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Ocean Remote Sensing

Advanced remote sensing capabilities have provided unprecedented opportunities for monitoring and studying the ocean environment as well as improving ocean and climate predictions. Synthesis of remote sensing data with in situ measurements and ocean models have further enhanced the values of oceanic remote sensing measurements. This session provides a forum for interdisciplinary discussions of the latest advances in oceanographic remote sensing and the related applications and to promote collaborations.

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.

Public information:

Final schedule at https://tinyurl.com/EGU-ORS-2022



Convener: Aida Alvera-Azcárate | Co-conveners: Craig Donlon, Guoqi Han, Tong Lee, Adrien Martin
| Tue, 24 May, 08:30–11:44 (CEST)
Room N2
Public information:

Final schedule at https://tinyurl.com/EGU-ORS-2022



Tue, 24 May, 08:30–10:00

Chairpersons: Tong Lee, Aida Alvera-Azcárate, Adrien Martin

Witold Podlejski et al.

Since 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only alternative to monitor such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and others phenomena. All together, they lead to false detections that cannot be discriminated with classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model based on spatial features to filter false detections. More specifically, Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum detections, five spatial indices were extracted for describing their shape and surrounding context and then used by a random forest binary classifier. Contextual features were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the classifier performs the filtering of daily false detections with an accuracy of 90%. This leads to a reduction of detected Sargassum pixels of 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution on 2016-2020 is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. In particular, it retrieves the two areas of consolidation in the western and eastern part of the Tropical Atlantic Ocean associated with distinct temporal dynamics. At full resolution, the dataset allowed us to semi-automatically extract Sargassum aggregations trajectories from successive filtered images. Using those trajectories will help to better quantify the drift of aggregations with respect to the currents, the wind and sea state. Overall, this new dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models.

How to cite: Podlejski, W., Descloitres, J., Chevalier, C., Minghelli, A., Lett, C., and Berline, L.: Sargassum observations from MODIS: using aggregations context to filter false detections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2900, https://doi.org/10.5194/egusphere-egu22-2900, 2022.

Aida Alvera-Azcárate et al.

Coastal ocean areas are very dynamic regions subject to strong anthropogenic pressure (e.g. industry, tourism, renewable energies, population). Satellite data constitute a unique tool that allows one to study and monitor these areas at a unique spatial and temporal resolution. The spatial and temporal scales needed to assess changes at coastal regions are higher than what can be rendered by individual missions: for example, satellites like Sentinel-3 provide daily temporal resolution, but the sensors onboard these satellites do not measure at the necessary high spatial resolution to resolve complex coastal dynamics; on the other hand, high spatial resolution sensors, like MSI onboard Sentinel-2 (10 m resolution), are able to resolve these small scales, but with a low temporal revisit time (about 5 days). Both high spatial resolution datasets and traditional ones are hindered by the presence of clouds, resulting in a large amount of missing data. The complementarity of Sentinel-2 and Sentinel-3 datasets can be exploited to derive a super-resolution dataset of total suspended matter and chlorophyll concentration in the Belgian coast (North Sea), with the spatial resolution of Sentinel-2 and the temporal resolution of Sentinel-3. Moreover, as the approach used is based in DINEOF (Data Interpolating Empirical Orthogonal Functions), missing data can be interpolated as well, to provide a gap-free super-resolution dataset retaining the spatial scales of the highest resolution dataset (Sentinel-2 in this case). The influence of ocean dynamics in the region will be assess using these ocean colour variables.


How to cite: Alvera-Azcárate, A., Van der Zande, D., Barth, A., and Beckers, J.-M.: Super-resolution total suspended matter and chlorophyll concentration using Sentinel-2 and Sentinel-3 data., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7545, https://doi.org/10.5194/egusphere-egu22-7545, 2022.

Clément Haëck et al.

The contribution of sub-mesoscale fronts (10-100km) to phytoplankton growth and biodiversity is still poorly understood. It is expected that these fronts generate vertical ageostrophic secondary circulations, and thus act locally on stratification and vertical nutrient supply.
The response of phytoplankton to this input has been observed in-situ and in numerical models, but to a lesser extent by satellite imagery, which presents the opportunity to quantify results at larger spatiotemporal scales.
We improve an existing method to define sub-mesoscale frontal areas from satellite SST data in the North Atlantic. The study area is divided geographically into three zones: North of the Gulf-Stream jet, South of—and including—the jet, and further South part of the olligotrophic gyre.
For each zone the distribution of Chlorophyll-a values are estimated by satellite inside and outside these fronts. For all zones the Chlorophyll-a is found more concentrated in fronts, with differences between zones in magnitudes and seasonal cycles. The overall impact of fronts on the total Chlorophyll-a amount is quantified. We also quantify the temporal advance of the spring bloom onset in fronts. Finally, interannual trends over the last two decades are studied.

How to cite: Haëck, C., Lévy, M., and Bopp, L.: Satellite Signature of Phytoplankton in Ocean Fronts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8668, https://doi.org/10.5194/egusphere-egu22-8668, 2022.

David Cotton and the HYDROCOASTAL


HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.

New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.

A series of case studies will assess these products in terms of their scientific impacts.

All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided



The scientific objectives of HYDROCOASTAL are to enhance our understanding  of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes


The technical objectives are to develop and evaluate  new SAR  and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.



The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set focusing on performance at the coast. It will also present the results of a study assessing regional tidal models.

How to cite: Cotton, D. and the HYDROCOASTAL: Improving SAR Altimeter processing over the coastal zone - the ESA HYDROCOASTAL project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1337, https://doi.org/10.5194/egusphere-egu22-1337, 2022.

Bjarke Nilsson et al.

Using global high-resolution elevation measurements from the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2), it is possible to distinguish individual surface ocean waves. As the majority of ocean surveying missions are radar satellites, ICESat-2 observations are an important addition to ocean surveys and can provide additional observations not possible with radar. By utilizing the coincident orbits between CryoSat-2 and ICESat-2 during the CRYO2ICE campaign, the observations from ICESat-2 are compared along long stretches of the ground tracks, rather than at the usual crossover points. Therefore, from August 2020 to August 2021, 136 orbit segments from ICESat-2 in the Pacific and Atlantic oceans are used in the comparison. To allow for comparison of ICESat-2 during the coincident orbits, CryoSat-2 is validated against in-situ stations as well as satellite altimetry measurements. Using the validated CryoSat-2 observations, the significant wave height (SWH) is determined from the individual photon heights observed by ICESat-2, by three different methods. First, by using the standard ocean data output (ATL12), the SWH determined from this can be further validated. Then, the two methods derived in this study contain a model of deriving the SWH directly from the observed surface waves, as well as a model using the same method as ATL12, to act as a baseline for the wave-based model. The validation of this wave-based model for extended stretches with CryoSat-2 would allow for the further use of this model for studies. The carried out comparisons result in correlations between ICESat-2 and CryoSat-2 of 0.97 for ATL12 and 0.95 for the wave-based model, with a small mean deviation between the altimeters. The observations from ICESat-2 experience a larger variance than other altimeter crossover-comparison studies, however being constrained by a larger time-lag (<3h) between the coincident orbits for ICESat-2 and CryoSat-2 this is expected. From the study, ICESat-2 is found to agree with observations from CryoSat-2, and utilizing the possibility of distinguishing the surface waves, would therefore provide beneficial for ocean observations.

How to cite: Nilsson, B., Andersen, O. B., Ranndal, H., and Rasmussen, M. L.: Consolidating ICESat-2 ocean wave characteristics with CryoSat-2 during the CRYO2ICE campaign, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10395, https://doi.org/10.5194/egusphere-egu22-10395, 2022.

Benjamin Loveday et al.

Following the successes of the first phase of the European Commission Copernicus programme, EUMETSAT is continuing and expanding its offer of data access services, marine data products, as well as marine training activities and user support services, under phase two. 

EUMETSAT operates the Sentinel-3, Sentinel-6 and Jason-3 satellites, and provides level-1 and level-2 marine data products for ocean colour, sea surface temperature, and altimetry science and applications.  

User support services include data access, customisation, and visualisation platforms, web-based technical information about products, as well as a helpdesk available to answer a full range of user queries on the products and their use. 

Interactive training activities are designed to accommodate a diverse range of audiences, both research and operational, putting trainee needs and interests at the centre of learning objectives. A focus on co-development of resources and participant-led learning interventions allows participants to tailor their own experiences towards development of the skills and knowledge that will help them in their own applications and work tasks. Building on four years of successful general courses, EUMETSAT now seeks to develop further specialised training and advanced courses for the marine community.  

This presentation will showcase existing services and resources, and provide information on planned training events for 2022 and beyond. It will expand on our training approaches and provide further information on opportunities for collaboration with the wider marine community, during the UN ocean decade. 

How to cite: Loveday, B., Träger-Chatterjee, C., Wannop, S., Evers-King, H., Brando, V., Rosmorduc, V., Ruescas, A., and Troupin, C.: Expanding the use of Copernicus marine satellite data: EUMETSAT’s user support and training activities., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11436, https://doi.org/10.5194/egusphere-egu22-11436, 2022.

Atefe Choopani et al.
Jacqueline Boutin et al.

Sea Surface Salinity (SSS) is an increasingly-used Essential Ocean and Climate Variable. The SMOS, Aquarius, and SMAP satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data [Boutin et al., 2021]. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well-suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale.

In this presentation, we review recent advances and performances of the last (version 3) CCI+SSS product.

The CCI v3 processing has been updated to improve the long-term stability of the SMOS SSS [Perrot et al., 2021] and to improve the level 4 SSS uncertainty estimates. A correction for the instantaneous rainfall impact [Supply et al., 2020] is applied, so that, in rainy regions the CCI v3 fields are close to bulk salinities. In the level 4 optimal interpolation, a full least square propagation of the errors is implemented, instead of a simplified propagation.

When compared with Argo upper salinities, the robust standard deviation of the pairwise difference is 0.16 pss. However, this number includes a sampling mismatch between the in-situ near-surface salinity done at a single space and time and the two-dimensional satellite SSS. We use a small-scale resolution simulation (1/12° GLORYS) to quantitatively estimate the sampling uncertainty. A quantitative validation of CCI v3 SSS and its associated uncertainties is performed by considering the satellite minus Argo salinity normalized by the sampling and retrieval uncertainties [Merchant et al., 2017]. We find that, at global scale, the sampling mismatch contributes to ~20% of the observed differences between Argo and satellite data; in highly variable regions (river plumes, fronts), the sampling mismatch is the dominant term explaining satellite minus Argo salinity differences.


Boutin, J., et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, JGR-Oceans, 126(11), doi:10.1029/2021JC017676.

Merchant, C. J., et al. (2017), Uncertainty information in climate data records from Earth observation, Earth Syst. Sci. Data, doi:10.5194/essd-9-511-2017.

Perrot, X., et al. (2021), CCI+SSS: A New SMOS L2 Reprocessing Reduces Errors on Sea Surface Salinity Time Series, IGARSS proceedings, doi: 10.1109/IGARSS47720.2021.9554451.

Supply, A.et al. (2020), Variability of Satellite Sea Surface Salinity Under Rainfall, in Satellite Precipitation Measurement: Volume 2, doi:10.1007/978-3-030-35798-6_34.

How to cite: Boutin, J., Martin, A., Thouvenin-Masson, C., Reul, N., Catany, R., and Consortium, C. C. I. S. S. S.: Sea Surface Salinity and its uncertainty in 2010-2020 CCI version 3 fields, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3071, https://doi.org/10.5194/egusphere-egu22-3071, 2022.

Haodi Wang et al.

Sea surface salinity (SSS) is an important indicator of hydrological cycle, oceanic processes and climate variability, and has been obtained from various methods including remote sensing, in-situ observations and numerical modelings. Due to the differences of instruments used, error correction algorithm and gridding strategy, each dataset has unique strengths and weaknesses. In this study, we conducted a multi-scale comparison of SSS among eight datasets, including satellite-based, in-situ-based and ocean reanalysis products from 2012 to 2020. Compared with WOA18 climatology, all products show good consistency in describing the dominant mode of global SSS distribution. Among eight datasets, the ISAS20 product is of the best quality, and observation-based products are generally more accurate than reanalysis products. Analysis on zonal average shows that positive bias appears in subtropic regions while negative bias distributes in subpolar areas. It was found that reanalysis products have significantly large negative biases at the polar region compared with satellite products and in-situ observations. On both the seasonal and interannual scales, high correlation coefficients (0.65-0.95) are found in the global mean SSSs between individual satellite products, in-situ analysis and ocean reanalysis products, with the differences relatively smaller among the same types of datasets. This analysis provides information on the consistency and discrepancy of different SSS products to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

How to cite: Wang, H., Bao, S., Ni, W., Chen, W., Wang, W., and Ren, K.: Intercomparison of Global Sea Surface Salinity from Remote Sensing, Reanalysis and In-situ Products, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12714, https://doi.org/10.5194/egusphere-egu22-12714, 2022.

George Vanyushin and Tatyana Bulatova




G.P. Vanyushin and T.V. Bulatova


Russian Federal Research Institute of Fisheries and Oceanography (VNIRO), Moscow, Russia

e-mail: ladimon@mail.ru



For fishing purposes, satellite monitoring of SST is used to study the influence of temperature conditions in water areas important for the development of aquatic organisms on the formation of their biological productivity. One of such water areas is the Lofoten Islands zone in the Norwegian Sea. This area (as a reference zone) is known as the main spawning region of one of the main fishing objects - the north-eastern Arctic cod. A digital massif of infrared data from NOAA satellites is used to monitor temperature conditions. Verification of satellite data was carried out by using quasi-synchronous measurements of water temperature from ships, buoys and shore stations. Matrices created on the basis of weekly SST maps for the period 1998-2020 were used to assess the temperature situation in this water area when analyzing the interannual variability of SST. Calculations of the average monthly and long-term average values of SST and SST anomalies were made, the dynamics of SST was evaluated.

The results of the analysis showed that in general, in 1998-2020, there was a positive trend of SST growth in the Lofoten Islands area. In the period from 1998 to 2001, the average annual indicators of SST, gradually decreasing, reached a minimum (6.91 °C) in 2001, but then up to 2006 (8.29 °C) showed high growth rates. In the period 2006-2020, the temperature situation in the area of the Lofoten Islands somewhat stabilized, the average annual SST values fluctuated from 7.85 °C (2008) to 8.55 ° C (2017). After the maximum reached in 2017, there was a slight decline in SST (to 8.05 °C). The comparative assessment of SST indicators with climatic data is especially relevant for the period of the main spawning of the north-eastern Arctic cod, which takes place on March-April. The obtained results showed that in 1998-2020, the SST in the period March-April was on average higher than the climatic one. Negative anomalies of SST were noted only in 2001 (-0.26°C). In general, the analysis of the dynamics of the long-term course of seasonal average values of SST in the period March-April 1998-2020 showed the presence of a trend for an increase in seasonal average anomalies of SST in area near the Lofoten Islands. A decrease in the values of SST anomalies (from 0.86 °C to -0.26 ° C) was noted only for the period 1998-2001. After 2001 began an increase of temperature anomalies which reached a maximum in 2015 (2.04 °C). Since this year, a certain decrease in the values of SST anomalies has been observed in the studied water area, which continues to the present.

Keywords: satellite monitoring, sea surface temperature (SST), the Lofoten Islands, the North-East Atlantic (NEA) cod, spawning area, comparative analysis.

How to cite: Vanyushin, G. and Bulatova, T.: Interannual variability of SST the Norwegian sea near the Lofoten Islands in 1998-2020 according to satellite monitoring during the cod spawning period, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2207, https://doi.org/10.5194/egusphere-egu22-2207, 2022.

Tue, 24 May, 10:20–11:50

Chairpersons: Aida Alvera-Azcárate, Guoqi Han, Craig Donlon

Alexandre Barboni et al.

Anticyclonic (cyclonic) eddies are commonly considered to have a warm (cold) signature on the sea surface temperature (SST). However several recent studies revealed the existence of a non-negligible fraction of "inverse" eddy-induced SST anomalies : cold-core anticyclones and warm-core cyclones. Using remote sensing and in situ observations in the Mediterranean sea over 3 years (2016-2018), we built an eddy core surface temperature index and showed that these inverse SST signatures have a seasonal distribution, scarce in winter but very common and even predominant in early summer. Warm-core cyclones and cold-core anticyclones proportion gets a maximum of 70% of the signatures in May and June, with a quick rise in coincidence with spring restratification and mixed layer depth (MLD) shallowing.

To understand further the physical processes we used a simple 1D vertical model of a water column forced by a seasonal surface temperature flux. It is a known observation that MLD is deeper (shallower) inside anticyclones (cyclones), and we tested if this difference of vertical structure alone was sufficient to reproduce eddy-induced SST signature inversion during spring restratification. This proved not to be enough, and it is only by taking into account a differential diapycnal eddy mixing - increased in anticyclones and reduced in cyclones - that we reproduce correctly, in agreement with the observations, the eddy surface temperature inversion. Furthermore, idealized 3D numerical simulations (so far for an anticyclone) at sufficiently high resolution were able to reproduce the shift from a winter warm-core eddy to a summer cold-core eddy, and they revealed a dependence on the wind forcing strength and frequency in the magnitude of the eddy-induced SST signature.

This simple 1D model tends to show that vertical mixing modulation by mesoscale eddies might be a key mechanism explaining inverse eddy SST signatures. It also suggests beyond that these signatures could represent an integrated signal of both temperature and momentum flux forcings at the scale of the eddy. 

How to cite: Barboni, A., Stegner, A., and Moschos, E.: What can we learn from eddy-induced signatures on sea surface temperature ?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2336, https://doi.org/10.5194/egusphere-egu22-2336, 2022.

Manal Hamdeno and Aida Alvera Azcárate

Marine heatwaves (MHWs) are prolonged discrete anomalously warm water events that last for more than five successive days and can be described by their duration, intensity, rate of evolution, and spatial coverage. These episodes of large-scale anomalously high ocean temperatures can have many impacts on the marine ecosystems and major implications for the fisheries as well. As a result of the anthropogenic climate change, MHWs have been observed in many parts of the world's oceans and their intensity and frequency are expected to increase in the future. This work investigates the spatial and temporal variability of the Marine Heatwaves in the Mediterranean Sea over 39 Years (1982 - 2020), the net air-sea heat exchange anomaly associated with these MHW events, and their possible physical drivers. The results have shown dissimilarities between the western (WMED) and eastern (EMED) Mediterranean basins in the detected MHW events during the study period. In other words, the WMED marine heatwaves were more frequent and more intense than the EMED marine heatwaves, while the marine heatwaves that occurred in the EMED were longer in terms of the duration than the ones that occurred in the WMED. Moreover, the fluctuation of the net heat flux anomalies during the study period were linked to the occurrence of the MHWs, the high SSTa of the MHW events were combined with positive (gain) heat flux anomaly but, during a few events, the high SSTa induced a negative (loss) heat flux anomaly especially in the EMED basin. In addition, a combination of some physical conditions such as shallower mixed layer depth, high air temperature (> 25 oC), high MSLP (> 1014 hPa), and low to no wind shear were observed to significantly contribute to the formation of the Mediterranean Sea marine heatwaves.

How to cite: Hamdeno, M. and Alvera Azcárate, A.: Spatial and temporal variability of marine heatwaves in the Mediterranean Sea over 39 years, and their possible physical drivers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6944, 2022.

Christine Gommenginger and Adrien Martin

High-resolution satellite images of sea surface temperature and ocean colour reveal an abundance of ocean fronts, swirls, vortices and filaments at horizontal scales below 10 km that permeate the global ocean, especially near mesoscale jets and eddies, in coastal seas and close to sea ice margins. These small-scale ocean features are the fingerprints of dynamic atmosphere-ocean interactions and intense ocean vertical processes that mediate exchanges across all the fundamental interfaces of the Earth System – between the atmosphere, the ocean surface, the ocean interior, the cryosphere and land – and impact major aspects of the global climate system.

Numerous research studies and high-impact scientific publications confirm the key role of submesoscale processes in air-sea interactions, upper-ocean mixing, lateral transports and vertical exchanges with the ocean interior. Small-scale processes also visibly dominate in coastal, shelf seas and polar seas - regions of disproportionally high strategic and societal value as hosts to numerous human activities and natural resources. This paper will review some of the evidence about the fundamental role of small-scale ocean dynamics in the Earth System, making the case for new observations from space to characterise these important phenomena. The contribution ends by outlining the science drivers and objectives of the SEASTAR satellite candidate mission currently under study as one of four candidates to the European Space Agency Earth Explorer 11 programme.

How to cite: Gommenginger, C. and Martin, A.: Observing small-scale ocean surface dynamics and vertical ocean processes in coastal, shelf and polar seas with the Earth Explorer 11 SEASTAR mission candidate., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4502, https://doi.org/10.5194/egusphere-egu22-4502, 2022.

Anis Elyouncha et al.

The aim of this study is to investigate the potential of spaceborne synthetic aperture radar (SAR) to monitor the Baltic Sea inflow/outflow circulation through the Danish straits. The flow in the Danish straits is mainly driven by changes in atmospheric forcing and is dominated by irregular inflow and outflow events. SAR provides high spatial resolution observations of the sea surface, which are particularly relevant in coastal areas and shelf seas. During the last decade, a new application of SAR measurements based on the analysis of the Doppler shift has emerged. The SAR Doppler shift is directly related to the surface circulation, thus direct measurements of surface currents are possible. It is however a challenging problem in practice due to the wave contribution to the observed Doppler shift.

The main limitation of spaceborne SAR for monitoring fast evolving ocean processes is the long revisit time. In order to overcome this limitation, data from three satellites are combined in this study, namely Sentinel-1A, Sentinel-1B and TanDEM-X. Sentinel-1 is a conventional single-antenna SAR, while TanDEM-X is an along-track interferometric SAR. In addition, the two systems differ in the operating frequency and in the imaging mode. In this study, two months of opportunistic data (June and July 2020) covering the Danish strait (Fehmarn Belt) are used. This time period is constrained by the availability of coincident (Sentinel-1 and TanDEM-X) data covering the area of interest. Since TanDEM-X is not an ocean-dedicated mission, acquisitions suitable for ocean current retrieval are sporadic.

Comparison of the derived radial velocities shows a good agreement between Sentinel-1 and TanDEM-X, provided both datasets are calibrated over land and the time delay between acquisitions is below ~20 min. The residual difference is probably due to the wave-induced Doppler shift. The SAR derived velocities are compared to the Copernicus analysis product (BALTICSEA\_ANALYSIS\_FORECAST\_PHY\_003\_006) and in-situ measurements. A reasonable agreement is found, provided that the wave-induced Doppler shift is taken into account. The study also investigates the relationship between the surface current along the Fehmarn Belt, the sea surface wind and the sea level, as an attempt to understand the main drivers of the surface flow. First, a high variability in the duration of inflow/outflow is observed. The shortest and the longest durations are one day and 10 days, respectively. Second, it is found that the surface current is predominantly in the east-to-west direction (outflow). Third, the relationship between the local wind and the surface current is stronger in the outflow situation, whereas the relationship between the surface current and the sea level gradient is stronger in the inflow situation. Though these observations agree with previous studies, it is however difficult to draw firm conclusions on the driving force from these limited dataset, hence additional data are required to verify these results. However, the study clearly demonstrates the potential of SAR for monitoring sea surface flows.

How to cite: Elyouncha, A., Eriksson, L., Brostöm, G., and Axell, L.: Monitoring the in/out flow through the Danish straits using satellite synthetic aperture radar, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7853, https://doi.org/10.5194/egusphere-egu22-7853, 2022.

Stefan Weichert et al.

Detailed knowledge of the subsurface current in the ocean environment allows for more accurate modelling of, e.g., exchange of mass and heat with the atmosphere. Measuring the vertical profile of the current in situ poses a range of costs and difficulties. Therefore, methods were developed to infer this information from measured Doppler shift velocities, i.e., from changes in the waves’ phase velocities owing to the background current, which are obtainable from, e.g., optical or radar imaging of the surface. Notably, the “polynomial effective depth method” (PEDM), due to Smeltzer et al. [1], was shown to be a promising candidate. These methods, however, typically assume the current to be uniform in the horizontal plane.

In this work we study the effects of slow horizontal variations on the accuracy of the extracted Doppler shifts. Synthetic data is generated by propagating waves from still water into a region of horizontal and vertical shear, where the propagation is governed by the dispersion relation as given by Steward and Joy [2]. The numerically generated wave fields then serve as the raw video data for the extraction of current-induced Doppler shifts whence the vertical shear current is estimated and compared to the prescribed one.

The simulation of the wave fields is based on the method of characteristics. Given a wave spectrum in the quiescent region, for each wavelength, a phase field is obtained from propagating waves along rays. These fields then form the basis for constructing a “movie”.

Results for different horizontal velocity fields and wave spectra are compared to investigate their effect on the accuracy of the vertical profile retrieved by the PEDM.

[1] Smeltzer, B. K., Æsøy, E., Ådnøy, A., & Ellingsen, S. Å. (2019). An improved method for determining near-surface currents from wave dispersion measurements. Journal of Geophysical Research: Oceans, 124, 8832– 8851.

[2] Stewart, R. H. & Joy, J. W. (1974). HF radio measurements of surface currents. Deep Sea Research and Oceanographic Abstracts, 21, 1039-1049

How to cite: Weichert, S., Smeltzer, B. K., and Ellingsen, S. Å.: How horizontal current shear affects the remote sensing of current depth profiles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8498, https://doi.org/10.5194/egusphere-egu22-8498, 2022.

yann guichoux et al.

Accurate, high-resolution estimate of ocean surface currents is both a challenging issue and a growing end-user requirement. Yet, the global circulation is only indirectly monitored through satellite remote sensing; to benefit the end-user community (science, shipping, fishing, trading, insurance, offshore energy, defence), current information must be accurately constructed and validated from all relevant available resources. eOdyn develops since 2015 a transformative method to derive surface currents from ship motion and Automatic Identification System (AIS) data [1][2]. Currents, derived from AIS data, a complementary in- situ observing system so far under-exploited, have the potential to complete surface current picture with high- frequency part of ocean dynamics in areas with intensive marine traffic activities. The presentation will focus on recent results, using AIS data collected thanks to low earth orbit satellites and ship behaviour analysis to produce relayable high resolution ocean surface current measurements to monitor different currents of interest (off the south African coastline, the Indian ocean and the Mediterranean sea). Comparisons between AIS derived surface currents and independant data sets from altimetry satellites, HF radars and drifters will be presented. The use of this new technology to complement exisiting measurement systems will be demonstrated. [1] Clément Le Goff, Brahim Boussidi, Alexei Mironov, Yann Guichoux, Yicun Zhen, Pierre Tandeo, Simon Gueguen, and Bertrand Chapron. Monitoring the Greater Agulhas Current with AIS Data Information, Published in Journal of Geophysical Research: Oceans, 2021. [2] Guichoux, Y., Lennon, M. and Thomas, N., Sea surface currents calculation using vessel tracking data, Proceedings of theMaritime Knowledge Discovery and Anomaly Detection Workshop. Michele Vespe and Fabio Mazzarella. JRC Conference and Workshop Reports, pp.31-35, 2016.  

How to cite: guichoux, Y., le goff, C., boussidi, B., and mironov, A.: (encore abstract)  Ship drift and Automatic Identification System analysis used to monitor ocean surface currents, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8904, https://doi.org/10.5194/egusphere-egu22-8904, 2022.

Mathias Tollinger et al.

High-resolution sea surface observations by spaceborne synthetic aperture radar (SAR) instruments are sorely neglected resources for meteorological applications in polar regions. Such radar observations provide information about wind speed and direction based on wind-induced roughness of the sea surface. The increasing coverage of SAR observations in polar regions calls for the development of SAR-specific applications that make use of the full information content of this valuable resource. Here we provide examples of the potential of SAR observations to provide details of the complex, mesoscale wind structure during polar low events, and examine the performance of two current wind retrieval methods. Furthermore, we suggest a new approach towards accurate wind vector retrieval of complex wind fields from SAR observations that does not require a priori wind direction input that the most common retrieval methods are dependent on. This approach has the potential to be particularly beneficial for numerical forecasting of weather systems with strong wind gradients, such as polar lows.

How to cite: Tollinger, M., Graversen, R. G., and Johnsen, H.: High-Resolution Polar-Low Winds Obtained from Unsupervised SAR-Wind Retrieval, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3844, https://doi.org/10.5194/egusphere-egu22-3844, 2022.

Leon Ćatipović et al.

Measuring data efficiently in the framework of geosciences has proven to be more cumbersome than expected, despite technological advances. While remote sensing techniques, such as satellite observations, provide extraordinary spatial coverage, they still lack the fine spatial and temporal resolution of in situ measuring techniques. Naturally, the level of coverage obtained by remote sensing techniques could be replicated with physical measuring
stations and devices, however, the financial cost would be immense. Therefore, if we are to broaden the spatial coverage while retaining both resolutions and minimising cost, we need to strategically deploy as few sensors as possible. In order to tackle this problem, we have
utilised three unsupervised learning (clustering) methods not only to demonstrate how a smaller subset of sensors can provide significant measurement accuracy, but also to show that there exists an optimal sensor placement (as opposed to random placement). Data used for this
demonstration is ERA5 wind components at 10m height from 1979 to 2019 over the Mediterranean sea, at a spatial resolution of 0.5° × 0.5° every 6 hours.
Clustering methods used are K-means clustering, Self-Organising Maps (SOM) and Growing Neural Gas (GNG). We have clustered the data into 5, 10, 20, 50, 100, 200 and 500 groups and treated the median centers of the resulting domains as the optimal placement for sensors. After the clustering was completed, we have attempted to reconstruct the missing data using two regression models: linear and K-Nearest Neighbours. Reconstructed data was compared (in both size and angle) to original data, and the results show that with just 5 points (out of a grand total of 1244 wet points), reconstruction accuracies are as follows: 65.6, 65 and 62.5% for linear regression reconstruction and 71.6, 71.2 and 70.5% for KNN reconstruction, when applied to GNG, K-means and SOM respectively. Increasing the number of points has diminishing returns (especially in excess of 100 points), with linear regression reconstruction accuracy peaking at ≈ 95% and KNN reconstruction remaining in the high 70%. As demonstrated, GNG and K-means performed slightly better than SOM, due to the nature of SOM’s rigid algorithm.

This work has been supported by Croatian Science Foundation under the project UIP-2019-04-1737.

How to cite: Ćatipović, L., Kalinić, H., and Matić, F.: Optimal sensor placement using learning models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7359, https://doi.org/10.5194/egusphere-egu22-7359, 2022.

Matthew Hammond et al.

Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems (GNSS), which have been reflected off the Earth’s surface. GNSS-R is particularly promising as it does not require a dedicated transmitter, thereby reducing mass and power requirements of the instrument, which gives an opportunity to build a constellation of low-cost sensors providing short revisit times and unprecedented sampling capabilities.

Ocean data has been collected regularly from GNSS-R instruments since 2014 from technology demonstration missions, e.g. TechDemoSat-1 (TDS-1, 2014 – 2018) and DoT-1 (2019 – Present), as well as operational missions, e.g. CyGNSS (2016 – Present). These missions have had different aims and setups, providing different perspectives on the capabilities of GNSS-R. TDS-1 and DoT-1 are polar orbiting, allowing the additional collection of data over sea-ice, where GNSS-R has shown strong signal sensitivity in coherent scattering conditions. The CyGNSS mission is the first GNSS-R constellation and consists of eight small satellites in an orbit that provides revisit times of only a few hours between the latitudes of ±35º, achieving a much higher sampling rate and faster revisit than TDS-1. Additionally, DoT-1 demonstrates onboard processing of signals originating from both GPS and Galileo satellites. The strong signal sensitivity to geophysical parameters such as ocean wind speed, when using signals from the Galileo system, shows the potential for further improvement in sampling when using signals from multiple Global Navigation Satellite Systems.

Both the CYGNSS and TDS-1 missions have shown consistent performance in the retrieval of ocean wind speed once instrument calibration steps have been taken to mitigate some of the challenges inherent to GNSS-R technology. However, a number of geophysical variables theorised to be impacting the GNSS-R observables were investigated over a range of ocean wind speeds. The major dependencies affecting wind speed retrieval appear to be significant wave height and precipitation, which have their greatest impact at low wind speeds, with sea surface temperature having a weaker impact but across all wind speeds. These geophysical dependencies, additional to wind speed, can have a significant impact on retrievals and may need to be isolated for accurate retrievals.  

GNSS-R has shown strong capabilities for ocean remote sensing of multiple variables, using platforms and instruments that are still advancing to most effectively utilise the technology. A number of such advancements will be employed in the forthcoming ESA HydroGNSS mission, where the National Oceanography Centre (NOC) are leading the development of ground segment processors for Level-1 signal calibration and Level-2 ocean surface wind speed and sea-ice extent products.

How to cite: Hammond, M., Foti, G., Gommenginger, C., Srokosz, M., and Floury, N.: GNSS-Reflectometry for Ocean Remote Sensing , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10631, https://doi.org/10.5194/egusphere-egu22-10631, 2022.

Ankita Misra et al.

Bathymetry plays an important role in ship navigation and as an input to numerical models that carry out the accurate estimation of various coastal processes such as storm surge, associated coastal flooding, and sediment dynamics like erosion-accretion of coastline. However, in-situ measurements of depths become challenging by virtue of the acquisition costs involved in deploying conventional methods, such as ship-based echo-sounding and LiDAR based techniques. Resultantly, several studies have been conducted related to Satellite Derived Bathymetry estimation using Optical Remote Sensing and empirical approaches, and most recently Machine Learning (ML) techniques. The advantage of ML methods is that they account for the non-linear relationship that exists between depths and reflectance in complex coastal environments. The present study evaluates the relative performance of 13 different linear and non-linear ML approaches, (1) Least absolute shrinkage and selection operator (LASSO) (2) Least-angle regression (LARS) (3) LASSO-LARS regression (4) Automatic relevance determination regression (ARD) (5) Bayesian ridge regression (BRR) (6) Multilayer perceptron (MLP) (7) K-nearest neighbors (KNN) (8) Support vector regression (SVR) (9) Random forest regression (RF) (10) Extra Trees regression (ET) (11) Gradient boosting regression (GBR) (2) Bagging regression (BR), for depth estimation using Landsat 8 OLI imagery in Labuan, Malaysia. The estimated depths are compared with in-situ measurements and various descriptive statistics are reported. It is observed that for all the study areas, ET, KNN, RF and BR consistently provide better results in comparison to other algorithms. The primary aim of the study is to highlight the best available ML methods that can be used with Medium Resolution satellite imagery to derive bathymetry as well as discussing the pros and cons of using ML for coastal bathymetry estimation . 

How to cite: Misra, A., Muslim, A. M., and Bhardwaj, S.: Assessing the advantages and limitations of Coastal Depth Estimation using Machine Learning techniques, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-581, https://doi.org/10.5194/egusphere-egu22-581, 2022.