Water Depth Estimation Research Articles

Satellite derived bathymetry using empirical and machine learning approaches: a case study in the highly dynamic coastal water

ABSTRACT The estimation of nearshore water depth is essential in many near-shore marine sciences and coastal engineering. However, direct measurements using fieldwork methods can be expensive and time-consuming. Also, near-shore waters are typically turbid and very dynamic, which decreases the accuracy of previous methods for clear waters. In this study, we compared the performance of two Satellite-Derived Bathymetry approaches, including empirical (in particular, the Stumpf and multi-linear regression models) and five non-linear regression of machine learning algorithms (Random Forest, Support Vector Machine, CastBoost (CB), Extreme Gradient Boosting, and Light Gradient Boosting Machine) to provide an efficient method for deriving accurate bathymetric data in a high dynamic coast, the Hasaki located in Ibaraki Prefecture, Japan, from the multi-spatiotemporal satellite images of the Sentinel-2 constellation. A total of 70% depth data points of each year (2016, 2018, and 2020) obtained from boat-based echo-sounding measurements are used for training, and the remaining 30% are used for testing each approach. CB revealed an outstanding performance (R 2 ranging from 0.84 to 0.92) in coastal area of Hasaki down to ~15 m depth at all the 3 years of datasets, with mean RMSE values less than 0.5 m when the depth less than 7 m.

Enhancing water depth inversion accuracy in the Yangtze River's Nantong Channel using random forest and coordinate attention mechanisms

Accurate bathymetry information is crucial for safe navigation and efficient management of the Yangtze River Channel, a vital shipping corridor in China. Traditional bathymetric surveying methods are time-consuming and labor-intensive, limiting their application in large-scale and real-time monitoring. This study proposes a novel approach for bathymetry inversion in the Yangtze River Nantong Channel by integrating geolocational features obtained from the ZY-1E satellite with high-resolution multibeam data using the random forest algorithm. Our approach incorporates geographical coordinates enhancing the predictive capabilities of conventional models. The random forest with longitude/latitude (RF-Lon./Lat.) model, which incorporates geographical information, outperformed conventional methods, achieving an R2 of 0.57, MAE of 1.99 m, and RMSE of 2.96 m. The successful application of the RF-Lon./Lat. model highlights the effectiveness of integrating geolocational features with machine learning algorithms for accurate bathymetry inversion in the complex and turbid waters of the Yangtze River Channel. This innovative approach offers a promising solution for precise and efficient water depth estimation, which is essential for various applications in the Yangtze River Basin, including channel management, waterway maintenance, and hydrological studies. The insights gained from this study contribute to the growing body of knowledge on the application of machine learning and remote sensing techniques for bathymetric mapping in complex river environments, particularly in the context of the Yangtze River Channel.

Estimating Water Depth of Different Waterbodies Using Deep Learning Super Resolution from HJ-2 Satellite Hyperspectral Images

Hyperspectral remote sensing images offer a unique opportunity to quickly monitor water depth, but how to utilize the enriched spectral information and improve its spatial resolution remains a challenge. We proposed a water depth estimation framework to improve spatial resolution using deep learning and four inversion methods and verified the effectiveness of different super resolution and inversion methods in three waterbodies based on HJ-2 hyperspectral images. Results indicated that it was feasible to use HJ-2 hyperspectral images with a higher spatial resolution via super resolution methods to estimate water depth. Deep learning improves the spatial resolution of hyperspectral images from 48 m to 24 m and shows less information loss with peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and spectral angle mapper (SAM) values of approximately 37, 0.92, and 2.42, respectively. Among four inversion methods, the multilayer perceptron demonstrates superior performance for the water reservoir, achieving the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of 1.292 m and 22.188%, respectively. For two rivers, the random forest model proves to be the best model, with an MAE of 0.750 m and an MAPE of 10.806%. The proposed method can be used for water depth estimation of different water bodies and can improve the spatial resolution of water depth mapping, providing refined technical support for water environment management and protection.

Assessing braking performance on wet-road through water-depth estimation and vehicle-pavement dynamic simulation

ABSTRACT The presence of water on wet roads due to rainfall has emerged as a critical factor influencing driving safety. During severe weather, the non-uniform water distribution on the road can significantly diminish pavement friction, elevating the risk of traffic accident. Furthermore, the tire-pavement friction coefficient varies with vehicle velocity, rendering wet braking analysis complex. Conventional braking risk analysis often assumes a fixed friction coefficient, neglecting the impact of uneven pavement-induced water depth irregularities. This paper introduces a novel braking performance assessment method based on water-depth estimation and vehicle-pavement simulation. The uneven water-depth distribution is first estimated using LiDAR-measured pavement geometry. A co-simulation framework is then proposed to analyze the tire-pavement friction and study the dynamic braking performance. The 85th percentile stopping distance is adopted as the evaluation index for quantifying braking risk. Results from case studies highlight the substantial influence of rainfall intensity and vehicle velocity on braking risk. Additionally, pavement rutting accumulates deeper water depth, thereby elevating braking safety risks. The proposed model offers a novel perspective on vehicle braking risk analysis and can serve as a valuable safety indicator for highway transportation management and decision-making in driving strategies during wet weather conditions.

An improved ResNet method for urban flooding water depth estimation from social media images

Social media images featuring high timeliness, large data volume, and low cost, is utilized for flooding depth estimation. Common flood depth estimation methods are to segment each reference instance, resulting in local water depths and contradictory results. This study added Coordinate Attention into Residual Neural Network (ResNet), and proposed Coordinate Attention-Residual Neural Network (CA-ResNet) to acquire global submerged water depths. The 2021 Henan rainstorm in China was used as case study and a flooding dataset from Chinese Sina Microblog was constructed, with total 5676 images and 4189 flooded. All the flooded depths class 0–9 reflected from the images were annotated manually. The proposed CA-ResNet method was compared with VGGNet, ResNet, GoogleNet, and EfficientNet, both accuracy and F1 score improved by 1% − 3%. The average accuracy of level 0 – 8 within the error range of CA-ResNet reached 83.14%. It is proved that CA-ResNet could achieve flood depth estimation, facilitating efficient decision support for flood responding.

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.

Water depth estimate and flood extent enhancement for satellite-based inundation maps

Abstract. Floods are extreme hydrological events that can reshape the landscape, transform entire ecosystems and alter the relationship between living organisms and the surrounding environment. Every year, fluvial and coastal floods claim thousands of human lives and cause enormous direct damages and inestimable indirect losses, particularly in less developed and more vulnerable regions. Monitoring the spatiotemporal evolution of floods is fundamental to reducing their devastating consequences. Observing floods from space can make the difference: from this distant vantage point it is possible to monitor vast areas consistently, and, by leveraging multiple sensors on different satellites, it is possible to acquire a comprehensive overview on the evolution of floods at a large scale. Synthetic aperture radar (SAR) sensors, in particular, have proven extremely effective for flood monitoring, as they can operate day and night and in all weather conditions, with a highly discriminatory power. On the other hand, SAR sensors are unable to reliably detect water in some cases, the most critical being urban areas. Furthermore, flood water depth – which is a fundamental variable for emergency response and impact calculations – cannot be estimated remotely. In order to address such limitations, this study proposes a framework for estimating flood water depths and enhancing flood delineations, based on readily available topographical data. The methodology is specifically designed to accommodate, as additional inputs, masks delineating water bodies and/or no-data areas. In particular, the method relies on simple morphological arguments to expand flooded areas into no-data regions and to estimate water depths based on the terrain elevation of the boundaries between flooded and non-flooded areas. The underlying algorithm – named FLEXTH – is provided as Python code and is designed to run in an unsupervised mode in a reasonable time over areas of several hundred thousand square kilometers. This new tool aims to quantify and ultimately to reduce the impacts of floods, especially when used in synergy with the recently released Global Flood Monitoring product of the Copernicus Emergency Management Service.

Modelling Water Depth, Turbidity and Chlorophyll Using Airborne Hyperspectral Remote Sensing in a Restored Pond Complex of Doñana National Park (Spain)

Restored wetlands should be closely monitored to fully evaluate the effectiveness of restoration efforts. However, regular post-restoration monitoring can be time-consuming and expensive, and is often absent or inadequate. Satellite and airborne remote sensing systems have proven to be cost-effective tools in many fields, but they have not been widely used to monitor ecological restoration. This study assessed the potential of airborne hyperspectral remote sensing to monitor water mass characteristics of experimental temporary ponds in the Mediterranean region. These ponds were created during marsh restoration in Doñana National Park (south-west Spain). We used hyperspectral images acquired by the CASI-1500 hyperspectral airborne sensor to estimate and map water depth, turbidity and chlorophyll a in a subset of the 96 new ponds. The high spatial and spectral resolution of the CASI sensor allowed us to detect differences between ponds in water depth, turbidity and chlorophyll a, providing accurate mapping of these three variables, and a useful method to assess restoration success. High levels of spatial variation were recorded between different ponds, which likely generates high diversity in the animal and plant species that they contain. These results highlight the great potential of hyperspectral sensors for the long-term monitoring of wetland complexes in the Mediterranean region and elsewhere.

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 < 3.5 m, while in depths > 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.

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.

Multi-spectral remote sensing bathymetry based on the variation of substrate spectra and empirical linear relationship

The high spatial resolution and wide applicability of multi-spectral remote sensing make it significant for estimating shallow water depth. In areas with mixed substrates, determining the contribution of substrate reflection to the sensor’s received signal can effectively enhance the precision of multi-spectral remote sensing bathymetry. Therefore, this paper proposes a Two Factors constrained Quasi-Analytical Algorithm (TF-QAA). Firstly, the algorithm considers the spectral variation of the mixed substrates. Secondly, a robust correlation between inherent optical parameters and water depth is established in this study, which is used to enforce a linear relationship constraint on the initially estimated water depth. Sentinel-2 image data was used to verify the algorithm in the Antelope Reef area of the South China Sea. The best result from images taken at different times indicates that the TF-QAA method has the average absolute error (MAE) of 0.92 m, the root mean square error (RMSE) of 1.20 m, the average relative error (MRE) of 10.6%, and the correlation coefficient (R2) of 0.97. The results suggest that the algorithm is effective for Case 1 water, providing a novel approach for extensive bathymetric mapping in shallow water areas of islands, with or without LiDAR-measured depth data.

Used of the light attractor in traps for catches in Bengkunat Waters, Pesisir Barat Regency

Pesisir Barat is one of the locations that has a great opportunity for capture fisheries activities in Lampung. Bubu is a passive fishing gear, made of thick steel frames, woven nets with small openings. Bubu is usually used to catch fish or other small crustaceans, by luring them to swim and trapping them inside. In this study, we used a light attractor as bait. Thus, increasing the effect of the trap. It exploits the phototactic behavior exhibited by many fish species, which are attracted by light itself, or preys on light-attracted phototactic invertebrates. In this study we used two kinds of traps. First, those with light attractors, then those without light attractors. The purpose of this study was to determine the composition of the catch and comparing the result of both traps. This research was carried out from June to July 2022 with an estimated water depth of ±4-6 meters. The trap size is 120 cm x 70 cm x 80 cm. The composition of the catch on trap fishing gear consisted of several types of catch including blue-spotted grouper, red snapper, kuniran, baronang, cuttlefish, shrimp, squid and octopus. The result are quite different. The traps with LEDs having more result compared to traps without LED modifications.

Wetlands Water Level Measurements From the New Generation of Satellite Laser Altimeters: Systematic Spatial‐Temporal Evaluation of ICESat‐2 and GEDI Missions Over the South Florida Everglades

AbstractThe ICESat‐2 and GEDI missions were launched in 2018, becoming the new generation of space‐borne laser altimeters. These missions provide unprecedented global geodetic elevations, opening great opportunities for water level monitoring. The potential of these altimeters has been demonstrated in open‐water environments such as lakes, rivers, and reservoirs. However, detailed evaluations in vegetated environments, such as wetlands, floodplains, and other areas not constrained by water canal networks, are essential for continued improvement and further hydrological application. We developed a systematic accuracy assessment of ICESat‐2 ATL08, and GEDI L2A products to monitor spatial‐temporal water level and depth dynamics over the South Florida Everglades wetlands. The evaluation was performed on data acquired between 2020 and 2021, using gauge‐based water level and depth estimates as references. The results showed an RMSE of 0.17 m (water level) and 0.15 m (water depth) for ICESat‐2 and 0.75 m (water level) and 0.37 m (water depth) for GEDI. The analysis suggested that nighttime acquisitions were more accurate for both missions than daytime ones. The low‐power beams achieved slightly higher accuracies than those of the high‐power beams over the evaluated wetlands. Water level retrieval was more problematic in densely vegetated areas; however, we derived a correction model based on the leaf area index that improved the accuracy by up to 75% for water depth retrievals from GEDI. Furthermore, the analysis provides new insights to understand the potential of the altimeters in monitoring the spatial‐temporal dynamics of water levels in the evaluated wetlands.

Performance and accuracy of cross‐section tracking methods for hydromorphological habitat assessment in wadable rivers with sparse canopy conditions

AbstractThis article investigates the performance and accuracy of continuous Real‐Time Kinematic (RTK) Global Navigation Satellite System (GNSS) position tracking for hydromorphological surveys, based on a comprehensive river restoration monitoring campaign. The aim of the research was to assess the method's suitability for efficient data collection in turbid, wadable rivers with sparse canopy conditions, and responds to the water management sector's increasing demand for efficient, low‐cost, and robust survey techniques. The methodological approach involved comparing manual, cross‐sectional water depth measurements to water depth estimations obtained by applying different interpolation methods to the continuous tracking data. The results demonstrate good agreement between both datasets (R2 = 0.77, RMSE = 0.13 m). When using a local standard deviation filter to remove noisy RTK‐GNSS measurements, estimation performance increased significantly (R2 = 0.96, RMSE = 0.06 m). The filter's influence on the hydromorphological habitat statistics mean water depth and coefficient of variation was limited but proved to be relevant for reach‐scale assessments of hydromorphological diversity. Based on a correlation analysis of >106 RTK‐GNSS position logs, we furthermore assessed the impact of tree canopy on RTK‐GNSS measurement accuracy and observed a strong influence within 6.5 m from the canopy border. Estimated accuracy deteriorated noticeably when canopy penetration exceeded 1 m, and accuracies >1 m were common beyond 4 m penetration. The study highlights the efficiency gains achieved with RTK‐GNSS tracking, and showcases its potential for hydromorphological surveys and streamgaging applications in challenging conditions, making it a promising alternative to traditional methods and remote sensing techniques.

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.

Estimating Water Depths of Upper Cretaceous Pilot Knob Volcanic-Related Strata: The McKown and Pflugerville Formations and Pyroclastic Ash at the Lower Falls Section, McKinney Falls State Park, Austin, Texas

The McKown and Pflugerville formations and pyroclastic ash beds at McKinney Falls State Park in Austin, Texas, are closely associated with the Lower Campanian Pilot Knob volcano. This volcano is one of over 400 known Late Cretaceous volcanoes in the Balcones Igneous Province. The center of the Pilot Knob volcano is 1.5 mi (2.4 km) southeast of the McKinney Falls State Park. The McKown strata at the Lower McKinney Falls within the park have been interpreted as a beach complex deposited on the shallow-water north flank of the Pilot Knob volcano. Previous researchers based this interpretation on the presence of a shallow-water macrobiota and the coarse-grain texture of the limestones. However, a new investigation of the Lower McKinney Falls outcrops has developed evidence that the McKown limestones were deposited in deeper water (150 to 300 ft [50 to 100 m]) on the flank of the Pilot Knob volcano by gravity-flow processes that formed carbonate debrites and hyperconcentrated-density-flow deposits. The new evidence comes from micropetrography data that demonstrates the matrix of the packstones is dominated by coccolith hash and that there are calcispheres and planktic foraminifers in both the packstones and grainstones. The composition of the McKown limestone is a mixture of shallow- and deep-water biotas, indicating the shallow-water biota were resedimented into a deeper-water setting. Also, McKown bedding relationships with the pyroclastic ash beds below indicates deposition in a low-energy setting. The pyroclastic volcanic beds contain coccoliths and are interpreted as being deposited in a deeper-water, lower-energy setting. Also, the Pflugerville limestone section is very similar to the McKown limestone section and evidence indicates Pflugerville limestones were not deposited in a shallow-water setting. The evidence produced by the present investigation substantiates that the carbonate strata at the Lower McKinney Falls were not deposited in a beach or shoaling complex but were deposited on the deeper flank of the Pilot Knob volcano. The concepts developed by this investigation of these deeper-water carbonates can be applied to interpreting the depositional setting of other carbonates associated with the Balcones Igneous Province volcanic mounds.

Satellite-Derived Bathymetry Using a Fast Feature Cascade Learning Model in Turbid Coastal Waters

Obtaining accurate bathymetric maps is very valuable for marine environment monitoring, port planning, and so on. Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters challenges originating from low water transparency, but it is limited by the quantity, quality, and water quality of samples. This study introduces a fast feature cascade learning model (FFCLM) to enhance the accuracy of bathymetric inversion from multispectral satellite images, particularly when limited field samples are available. FFCLM leverages spectral bands and in situ data to derive effective inversion weights through feature concatenation and cascade fitting. Field experiments conducted at Nanshan Port and Rushikonda Beach gathered water depth, satellite, and in situ data. Comparative analysis with conventional machine learning algorithms, including support vector machine, random forest, and gradient boosting trees, indicates that FFCLM achieves lower errors and demonstrates more robust performance across study areas. This is especially more pronounced when using small training samples ( n < 100). Examination of key parameters and water depth profiles highlights FFCLM’s advantages in generalization and deep-water inversion. This study presents an efficient solution for small-sample bathymetric mapping in turbid coastal waters, utilizing spectral and physical information to overcome sample size limitations and enhancing satellite remote sensing capabilities for shallow water monitoring.

Estimation of Road Wetness from a Passenger Car

This paper presents an evaluation of a system aiming at estimating water depths on a road surface. Using accelerometers, the system records the vibrations of a wheel arch liner due to impacts of water droplets. The system setup, including the location of the accelerometers on a wheel arch and the data acquisition, is described. Tests were performed with a passenger car on various road surfaces and at different vehicle speeds and water depths. Signals recorded by the accelerometers are filtered and processed. The link between the acceleration amplitude, the water depth, and the vehicle speed is consistent with results from previous studies. The effect of the surface texture is less obvious and needs further investigations. A mathematical model has been developed to relate the acceleration amplitude to the water depth. The potential application of the developed system to on-board evaluation of pavement wetness, and consequently the pavement skid resistance, is discussed. Perspectives for driver assistance, or more generally, for autonomous driving to improve traffic safety, are also highlighted.

Nearshore Depth Estimation Using Fine-Resolution Remote Sensing of Ocean Surface Waves

In the field of water depth inversion using imagery, the commonly used methods are based on water reflectance and wave extraction. Among these methods, the Optical Bathymetry Method (OBM) is significantly influenced by bottom sediment and climate, while the wave method requires a specific study area. This study introduces a method combining the FFT and spatial profile measurement to invert the wavelength of the wave bathymetry method (WBM), which enhances accuracy and reduces workload. The method was applied to remote sensing images of Sanya Bay in China, obtained from the Worldview satellite. The average error of the inverted depth results after applying the wavelength inversion technique was 15.9%, demonstrating consistency with the depth measurements obtained through the OBM in clear water of the bay. The WBM has notable advantages over the OBM, as it is unaffected by water quality. In addition, the influence of wave period on the accuracy of water depth retrieval was theoretically evaluated, revealing that a larger wave period leads to a better depth measurement. The depth measurement from two images with different wave periods aligned with the theoretical analysis. These results showcase the applicability and potential of the WBM for accurately estimating water depth in various coastal environments.

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.