Search Results (161)

Satellite-Derived Bathymetry (SDB) based on the Stumpf log-ratio method routinely uses a fixed parameter n = 1000, a convention that has rarely been evaluated systematically. This study assesses empirically how SDB error varies with n across 31 bathymetric scenarios at 18 coastal sites with contrasting morphological and water quality conditions using Sentinel-2 imagery, ACOLITE atmospheric correction and in situ reference depths in the −0.5 to −6 m range. For each scenario, an optimal n was retrieved by minimizing RMSE against the reference bathymetry, and the response of the error to n was characterized. Two stable regions (a left and a right plateau) are identified in the error-versus-n curve, with most scenarios (26 of 31) reaching their optimum on the left plateau at small n values. Replacing n = 1000 with the site-specific optimum, the mean R² increases from 0.70 to 0.84 and reduces RMSE by 21.9% on average (~18 cm). A single generalized value n_median(LP) = 2.2, transferable across sites, recovers most of this gain; the unscaled, parameter-free case n = 1 alone already reduces RMSE by 15.9% on average relative to n = 1000 and outperforms it in 25 of the 31 cases. The margin of improvement obtainable by tuning n is strongly correlated (Spearman) with hue angle, chlorophyll-a and Trophic State Index, while turbidity and Secchi depth show no correlation, consistent with the loss of reliability of these last two indicators in optically shallow waters. The benefit of using a small n is greatest in greener, higher-chlorophyll waters, whereas in very clear, blue waters (hue > 160°, Chl-a < 2 µg/L) n = 1000 remains a defensible choice. These results support replacing n = 1000 by n ≈ 2–3 as the practical default for Stumpf-based SDB in the studied depth range and provide a water-quality-based criterion to anticipate the expected benefit of parameter tuning. These improvements make it possible to use imagery from less clear waters for SDB extraction, broadening the range of images suitable for beach monitoring. Full article

Floating photovoltaics (FPV) offer a sustainable approach to renewable energy production while enhancing water resource management by reducing land use, improving module efficiency through natural cooling, and limiting water evaporation. However, large-scale deployment requires careful site selection, operational feasibility assessment, and integration with existing water management practices. This study presents a fully cloud-native Sentinel-2-based framework to map recent water surface extents of reservoirs and reconstruct their bathymetric profiles. Developed within the Google Earth Engine (GEE) environment, the approach employs harmonized time series, automated cloud filtering, and median composites combined with adaptive NDWI/MNDWI thresholds to generate high-resolution (10 m) water masks. These dynamic surface extents are integrated with monitored water levels to estimate bathymetry through an annual multitemporal log-ratio band-switching configuration. The method has been tested across the nine main strategic reservoirs of the Basilicata region (Southern Italy). The water-surface extraction approach achieved an Overall Accuracy of 95.33% and a Kappa coefficient of 0.907 in the internal thematic assessment. The bathymetric reconstruction results led to relative volume errors for medium-to-large water bodies ranging between 13.8% and 35.1%, while larger percentage discrepancies in smaller impoundments were driven by scale-dependent normalization effects on low storage volumes. Overall, the proposed method offers a scalable, cost-effective, and Earth Observation (EO)-driven screening tool for the initial site selection, capacity assessment, and planning of FPV systems, particularly in data-scarce regions lacking updated bathymetric surveys. Full article

The use of Satellite-Derived Bathymetry (SDB) constitutes an efficient, cost-effective, time-saving, and scalable approach for generating high-resolution shallow-water bathymetry. In this context, SDB can be proven to be a valuable method for supporting bathymetric surveys of submerged archaeological sites. This paper focuses on the ancient city of Asopos, located on the coast of the modern village of Plytra in Laconia, SE Peloponnese, Greece. The site occupies an extensive coastal area characterised by numerous ancient remains and geomorphological features distributed along the shoreline and within the nearshore zone. To achieve the objective of producing a very-high-resolution SDB map, one empirical and four machine learning approaches were evaluated: (a) the quadratic Stumpf band-ratio model; (b) Categorical Boosting; (c) Random Forest; (d) Extreme Gradient Boosting, with all models trained using the same three optimal spectral band ratios; and (e) the CatBoost model trained using principal components derived from a Principal Component Analysis of the multispectral dataset. These approaches were developed using multispectral WorldView-2 imagery and 208 in situ depth measurements as ground truth, collected across a different range of depths and distances from the coastline. Pan-sharpened imagery was used for visual interpretation. Model accuracy was assessed using an additional validation dataset of 57 depth measurements. This combined approach demonstrates that VHR SDB mapping of the nearshore zone is highly effective when employing three band ratios involving the blue, green, and yellow bands, achieving a root mean squared error of less than 1 m, and can therefore serve as a reliable method for shallow-water geoarchaeological investigations. Full article

This study presents an integrated, data-driven framework for satellite-derived bathymetry and morphodynamic assessment in large, regulated rivers, providing a spatial database to support reach-scale hydromorphological monitoring and river management. Satellite-derived bathymetry (SDB) was developed using 24,768 in situ depth measurements and Sentinel-2 multispectral data to train Random Forest (RF) and Artificial Neural Network (ANN) models. Under turbid water conditions, the Random Forest model outperformed the Artificial Neural Network model in simulating the non-linear relationship between the water spectrum and water depth; the RF model achieved an R² of 0.828 and an RMSE of 0.93 m, while the ANN model produced an R² of 0.608 and an RMSE of 1.40 m. Depth-dependent errors were smallest at intermediate depths and larger in shallow and deep water. Morphometric parameters, including the Sinuosity Index (SI) and Braiding Index (BI), were calculated for 2017, 2019, and 2021 using the NDWI-based water mask to define channel boundaries. The reach exhibited moderate sinuosity (SI ≈ 1.16), and an increase in braiding was observed (BI ranging from 1.33 to 1.36). From 2017 to 2019, erosion (3.51 km²) exceeded deposition (1.25 km²). In contrast, the 2019–2021 period showed approximately equal areas of erosion and deposition (1.63 km² each). The analysis is constrained by a single 2015 calibration survey, the optical penetration limit of Sentinel-2, and the reliance on three morphometric snapshots (2017, 2019, 2021), which may not capture short-term adjustments. The novelty of this study lies in integrating ML-based Sentinel-2 bathymetry with multi-temporal morphometric indicators to characterize the vertical and horizontal dynamics of regulated rivers jointly. Full article

Satellite-derived bathymetry (SDB) provides an efficient approach for shallow-water mapping because of its wide spatial coverage and repeated observation capability. However, multi-temporal bathymetric results derived from optical imagery often exhibit substantial inconsistencies due to variations in atmospheric conditions, water optical properties, bottom reflectance, and imaging geometry. Moreover, different bathymetric intervals usually exhibit distinct uncertainty characteristics, while conventional global fusion methods generally apply a single statistical strategy to the entire depth range. To address this limitation, this study proposes an ICESat-2-constrained adaptive segment-wise rank-statistic fusion framework for multi-temporal SDB. The bathymetric range is adaptively divided into multiple depth intervals using ICESat-2 bathymetric control points, and the optimal rank-statistic fusion strategy is independently selected for each interval according to local RMSE evaluation. In this way, shallow-water outliers can be effectively suppressed, while deep-water systematic underestimation can be alleviated simultaneously. Experiments conducted in Ganquan Island, Dong Island, and Key Biscayne demonstrate that the proposed framework consistently outperforms individual single-scene results as well as conventional mean and median fusion methods. Compared with conventional mean and median fusion methods, the RMSE was reduced by up to 27.5%, while the coefficient of determination (R²) reached 0.95. Significant improvements were particularly observed in deeper bathymetric intervals and complex benthic environments. The results indicate that adaptive segmented rank-statistic fusion can effectively characterize bathymetric-dependent error variations and achieve unified optimization for shallow-water outlier suppression and deep-water bias correction. Full article

Satellite-derived bathymetry (SDB) provides a cost-effective means for mapping shallow-water depths, yet its scalability and cross-regional generalizability remain challenging in optically complex coastal environments. This study systematically evaluates machine learning (ML) and deep learning (DL) approaches for transferable SDB over the 0–20 m depth range using multispectral Sentinel-2 imagery. A Random Forest model and four deep learning architectures–ResNet-50, ResNet-101, EfficientNet-B4, and ConvNeXt-Large–are developed and trained using data from Pratas Island (South China Sea) and selected reef regions of the Great Barrier Reef (GBR), and subsequently evaluated on spatially independent intra-regional and cross-regional test areas to assess generalization performance. Model sensitivity is investigated with respect to key training configurations, including loss-function design and data-splitting strategy. To enhance shallow-water learning, we introduce a Smooth Weight Function (SWF)-weighted RMSE loss that emphasizes near-surface depths and compare it with conventional RMSE and relative percentage error (RPE) objectives. In terms of training data, preserving spatial continuity during training substantially improves both numerical accuracy and structural consistency of predictions compared with random patch splitting. While the Random Forest model performs competitively in intra-regional tests, its accuracy degrades under cross-regional transfer (RMSE increasing from 1.53 m to 2.99–3.78 m). Deep learning models, although not always outperforming Random Forest in intra-regional settings, exhibit greater robustness to geographic shift. Using the spatially continuous training strategy, intra-regional RMSE ranges from 1.15 to 1.92 m over the full 0–20 m range, with shallow-water RMSE as low as 0.26 m for depths ≤ 3 m. Cross-regional transfer to geographically independent reefs yields moderate RMSE values of approximately 2.46–2.98 m (0–20 m range), indicating that geographic transfer remains challenging despite meaningful improvements over Random Forest. We further benchmark the proposed architectures against a task-specific bathymetry network using the public MagicBathyNet dataset. Under a unified 0–16 m shallow-water configuration using aerial RGB imagery, the proposed models achieve RMSE values between 0.19 and 0.22 m, outperforming both the baseline U-Net and the transformer-based bathymetry architecture while using substantially fewer parameters. In addition, we exploit multi-temporal repeat imagery for both training and inference, which increases training diversity and improves robustness to temporal variability arising from changing sun angles, atmospheric conditions, water properties, and tides. During inference, predictions from multiple repeat images are aggregated using the median to reduce noise and improve stability. Finally, we release optimized network architectures and pretrained weights to facilitate scalable application to new sites. This work demonstrates a practical pathway toward transferable, large-area SDB from multispectral satellite imagery using deep learning. Full article

Traditional satellite-derived bathymetry (SDB) often suffers from systematic optical path distortions due to the neglect of seafloor slope effects, leading to significant accuracy degradation in high-gradient coastal areas. This study proposes a Slope-Aware Physics-Informed Neural Network (SA-PINN) framework that synergistically utilizes ICESat-2 bathymetric photons and Sentinel-2 multispectral imagery. The core innovation involves a slope-aware operator, integrated into the radiative transfer-based physics loss function, which explicitly rectifies directional optical path deviations induced by seafloor inclination. By fusing physical mechanisms with data-driven features, the model utilizes a seven-dimensional feature space comprising four spectral bands, two directional slope components, and prior depth. Applications at Culebra, Maui, and Molokai demonstrate that SA-PINN significantly outperforms the Stumpf model, Random Forest, and standard CNNs, achieving root mean square errors (RMSE) of 1.36 m, 2.91 m, and 1.34 m, respectively. Ablation studies confirm that SA-PINN reduces RMSE by up to 37% compared to CNN in complex regions with slopes exceeding 10°, ensuring superior physical consistency and spatial continuity. This research provides a robust, in situ-free automated solution for high-resolution bathymetric mapping in remote and steep coastal environments globally. Full article

Satellite-derived bathymetry (SDB) offers a practical alternative for mapping shallow reefs in remote oceanic settings where acoustic surveys are costly and logistically constrained. Here we benchmark PlanetScope 8-band (3 m) surface reflectance—an underused commercial constellation for reef SDB—using ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) ATL03 photon data (Release 006) as independent vertical control. Seventeen ATL03 ground tracks (2019–2025) were processed using geometric filtering, photon classification, and explicit air–water refraction correction. This yielded 5171 candidate seafloor observations, of which 5021 were co-located with valid PlanetScope water pixels after Usable Data Mask screening (UDM2/UDM2.1), sun-glint correction, and reflectance quality screening. Four SDB formulations (Lyzenga, Bierwirth, and Stumpf) were calibrated and independently validated using depth-stratified train/validation partitions (70/30, 80/20, and 90/10). Across partitions, the multiband polynomial model of Lyzenga 2006 generalized best (R² = 0.843–0.859; RMSE = 1.734–1.813 m; bias = −0.070 to −0.081 m), followed by Bierwirth (R² = 0.826–0.845; RMSE = 1.818–1.904 m). Lyzenga 1985 reported lower skill (RMSE ≈ 3.1 m), while the Stumpf log-ratio failed in independent validation. ICESat-2 photon bathymetry provides repeatable point-based control in clear waters but remains less precise than echo sounding due to photon classification and spatial-support effects; therefore, uncertainties and applicability limits must be reported. Overall, PlanetScope 3 m, 8-band surface reflectance supports reproducible reef-scale SDB in Seaflower under the evaluated conditions, with Lyzenga 2006 as a robust baseline. Full article

This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem. Full article

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a 532 nm laser with strong water-penetration capability, making it well suited for satellite-derived bathymetry in shallow waters; however, the effective denoising of photon-counting data remains essential due to strong solar background and intrinsic instrument noise. To address this challenge, this study proposes a novel photon denoising method, termed the Directional Nearest Neighbor Distance-based Algorithm (DNNDA), for robust extraction of signal photons from shallow-water ICESat-2 data. Unlike existing methods that rely heavily on density or terrain features and often degrade under high-noise conditions, DNNDA systematically exploits both scale-corrected spatial relationships and directional distribution characteristics of photons. By quantitatively characterizing the directional features of photon distributions and embedding this information into a density representation, DNNDA amplifies the density contrast between signal and noise photons, rendering the seafloor signal photons more distinct and easier to extract. An evaluation index was further designed to automate optimal parameter determination. Validation using multiple global ICESat-2 datasets demonstrates that DNNDA achieves superior seafloor photon extraction performance, with F1-scores exceeding 95%. Further regression analysis against high-precision CUDEM data in the Puerto Rico region yields root-mean-square errors below 0.57 m. By jointly correcting scale anisotropy and incorporating directional information, DNNDA enables reliable and adaptive signal photon extraction across local and global scales, providing a robust solution for shallow-water bathymetry in complex, high-noise environments. Full article

High-precision inversion of shallow-water depth is crucial to marine resource development, ecological protection, and national defense security. Traditional acoustic detection, LiDAR, and empirical models are limited by high cost, low efficiency, or water quality dependence, struggling to meet people’s growing demand for shallow-water depth. With the rapid development of theories and technologies such as remote sensing information, computer science, and artificial intelligence, bathymetric inversion based on remote sensing images and deep learning models has become a research hotspot. In this study, journal articles and conference papers were searched in the Web of Science (WOS) and Google Scholar databases using keywords such as “remote sensing image”, “bathymetry”, and “deep learning model”. The publication time of the papers ranges from January 2021 to September 2025. A total of 309 relevant studies were retrieved and, after screening and quality control, 132 core studies were finally selected as the research objects for this review. These studies were classified according to deep learning models, including CNN, U-Net, MLP, and RNN. The study analyzed and summarized the characteristics of different deep learning models in bathymetric inversion, as well as their data source selection, inversion accuracy, and limitations. Additionally, the future development trends were discussed in combination with the latest research results. Full article

Satellite-derived bathymetry holds significant value for acquiring nearshore bathymetric data. However, in coastal waters, bathymetry is affected by in-water particle scattering and seafloor substrate variability, leading to spatial inconsistency between the logarithmic green band profile derived from multispectral satellite imagery and the actual water depth profile. According to the position information of interpolated points and the inverse distance square relationship with the surrounding 16 points from low-reference bathymetric data (such as the bathymetric map from GEBCO, NOAA Electronic Navigational Charts), this model adopts a third-order inverse distance square bicubic convolution interpolation method to resample a high-resolution bathymetric map with the size of the satellite image. Normalized underwater topography trend data (derived from the low-resolution reference bathymetric map) were combined with normalized green band data to compute an averaged dataset. In this way, a linear bathymetric model was constructed. We invert this model’s parameters and calculate the water depth by using the average data and reference points from reference bathymetric data. Validation tests were conducted across three test areas using independent validation bathymetric data: Weizhou Island, China (Case II waters); Saipan, Northern Mariana Islands, USA (Case I waters); and Molokai Island, Hawaii, USA (Case I waters). Each test area was studied using five error analysis methods (i.e., scatterplot, error histogram, regional bathymetric error, three check lines, and seven check points). Compared to four classic bathymetric models (i.e., single-band model, log-ratio model, ratio-log model, and multi-band model), the proposed model achieved lower root mean square errors (RMSE) of 2.08 m, 1.40 m, and 2.01 m in the three test areas, representing reductions of 35%, 43%, 45%, and 20% and overall averages of 48%, 62%, 64%, and 43%, respectively. Its goodness of fit (R²) reached 0.87, 0.97, and 0.97, showing improvements of at least 5%, 5%, 9%, and 9% and overall averages of 17%, 77%, 84%, and 12%, respectively. The results demonstrate that the proposed model significantly improves bathymetry accuracy while maintaining algorithmic simplicity, providing a new model for acquiring nearshore foundational bathymetric maps. Full article

Accurate nearshore bathymetry is essential for various marine applications, including navigation, resource management, and the protection of coastal ecosystems and the services they provide. This study presents an approach to enhance the accuracy of bathymetric estimates derived from high-spatial- and high-temporal-resolution optical satellite imagery. The proposed technique is particularly suited for multispectral sensors that acquire spectral bands sequentially rather than simultaneously. PlanetScope SuperDove imagery was employed and validated against bathymetric data collected using a multibeam echosounder. The study area is the Gulf of Sciacca, located along the southwestern coast of Sicily in the Mediterranean Sea. Here, multibeam data were acquired along transects that are subparallel to the shoreline, covering depths ranging from approximately 7 m to 50 m. Satellite imagery was radiometrically and atmospherically corrected and then processed using a simplified radiative transfer transformation to generate a continuous bathymetric map extending over the entire gulf. The resulting satellite-derived bathymetry achieved reliable accuracy between approximately 5 m and 25 m depth. Beyond these limits, excessive signal attenuation for higher depths and increased water turbidity close to shore introduced significant uncertainties. The innovative aspect of this approach lies in the combined use of spectral averaging among the most water-penetrating bands, temporal averaging across multiple acquisitions, and a liquid-facets noise reduction technique. The integration of these multi-layer inputs led to improved accuracy compared to using single-date or single-band imagery alone. Results show a strong correlation between the satellite-derived bathymetry and multibeam measurements over sandy substrates, with an estimated error of ±6% at a 95% confidence interval. Some discrepancies, however, were observed in the presence of mixed pixels (e.g., submerged vegetation or rocky substrates) or surface artifacts. Full article

This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all satellite derived bathymetry (SDB) methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the iterative closest point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error. However, our analysis demonstrates that performing detrending where possible can achieve an error distribution as close to normality as possible for which error metrics are more diagnostic. Full article

Coastal sandbars play a crucial role in shoreline protection, yet monitoring their dynamics remains challenging due to the cost and limited temporal coverage of traditional surveys. This study assesses the feasibility of using Sentinel-2 multispectral imagery combined with the logarithmic band ratio method to automatically detect submerged sandbar crests along three morphologically distinct beaches on the northwestern Mediterranean coast. Pseudo-bathymetry was derived from log-transformed band ratios of blue-green and blue-red reflectance used to extract the sandbar crest and validated against high-resolution in situ bathymetry. The blue-green band ratio achieved higher accuracy than the blue-red band ratio, which performed slightly better in very shallow waters. Its application across single, single/double, and double shore-parallel bar systems demonstrated the robustness and transferability of the approach. However, the method requires relatively clear or calm water conditions, and breaking-wave foam, sunglint, or cloud cover conditions limit the number of usable satellite images. A temporal analysis at a dissipative beach further revealed coherent bar migration patterns associated with storm events, consistent with observed hydrodynamic forcing. The proposed method is cost-free, computationally efficient, and broadly applicable for large-scale and long-term sandbar monitoring where optical water clarity permits. Its simplicity enables integration into coastal management frameworks, supporting sediment-budget assessment and resilience evaluation in data-limited regions. Full article