In November 2009, the first L-band interferometric radiometer was launched as the second Earth Explorer mission by the European Space Agency (ESA), with the primary goal of measuring soil moisture over land and sea surface salinity over the oceans. After more than 15 years, the mission continues to provide excellent monitoring of these two essential climate variables, offering unprecedented spatial and temporal coverage and resolutions.  Over these 15 years, the data processing algorithms for the salinity retrieval have had to evolve to address significant challenges in the observation acquisition process. These challenges include: i) Land-sea  and ice-sea contamination, which results in spurious biases  near the coastlines and ice-edges due to sharp transitions between the high values of the brightness temperature over land and ice, and the low values over the ocean; ii) Degradation of the signal caused by Radio Frequency Interferences sources, which unexpectedly (and illegally) occupied the frequency reserved for Earth Observation. 

After huge efforts from all the expert support laboratories at level 2, the SMOS sea surface salinity maps have achieved an accuracy that has led to a wave of influential advances across many fields, especially in physical oceanography and climate change. These advancements include insights into the intensification of the water cycle, drivers of the sea-ice retreat in Antarctica, among others. As a testament to the credibility and robustness of the satellite salinity observation, the Climate Change Initiative has not only included salinity in its program, but also brings the opportunity to add new essential variables that use satellite salinity for their estimation.

Looking ahead, it is time to consider a successor to SMOS. Starting in 2028, the Copernicus Imaging Microwave Radiometry (CIMR) Copernicus expansion mission is expected to continuously monitor the L-band (along with other frequencies), albeit with slightly degraded spatial resolution but significantly increased signal-to-noise ratio compared to SMOS. The SMOS support laboratories are committed to be at the international forefront of the acquisition of the L-band measurement. For this reason, we are not completely satisfied with “only” continuous monitoring of the L-band; we also aim to enhance the resolution of the signal. To this end, two different proposals were submitted in the last ESA Earth Explorer 12 call for ideas. Although neither was selected, both have received excellent evaluations, encouraging the teams to mature and resubmit their proposals in the next call.

How to cite: Olmedo, E., Turiel, A., González-Gambau, V., García-Espriu, A., Reul, N., Tenerelli, J., Boutin, J., Vergely, J.-L., Parracho, A., Bonjean, F., Jeansou, E., Rey-Ricord, Y., Bertaud, N., Belaid, A., Sabia, R., Crapolicchio, R., and Scipal, K.: SMOS Sea Surface Salinity retrieval after 15 years of mission, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18038, https://doi.org/10.5194/egusphere-egu25-18038, 2025.

The Baltic Sea is a semi-enclosed shelf sea and characterized by its distinct geographical and oceanographic features. One of the Baltic’s most remarkable features is its surface salinity gradient that is horizontally decreasing from the saline North Sea to the near fresh Bothnian Sea in the north, and Gulf of Finland in the east. Additionally, a vertical gradient and strong stratification separate between less saline surface water and deep saline water. These salinity features are mainly driven by a combination of river runoff, net precipitation, wind conditions, and geographic features that lead to restricted and irregular inflow of saltwater into the Baltic and limited mixing. The overall positive freshwater balance causes the Baltic to be much fresher compared to fully marine ocean waters with a mean salinity of only about 7 g/kg. The Baltic Sea is particularly sensitive to climate change and global warming due to its shallowness,  small volume and limited exchange with the world oceans. Consequently, it is changing more rapidly than other regions. Recent changes in salinity are less clear due to a high variability but overall surface salinity seems to decrease with a simultaneous increase in the deeper water layers. Furthermore. the overall salinity distribution is indirectly linked to the general circulation of the Baltic Sea which consists mainly of cyclonic circulation cells comprising the main sub-basins of the Baltic Sea. Thus, improving the understanding of the salinity dynamics ultimately leads to a better understanding of the circulation in the Baltic Sea. 

Within the project 4DBALTDYN highly spatially resolved SMOS SSS (Sea Surface Salinity) satellite data will be combined with in situ observational data and numerical modeling to improve our understanding of the salinity dynamics of the Baltic Sea. SMOS SSS data (2011-2019) provide a continuous monitoring of the evolution of the surface salinity of the entire area of the Baltic Sea. 

How to cite: Lehmann, A., Catany, R., and Mehrtens, H.: Application of SMOS SSS L4 data to improve the understanding of the salinity dynamics and circulation of the Baltic Sea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6694, https://doi.org/10.5194/egusphere-egu25-6694, 2025.

CryoRad, a candidate mission under ESA’s Earth Explorer 12 program, is designed to carry an advanced wideband, low-frequency microwave radiometer operating from P- to L-band (0.4 to 2 GHz). One of the primary objectives of CryoRad is to enhance sea surface salinity (SSS) retrieval, particularly in cold water regions which are of increasing importance due to climate change. This improvement is because of the great sensitivity of P-band brightness temperature to changes in sea surface salinity. Achieving this objective hinges on the development of an accurate dielectric constant model for seawater over the entire frequency range of 0.4 to 2 GHz in order to compute the sea surface emissivity and retrieve SSS.

Over the past decade, progress has been made in the accurate measurement [1] and modeling of seawater dielectric constants at L-band (1.413 GHz) [2][3][4]. These efforts have been directed at improving SSS retrieval accuracy using data from satellite missions such as SMOS [5] and Aquarius [6]. Recently, the new P-band seawater dielectric measurements (0.707 GHz) [7] enable the possibility of using the data at the two frequencies to develop a new dielectric constant model, which is expected to provide insight into the dielectric constant of seawater across the entire frequency range of the CryoRad mission and its application in multi-frequency SSS retrieval.

This paper introduces the development of this wideband seawater dielectric constant model and its validation against experimental measurement data. A sensitivity analysis is also presented to highlight the benefits of using an accurate dielectric constant model over the frequency range. These results demonstrate the potential of the new model to support the CryoRad mission as well as future missions operating within this frequency band to improve multi-frequency SSS retrieval.

[1] R. Lang, Y. Zhou, C. Utku, and D. Le Vine (2016), “Accurate measurements of the dielectric constant of seawater at L-band”, Radio Science, vol: 51, pp. 2-24.

[2] Y. Zhou, R. Lang, E. Dinnat and D. Le Vine (2021), “Seawater Debye Model Function at L-band and Its Impact on Salinity Retrieval from Aquarius Satellite Data”, IEEE TGRS, vol. 59, no. 10 pp. 1-14. 

[3] D. Le Vine, R. Lang, Y. Zhou, E. Dinnat and T. Meissner (2022), “Status of the dielectric constant of sea water at L-Band for remote sensing of salinity”, IEEE TGRS, vol. 60, 4210114

[4] J. Boutin et al. (2023), “New seawater dielectric constant parametrization and application to SMOS retrieved salinity”, IEEE TGRS, vol. 61, 2000813

[5] Y. Kerr, et al. (2010), "The SMOS mission: New tool for monitoring key elements of the global water cycle." Proc. IEEE 98(5): 666–687

[6] D. M. Le Vine, G. Lagerloef and S.E. Torrusio (2010), “Aquarius and Remote Sensing of Sea Surface Salinity from Space”, Proc. IEEE, vol. 98, no. 5, pp. 688-703

[7] D. M. Le Vine, R. Lang, M. Li, E. Dinnat, J. Boutin and Y. Zhou (2025), “The Dielectric Constant of Sea Water at P-Band for Salinity from 0 to 150 pss”, IEEE TGRS, Early Access, 2025

How to cite: Zhou, Y., Boutin, J., Le Vine, D., Lang, R., Brogioni, M., Macelloni, G., and Drusch, M.: Modeling the Dielectric Constant of Seawater from 0.4 GHz to 2 GHz: A feasbility study for the CryoRad mission, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18469, https://doi.org/10.5194/egusphere-egu25-18469, 2025.

The salinity of polar oceans is undergoing significant changes due to sea ice melt and increased continental runoff, which have resulted in a decrease in sea surface salinity (SSS) across most regions of the Arctic Ocean. Similarly, changes in the extent and thickness of Antarctic sea ice are altering SSS in the Southern Ocean, intensifying upper ocean stratification. These shifts profoundly impact ocean circulation, the ocean's capacity to absorb atmospheric heat and carbon, and ultimately, Earth’s climate. Notably, variations in SSS play a crucial role in the potential collapse of the Atlantic Meridional Overturning Circulation, with timing potentially earlier than anticipated by current climate models.

Accurate SSS estimates are essential for monitoring freshwater fluxes at ocean boundaries (e.g., sea ice melting and formation, river runoff, and precipitation), surface hydrography variability affecting deep water formation and overturning circulation, and exchanges with other ocean basins—all of which influence global climate. However, current climate models struggle to accurately represent high-latitude water mass properties due to simplistic depictions of processes like lateral mixing, convection, and entrainment, especially in marginal ice zones. These limitations hinder the models’ ability to predict climate change impacts effectively.

SSS is recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) and an Essential Ocean Variable by the Global Ocean Observing System (GOOS). While current 1.4 GHz (L-band) radiometer missions have revolutionized global SSS measurements at scales of 40–150 km with revisit intervals of 3 to 8 days, their sensitivity to SSS diminishes significantly in cold waters (by a factor of ~3 between 30°C and 0°C), leading to greater uncertainties in polar regions.

The CryoRad mission, an ESA Earth Explorer 12 candidate, features a radiometer with an extended frequency range of 0.4–2 GHz, designed to improve SSS measurement accuracy in cold waters by at least a factor of two compared to L-band radiometers.

As part of the CNES study on "Salinity Estimation in Cold Seas Using Multiband 0.4–2 GHz" and the ESA CryoRad Phase 0 Science and Requirements Consolidation Study (SciReC), we conducted simulations using a simplified CryoRad instrument model. These simulations demonstrate the mission's potential to enhance SSS retrieval at high latitudes. The uncertainties in SSS retrieval were evaluated, considering various radiometric measurement factors such as sea surface temperature, wind speed, and atmospheric influences, which were modeled using radiative transfer principles validated for L-band and extrapolated to lower frequencies.

This simulator was used to perform an initial sensitivity analysis for Level 2 and Level 3 salinity estimates. Our presentation will detail the simulator's implementation, including the direct and inverse models, inversion strategies, and the performance achieved in estimating SSS within the context of an academic case study.

How to cite: Vergely, J.-L., Boutin, J., Ferron, S., Frery, M.-L., Macelloni, G., Brogioni, M., Jeansou, E., and Bruniquel, V.: Estimation of sea surface salinity at high latitudes using the Cryorad 0.4-2GHz wideband radiometer., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8072, https://doi.org/10.5194/egusphere-egu25-8072, 2025.

Sea Surface Temperature (SST) is a key variable for understanding oceanographic processes and climate dynamics, and monitoring maritime environments and marine ecosystem management operations. The Forest constellation of CubeSats provides an innovative source to SST measurement by leveraging thermal infrared sensors with a spatial resolution of 200 meters and a swath width of 410 km. From April 25 onwards, the constellation achieves a revisit time of 12 hours anywhere on Earth, enabling near real-time monitoring of ocean thermal patterns. Designed with a radiometric accuracy target of 3K, the system is optimized for capturing fine-scale thermal structures with reliability comparable to traditional satellite platforms. Overpass times of the Forest constellation will be around late afternoon local time. This offers two advantages for SST data analysis. First, many public satellites have overpass times around midday and the afternoon orbits suffer from a coverage gap. Therefore, the Forest constellation will close a significant data gap in the afternoon. Second, the late afternoon overpasses align with the peak diurnal warming of ocean temperatures. As maximum temperatures are often the primary variables of interest when analyzing ecosystem effects, the afternoon orbits may offer special insights for this. 

At EGU, we want to showcase examples of our SST data from Forest 2, the current generation in orbit. We outline the methodology for processing, calibrating, and validating SST measurements derived from the Forest constellation. We evaluate the data quality and its potential applications in areas such as ocean circulation modeling, marine resource management, maritime sustainability and climate change monitoring. Preliminary results highlight the ability of the Forest constellation to deliver high-resolution SST data at high revisit rates, offering a cost-effective and accessible solution for global ocean observation. This adds to demonstrate the transformative potential of CubeSat technology in advancing Earth system science.

How to cite: Sarelli, A., Liesenhoff, L., Mollière, C., Spichtinger, A., Fotopoulos, G., Wahbe, J., Laux, D., Umstädter, K., Wong, J., Langer, M., and Gottfriedsen, J.: High Resolution and High Frequency Sea Surface Temperature Measurements from the Forest-Constellation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18602, https://doi.org/10.5194/egusphere-egu25-18602, 2025.

Sea surface temperature (SST) and sea-ice surface temperature (IST) are both essential climate variables (ECVs) and long-term stable observational records of these (and other ECVs) are crucial to monitor, characterize and understand the state of climate as well as its variability and changes. The Copernicus Marine Service (CMS) and Copernicus Climate Change Service (C3S) are responsible for complementary reprocessing activities using satellite ocean observations. CMS encompasses reprocessing at global and regional scales of all satellite observations including all observations available at a given time (reprocessing of Essential Ocean Variables, EOVs). C3S fosters climate reprocessing, typically at global scale, with special focus on the most accurate observations and homogeneous time series (reprocessing of Essential Climate Variables, ECVs).

The Danish Meteorological Institute (DMI) serves as a Production Unit (PU) for the Sea Surface Temperature (SST) and Sea Ice (SI) Thematic Assembly Centers (TAC) of CMS and the SST ECV of C3S. Within both frameworks, a suite of GHRSST-compliant L3S and L4 SST and combined SST/IST products for the Baltic and North Sea (CMS), Pan-Arctic (CMS) and Global Ocean (C3S) are produced. In the beginning of 2025, the new C3S SST/IST global L4 Climate Data Record (1982-2024) was released providing a unique opportunity for assessment of temperature changes over the global ocean including regions with sea-ice cover. Satellite observations from infrared and microwave sensors (independent of in situ measurements) have been blended using an optimal interpolation scheme to provide daily gap-free fields of combined SST and IST on a global 0.05 regular latitude-longitude grid. For consistency with existing L4 SST products, the global C3S SST/IST CDR also includes an estimate of the under-ice water temperature (UISST) in sea-ice covered regions. However, the combination of SST and IST provides a much better and more consistent indicator of climate change and surface temperature trends in the high latitudes, where the coverage of sea ice changes rapidly. The global combined SST and IST has risen 0.5 ^◦C over the period 1982-2024, which is ~25% more than observed in existing global L4 SST products considering the global ocean (using the under-ice SSTs) and the region between 60S and 60N, respectively. This highlights the importance of the combined SST and IST indicator for monitoring the actual surface temperature trends in high latitudes.

In early 2025, a reprocessed version of the Baltic Sea and North Sea SST Reanalysis product (1982-2024) will be released using the latest version of the ESA SST_cci L2P data as input. This product only uses satellite-based SSTs from infra-red sensors covering the North and Baltic Sea basins, at a 0.02 degrees regular latitude-longitude grid. Although it does not directly ingest IST, it uses a new approach for estimating the freezing point temperature of sea water depending on climatological salinity, which is a complex variable in the enclosed Baltic Sea basin. This presentation provides an overview of the existing and new products and their quality, along with a summary of implemented and future improvements.

How to cite: Karagali, I., Englyst, P., Lundtorp Olsen, I., Gacitua, G., Hayward, A., Kolbe, W., and Høyer, J.: SST and Combined SST/IST Products Overview: DMI's Contribution to Copernicus Marine and Climate Change Services", EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20235, https://doi.org/10.5194/egusphere-egu25-20235, 2025.

This presentation presents results discussed in [1] where a methodology based on the use of dynamical systems ideas to assess the quality of the results obtain from different configurations of a high resolution coastal ocean model is used. The aim is to leverage satellite imagery, which provides observations at a lower cost than in situ observations, and to propose a strategy for quantifying the quality of model results. Accessing in situ data across all small coastal areas is not feasible, as in situ observations are scarce and obtained through dedicated ships or instruments in limited and selected regions. Our work aims to use alternative remote sensing information to overcome this challenge. Examples are discussed in selected coastal areas.

Acknowledgments

Support from PIE project Ref. 202250E001 funded by CSIC, from grant PID2021-123348OB-I00 funded by MCIN/ AEI /10.13039/501100011033/ and by FEDER A way for making Europe, from IMPRESSIVE, a project funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821922, from SIRENA, funded by the European Maritime, Fisheries, and Aquaculture Fund (EMFAF) under the Pleamar 2023 Program of the Biodiversity Foundation.

References:

[1] G. Garcia-Sanchez, A.M. Mancho, A. G. Ramos, J. Coca, J.A. Jimenez-Madrid. Dynamical systems for remote validation of very high-resolution ocean models. Nonlinear Dynamics 112, 8653-8673 (2024).

How to cite: Mancho, A. M., García-Sánchez, G., Ramos, A. G., Coca, J., and Jiménez Madrid, J. A.: Validation of High-Resolution Ocean Models Through Remote Sensing Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13611, https://doi.org/10.5194/egusphere-egu25-13611, 2025.

A near-real-time version of the blended sea surface wind (BSSW) data product from multiple satellites, as well as the data processing method, and data accuracy analysis is introduced in this paper. The BSSW used sea surface winds provided by the virtual satellite constellation composed of HY-2 series satellites, Metop series satellites and DMSP series satellites as input. Error analysis, cross-calibration and 2D-Var processing is applied to blend these winds derived from different platform. With these methods, a near-real-time blended sea surface product with 6 hours interval and a spatial resolution of 25 kilometers is produced and released operationally by National Satellite Ocean Satellite Application Service. Comparing to buoy data, the RMSE is below 1.6 m/s for wind speed and below 18° for wind direction. While comparing to ERA5 data, the RMSE is below 1.3 m/s for wind speed and below 11° for wind direction. The validation results show that the BSSW is consistent with the buoy winds and ERA5 winds, indicating that BSSW can be of great importance to ocean and atmospheric numerical forecast model, marine disaster prevention and reduction, as well as scientific research on ocean.

How to cite: Zou, J., Lin, W., Wang, Z., and Lu, Y.: A NEAR-REAL-TIME Blended Sea-Surface Wind Product Based on Data from Multiple Satellites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2249, https://doi.org/10.5194/egusphere-egu25-2249, 2025.

The integration of artificial intelligence (AI) into marine radar systems holds transformative potential for ocean monitoring, particularly in South Korea, where the spatial measurement network for the ocean remains underdeveloped. This study seeks to overcome the limitations of marine radar, especially in mitigating noise and accurately capturing wave field dynamics under both calm and extreme sea conditions. To achieve this, we enhance the radar's 3D Fast Fourier Transform (FFT) using a custom-made denoising autoencoder (DAE) architecture. Although the stereo camera system was initially planned to provide the training ground truth data for the AI model, this study instead tests the AI using synthetic real sea surface datasets to offer greater flexibility in simulating various wave conditions. Wave parameters were extracted from the 3D FFT and analyzed across multiple Beaufort-scale scenarios. The DAE application resulted in substantial noise reduction, with signal-to-noise ratio (SNR) improvements of over 13 dB, thus improving the clarity and accuracy of wave patterns in radar returns. The results highlight the potential of AI-enhanced radar systems to refine wave field analyses, particularly in complex and extreme sea states. Future work will focus on further optimizing the AI architecture for real-world marine radar and stereo camera datasets, advancing its operational readiness for disaster mitigation and oceanographic research.

How to cite: Amahoru, A. H., Yoo, J., Abdullah, F. A. R., Son, D., and Bang, M.: Noise Reduction in Synthetic Radar Returns via Denoising Autoencoder for Estimating Ocean Wave Parameters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15069, https://doi.org/10.5194/egusphere-egu25-15069, 2025.

Mesoscale eddies are ubiquitous features of the global ocean. They can trap and transport water over large distances and therefore play a key role in regulating the ocean’s energy, heat and biogeochemical cycles. Furthermore, they impact the transport of surface tracers and therefore have an important influence in regulating the geographical distribution of surface tracers and ecological niches. Global observations of the large-scale mesoscale circulation (i.e. scales > 100 km) are provided daily by the sea surface heights maps obtained from the merging of the along-track measurements acquired by the current constellation of satellite altimeters. Due to large spatial and temporal extent of such datasets, mesoscale studies based on satellite altimetry required the development of automated methods to identify and track individual mesoscale eddies.

The META-Networks (Mesoscale Eddy Trajectories Atlas – Networks) is a new eddy dataset based on the fields of absolute dynamic topography reconstructed using the new Multiscale Interpolation gridding (MIOST) from 1993 to present. Like other existing datasets, META-Networks provides mesoscale eddy characteristics and tracks over the global ocean. The tracks are reconstructed by combining together the individual eddies detected from sea level elevation using the Py-Eddy-Tracker algorithm (PET) described by Mason et al., 2014, and available here https://github.com/AntSimi/py-eddy-tracker.

A key additional feature of META-network is that individual tracks are combined together in a series of eddy-networks which takes into account eddy-eddy interactions at the beginning and end of each track. Such interactions are identified via eddy overlapping from individual tracks, and the tracks are associated in the same network if the overlap ratio (defined as the intersection of the area of the two eddies divided by the union of their areas) is larger than a given threshold. Such network representation enables the analysis of eddy properties not only during individual along-track evolution, but also at times of merging and splitting interactions with other eddies, providing an additional point of view for the characterization of mesoscale activity in the global ocean.

Here we present a global overview of the identified eddy networks. Each network is characterized based on its spatial and temporal extent and as well as on the statistical properties (duration, size, intensity etc.) of the individual eddies that comprise it. Within each network, eddy-eddy interactions are analyzed to highlight the regional and dynamical conditions that favor the occurrence of merging and splitting events. Finally, to qualitatively validate the reconstructed networks and identified synergies, a few examples of synergies with other remotely sensed variables (e.g. ocean color and sea surface temperature) at the time of specific eddy merging and splitting events are also investigated and discussed.

How to cite: Nencioli, F., Gamot, J., Delepoulle, A., Pujols, I., and Dibarboure, G.: A network-based characterization of eddy activity via the META-Networks dataset from multi-satellite altimetry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19106, https://doi.org/10.5194/egusphere-egu25-19106, 2025.

The DUACS system (Data Unification and Altimeter Combination System) produces, as part of the CNES/SALP project, Copernicus Marine Service and Copernicus Climate Change Service, high quality multi-mission altimetry Sea Level products for oceanographic applications, climate forecasting centers, geophysics and biology communities. These products consist in directly usable and easy to manipulate Level-3 (L3; along-track cross-calibrated SSHA) and Level-4 products (L4; multiple sensors merged as maps or time series).

Level-3 algorithms used for nadir altimeters have been extended to handle SWOT’s unique swath-altimeter data: upgrades with state-of-the-art Level-2 corrections and models from the research community, a data-driven and statistical approach to the removal of spurious and suspicious pixels, a multi-satellite calibration process that leverages the strengths of the pre-existing nadir altimeter constellation, a noise-mitigation algorithm based on a convolutional neural network. The objective of this presentation is to present the uniqueness of Level-3 algorithms and datasets and the regular changes made every 6 months with restatements. The changes introduced by version 2 of the L3 products published in December 2024/January 2025 are as follows:

• Geophysical standards changes
  
  • Internal tides model HRET14
  • Quick fix of the SSB/SSHA offset in polar transitions
  • Addition of 5 cm offset on MDT and ADT to be consistent with other L3 products
• Coverage improved :
  • Eclipse data gaps retrieved with good quality
  • Polar and coastal regions
• Cross-calibration improved, especially for coastal areas and polar seas
• Coastline and distance to coast improved
• Addition of surface classification (ice/leads) in editing flag
• Addition of new variables :
  • Unfiltered geostrophic velocities
  • Internal tide model
  • Cross-track distance

2D topography images from SWOT have been added to nadir altimeter data inside mapping algorithms (MIOST, 4DvarNET, 4DvarQG) to produce Level-4 products. The wide swath data provided by the SWOT mission help to reduce mapping errors mainly in energetic ocean currents, to better position oceanic structures (eddies, fronts…) and to have finer resolution in maps.

How to cite: Anadon, C., Treboutte, A., Chevrier, R., Delepoulle, A., Ballarotta, M., Pujol, M.-I., Ubelmann, C., and Dibarboure, G.: SWOT-KaRIn Level-3 and Level-4 Algorithms and Products Overview, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11212, https://doi.org/10.5194/egusphere-egu25-11212, 2025.

Mean Sea Surface (MSS) is a crucial information to get an accurate Sea Level Anomaly (SLA). This study introduces an advanced MSS model, developed by combining data from the Surface Water and Ocean Topography (SWOT) mission Ka-band Radar Interferometer (KaRIn) with over 30 years of nadir altimetry observations. SWOT KaRIn provides two groundbreaking advantages: unmatched precision for resolving small-scale ocean features and two-dimensional measurements that offer a complete view of ocean surface structures, in contrast to the one-dimensional cross-sectional data from nadir altimeters. By integrating SWOT’s exceptional spatial resolution with the long-term temporal stability of nadir altimetry, this new MSS model delivers a more accurate and comprehensive representation of sea surface topography. The model is constructed using a gridded draft MSS based on state-of-the-art MSS to represent large-scale features, which is then refined using two wavelength-specific approaches. The first leverages the mean profile from SWOT KaRIn’s science phase. The second incorporates the static component of the Sea Surface Height (SSH) signal derived through the Multiscale Inversion of Ocean Surface Topography (MIOST) method. Compared to state-of-the-art MSS model, this new MSS reveals previously undetected seamounts, reduces geodetic residuals in SWOT KaRIn Sea Level Anomaly (SLA) signals, and improves overall accuracy. 

How to cite: Charayron, R., Schaeffer, P., Ballarotta, M., Delepoulle, A., Laloue, A., Pujol, M.-I., and Dibarboure, G.: Blending data from SWOT KaRIn science phase and 30 years of nadir altimetry to improve Mean Sea Surface models. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8570, https://doi.org/10.5194/egusphere-egu25-8570, 2025.

Mesoscale eddies play a significant role in transporting heat, salinity, and nutrients. The sea surface height (SSH) mapped by nadir data, limited by the spatial coverage and resolved wavelength of SSH observations, cannot fully resolve eddies with wavelengths smaller than 150 km. However, with the launch of the Surface Water and Ocean Topography (SWOT) mission, the spatial coverage and resolved wavelength of SSH observations have been significantly improved, resulting in an enhancement in the effective spatial resolution of SSH maps. Here, we further investigate the effect of the enhancement brought by SWOT on the study of mesoscale eddies. As the long wavelength error, which can introduce spurious signals during SSH mapping, remains in the level-3 SWOT ocean product, we propose an interpolation method to reduce LWE, thereby ensuring the accuracy of mesoscale eddy reconstruction. Two versions of SSH maps were generated by the proposed method, one using both nadir data and SWOT data, and the other using only nadir data. With the contribution of SWOT data, more mesoscale eddies with scales smaller than 150 km are reconstructed in the SSH maps, with a corresponding increase in eddy kinetic energy (EKE) up to 0.01 m²/s². In the Kuroshio, where the mean EKE is about 0.12 m²/s², the EKE increases by about 6%, whereas it increases by 20% in the California Currents (with a mean EKE of 0.014 m²/s²) and by 35% for the Open Ocean (with a mean EKE of 0.004 m²/s²). As the mean EKE decreases, the contribution of SWOT to the study of mesoscale eddies becomes more pronounced. Notably, the significant increase in EKE in the Open Ocean is accompanied by additional monthly variations in EKE. 

How to cite: Shi, J., Jin, T., and Jiang, W.: The contribution of SWOT to the mesoscale eddy activity estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3095, https://doi.org/10.5194/egusphere-egu25-3095, 2025.

Monitoring ocean color is a crucial tool in understanding marine ecosystems and their health, as it provides quantitative information on chlorophyll-a (chla) concentration, absorption by colored dissolved organic matter (CDOM). Ocean color data can be used to characterize phytoplankton pigment groups from the ocean, by using a few color bands (multispectral imaging). Hyperspectral sensors have the potential to distinguish pigment groups in more detail. This capability is necessary for detailed monitoring of local ecosystems and improving predictions of algae blooms. As part of the European Copernicus Programme, the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) will provide routine hyperspectral observations globally over land and coastal zones in support to European Union policies for the management of natural resources, ecosystem services and societal benefits. CHIME primarily focuses on land applications, however, secondary applications include, among others, detection of floating debris and water quality monitoring in inland and coastal waters.

Radiative transfer modelling (RTM) is a useful tool for determining the expected level of detail in which phytoplankton pigment groups can be distinguished with a new observing system (e.g. satellite mission). The forward RTM simulation uses assumed absorption and scattering properties of phytoplankton in each depth layer to calculate light progression and the reflectance spectrum. Since these properties are difficult to determine and vary with factors such as depth [1] and the community that the pigment groups appear in [2], it can lead to inaccurate model output.

During an observation campaign in September 2024, we sampled at two different locations in Trondheimsfjorden: one located in the middle of the fjord, to compare results with satellite data while minimizing land-mixing, and the other located at the critical Gaula river outlet, which is important for sediment and nutrient transport into the fjord. We measured among others phytoplankton accessory pigments and taxonomy, CDOM, the surface reflectance spectrum, and the downwelling irradiance in the water column. This data allows us to compare the model output to the observation while we tune absorption and scattering properties. An optimization process lets us tune them to minimize the deviation in the output, thereby resulting in estimates of absorption and scattering properties.

The forward RTM exercise was set up to simulate the reflectance of the water surface and the downwelling irradiance in the water column. Measured chla and CDOM concentrations were used as inputs. In the optimization exercise, the sum of squares of the difference between the measured vertical downwelling irradiance profile and the simulated vertical downwelling irradiance profile is minimized, by tuning the absorption and scattering coefficients. For testing, the measured reflectance spectrum and RTM simulated reflectance spectrum were compared.

The final absorption and scattering coefficients were compared to measured absorption properties [3] and simulated absorption and scattering properties [2].  These simulations can better determine the sensitivity of future hyperspectral missions (e.g. CHIME) to distinguishing phytoplankton pigment groups.

[1] Sundarabalan et al., 2013. Journal of Quantitative Spectroscopy and Radiative Transfer. https://doi.org/10.1016/j.jqsrt.2013.01.016
[2] Lain et al., 2023. Scientific Data. https://doi.org/10.1038/s41597-023-02310-z
[3] Johnsen et al., 2007. Journal of Phycology. https://doi.org/10.1111/j.1529-8817.2007.00422.x

How to cite: Oudijk, A. E., Osanen, J., Singh, A., Perumthuruthil Suseelan, V., Sanchez, N., Hvammen Sikkerbøl, A.-M., Moreira Fragoso, G., Celesti, M., Nieke, J., Johansen, T. A., and Omholt Alver, M.: Determining Absorption and Scattering Coefficients by Optimizing Radiative Transfer Modelling with In-Situ Coastal Hyperspectral Reflectance Spectra, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8871, https://doi.org/10.5194/egusphere-egu25-8871, 2025.

Pumice rafts are considered to be a long-range drifting agent that promotes material exchange and the dispersal of marine species. Large ones can also interfere with vessel navigation and have a negative impact on the social economy and marine ecosystems. Synoptic observations from the Multispectral Instrument (MSI) on-board Sentinel-2, with a spatial resolution of up to 10 m, provide an excellent means to monitor and track pumice rafts. In this study, the use of a Spectral Feature-Based Extraction (SFBE) algorithm to automatically discriminate and extract pumice on the ocean surface from submarine volcano eruptions was proposed. Specifically, a Pumice Raft Index (PRI) was developed based on the spectral signatures of pumice in MSI imagery to identify potential pumice features. After pre-processing, the PRI image was then subjected to a series of per-pixel and object-based processes to rule out false-positive detections, including shallow water, striped edges, mudflats, and cloud edges. The SFBE algorithm showed excellent performance in extracting pumice rafts and was successfully applied to extract pumice rafts near the Fiji Yasawa islands in 2019 and Hunga Tonga island in 2022, with an overall pumice extraction accuracy of 95.5% and a proportion of pixels mis-extracted as pumice of <3%. The robustness of the algorithm has also been tested and proved through applying it to data and comparing its output to results from previous studies. The timely and accurate detection of pumice using the algorithm proposed here is expected to provide important information to aid in response actions and ecological assessments, and will lead to a better understanding of the fate of pumice.

How to cite: Sun, S., Chen, X., and Ou, Z.: Spectral Discrimination of Pumice Rafts in Optical MSI Imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1734, https://doi.org/10.5194/egusphere-egu25-1734, 2025.

Phytoplankton abundance and community structure are crucial to the natural carbon cycle and the sustainability of marine ecosystems. However, their long-term natural variability and their response to anthropogenic climate change remain poorly understood. Since 1997, numerous algorithms have been developed to estimate these two parameters using ocean colour remote sensing. These tools have enabled 25 years of continuous, global-scale observations, providing invaluable insights into the variability of phytoplankton. However, analysing recent trends using these datasets presents significant challenges. Temporal inconsistencies and discontinuities in satellite time series - resulting from sensor transitions or decommissioning - introduce biases that complicate trend detection. Here, we present an inter-comparison framework designed to address these limitations by analysing multiple ocean colour remote sensing products. We implemented a robust statistical approach to minimize the influence of temporal discontinuities on trend detection. Results reveal both regions with robust trends across products and areas of significant disagreement, with the latter being largely prevalent. This study provides novel insights into recent phytoplankton dynamics while highlighting current limitations in our capacity to monitor these changes. Our findings emphasize the importance of multi-product analyses for reliable trend assessment in ocean colour remote sensing.

How to cite: Doléac, S., Ollier, L., Bopp, L., and Lévy, M.: Can we really estimate trends in phytoplankton abundance and community structure over the 25 year ocean colour satellite era ?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5711, https://doi.org/10.5194/egusphere-egu25-5711, 2025.