We welcome contributions on all aspects of the oceanic remote sensing and the related applications. Topics for this session include but are not limited to: physical oceanography, marine biology and biogeochemistry, biophysical interaction, marine gravity and space geodesy, linkages of the ocean with the atmosphere, cryosphere, and hydrology, new instruments and techniques in ocean remote sensing, new mission concepts, development and evaluation of remote sensing products of the ocean, and improvements of models and forecasts using remote sensing data. Applications of multi-sensor observations to study ocean and climate processes and applications using international (virtual) constellations of satellites are particularly welcome.
Orals: Tue, 5 May, 14:00–17:55 | Room 1.34
Monitoring sea state is essential for navigation safety, vessel operability, and offshore/coastal engineering, as it supports the estimation of key parameters such as sea-surface geometry and significant wave height. Traditionally, sea-state information is obtained from in situ instruments (e.g., wave buoys and ship motion sensors) and remote observations (e.g., radar-based systems and satellite products). While these approaches are mature, they may be limited by spatial/temporal coverage, deployment, and maintenance constraints. In contrast to well-established fixed-station solutions, shipborne observations are emerging, coping with non-stationary viewing geometry and vessel dynamics. This motivates system integration, such as stereophotogrammetry with Global Navigation Satellite System (GNSS) observations to provide an alternative route to measure wave metrics from the reconstructed sea surface geometry. The present work describes a shipborne sensing system built around a time-disciplined camera and GNSS synchronization to enable stereo-vision processing and subsequent generation of a 3D point cloud of the sea surface. The whole project approach is technically challenging in realistic marine conditions (e.g., changing illumination, specular reflections, low texture, and intermittent occlusions). The acquisition chain utilizes a GNSS Pulse-Per-Second (PPS) signal to trigger two industrial RGB cameras synchronized via hardware, with deterministic triggering and logging managed by a Raspberry Pi 4. PPS-based triggering provides stable frame time-stamping, enabling coherent fusion with and motion (ship attitude and trajectory) information provided by GNSS. Ongoing tests focus on end-to-end robustness (timing stability, synchronization, and motion sensitivity) and on comparison against independent references when available (e.g., onboard motion sensors and nearby in situ records). The proposed configuration provides a useful instrument for sea-state monitoring, in a scalable and low-cost method.
How to cite: Mammadova, S., Ferrando, I., and Sguerso, D.: Integrated stereo vision and GNSS approach for sea-state monitoring on a moving vessel, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2994, https://doi.org/10.5194/egusphere-egu26-2994, 2026.
Satellite remote sensing has fundamentally advanced the observation of directional ocean wave spectra, progressing from empirical and model-assisted retrievals toward physically based, direct spectral estimation methodologies, now supported by simulation capabilities enabled by higher computing performances. Current missions provide complementary spectral coverage across a broad range of ocean wave wavelengths. Sentinel-1/SAR (Copernicus missions) retrieves intermediate- to long-wavelength swell up to 800 m, primarily from Wave Mode (WV) acquisitions in the open ocean. More recently, Terrain Observation by Progressive Scans (TOPS) acquisitions, particularly over coastal regions, have enabled advanced mapping of the SAR-observed cross-spectra, through advanced signal-processing algorithms. CFOSAT/SWIM (Franco-China cooperation) provides multi-incidence directional measurements resolving intermediate wave components up to about 800m, while SWOT (Franco-American cooperation) extends observations toward longer swell wavelengths, reaching approximately 1200 m for some extreme cases. Together, these missions enable a multi-scale, multi-geometry characterization of ocean wave spectra. Forthcoming missions, currently under design and development, are expected to provide sensitivities to longer ocean wave and result in different imaging sensitivities for directional ocean wave spectra (ROSE-L) and offering multi-static measurements to enhance angular sampling and directional retrievals (Harmony).
Despite these advances, no single sensor provides complete coverage of the ocean's directional wave spectra and wave products are often expressed across different spectral domains (frequency vs. wavenumber), physical variables (wave height vs. slope), and coordinate systems (Cartesian vs. polar). Limitations arise from radar band, incidence geometry, capability to resolve wave direction ambiguity and imaging mechanism: long-period swells remain largely beyond the reach of SWIM, which also suffers from contamination such as a long-track speckle noise; SAR systems are limited by azimuth cut-off effects and rely on quasi-linear inversion of the measured cross-spectra ; the directional wave spectra are ambiguous with respect to satellite azimuth. Additional constraints include contamination from non-geophysical signals (rain cells, atmospheric front, low winds, etc.) leading to non-geophysical wave products.
To address these limitations, integrating multiple missions and performing systematic inter-comparisons and “normalization” is essential. By aligning overlapping spectral ranges, filling observational gaps, and accounting for mission-specific artefacts, this study aims at reconstructing a continuous and geophysical consistent directional wave spectrum, including the separation and partition-based evaluation of individual swell systems and the local wind-sea component. Complementary strategies—including advanced AI- or data-driven algorithms applied across the full spectral field—support robust quality control and the accurate computation of total and wind-sea significant wave heights.
Integrating multi-mission observations with full-spectrum retrieval enables the most comprehensive reconstruction of directional ocean wave spectra from space. Independently validated against numerical wave models and in situ measurements, this framework provides a quantitative and robust characterization of wave spectra across multiple spatial and temporal scales. It establishes a scalable methodology for ensuring observational consistency and lays out the groundwork for next-generation remote sensing missions and advanced algorithmic developments in global ocean wave dynamics.
How to cite: Benchaabane, A., De Carlo, M., Husson, R., and Peureux, C.: Bridging Satellite Observations: A Comprehensive Intercomparison of Wave Spectra, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20698, https://doi.org/10.5194/egusphere-egu26-20698, 2026.
The SWOT satellite was launched in December 2022 and employs a novel SAR-in technology to achieve high-precision measurements over inland waters and the ocean. For ocean observations, SWOT provides low-rate data products, referred to as LR data. Currently, the mainstream post-processed LR products are released at Level-3 (L3). However, the accuracy level of the SWOT L3 data still requires comprehensive evaluation and analysis. This study primarily focuses on the assessment of SWOT SSH data in both coastal regions and the open ocean.
First, a crossover analysis was conducted using 2-km spatial resolution data over the open ocean and coastal areas. The results indicate that the mean and standard deviation of SSH crossover differences from SWOT are consistent with the accuracy levels of conventional satellite altimetry missions. Second, in situ tide gauge observations were used to evaluate SWOT L3 SSH data with spatial resolutions of 2 km and 250 m within 20 km of the coastline. The results demonstrate good correlation between SWOT L3 data at both resolutions and tide gauge measurements. However, at certain stations—such as those located in regions with complex nearshore bathymetry or around islands and atolls—the correlations are relatively lower.
In addition, an evaluation was performed for SWOT L3 data across different cross-track distance ranges. The results similarly show that, provided valid data are available, SWOT L3 SSH data exhibit good correlation with tide gauge observations across various cross-track distances. Both datasets reveal a consistent pattern of degraded data quality toward the swath edges, while relatively higher quality is observed near the nadir region. Furthermore, a statistical analysis of data loss rates within 5 km of the coastline indicates that the average data loss rate reaches approximately 70% for the 2-km resolution data, whereas the 250-m resolution data exhibit an average loss rate of about 31% within the same nearshore zone.
Finally, comprehensive analysis of all results reveals noticeable differences in data quality between the left and right swaths, which may be related to the satellite’s flight attitude. This issue requires further investigation.
How to cite: Huan, C., Jin, T., Gao, X., Zhou, M., and Shi, J.: Evaluation of SWOT Level 3 Sea Surface Height Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9047, https://doi.org/10.5194/egusphere-egu26-9047, 2026.
Satellite altimetry has made a fundamental contribution to our understanding of ocean circulation during more than three decades, the so-called altimetry era. The wide-swath altimetry concept explored by the SWOT mission has started a new one, the SWOT-era. As SWOT extends the capability of nadir altimeters to two-dimensional mapping and sampling of the ocean surface at an unprecedented spatial resolution, this has opened the opportunity to constrain better the mesoscale variability, also in areas with a reduced number of the available altimetry missions and small Rossby radii. The results presented in this work rely on a comparison of SWOT retrievals with conventional altimeters and characterize the representation of the mesoscale field in the global ocean emerging from the different altimetry concepts. While this study aims to an assessment of the eddy-induced anomalies at the surface in the global ocean, specific areas of interest along the boundary currents and eddy-rich areas, were selected to investigate the mesoscale contributions to ocean dynamics and thermodynamics (heat transport) building on satellite sensor synergies. The detailed knowledge of the mesoscale eddies (number, size, polarity) is then used to design and train a machine-learning (ML) based detection of the eddies. The accuracy of the results is assessed against the dynamical-based approach applied to the same output fields.
How to cite: Bonaduce, A., Cipollone, A., Broccoli, M., and Raj, R.: Eddy induced anomalies in the global ocean: new insights from wide-swath altimetry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11662, https://doi.org/10.5194/egusphere-egu26-11662, 2026.
For more than 30 years, satellite altimetry has been providing a unique data record, crucial for many applications such as ocean circulation and the monitoring of sea level rise at global and regional scale. The stringent requirements of such applications set the need for continuous improvement of satellite altimeter products, including those of past missions, in order to get consistent and homogeneous datasets.
The accurate retrieval of sea surface heights from satellite altimetry requires the measured ranges to be corrected for a number of atmospheric and sea state effects. Amongst these effects, the path delay induced by the water vapor and cloud liquid water, the wet tropospheric correction (WTC), is still a major source of uncertainty, in particular in the long-term sea level trend.
Sponsored by the European Space Agency, the FDR4ALT project aims at generating improved, up-to-date altimetric records for the historical multidisciplinary 35-day repeat missions, ERS-1, ERS-2 and Envisat. To achieve this goal, a new set of wet tropospheric corrections based on the GNSS-derived Path Delay Plus (GPD+) algorithm, has been developed for these missions and is the focus of this study. GPD+ are wet path delays, obtained by data combination through objective analysis, using all available WTC sources, including onboard radiometers, GNSS, external imaging radiometers and an atmospheric model.
Since the GPD+ WTC are combined values, the intercalibration of the various datasets is a crucial step. To this end, a novel method to intercalibrate the various WTC datasets against one of the current best radiometric references, the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), hereafter designated SSMIS, has been developed.
The sensor calibration consists of the adjustment of each dataset to SSMIS using a time-varying set of two parameters (2P), offset and scale factor. For this purpose, 5°x5° grids of WTC mean values for the period of each altimeter cycle and each month, respectively, are computed for SSMIS. Similar grids of mean WTC values for the period of interest are computed for each non-SSMIS sensor, including the MWR onboard each altimeter mission. Using the set of corresponding grid cells for each pair (non-SSMIS, SSMIS) the 2P of the adjustment of each sensor to the SSMIS dataset has been computed. Finally, for the whole study period (1991-2012), ERA5 model and the GNSS dataset have also been adjusted to SSMIS by means of a similar 2P model, with yearly parameters.
This paper summarizes the results, focusing on the impact of the inter-calibration and the added value if these corrections for the FDR4ALT products.
The new calibration revealed to be robust, less sensitive to sampling issues and able to intercalibrate any set of sensors, even with different orbits and sampling patterns. Results show that the new GPD+ WTC are continuous, consistent and intercalibrated datasets, valid over all surface types. In addition to open ocean, they also cover conditions where the measurements from the onboard microwave radiometers are invalid, mostly coastal and polar regions, recovering, on average, 25-30% of the ocean points.
How to cite: Fernandes, M. J., Vieira, T., Aguiar, P., and Lázaro, C.: Improving the Historical ERS and ENVISAT Fundamental Data Records by Means of a New Set of GPD+ Wet Tropospheric Corrections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12562, https://doi.org/10.5194/egusphere-egu26-12562, 2026.
Satellite altimetry provides observations of sea surface height and derived geostrophic currents through the operational DUACS/Copernicus Marine Service Sea Level Thematic Assembly (SLTAC). However, these currents represent only part of the total surface circulation, which also includes ageostrophic components such as wind-driven currents (Ekman and Near-Inertial Oscillations (NIOs)).
To address this limitation, complementary observation-based approaches have been developed to optimally combine satellite and in-situ data, improving the physical realism and temporal resolution of upper-ocean circulation fields. In this study, we use the Multiscale Inversion for Ocean Surface Topography (MIOST) variational tool (Ubelmann et al., 2020) to retrieve both geostrophic and ageostrophic currents. MIOST achieves this by decomposing the signal into representors accounting for different spatial and temporal scales (mesoscale to large-scale) and physical processes (geostrophy, wind-driven currents, NIOs).
Using MIOST, altimetry data are combined with hourly drifter velocities from the Copernicus Marine database (INSITU_GLO_PHY_UV_DISCRETE_MY_013_044, https://data.marine.copernicus.eu/product/INSITU_GLO_PHY_UV_DISCRETE_MY_013_044/description) to represent wind-driven currents, in particular the energetic NIOs, which remain a major challenge for satellite observation (SKIM mission concept, Ubelmann et al., 2021; ODYSEA mission). Previous studies within CNES-funded DUACS project have shown that combining altimetry and hourly drifter data can represent about 30–40% of NIOs in regions with sufficient drifter coverage (e.g., North Atlantic gyre). Additionally, ESA-WOC project has shown that wind reanalyses (ERA5) can predict a substantial fraction (~40%) of NIO variability (Ubelmann et al., 2025).
The objective of this work is to combine these two complementary approaches: starting from prior ageostrophic currents (ESA-WOC) and applying the MIOST variational method to reconstruct geostrophic currents and residual NIO signals contained in drifter observations. Parameter tuning of MIOST modes was required since the statistical mapping is applied to residuals rather than the full signal.
The reconstructed currents are intercompared with existing products, including Copernicus-Globcurrent total surface currents (MULTIOBS_GLO_PHY_MYNRT_015_003) and ESA WOC total surface currents.
Elipot, S., R. Lumpkin, R. C. Perez, J. M. Lilly, J. J. Early, and Sykulski, A.M.: A global surface drifter dataset at hourly resolution, J. Geophys. Res. Oceans, 121, doi:10.1002/2016JC011716, 2016
Ubelmann, C., Dibarboure, G., Gaultier, L., Ponte, A., Ardhuin, F., Ballarotta, M., & Faugere, Y. (2021). Reconstructing ocean surface current combining altimetry and future spaceborne Doppler data. Journal of Geophysical Research: Oceans, 126, e2020JC016560. https://doi.org/10.1029/2020JC016560
Ubelmann, C., Farrar, J. T., Chapron, B., Gaultier, L., Gomez-Navarro, L., Rio, M.-H., and Dibarboure, G.: A data-driven wind-to-current response function and application to ocean surface current estimates, Ocean Sci., 21, 2915–2928, https://doi.org/10.5194/os-21-2915-2025, 2025.
How to cite: Jousset, S., Ubelmann, C., and Dibarboure, G.: Improving Total Surface Currents by Combining Altimetry and In-Situ Observations Using the MIOST Variational Method, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11828, https://doi.org/10.5194/egusphere-egu26-11828, 2026.
Gridded geostrophic currents derived from satellite altimetry are a cornerstone for investigating ocean surface circulation. However, their effective spatial resolution and dynamical fidelity are highly sensitive to processing choices. Here, we present a comprehensive global intercomparison of several daily, gridded (Level-4) freely available altimetry-based datasets that employ distinct gridding strategies.
Dataset performance is evaluated using a suite of complementary metrics. Spectral diagnostics, including power spectral density and eddy kinetic energy, are employed to quantify the representation of mesoscale variability across a range of spatial scales, dynamical regimes, and energy levels. In addition, a Lagrangian framework is adopted in which virtual drifters are deployed and advected along observed drifter trajectories, enabling robust statistical comparisons of transport and dispersion characteristics among the datasets. The results are consistent across metrics and highlight the strong performance of recently developed products, including neural network–based approaches such as NeurOST, and advanced multi-mission datasets, such as those provided by the Copernicus Marine Environment Monitoring Service (CMEMS) data store.
This intercomparison is conducted within the framework of ESA’s Climate Change Initiative, which aims to expand ESA’s portfolio of Essential Climate Variables. It supports the development of a novel Ocean Surface Heat Flux (OSHF) product, generated to address the observational gaps and limitations inherent in existing OSHF estimates.
How to cite: Juhl, M.-C., Müller, F., Hart-Davis, M., Dettmering, D., Olmedo, E., and Arias, M.: Evaluation of global satellite-derived geostrophic ocean current products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18810, https://doi.org/10.5194/egusphere-egu26-18810, 2026.
Thanks to its current accuracy and maturity, altimetry is considered as a fully operational observing system dedicated to various applications such as climate studies. Altimeter measurements are corrected from several geophysical parameters in order to isolate the oceanic variability and the tide correction is one of the most critical. The accuracy of the tide models has been much improved for the last 30 years, and the most recent FES2022 model allowed reducing the residual errors in shelf and coastal seas and at high latitudes compared to previous version FES2014 and to other state-of-the-art global tide models. However improvements are still needed in these areas were the omission and the modelling error are still significant, but also on the global ocean.
In order to answer the new challenges of the tide correction for HR altimetry, in particular the SWOT mission, which gives access to unprecedent high-resolution sea surface measurements and very narrow coastal areas, a new global tide model FES2026 is currently under development.
This new model will benefit from new tidal frequencies that were not yet included in global tide models, as some minor third order of the tide potential tides and also some complementary non-linear frequencies that are most important in shallow and coastal zones. A dedicated omission error analysis was performed and gives some information about the main non-linear frequencies to take into account. Some evolutions are also envisioned concerning the improvement of the extrapolation procedure at the transition between ocean and land particularly in narrow fjords regions sampled by SWOT KaRIn measurements.
Some comparison with FES2022 and other global tide models (GOT5.6, R. Ray) are proposed. Preliminary validation results using several altimeter missions including SWOT mission, and also some in situ measurements will be presented.
How to cite: Lyard, F., Carrere, L., Marlier, G., Dabat, M.-L., Skandrani, C., and Dibarboure, G.: Preliminary results of the new global barotropic tide model FES2026 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20160, https://doi.org/10.5194/egusphere-egu26-20160, 2026.
The accurate estimation of kilometer-scale ocean surface wind fields is a cornerstone of modern marine meteorology, impacting weather forecasting, offshore wind energy development, and maritime safety. Synthetic Aperture Radar (SAR) remains a primary tool for these estimations due to its high spatial resolution and sensitivity to sea surface roughness. Traditionally, wind retrieval from SAR data relies on Geophysical Model Functions (GMFs) that relate the Normalized Radar Cross Section (NRCS) to wind speed and direction. However, because SAR sensors typically operate with a single fixed antenna, the inversion process is inherently under-determined, frequently requiring ancillary data from Numerical Weather Prediction (NWP) models to resolve wind direction ambiguities. These priors can introduce errors due to spatiotemporal lags or the underestimation of extreme events. To address this, more SAR observables can be exploited to gain independence with respect to model a priori. Here, we focus on the Co-Cross-Polarization Coherence (CCPC), defined as the complex cross-correlation between co-polarized and cross-polarized channels, has emerged as a valuable observable to supplement traditional NRCS measurements.
Recently, the CCPC has been formulated as piecewise log-normal function optimized on an extensive dataset of over 25k Sentinel-1 Interferometric Wide Swath (IW) observations. This modeling ensures the model remains well-behaved and physically consistent even for wind regimes higher than 20 m/s. The utility of the new PGMF-2 has been demonstrated through a Bayesian inversion scheme that integrates the co-polarization, the cross-polarization, and the real and imaginary parts of the CCPC. Monte Carlo simulations confirm that the unique odd-symmetry of the CCPC provides critical directional constraints that complement the even-symmetry of the NRCS. This combination enables wind direction retrieval without the need for NWP priors, particularly in the 7 to 15 m/s range, where the SAR-only inversion outperforms traditional NWP-based methods.
Moving on from simulation, we applied the inversion on real-world observations. Though a prior remain necessary for some wind field configurations, notably when the wind direction is parallel or ortogonal to the satellite track, as the CCPC is zeroed under these directions, the SAR-retrieved field is coherent with both NWP and scaterrometer data. In addition, the method proposed a unified methodology to introduce new parameters, such as the Imax and the streak direction, to further constrain the SAR-inverted wind field. Through the residual of each component, it also provide a quantitative evaluation of the inversion quality, which is primordial for the detection of pathoological cases and provide warning for the users where the SAR data is noised
This research highlights the potential of using additional SAR observables
CCPC to enable autonomous, high-resolution SAR wind field mapping, which is essential for monitoring rapidly evolving extreme weather systems and optimizing offshore energy resources. This opens new wind retrieval perspectives for current and future SAR missions such as Harmony, ROSE-L and Sentinel-1NG.
How to cite: Husson, R., Colin, A., Mouche, A., Grouazel, A., Nouguier, F., Pinheiro, M., and Longepe, N.: Bayesian wind fields estimates from C-band SAR without NWP prior, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21226, https://doi.org/10.5194/egusphere-egu26-21226, 2026.
Wind vector retrieval from radar measurements plays a fundamental role in ocean remote sensing, providing essential information for weather forecasting, climate studies, and maritime operations. Traditional scatterometer missions, including the ASCAT series aboard METOP satellites, the Chinese HY-2 constellation, and CFOSAT-SCAT on the CFOSAT mission, have established robust operational wind processing chains that have been successfully deployed for decades. These systems rely on well-validated inversion algorithms and ambiguity removal techniques to derive ocean surface wind vectors from radar backscatter measurements. However, the expanding diversity of spaceborne radar systems capable of providing wind information—such as Synthetic Aperture Radars (Sentinel-1), altimetry missions (Sentinel-3, Sentinel-6, SWOT), and emerging constellation concepts—creates a growing need for flexible processing frameworks that can accommodate different instrument geometries, viewing configurations, and measurement characteristics.
In this work, we present GUST (General Utility Scatterometry Tool), a modern Python-based framework designed to provide a unified approach to wind vector retrieval from diverse radar observations. GUST implements established wind-retrieval algorithms, including maximum-likelihood estimation (MLE) inversion with configurable geophysical model functions, and advanced ambiguity-removal techniques such as the two-dimensional variational method (2DVAR), originally developed for operational scatterometer processing. The implementation leverages PyTorch for GPU-accelerated computations, enabling efficient processing of large satellite datasets while maintaining algorithmic transparency and modularity.
In its baseline configuration, GUST fully reproduces the functionality and accuracy of the KNMI Advanced Wind Data Processor (AWDP), the operational standard for ASCAT data processing. Validation against AWDP reference products demonstrates excellent agreement: wind speed RMSE of 0.02 m/s, correlation exceeding 0.99, and approximately 98% agreement in ambiguity selection. These results confirm that the Python implementation maintains the scientific rigor of established Fortran-based processors while providing a more accessible and modifiable codebase.
The key advantage of GUST lies in its flexible architecture. The processing workflow—comprising data reading, quality control, inversion, ambiguity removal, and output generation—is organized as independent, configurable modules. This modular design enables rapid implementation of new retrieval algorithms tailored for emerging observation techniques: cross-polarisation measurements, Doppler-based retrievals, multi-band and multi-angle configurations, or bi-static observation geometries. Furthermore, GUST supports the development of multi-parametric geophysical model functions that incorporate additional ocean state variables—such as sea surface currents and sea state conditions—into the wind retrieval process, advancing beyond traditional wind-only inversions toward more comprehensive air-sea interaction characterisation.
Looking forward, GUST provides a foundation for processing data from next-generation missions with enhanced sensing capabilities, such as Metop-SG and the Harmony mission concept. The Python-based implementation enables straightforward incorporation of new algorithms, including machine learning approaches, and supports collaborative development within the scientific community. By bridging traditional operational processing with modern software practices, GUST aims to accelerate the development of wind-retrieval capabilities in the evolving landscape of ocean observing systems.
Acknowledgement This work was performed as a part of CFOSAT IFREMER Wind and Wave Operation Center (IWWOC) development. The IWWOC is co-funded by CNES and IFREMER.
How to cite: Mironov, A.: GUST: A General Utility Scatterometry Tool for Multi-Platform Ocean Wind Retrieval, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5630, https://doi.org/10.5194/egusphere-egu26-5630, 2026.
Storm surge is one of the primary causes of marine disasters in China, especially tropical storm surges. Numerical model forecasts serve as a critical reference in storm surge operational prediction. The precision of the storm surge simulation is largely contingent upon the accuracy of the input driving forces. The prevailing typhoon storm surge numerical models typically utilize a model wind field derived from tropical parameters as the driving force, ensuring both accuracy and timeliness. However, these statistical models based on the classical wind-pressure relationship or the principle of gradient wind often fail to capture the true asymmetry of the tropical wind field, particularly when the tropical structure is distorted by coastal topography or interactions with other atmospheric systems. Therefore, we upgraded the GPU version of the high-precision operational storm surge forecasting model for the China Sea, by incorporating remote sensing wind data from HY-2B、2C and 2D satellites to assimilate and correct the model wind driven field. Hindcast and forecast cases demonstrated the improvements of calculation results, especially in areas impacted by the right half of the tropical.
How to cite: Fu, X., Gao, Y., Liang, S., and Li, M.: Assimilation of operational storm surge forecasting model with HY-2 satellite wind, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6257, https://doi.org/10.5194/egusphere-egu26-6257, 2026.
The dynamics of river plumes in coastal environments play a key role in sediment transport, water quality, and ecosystem functioning. This study aims to characterize the spatial and temporal variability of the Mondego River plume (Figueira da Foz, Portugal) through satellite observations, assessing how different methodological approaches influence plume detection and interpretation.
High resolution satellite data, including Sentinel-2 and Sentinel-3 were used as the primary source in this study. Environmental datasets from various sources (SNIRH, Copernicus, IPMA) were used to support image interpretation. The analysis covers the period from 2019 to 2025, including a representative set of cloud free satellite images. Different atmospheric correction approaches, such as Sen2Cor (ESA L2A) and Dark Spectrum Fitting (ACOLITE), were applied to evaluate the impact of atmospheric pre-processing on plume detection and analysis. Several plume detection techniques were tested, including reflectance and turbidity thresholds, spectral distance and unsupervised clustering methods. This analysis was conducted in two phases: an initial phase following well-documented methodologies successfully applied in previous studies in literature, followed by an exploratory approach in which case-specific tests were designed to improve plume detection.
Results reveal pronounced seasonal and event driven variability in plume extent and dispersion, consistent with variations in river discharge, tides and wind forcing. Differences between atmospheric correction methods lead to variations in plume detectability and estimated spatial extent, highlighting the sensitivity of satellite derived products. Clustering based approaches capture plume morphology and spatial continuity, while threshold based methods provide rapid and easily interpretable estimates of plume extent.
Overall, this study demonstrates how satellite based plume characterization strongly depends on methodological approaches and pre-processing strategies, providing a reproducible framework for monitoring plume dynamics. This work is relevant for coastal management, while also underlining limitations related to atmospheric conditions and ancillary data quality.
How to cite: T. Silva, B., B. Oliveira, P., and A. Silva, P.: Spatial and Temporal Variability of the Mondego River Plume: A Satellite Based Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10289, https://doi.org/10.5194/egusphere-egu26-10289, 2026.
Coastal zones are closely associated with human activities and socio-economic development. Satellite altimetry is a key technology for coastal sea-level monitoring owing to its global coverage and all-weather capability. Synthetic Aperture Radar (SAR) altimetry has become an important tool for coastal sea-level monitoring due to its enhanced along-track resolution (~300 m). However, the large cross-track footprint (~15 km) allows land contamination and complex nearshore scattering to distort radar echoes, leading to significant deviations from standard ocean waveforms. Waveform retracking is an effective approach for mitigating these effects. Nevertheless, existing retrackers typically rely on independent single-waveform or 20 Hz along-track processing, which has limited effectiveness under complex coastal conditions. Furthermore, commonly used retrackers often require leading-edge fitting to obtain initial significant wave height (SWH) estimates, which is sensitive to leading-edge distortions. As a result, sea surface height (SSH) measurements within 5 km of the coastline, especially in the nearshore zone within 3 km, suffer from low data availability and reduced precision. To address these limitations, we propose a spatiotemporal continuity-constrained multi-parameter sub-waveform retracker (MulPOS-C), based on the multi-parameter optimized sub-waveform (MulPOS) determination framework. MulPOS-C organizes repeated-cycle altimetric observations into a two-dimensional (2D) grid (along-track bin × cycle) and identifies optimal sub-waveforms independently along the spatial and temporal dimensions. A spatiotemporal continuity function is then introduced to reconcile directional discrepancies, yielding a consistent set of optimal sub-waveforms across the 2D domain. Sea surface height (SSH) is subsequently retrieved using a fixed power threshold to define the start and end gates of the water-related sub-waveform. This strategy ensures physical consistency and controllable errors relative to full-waveform physical retrackers, while avoiding the instability associated with initial significant wave height (SWH) estimation. Global validation against four retrackers demonstrates that MulPOS-C substantially improves both data availability and precision. Within 1 km of the coastline, 81.8% of stations achieve the lowest RMSE among all methods. At 1 km offshore, the RMSE and SSH noise are reduced to 9.4 cm and 10.6 cm, respectively, representing reductions of at least 4.3 cm in RMSE and 3.5 cm in SSH noise compared with the four retrackers. At 2 km offshore, the RMSE decreases to 7.7 cm and the proportion of gross errors is reduced to 1.8%, significantly outperforming the other retrackers, with all metrics approaching open-sea levels. At 3 km offshore, all metrics reach open-sea levels. In addition, MulPOS-C is methodologically generic and can be readily extended to conventional Low-Resolution Mode (LRM) altimeter data.
How to cite: Gao, X., Jin, T., and Li, J.: MulPOS-C: A Spatiotemporal Continuity-Constrained Multi-Parameter Sub-Waveform Retracker for Coastal Altimetry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15542, https://doi.org/10.5194/egusphere-egu26-15542, 2026.
Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a transformative remote sensing technique for ocean geophysical parameter monitoring. However, existing wind speed retrieval algorithms often encounter accuracy bottlenecks due to an incomplete accounting of non-local swell modulations in different sea state conditions. This study identifies that, beyond Significant Wave Height (SWH), the swell period is a critical driver of GNSS-R signal variations, exerting an inherently non-linear influence on sea surface roughness across different wind speed regimes. To address this, we propose a refined retrieval framework. First, the swell period is introduced as a corrective parameter to calibrate scattering cross-section deviations induced by non-local waves, addressing the mismatch between local wind stress and surface roughness. Second, the instantaneous change rate of the Normalized Bistatic Radar Cross Section (NBRCS) is incorporated as a dynamic sensitivity indicator. This feature effectively enhances the model’s capability to distinguish between developing and fully developed seas, providing a temporal dimension to the traditionally static retrieval approach. A Multi-Layer Perceptron (MLP) architecture was optimized using CYGNSS mission data for end-to-end estimation. Validation against ERA5 reanalysis data demonstrates that the integration of swell dynamics and NBRCS temporal variations significantly enhances retrieval robustness across all wind speed ranges. While the model achieved a correlation coefficient (R) of 0.86 and an RMSE of 1.37 m/s across all ranges, the proposed model improves these metrics to R = 0.87 and RMSE = 1.32 m/s. Specifically, the model exhibits improved performance in both the high wind speed range (> 15 m/s), where the RMSE decreased from 2.79 m/s to 2.37 m/s (a 15% improvement), and the low wind speed range (< 10 m/s), with an RMSE of 1.21 m/s. These results underscore the necessity of accounting for swell-related wave age and temporal signal variations to achieve high-precision GNSS-R sensing under diverse and complex global sea conditions.
How to cite: Xian, H. and Jin, T.: A Refined Spaceborne GNSS-R Wind Speed Retrieval Framework Considering Swell Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16293, https://doi.org/10.5194/egusphere-egu26-16293, 2026.
The Arabian Gulf (Gulf) is a dynamic marine ecosystem and optically complex marginal sea, where the retrieval of chlorophyll-a (Chl-a) from ocean color observations is challenging due to intense atmospheric dust, high suspended sediments, and shallow bathymetry. Nevertheless, satellite-derived Chl-a, widely used as a proxy for marine primary productivity, is one of the key essential climate variables for monitoring long-term marine ecosystem variability and climate change. Given the absence of a single satellite mission spanning the full historical record of ocean color data, the construction of continuous, multi-decadal Chl-a time series relies on the integration of multiple sensors, highlighting the need for careful regional evaluation of available products. This study assessed the performance and consistency of widely used single-sensor and multi-sensor merged Chl-a products in the Arabian Gulf, including MODIS-Aqua, the Ocean Colour Climate Change Initiative (OC-CCI), and GlobColour datasets. The assessment was based on statistical comparisons with available in situ Chl-a measurements and analyses of spatiotemporal variability. Temporal consistency was also examined during both common operational periods and continuous overlapping lifespans of the datasets, with analyses covering time period from September 1997 to September 2025.
The results showed strong and stable correlations among the products, indicating robust temporal coherence in the Gulf. All products demonstrated valuable insights into Chl-a variability; however, notable differences were observed in spatial pattern and long-term trends, particularly over coastal and shelf regions. OC-CCI provided better spatial and temporal coverage with greater overall stability, making it particularly suitable for long-term environmental monitoring in the Gulf. Trend analysis revealed contrasting spatial patterns among the products, with OC-CCI indicated an overall declining Chl-a trend with localized coastal increases, while GlobColour exhibited increasing trends along the northern and southern coasts and a declining trend along the eastern coast. These differences are likely linked to known quality limitations in the early phases of merged datasets, especially during periods dominated by single-sensor contributions. Consistent with this, the OC-CCI record during 1997-2002 also shows regional discrepancies and less coherent trend behavior, which become substantially reduced after 2003, coinciding with increased sensor overlap and improved temporal consistency. This study highlights the necessity of regional validation of global ocean-color products in optically complex environments and identifies the most reliable dataset for developing long-term Chl-a records in the Gulf, supporting improved assessments of marine productivity and climate change impacts.
How to cite: Shafeeque, M., Saleem, A. A., Chaitanya, A. V. S., Lateef, S., and Albert, J.: Inter-comparison of Ocean Color chlorophyll products for assessing the marine productivity of the Arabian Gulf, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20793, https://doi.org/10.5194/egusphere-egu26-20793, 2026.
The ocean archives massive, stable remote sensing datasets, and leveraging these data to achieve intelligent real-time recognition of marine organisms has become a core task in the field of marine remote sensing. However, existing object recognition algorithms primarily focus on determining the location of objects, neglecting the demand for refined object recognition. When these algorithms that only identify location and category applied to marine remote sensing, they fail to meet the requirements of marine fisheries, species conservation, and precise underwater operations. To expand the application scenarios of marine target recognition, we propose a Key Point Refined Network (KPR-Net) for fish contour recognition, a two-stage adaptive target detection algorithm that first determines the positions of target feature points through feature extraction and sufficient fusion processing, then delineates the target contour via simple topological relationships. Our proposed KPR-Net efficiently and accurately predicts the positions of marine target feature points by integrating a self-attention mechanism with multi-feature aggregation. Furthermore, to incorporate biological characteristics and enhance the detection performance of fish targets, we take the natural contour features of fish as constraints, accurately determine the arrangement of these vertices based on the positions and directions of the target feature points, and sequentially connect them according to the determined arrangement to obtain the target contour. Experiments on multiple challenging marine datasets demonstrate the accuracy of our proposed method in multi-category target detection tasks. Particularly in the refined presentation stage of recognition results, the determination of target contours enriches the output content of the target detection algorithm, providing more detailed and comprehensive recognition information.
How to cite: Rao, W., Jeboyedoff, M., and Chen, G.: An Improved Object Recognition Algorithm via Feature Point: A Case Study of Fish Contour Recognition in Marine Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5193, https://doi.org/10.5194/egusphere-egu26-5193, 2026.
Meteotsunamis, as the name suggests, are atmospherically generated tsunamis, most often formed during fast passage of a mesoscale atmospheric disturbance, such as convective storm capable of ducting gravity waves and rapidly changing surface air pressure. Energy from the atmosphere is transferred to the water body and the resulting high-frequency (T < 2h) sea level disturbances can exceed one meter if amplified by Proudman or Greenspan resonance. These extreme events can cause a substantial damage to coastal communities and may even lead to human causalities, which have been reported around the world. Thus, it is vitally important to be able to forecast such events in a timely manner. However, such meteotsunami events are hard to predict due to limited spatial coverage of tide gauges as well as due to convective storms, i.e., their shape, speed, propagation direction and intensity changing in time, to which—on top of coastal ocean bathymetry modulations—intensity of meteotsunamis is dependent. Previous studies have shown that various hazardous natural phenomena—such as earthquakes, volcano eruptions, strong convective storms and tsunamis—can produce detectable ionospheric disturbances measurable by perturbations of the ionospheric total electron content (TEC) opening a possibility for earlier warning. To date, only one study has investigated ionospheric TEC perturbation associated with meteotsunamis, focusing on an event along the eastern coast of the United States, with encouraging result. To check whether similar approach could be applied for other parts of the globe, this study investigates a meteotsunami observed at Australian south-east coast which formed on 30 April 2020. Conditions for the upward propagation of meteotsunami-generated atmospheric gravity waves (AGWs) into the ionosphere are checked by using linear gravity wave theory. Observational evidence is assessed by utilizing space-based radio occultation (RO) measurements from Global Navigational Satellite System (GNSS), Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperatures and ground-based GNSS TEC observations. Our result reveals evidence of free propagation of meteotsunami-generated AGWs into the upper atmosphere along with associated variability in ionospheric TEC of approximately 1 TECU. These findings support the potential of ionospheric observations as a complementary tool for meteotsunami signal detection and possibly early warning.
How to cite: Radovan, A. and Vilibić, I.: A sea in the sky? Evidence of meteotsunami signatures in the ionosphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4857, https://doi.org/10.5194/egusphere-egu26-4857, 2026.
Antarctic open-ocean polynyas (OOPs) are critical windows for air-sea interaction, significantly enhancing heat exchange and driving Antarctic Bottom Water (AABW) formation. While their importance is recognized, their long-term spatiotemporal evolution and the underlying subsurface drivers remain incompletely understood. This study identifies recurrent deep-water OOPs and reveals a pronounced longitudinal asymmetry and complex subsurface dynamics through a refined 20-year analysis.
Using the DEEP-AA daily polynya edge dataset (2003–2022), we tracked open-water features during austral winters (April–October). By applying rigorous spatiotemporal filters—excluding short-lived (<20 days), small-scale (<100 km²), or coastal-influenced features—we identified 16 primary OOPs. Among these, eight were classified as "deep-water OOPs" based on their location over abyssal depths (>3,500 m) or major topographic features, such as seamounts and mid-ocean ridges.
Our results highlight a strong clustering of deep-water OOPs within the 90°W–90°E sector, governed by coupled dynamic-thermodynamic processes. In the 55°S–65°S latitudinal band, Antarctic Circumpolar Current (ACC) shear amplifies Ekman transport and mesoscale eddy activity. Topographic features, such as Maud Rise, induce flow divergence that sustains surface openings. In contrast, the northward deflection of the ACC west of 90°W promotes sea-ice expansion in the Ross and Amundsen Seas. These OOPs act as vital conduits for AABW formation; topographic uplift facilitates the upward transport of warm Circumpolar Deep Water (CDW; T > 0°C, S ≈ 34.6 psu) into the mixed layer, effectively inhibiting ice growth.
A key novelty of this study is the identification of "Silent Periods"—intervals where the surface appears frozen (days to months) due to extreme winter cooling, yet subsurface heat transport remains active. We found that residual thermal anomalies create an "Oceanic Heat Memory" effect, which preconditions the polynya for rapid reactivation. Conventional sea-ice concentration (SIC) threshold methods fail to capture these subsurface signals, consistently underestimating both polynya persistence and total ocean-to-atmosphere heat fluxes.
These findings demonstrate that seabed topography, environmental conditions, and ocean circulation are the primary determinants of deep-water polynyas distribution. By elucidating the mechanisms of topographic preconditioning and the limitations of surface-only observations, this work provides essential insights for improving ice-ocean coupling in Earth System Models and refining projections of Southern Ocean climate change.
How to cite: Luo, X. and Fan, X.: Spatial Heterogeneity and Multi-Decadal Dynamics of Antarctic Deep-Water Polynyas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3297, https://doi.org/10.5194/egusphere-egu26-3297, 2026.
Accurate estimates of ocean surface heat fluxes (OSHF) are essential for assessing and improving climate projections and supporting adaptation strategies, yet direct measurements are challenging, costly, and not feasible at global scales. The GCOS Implementation Plan 2022 emphasizes the urgent need to enhance estimates of latent and sensible heat fluxes, recommends greater use of satellite data, and highlights that current in-situ observing systems, such as Argo, are insufficient to provide the high-resolution data required for climate modeling and model validation.
The CCI OSHF project addresses this gap by generating a new satellite-based OSHF product aiming at meeting the climate community requirements for spatial and temporal resolution, timeliness, uncertainty, and long-term stability. By relying on fundamental physical principles rather than parameterizations and leveraging multiple satellite missions and in-situ observing networks, the product aims at reducing uncertainties.
After one year of the project, we will present the user requirements outcomes from the dedicated user consultation action, describe the methodology used to generate the product, and show preliminary results on the performance of the first 15-year version of the new satellite-derived OSHF dataset.
How to cite: Olmedo, E., Arias, M., Bergas-Ques, J., Cornes, R., González-Gambau, V., Haimberger, L., Hart-Davis, M., Juhl, M.-C., Kent, E., Mayer, M., Merchant, C., Müller, F., Ruiz-Sebastián, A., Sagués, A., Storto, A., Turiel, A., Winkelbauer, S., Yang, C., and Pinnock, S.: CCI-OSHF: A new ESA Climate Change Initiative to enhance ocean surface heat fluxes estimates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17849, https://doi.org/10.5194/egusphere-egu26-17849, 2026.
Distributed Acoustic Sensing (DAS) provides dense, distributed measurements along subsea fibre-optic cables and has demonstrated potential for passive detection, e.g., of surface vessels, whales, and similar objects. Beyond detection and localisation, the spectral content of the acoustic signals contains information about the object, such as propulsion, machinery, and operational state for ships. In hydrophones, such information is often extracted using spectral and modulation-based techniques, including Low-Frequency Analysis and Recording (LOFAR) and Detection of Envelope Modulation on Noise (DEMON). However, the direct transfer of these methods to DAS is non-trivial due to differences in frequencies, coupling conditions, and the limited acoustic bandwidth typically available in DAS systems.
In this study, we investigate how ship radiated noise can be spectrally characterised using DAS by adapting analysis concepts originally developed for hydrophone-based passive acoustics. We focus on frequency bands accessible to DAS low-frequency tonal components are observable. A preprocessing pipeline is applied to enhance signal-to-noise ratio and suppress non-acoustic contributions. Within this framework, DEMON-inspired envelope analysis is explored in a form compatible with DAS bandwidth constraints, alongside direct low-frequency spectral methods.
To place the extracted spectral features in a physical and operational context, the DAS-derived signatures are compared with ship information from AIS data and auxillary ship information. This comparison enables assessment of how observed spectral characteristics relate to known ship properties and operating conditions, and clarifies which elements of classical underwater radiated noise theory are transferable to DAS observations.
By focusing on spectral characterisation rather than localisation performance, this work aims to bridge the gap between traditional hydrophone-based ship acoustics and emerging DAS-based maritime monitoring, providing a methodological foundation for interpreting DAS detections in terms of vessel class and behaviour.
How to cite: Soerensen, K. A., Orellana, L., Pedersen, H., Nezhad, K., and Heiselberg, H.: Spectral Characterisation of Undewater Radiated Noise Using Distributed Acoustic Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20071, https://doi.org/10.5194/egusphere-egu26-20071, 2026.
Marine chlorophyll-a plays a crucial role in marine ecosystems worldwide and is essential for photosynthesis in certain plankton and aquatic plants. The Arabian Sea is vital for biodiversity and climate regulation. Hence, remote sensing techniques are employed to monitor chlorophyll-a and understand the spatiotemporal fluctuations of phytoplankton and biomass in these waters. This research investigates the spatiotemporal trends of relative humidity acquired from AIRS, MERRA-2 model’s dust column density, and MODIS retrieved Aerosol Optical Depth (AOD) and Chl-a across the Arabian Sea (4ᵒN-26ᵒ N to 50ᵒE-78ᵒE) spanning from 2003 to 2023. Cross-wavelet analysis and correlation of Chl-a with relative humidity, dust, and AOD over the AS are also analyzed in this research. An anti-phase and phase lag state has been found between Chl-a and these variables, showing a negative correlation. Correlation maps reveal that Chl-a in the Arabian Sea has a negative correlation with dust (-0.6 to -0.1) across most areas, suggesting that dust deposition may inhibit phytoplankton growth due to reduced light penetration or other factors. Similarly, the negative correlation with relative humidity (-0.9 to -0.1) could reflect adverse climatic conditions during humid periods. In contrast, the positive correlation with AOD (0.1 to 0.9) in the southern region implies that aerosols might enhance phytoplankton productivity through nutrient deposition, while negative values in the northern region (-0.9 to -0.4) highlight contrasting dynamics. While dust column mass density and Chl-a show decreasing trends, aerosol optical depth and relative humidity are increasing. Moreover, the highest variability of Chl-a has been observed in DJF close to the shores of Oman, Iran, and Pakistan.
How to cite: Tariq, S.: Atmospheric Interactions and Marine Productivity: Assessing Chlorophyll-a Responses to Dust, AOD, and Humidity in the Arabian Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-71, https://doi.org/10.5194/egusphere-egu26-71, 2026.
Phytoplankton are the drivers of primary production in aquatic ecosystems, forming the foundation of the oceanic food web and playing a pivotal role in global carbon cycling. Satellite ocean colour is used to monitor the phytoplankton distribution and is critical for understanding ecosystem dynamics and climate interactions. The Moderate Resolution Imaging Spectroradiometer (MODIS) has provided invaluable multispectral ocean colour observations for over two decades. Recently, NASA launched the Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) mission introduces hyperspectral capabilities that greatly enhance spectral characterization and cloud–aerosol corrections. This study investigates cross-sensor consistency between MODIS-Aqua and PACE-OCI over the northern Indian Ocean, using a sequence alignment technique firstly applied to satellite imagery. Chlorophyll and remote sensing reflectance products from MODIS and PACE were intercompared for an 8-day period and validated using in-situ measurements from India’s Coastal Ocean Monitoring and Prediction System (COMAPS). The analysis integrates two approaches: (i) standard statistical metrics and clustering techniques, and (ii) a pixel-level comparison method using the Needleman–Wunsch algorithm (NWA), adapted from bioinformatics for spatially sensitive sequence alignment of satellite data. Results show a strong inter-sensor correspondence (R² > 0.9) in blue-green spectral bands (412–555 nm), with both sensors effectively capturing large-scale chlorophyll patterns and coastal–offshore gradients. Validation results indicate similar performance for both sensors, with PACE showing slightly better performance (R2 = 0.88, MSE = 0.008). The NWA-derived similarity maps indicate spatial deviations mainly in nearshore zones, highlighting region-dependent sensor performance. The study on hyperspectral and multispectral sensor comparison reveals PACE’s potential to continue and enhance MODIS’s long-term ocean colour climate data record. The proposed sequence alignment approach offers a robust, directionally sensitive alternative to conventional statistical comparisons, enabling detailed cross-sensor validation for future ocean colour applications.
How to cite: Puthukulangara, P. and Nidamanuri, R. R.: Cross-sensor evaluation of hyperspectral and multispectral Ocean colour products in the northern Indian Ocean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4677, https://doi.org/10.5194/egusphere-egu26-4677, 2026.
The Euphotic Zone Depth (Zeu) refers to the depth in the ocean where only 1% of surface light remains available for photosynthesis. It serves as a crucial indicator of water clarity, water quality, and the primary productivity of marine ecosystems. This study presents a detailed examination of the spatial and temporal dynamics of the Zeu over the Arabian Sea from 1998 to 2023, utilizing satellite-derived observations. Results reveal that Zeu exhibits pronounced variability on interannual, intra-annual, and decadal timescales. The analysis incorporates three key sub-regions — the Northern Arabian Sea (NAS), South Eastern Arabian Sea (SEAS), and South Western Arabian Sea (SWAS). The basin-wide annual mean Zeu ranges between 6 m and 80 m, indicating marked spatial differences in water transparency. Regionally, Zeu values vary from approximately 10–62 m in the NAS, 10–74 m in the SEAS, and 12–72 m in the SWAS. Empirical Orthogonal Function (EOF) analysis indicates that interannual variations explain 21.57% of the total variance, predominantly influenced by the Indian Ocean Dipole (IOD) and, to a lesser extent, the El Niño–Southern Oscillation (ENSO), particularly within the SEAS and SWAS. In the NAS, Zeu demonstrates a notable lagged relationship, with the first principal component (PC1) lagging the Dipole Mode Index (DMI) and ENSO by roughly 10 and 8 months, respectively. A significant positive trend in Zeu is observed across the Arabian Sea and within each sub region, with the NAS showing the greatest increase. Seasonal trend analysis further reveals consistent increases in Zeu across all seasons, with the most pronounced rise (0.47 m yr⁻¹) occurring in the NAS during the October–November (ON) season. A clear inverse relationship between Zeu and chlorophyll-a is observed across all regions indicating that variations in phytoplankton biomass are a primary control on light penetration and water clarity. Overall, this study provides the first comprehensive insight into the multi-decadal variability of Zeu across the Arabian Sea based on multi-sensor satellite observations. These findings carry direct implications for the Blue Economy, particularly in sectors such as fisheries, aquaculture, and marine biodiversity conservation. A deepening Zeu may signal ecosystem shifts that can affect fishery potential, primary production, and carbon cycling, highlighting the need for region-specific marine spatial planning and climate-resilient strategies. Monitoring Zeu variability can thus serve as a valuable indicator for assessing ocean health, in alignment with the Sustainable Development Goals (SDGs) 13: Climate Action and 14: Life Below Water.
How to cite: Francis, S., Murali R, M., and Pottekkatt Jayapalan, V.: Climatic Modulation of the Euphotic Zone in the Arabian Sea: Multi-Sensor Satellite Evidence from 1998–2023, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-490, https://doi.org/10.5194/egusphere-egu26-490, 2026.
Because of their spatial coverage and time history extending back over four decades, satellite retrievals of sea surface temperature (SST) are important to benchmark climate warming. However, the task of homogenization of satellite climate records is complicated by artefacts linked with stratified boundary layers on different depth and time scales, and also by the characteristic depth scales of the measuring instruments. To first order, the oceanic skin temperatures measured from earth orbit are biased cool by a few tenths of Kelvin with respect to in situ measurements at depths of centimeters-to-metres that have traditionally been used for comparison and calibration. This ‘cool skin’ effect is due to the fast response of the surface skin layer to surface heat flux loss compared with the vertical heat transport of mixing processes that are driven by wind speed. An additional consideration for satellite SST retrievals is the ‘warm layer effect’: the near surface stratification caused by solar heating during daytime, especially during light winds. This contribution presents an analysis of the modified Kantha and Clayson one-dimensional diffusion model to simulate the formation and evolution of the oceanic boundary layer on diurnal time scales with the aim of quantifying the ‘cool skin’ and ‘warm layer’ effects for satellite SST applications. The upper ocean model is forced by ERA5 reanalysis data over short time segments of two days, i.e., long enough to spin up the geophysical mixing processes and obtain a complete diurnal temperature cycle. Preliminary results illustrate how near surface diurnal warming effects vary as a function of wind speed and surface heat flux. The model is used estimate satellite SST bias with respect to in situ measurements from a global match-up data base from January 2024.
How to cite: Kettle, A. and Saux Picart, S.: An oceanic boundary layer model to understand near-surface temperature gradients for satellite temperature retrievals, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1785, https://doi.org/10.5194/egusphere-egu26-1785, 2026.
Seismic oceanography re-uses legacy marine multichannel reflection data (MCS) giving it a new porpose for ocean knowledge enrichement. Targeting the water column, detailed MCS processing for high-resolution water column imaging facilitates new interpretations and ocean modelling technics. Seismic acoustic response is intrinsically conected to the water column propreties, such as temperature an salinity (e.g., Azevedo, L. et al., 2021), this brings the study of fine-scale ocean processes to another level giving the possibility of lateraly predicting their spatiotemporal continuity.
To retrieve the water collumn reflections the MCS processing workflow requires specific steps that enhance the signal-to-noise ratio and preserve the true amplitude content (Duarte, et al., 2025) . Later, the resultant images can be interpreted within the context of expected and present ocean processes in the study region (Duarte, et al., 2024) and integrating measurements acquired in oceanographic campains.
Since seismic oceanography signal depends on the water column propreties, seismic oceanography inversion enables the re-constrution of the ocean temperature and salinity distributions. However, seismic inversion is inherently non-linear, identical seismic responses can be originated from different combinations of temperature and salinity pairs. Consequently, the inversion problem is ill-posed and admits multiple solutions, requiring smart strategies to condition and solve the ambiguity (Azevedo, L. et al., 2021).
Our results demonstrate a sucessful atempt on reconstructing the high-resolution temperature and salinty models for two Portuguese regions with stochastic seismic oceanography inversion. This aproach highlights how seismic oceanography can resolve fine-scale thermohaline structures, often neglected by conventional sampling technics and shades lighight into its spatial continuity while assessing the predictions of the uncertainty. The resulting models provide valuable insights confirming the potential of seismic data as a complement tool for oceanographic studies and encouragning its integration in further oceanographic studies.
How to cite: Duarte, A. F., Azevedo, L., and Mendes, R.: Seismic oceanography improving ocean knowledge, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19210, https://doi.org/10.5194/egusphere-egu26-19210, 2026.
Coastal mariculture areas exhibit high spatiotemporal heterogeneity in carbon source–sink dynamics due to the interaction of natural processes and human activities. A systematic understanding of the spatiotemporal variability and controlling mechanisms of seawater CO2 partial pressure (pCO2) and air–sea CO2 flux (FCO2) remains limited here. Lianjiang County, a leading county in China in terms of fishery output over the past five years, was selected as the study area. Based on Sentinel‑2 reflectance data, we developed a 10 m‑resolution sea surface pCO2 retrieval model using the XGBoost algorithm. The model demonstrated high accuracy, with a coefficient of determination (R²) of 0.87 and a root‑mean‑square error (RMSE) of 6.89 μatm. Sea surface pCO2 in the 2024–2025 mariculture period (autumn to spring) witnessed a “higher in warm seasons, lower in cold seasons” pattern, driven by both thermal dynamic and biological processes. Meanwhile, the retrieved pCO2 exhibited a distinct “mariculture signal”: Compared with ambient seawater, Macroalgae culture areas maintained lower pCO2 due to strong photosynthesis, whereas shellfish farming areas showed elevated pCO2 from respiration and calcification; In co-cultivation areas of shellfish and algae, pCO2 falls between those of their respective monocultures. Although the study area functioned as a weak source of atmospheric CO₂, this study reveals the potential of mariculture as a marine carbon dioxide removal (mCDR) strategy. By reducing background CO₂ efflux, macroalgae cultivation enhances carbon sequestration. In contrast, while shellfish farming elevates CO₂ emissions, integrated aquaculture provides a compensatory mechanism that partially offsets these emissions, reconciling economic value with climate objectives. This work provides a high-resolution remote-sensing approach for monitoring sea surface pCO2 in coastal mariculture areas, clarifies the joint regulation of carbon dynamics by natural and farming processes, and offers a scientific basis for carbon cycle management in mariculture zones.
How to cite: Yu, S., Xu, Y., Yang, W., Chen, Y., Guo, J., and Qi, D.: Satellite estimation of air-sea CO2 flux in marine aquaculture areas: a case study of Lianjiang County, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2728, https://doi.org/10.5194/egusphere-egu26-2728, 2026.
Abstract
The definition of end-members plays a central role in spectral decomposition of aquatic remote sensing reflectance, especially in highly turbid coastal waters where spectral signatures are strongly mixed and sensor dependent. End-members are defined as the purest reflectance spectra of water constituents and often could not be derived directly from observational data. Such data-driven end-members are often sensitive to noise, atmospheric correction uncertainties, and the reduced spectral resolution of multispectral sensors. Here, we examine how physically modeled end-members (MEMs) and end-members extracted from observations (EEMs) compare in terms of stability across different sensor types in the Dutch Wadden Sea.
Physically modeled end-members were generated using a validated bio-optical forward model constrained by realistic ranges of optically active constituents. In parallel, EEMs were extracted from in-situ hyperspectral reflectance and from Sentinel-2 MSI and Sentinel-3 OLCI data using a geometric end-member extraction approach. The stability of MEMs and EEMs was evaluated through geometric inclusion analyses, spectral similarity measures, and reflectance reconstruction following Gaussian-based spectral decomposition.
The comparison shows that MEMs remain consistent across in-situ hyperspectral and satellite-derived multispectral datasets, while EEMs tend to lose representativeness when applied to multispectral observations. This degradation is mainly linked to band aggregation effects and increased sensitivity to atmospheric correction uncertainties. In contrast, MEMs preserve their spectral geometry and reconstruction capability under these conditions.
By separating the role of end-member definition from subsequent retrieval steps, this study demonstrates that physically constrained end-members provide a more robust foundation for multispectral spectral decomposition in optically complex coastal waters. These findings are particularly relevant for operational satellite monitoring applications where stability and transferability are essential.
Keywords
Remote Sensing, Water constituents, Spectral Separation, Bio-optical Model, Remote Sensing Reflectance
How to cite: Arabi, B., Lu, M., and Moradi, M.: Stability of Physically Modeled versus Data-Extracted End-Members for Multispectral Decomposition of Coastal Water Reflectance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3429, https://doi.org/10.5194/egusphere-egu26-3429, 2026.
Satellite-derived products are essential for monitoring aquatic ecosystems. This study presents a first assessment of the capabilities of the hyperspectral Ocean Colour Instrument (OCI) onboard of NASA’s recently launched Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) satellite. OCI delivers spectral observations from 340 to 895 nm with bandwidth at 5 nm resolution and spectral steps of 2.5 nm sampling, marking a major advance in ocean colour monitoring. Because the launch is recent and the premises very encouraging, an early assessment of its performance both against existing sensors and in-situ data in complex coastal waters is of great interest for validating its capabilities. This work is focused on the coastal waters of southern Portugal, analysing three sampling areas off Sagres, Portimão and Armona Island. PACE/OCI Level-2 chlorophyll-a (Chl-a) products from multiple dates between February 2024 and July 2025 were extracted and compared with coincident opportunistic in-situ Chl-a measurements collected from water samples across various CIMA projects. To contextualize the performance of the new hyperspectral sensor, a parallel comparison was conducted using standard reduced resolution Level-2 Chl-a products (CHL_OC4Me, CHL_NN) from the multispectral Sentinel-3 Ocean and Land Colour Instrument (S-3/OLCI), which has a comparable spatial resolution with PACE/OCI, approximately 1.2 km. An array of statistical metrics (e.g. Median Absolute Percentage, MdAPD, and Coefficient of Determination, R²) was applied to evaluate the agreement between satellite-derived and in-situ Chl-a concentrations. Preliminary results, based on the available matchup dataset, indicate that PACE/OCI estimations show a closer alignment with in-situ measurements compared to those from S-3/OLCI in the study region, as evidenced by the higher R² and lower log-transformed MdAPD. This improved performance suggests that the hyperspectral capability of OCI sharpens the algorithmic quantification of the phytoplankton signal, using NASA’s combined OCx and Colour Index approach, within the optically complex conditions characteristic of coastal and oceanic waters. Despite the limited temporal coverage (the PACE was launched in the first months of the 2024) and the scarce number of matchups between the opportunistic in-situ data and the corresponding satellite data, this early analysis underscores the promising potential of PACE/OCI for delivering more accurate Chl-a estimates in marine coastal environments. The study provides a first overview on the potential of PACE/OCI in Portuguese coastal waters, and in general on the feasibility of hyperspectral observation, for future research and applications of ocean colour data in contexts like regional coastal management and Blue Economy sectors.
How to cite: Ciccarelli, C., Cristina, S., and Lega, M.: Assessing the Hyperspectral Advantage: A First Evaluation of PACE/OCI for Chlorophyll-a Retrieval in Southern Portuguese Coast, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7726, https://doi.org/10.5194/egusphere-egu26-7726, 2026.
Chlorophyll-a (Chl-a) concentration is a key climate variable. It is directly related to phytoplankton biomass and thus, reflects primary production, the foundation of all aquatic ecosystems. Studying the variation and distribution of Chl-a is crucial for understanding ecosystem functioning, monitoring ecosystem health and environmental changes. In this study we evaluate the reliability of satellite observations (level L3 and L4 data) in comparison to in-situ measurements and investigate the spatial and temporal distributions of Chl-a in the western part of the Black Sea (42°–43.7°N and 27°–29°E) for the period 1998-2025. Satellite products for the concentration of Chl-a were obtained from the Copernicus Marine Service, while in-situ data were collected by ship expeditions of the Institute of Oceanology at the Bulgarian Academy of Science.
The comparison between satellite and in-situ data close to the shore, along the shelf and in the open sea showed that the largest deviations are calculated near the coastline and decrease towards the open sea. Satellite L3 values demonstrate smaller value of the RMSD and higher Pearson correlation coefficient than L4. The analysis includes the surface and subsurface values of the Chl-a, thus revealing the importance of data availability in the whole euphotic layer.
A 28-year time series (1998-2025) of spatially averaged L3 daily values of Chl-a is analyzed in order to identify events of anomalously high concentrations of chlorophyll-a, which could signal the development of phytoplankton blooms. The threshold value is determined and the intra- and inter annual distribution of such events is found.
The L4 data are analyzed in order to capture complex spatiotemporal relations and to reveal the significant modes in the variability by applying EOF analyses. The investigation distinguishes four categories of blooms: (1) north-to-south spreading blooms likely influenced by Danube or other rivers’ nutrient input; (2) large-scale blooms along the entire Bulgarian coast linked to northward currents and coastal upwelling; (3) localized events in the Burgas and Varna Bays, possibly from anthropogenic sources; and (4) blooms south of Burgas, associated with mesoscale eddies concentrating phytoplankton.
How to cite: Pandourska, E. and Peneva, E.: Spatiotemporal patterns of chlorophyll-a concentration in the western part of the Black Sea for the period 1998-2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9035, https://doi.org/10.5194/egusphere-egu26-9035, 2026.
The rapid warming of the Arctic could drive some critical parts of the Earth system towards tipping points. For tipping elements involving ocean circulation (eg. Atlantic Meridional Overturning Circulation – AMOC, or sub-polar gyre - SPG), remote sensing of sea surface salinity (SSS), temperature (SST), height (SSH) or current (Total Surface Current Vector - TSCV) can detect fingerprints of the proximity of tipping points.
While spaceborne satellite sensors provide large-scale SSS coverage, their spatial (> 50 km) resolution is insufficient to capture coastal and (sub-) mesoscale processes, especially in dynamic regions like Greenland’s continental shelves with fast-changing sea ice. In situ platforms, though precise, lack the spatial and temporal coverage required to capture spatial structures, and monitor rapid changes and extreme events.
This contribution presents a requirement assessment for airborne SSS measurements along Greenland, focusing on the unique challenges posed by the encountered environmental conditions (low SST, presence of sea ice, …), and the need for low-carbon, scalable observing platforms. We evaluate the scientific and operational requirements for SSS retrievals, including spatial resolution (100 m–10 km), revisit frequency, and accuracy (0.1-1 pss), and discuss the trade-offs between platform endurance, payload capacity, and environmental impact.
We then review suitable instrumentation for airborne SSS mapping, with an emphasis on technologies compatible with low-carbon platforms such as drones, high-altitude pseudo-satellites (HAPS), and airships.
The potential for multi-sensor fusion—combining SSS with sea surface temperature, currents, and wind measurements—is also explored, as is the integration of airborne data with satellite and in situ networks.
How to cite: Martin, A., Gommenginger, C., Buckingham, C., McCann, D., and Marquez, J.: Airborne Sea Surface Salinity Monitoring Along Greenland with a low-carbon platform: Requirements Assessment and Review of Existing Instrument, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11771, https://doi.org/10.5194/egusphere-egu26-11771, 2026.
Understanding how ocean tides propagate within estuaries and rivers is important for quantifying upstream tidally influenced water level variability and flood risk under sea level rise. Conventional observations from tide gauges provide accurate tidal estimates, but are spatially sparse and confined to fixed locations, but have limited ability to resolve lateral and cross-channel structure. In this study, we use high-resolution wide-swath altimetry (100m L2 HR Raster) from the Surface Water and Ocean Topography (SWOT) Satellite mission to directly observe the two-dimensional spatial structure of the M2 tidal variability across estuarine and riverine systems. We characterize how tidal signals evolve spatially as they propagate inland, including regions of amplification, reduced wave speed and spatial gradients coincident with variations in channel geometry. The results reveal coherent spatial structures that are not captured by along-channel or point-based analysis and identify locations where tidal-river interactions may increase sensitivity to background sea level changes and compound flooding. We identify localized regions where the tidal wave exhibits a slowdown in propagation concurrent with an increase in amplitude, indicating spatially confined zones with a convergence of tidal energy. These regions represent sensitive locations where long wavelength disturbances, such as storm surges, may amplify as they propagate inland.
How to cite: Kolster, M. R., Nerem, R. S., and Minear, J. T.: Spatial and temporal structure of the M2 tide in Rivers and Estuaries as observed from SWOT, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18562, https://doi.org/10.5194/egusphere-egu26-18562, 2026.
Coastal high-frequency (HF) radars are widely used in operational oceanography to provide continuous, wide-area observations of surface currents, waves, and maritime targets. Operating in the 3–30 MHz frequency band, HF radars can achieve observation ranges exceeding 200 km, making them essential tools for coastal monitoring and exclusive economic zone (EEZ) surveillance.
Accurate target positioning in HF radar observations critically depends on the reliability of direction-of-arrival (DOA) estimation. Angular errors introduce significant uncertainty in target localization and propagate into radial velocity retrievals and tracking consistency, particularly under low signal-to-noise ratio (SNR) conditions commonly encountered in real coastal environments. Conventional Fourier beamforming suffers from high sidelobe levels that lead to angular ambiguity, while the Capon beamformer is highly sensitive to covariance estimation and often becomes unstable at low SNR.
This study evaluates a norm-constrained Capon (NC-Capon) beamforming approach as a strategy to enhance the robustness of spatial filtering in operational HF radar observations. By combining a norm constraint with diagonal loading, NC-Capon beamforming stabilizes spatial filtering and suppresses sidelobe leakage, resulting in more robust DOA estimation under noisy conditions. Field experiments were conducted using two operational coastal HF radar stations in northern Taiwan. A dedicated experimental vessel followed controlled trajectories at nearshore ranges of approximately 1–3 km, while a fixed offshore unloading platform served as a stable reference target. Radar-derived target positions obtained using Fourier, Capon, and NC-Capon beamforming were systematically compared with Automatic Identification System (AIS) data to quantify angular uncertainty under different azimuthal conditions and its impact on target localization results.
Results show a slight discrepancy between radar measurements and AIS target locations, particularly under low SNR conditions and at large azimuthal angles. Moreover, systematic bias occurs in one of the coastal radar observations, which is suspected to be related to the configuration of the radar system. These findings underscore the importance of enhancing spatial filtering robustness to improve the reliability of target localization using coastal HF radar.
How to cite: Chang, H.-M., Cheng, A., You, Z.-X., Chien, H., and Flament, P.: Reducing Angular Uncertainty in Target Localization of Operational Coastal HF RadarUsing NC-Capon Beamforming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15800, https://doi.org/10.5194/egusphere-egu26-15800, 2026.
Bistatic configurations are increasingly important for extending observation geometry in coastal HF radar systems. In this study, we implement a bistatic HF radar testbed in Taoyuan, Taiwan, based on the open-source Generic High Frequency Doppler Radar architecture developed by the University of Hawai‘i. The site supports both monostatic and bistatic operations, enabling direct, real-time comparisons under identical environmental conditions.
Monostatic HF radar observations are inherently constrained by viewing geometry and are further limited in practice by hardware redundancy requirements, site availability, and electromagnetic interference. Bistatic configurations provide an effective means to expand observation geometry and spatial coverage. We report practical experience from the deployment of a bistatic HF radar system in Taiwan, with emphasis on cross-site time synchronization and bistatic signal processing.
Accurate time synchronization between transmitting and receiving sites is a critical challenge in bistatic operation. Although monostatic systems typically rely on temperature-compensated crystal oscillators (OCXO), operational tests show that residual clock drift can degrade phase coherence in bistatic measurements. To address this issue, two synchronization strategies are implemented: (1) a GPS-disciplined oscillator (GPSDO) with pulse-per-second (PPS) signals and DDS-based phase-lock feedback to achieve progressive convergence toward a target timing accuracy, and (2) highly stable atomic clocks combined with PPS calibration to ensure long-term timing stability during continuous operation.
On the processing side, the monostatic framework is extended to bistatic geometry. Following established bistatic scattering theory, the inversion procedure includes scattering-point localization using elliptical geometry, formulation of bistatic Bragg frequency relationships, and estimation of velocity components. A bistatic current inversion scheme is further developed to enable cross-validation between monostatic and bistatic measurements and to synthesize vector surface current fields.
Overall, this work demonstrates the feasibility of bistatic HF radar systems for overcoming key limitations of monostatic observations. The presented hardware synchronization strategies and processing framework provide a practical foundation for future multi-station collaboration, system validation, and expanded coastal monitoring applications.
How to cite: Cheng, A., Chang, H.-M., Yu, H. Y., Chien, H., and Flament, P.: Bistatic HF Radar for Coastal Ocean Remote Sensing: System Implementation and Validation in Taiwan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12175, https://doi.org/10.5194/egusphere-egu26-12175, 2026.
