the health status of submerged ecosystems in OSWs.

METHODS FOR SATELLITE DERIVED BATHYMETRY FROM SENTINEL-2 IMAGES: A COMPARISON

Abstract. In recent decades, research has been developed to estimate near-shore bathymetry depth values using satellite imagery. Visible and infrared bands are used to derive elevation profile estimates, so to obtain bathymetric in rapid way without mobilisation of persons or equipment and saving the costs. For consequence, Satellite Derived Bathymetry (SDB) is seen as a valid approach for shallow waters survey: strongly supported by the activity of scholars and researchers, multiple methods are available in the literature. This article aims to investigate and compare different SDB methods for sea depth extraction from Sentinel-2 satellite multispectral images, with particular attention to the accuracy of the results. The experiments are conducted on imagery including Blue, Green, Red and Near Infrared bands, with 10 m resolution, concerning the Bay of Pozzuoli (Italy). After removing the glint, the effects caused by the reflection of sunlight through single scattering from sea surface, three methods are applied: Band Ratio method (BRM), 3rd-degree polynomial regression line method (3DPM), and principal component analysis method (PCAM). 3DPM can be seen as a variant of the BRM where the linear law that interprets the correlation between the band ratio values and the depth values is replaced by the third order function. Models are trained using depth data extracted from an Electronic Navigational Chart (ENC) at 1:7,500 scale, which is also used to verify result accuracy. The experiments demonstrate that the 3DPM is better able to obtain a more precise bathymetric model, confirming the greater adaptability of the 3rd order function to interpretate the variability of the interaction of light with water along the water column.

Rapid Bathymetry Mapping Based on Shallow Water Cloud Computing in Small Bay Waters: Pilot Project in Pacitan-Indonesia

Mapping coastal areas generally requires large data constellations in time series and requires analysis using complex mathematical and modeling approaches. In shallow-water bathymetric mapping, remote sensing plays an important role in supporting conventional bathymetric mapping, especially in areas that are difficult to access. This method called Satellite Derived Bathymetry (SDB). The cloud computing approach is a solution for mapping shallow water bathymetry rapid and effectively. This study using Google Earth Engine (GEE) to compute remote sensing data for produce near-shore bathymetry. The method of Li et al. (2021) performs bathymetric extraction without using depth samples but uses chlorophyll-A as input for depth extraction parameter calculations. This study examines a small bay in the waters of Pacitan, Anakan Bay, and the waters of Kemujan Island in the Karimunjawa Islands. Within this study area, significant differences in resulting depth are very limited, ranging from 0 to -17.8. The developed model, based on the algorithm proposed by Li et al. (2021), is estimated to be able to provide accurate predictions of up to around 90% in the waters studied, with a root mean error rate (RMSE) of 1.1 meters.

Physics-Based Satellite-Derived Bathymetry (SDB) Using Landsat OLI Images

The estimation of depth in optically shallow waters using satellite imagery can be efficient and cost-effective. Active sensors measure the distance traveled by an emitted laser pulse propagating through the water with high precision and accuracy if the bottom peak intensity of the waveform is greater than the noise level. However, passive optical imaging of optically shallow water involves measuring the radiance after the sunlight undergoes downward attenuation on the way to the sea floor, and the reflected light is then attenuated while moving back upward to the water surface. The difficulty of satellite-derived bathymetry (SDB) arises from the fact that the measured radiance is a result of a complex association of physical elements, mainly the optical properties of the water, bottom reflectance, and depth. In this research, we attempt to apply physics-based algorithms to solve this complex problem as accurately as possible to overcome the limitation of having only a few known values from a multispectral sensor. Major analysis components are atmospheric correction, the estimation of water optical properties from optically deep water, and the optimization of bottom reflectance as well as the water depth. Specular reflection of the sky radiance from the water surface is modeled in addition to the typical atmospheric correction. The physical modeling of optically dominant components such as dissolved organic matter, phytoplankton, and suspended particulates allows the inversion of water attenuation coefficients from optically deep pixels. The atmospheric correction and water attenuation results are used in the ocean optical reflectance equation to solve for the bottom reflectance and water depth. At each stage of the solution, physics-based models and a physically valid, constrained Levenberg–Marquardt numerical optimization technique are used. The physics-based algorithm is applied to Landsat Operational Land Imager (OLI) imagery over the shallow coastal zone of Guam, Key West, and Puerto Rico. The SDB depths are compared to airborne lidar depths, and the root mean squared error (RMSE) is mostly less than 2 m over water as deep as 30 m. As the initial choice of bottom reflectance is critical, along with the bottom reflectance library, we describe a pure bottom unmixing method based on eigenvector analysis to estimate unknown site-specific bottom reflectance.

Innovative Maritime Uncrewed Systems and Satellite Solutions for Shallow Water Bathymetric Assessment

Shallow water bathymetry is a topic of significant interest in various fields, including civil construction, port monitoring, and military operations. This study presents several methods for assessing shallow water bathymetry using maritime uncrewed systems (MUSs) integrated with advanced and innovative sensors such as Light Detection and Ranging (LiDAR) and multibeam echosounder (MBES). Furthermore, this study comprehensively describes satellite-derived bathymetry (SDB) techniques within the same geographical area. Each technique is thoroughly outlined with respect to its implementation and resultant data, followed by an analytical comparison encompassing their accuracy, precision, rapidness, and operational efficiency. The accuracy and precision of the methods were evaluated using a bathymetric reference survey conducted with traditional means, prior to the MUS survey and with cross-comparisons between all the approaches. In each assessment of the survey methodologies, a comprehensive evaluation is conducted, explaining both the advantages and limitations for each approach, thereby enabling an inclusive understanding for the reader regarding the efficacy and applicability of these methods. The experiments were conducted as part of the Robotic Experimentation and Prototyping using Maritime Unmanned Systems 23 (REPMUS23) multinational exercise, which was part of the Rapid Environmental Assessment (REA) experimentations.