SWOT is a pathfinder mission using new technology to address transformative questions on energy and water of the Earth System in a warming climate.  The excess heat energy entering the Earth system as a result of the greenhouse effect is largely stored in water, and changes in the water cycle and water resources have profound effects on life on Earth.            

SWOT is a next generation radar altimeter that uses synthetic aperture radar interferometry to measure the elevation of water surface over both continents and oceans in two dimensions with a radar footprint 1000 times smaller than that of a conventional altimeter. SWOT will cover the world between 78N and 78S every 21 days, leaving only small gaps comprising <5% of Earth’s surface. 

More than 90% of the heat from global warming since the industrial revolution has been absorbed and stored in the ocean.  A major part of this process takes place in the ocean on scales too small to be observed from space in the past.  SWOT will improve the two-dimensional spatial resolution of sea surface height from present 200 km to 20 km to address the processes of heat uptake from the atmosphere.

In a warming climate earth’s water cycle is accelerating, making it difficult to track and manage water resources as well as predicting floods and droughts.  The areal extent of surface water on land can be observed by conventional spaceborne sensors, but the volume of surface water in lakes and rivers will be surveyed by SWOT from space for the first time.  The numbers of rivers and lakes to be surveyed by SWOT are orders of magnitude more than the present observations.

The high-resolution data of SWOT near the coasts will allow us to study sea level variations in unprecedented detail. Storm surge and other impacts like salt water intrusion and river diversion will be exacerbated by the continuing sea level rise.  SWOT data will help improve models to monitor and forecast these impacts.

After nearly 20 years’ development, SWOT was launched on December 16, 2022 as a joint mission of NASA and the French Space Agency, CNES, with contributions from the Canadian Space Agency and the UK Space Agency. The satellite system was fully deployed within a week of launch and is in a 3-month phase of engineering checkout.  A 3-month calibration and validation phase will start afterwards in the one-day repeat initial orbit, which will transition into a 21-day repeat orbit during the science phase of the mission in mid 2023.  The release of SWOT data to the public for evaluation is expected to take place 10 months after launch.  The status of the mission at the end of April will be reported by this presentation.

How to cite: Fu, L.-L., Pavelsky, T., Morrow, R., Cretaux, J.-F., and Farrar, T.: The SWOT (Surface Water and Ocean Topography) Mission and Its Status, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17156, https://doi.org/10.5194/egusphere-egu23-17156, 2023.

OSCAR (Ocean Surface Current Airborne Radar) is a new airborne instrument which provides unique 2D synoptic views of ocean and atmosphere dynamics (currents, waves, winds) below km-scale. OSCAR is a Ku-band (13.5 GHz) SAR system with Doppler and scatterometry capabilities in three azimuth look directions. The OSCAR instrument features an along-track interferometric (ATI) baseline in two lines-of-sight squinted 45° fore and aft from the broadside direction. The fore and aft antenna pairs provide interferometric Doppler measurements in two views angularly separated by 90 degrees. This ensures two orthogonal measurements of the ocean surface motion velocity that enable the retrieval of the total ocean surface current vector. In addition, backscatter measurements from the broadside antenna in the zero-Doppler direction serve to retrieve wind direction and wind speed, which are critical to correctly measure total ocean surface currents.

In each line-of-sight, the ocean surface motion sensed by the microwave radar (after correcting for navigation and geometry) has two constituents: the total ocean surface current – consisting of all currents contributing to actual horizontal transport of water – and a measurement bias associated with the Doppler signature of the surface scatterers responsible for the backscatter, a term known as Doppler wave bias or Wind-wave induced Artefact Surface Velocity — WASV (Martin et al., 2016). The WASV is caused by the phase velocity of the surface scatterers responsible for the microwave backscatter (e.g. Bragg waves) and the effect of the orbital motion of longer ocean waves. The magnitude of the WASV can reach 0.5-1 m/s and is, at first order, a function of the wind direction. A number of geophysical model functions (GMFs) have been published in recent years to correct this effect.

In May 2022, OSCAR was flown during the SEASTARex campaign over Iroise Sea (French Brittany). The campaign consisted of three flights on three different days, including acquisitions over a well-instrumented site with ground truth measurements of total ocean surface current fields from a WERA HF radar, supported by data from an X-band marine radar, stereo-video and a down-looking ADCP. Here, we present the first results of the validation of the OSCAR retrieved current fields against data from independent ground truth sensors and models.

How to cite: McCann, D., Martin, A., Macedo, K., Gommenginger, C., Marié, L., Carrasco Alvarez, R., Meta, A., Martin Iglesias, P., and Casal, T.: OSCAR: validation of 2D total surface current vector fields during the SEASTARex airborne campaign in Iroise Sea, May 2022., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12743, https://doi.org/10.5194/egusphere-egu23-12743, 2023.

Synthetic aperture radar (SAR) offers the possibility to observe the sea surface circulation with very high spatial resolution. These observations are particularly relevant in coastal areas and shelf seas. SAR has been routinely providing valuable information on sea surface winds and waves for many decades. During the last decade, a new application of SAR measurements based on the analysis of the Doppler shift has emerged, opening the possibility to measure directly the surface currents. There are still however many unresolved questions and challenges. One of the challenging questions is the wave-current interaction and its effect on the wind and wave retrieval. 

The Agulhas Current is the strongest western boundary currents in the southern hemisphere. The region of the Agulhas Current is characterized by a complex upper ocean dynamics involving a wide range of mesoscale and submesoscale processes. It thus provides an ideal natural laboratory for oceanographers and remote sensing sensors and techniques. 

Unique acquisitions of the interferometric SAR TanDEM-X over the Agulhas Current with very high spatial resolution (100 - 200 m) are analyzed. Maps of SAR-derived surface velocity are compared to model data. The SAR velocity images accurately capture the boundary and the intensity of the Agulhas Current. Moreover, these maps show unprecedented fine structure of the Agulhas Current and its interaction with the wave field. The pattern depicted by the backscatter images is on the other hand very variable from scene to scene depending on the wind and sea state. Only in particular cases, the current structure can be discerned from the backscatter. The influence of the Agulhas Current on the wind and wave field retrieval is investigated. The inversion of the backscatter to wind speed without taking the current into account lead artificially high estimates of SAR-derived wind speeds. Note that the wind and wave field retrieval will also impact the current retrieval via the wave-induced Doppler shift. 

These SAR observations are analyzed together with collocated existing products of ocean surface wind, ocean surface current, sea surface temperature and significant wave height. Our analysis indicates that wave-current interactions in regions of strong current shear produce very different signatures of sea surface roughness. The roughness signatures depend on the wave propagation direction relative to the current. A case of particularly enhanced roughness, probably due wave breaking events, is discussed.

How to cite: Elyouncha, A. and Eriksson, L.: Observations of the Agulhas Current by along-track interferometric synthetic aperture radar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14595, https://doi.org/10.5194/egusphere-egu23-14595, 2023.

The sea-surface slope probability is an important physical parameter to describe the ocean surface and its interaction with the atmosphere. Its reference measurement dates back to the celebrated airborne experiment conducted by Cox and Munk (CM) in 1951 using sun glitter patterns. The obtained two-dimensional slope distribution function deviates slightly from the Gaussian distribution with pronounced up-to-crosswind and up-to-downwind asymmetries, a stronger peakedness and a slower decay at large values. It is classically parametrized by a Gram-Charlier representation with seven directional parameters describing the mean square slopes (MSSs) as well as the skewness and kurtosis coefficients for wind speeds up to 15 m/s. The MSSs are shown to follow a quasi-linear trend with wind speed, a result which has been confirmed by many subsequent airborne and spaceborne optical measurements and wave-tank experiments. The higher-order statistical coefficients have a non trivial dependence on wind speed as shown by the more recent results by Bréon and Henriot (2006); however, they are challenging to evaluate accurately and suffer from a larger uncertainty.

We re-examine the sea-surface probability by using radiances collected from space by the Infrared Atmospheric Sounder Interferometer (IASI) when looking down at ocean surface during the day. This is achieved by using about 300 channels between 3.6 and 4.0 μm and a physically-based approach which properly takes the contribution of the reflected solar radiation into account. This unique data set covers 13 years of observations over the world ocean, resulting in about 150 millions IASI appropriate spectra and as many wave-slope probabilities. Based on these experimental wave-slopes we revisit and discuss CM results and methodology and their limitations. We propose an original and robust approach for accurate retrievals of the Gram-Charlier parameters. Our findings for the MSSs are fully compatible with those of CM but our lower uncertainties enable to point out departures from the linear wind-speed dependencies and a slight overestimation of the upwind MSS described by the linear fit of CM at moderate wind speed. Our skewness and kurtosis coefficients show clear influences of the wind speed, with a steady decrease of the former and the alongwind kurtosis coefficient being maximal at moderate wind speeds, features that CM could not point out due to the limitations of their measurements. We revisit the renormalization procedure employed by CM to obtain the complete variances from truncated pdfs and show that it imposes stringent conditions on the kurtosis coefficients that allow to determine them accurately. We also provide measurements of the shifted position of the most probable slope as well as a demonstration of a qualitative change of regime in the updown wind asymmetry of the wave-slope probability when the wind speed increases.

How to cite: Guérin, C.-A., Capelle, V., and Hartmann, J.-M.: Determining the wave-slope statistics using IASI observations of the sea surface, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8159, https://doi.org/10.5194/egusphere-egu23-8159, 2023.

The growing satellite record of sea state observations is becoming increasingly important for climate change research, to improve ocean and weather forecasts and to inform climate change mitigation and investment strategies. In this context, coastal processes and impacts are of particular concern, driving a yet stronger research imperative. The Copernicus Sentinel-6 Michael Freilich (S6-MF) mission was launched in November 2020 by the European Space Agency to succeed Jason-3 (J3) as the long term satellite altimetry reference mission. S6-MF commissioning involved a unique 12-month Tandem Experiment during which S6-MF flew approximately 30 seconds behind J3 on the same ground tracks, resulting in an unprecedented global dataset of quasi-simultaneous collocated altimeter sea state measurements in Low-Resolution Mode (LRM) and Synthetic Aperture Radar (SAR) mode.

In this work, this unique dataset is examined to evaluate uncertainties in altimeter significant wave height (Hs) observations from the two missions in different operating modes and different sea state conditions. A particular focus is placed on the evaluation of uncertainties in the coastal zone by exploiting the large number of moored buoys located near the coast of the U.S. S6-MF and J3 data are compared with buoy measurements and reanalysis data using, amongst other methods, triple collocation (TC) analysis. Attention is paid to both the collocation methodology and possible correlation of random errors. Results indicate that, over both global and coastal oceans, J3 and S6-MF Low-Resolution Hs are almost identical, with near-zero bias, low RMS difference and very high correlation. This very high correlation precludes the use of triple collocation to the J3/S6-MF-SAR/buoy triplets. Comparing S6-MF SAR with J3 LRM and buoys confirms the positive sea-state dependent bias in SAR Hs. Triple collocation of J3, S6-MF and buoys reveals the sensitivity of measurement uncertainty to collocation criteria, particular in coastal areas. Further, we show how sea state dependence of measurement uncertainty varies between oceanic and coastal settings. In general, we find that steeper spatial gradients of sea state typically associated with coastal regions can hamper interpretation of TC analyses without undue consideration. These findings demonstrate the value of the Tandem Experiment to evaluate uncertainty and provide evidence of the stability and/or enhancements of new mission data contributing to the growing satellite climate record.

How to cite: Timmermans, B., Gommenginger, C., and Banks, C.: Coastal Sea State Uncertainty From a Triple Collocation Analysis of Observations During the Sentinel-6 Michael Freilich – Jason-3 Tandem Phase Experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9894, https://doi.org/10.5194/egusphere-egu23-9894, 2023.

Ocean surface salinity dataset is useful for research on climate change and its variability. In particular, a gridded daily ocean surface salinity product with high spatial resolution can provide information of short-term variability in the East China Sea (ECS) and the Yellow Sea (YS). Here, we conducted gap-filling of daily surface salinity product based on the Geostationary Ocean  Color Imager (GOCI) for the period 2011-2020 with spatial resolution of 500 m using machine learning approach. For this, we used GOCI-based daily surface salinity preoduct as ground-truth data with envrionemntal variables such as sea surface temperature (SST), sea surface height (SSH), eastward seawater velocity (uo), northward seawater velocity (vo), and seawater salinity (SS) as input data of machine learning model. To identify importance between daily surface salinity and environmental variables affecting daily surface salinity, feature importance ranking was used. Our model shows gap-free daily surface salinity product based GOCI. In addition, the successful application of machine learning model provides the information of long-term variation of daily surface salinity at high spatial resolution in the ECS and the YS.

How to cite: Shin, J., Kim, S.-H., and Jo, Y.-H.: Gap-filling processes for GOCI-based daily surface salinity product using environmental variables and machine learning approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10816, https://doi.org/10.5194/egusphere-egu23-10816, 2023.

The OSI SAF (Ocean and Sea Ice Satellite Application Facility) is a dedicated EUMETSAT centre for processing satellite data at the ocean-atmosphere interface. It’s a consortium constituted of Météo-France, as a leading institute, and four cooperating institutes: MET Norway, DMI (Denmark), Ifremer (France), KNMI (Netherlands). 

Utilizing specialist expertise the consortium processes and distributes near real-time products related to key parameters of the ocean-atmosphere interface as well as climate data records of these parameters. Main products are: Winds, Sea and Ice Surface Temperature (SST/IST) and Sea Ice Parameters at both poles: Concentration, Edge, Type, Emissivity and Drift.

The applications of products are numerous. The most general ones are the assimilation into models, the validation of models, oceanography, research and environmental monitoring. The presentation will showcase OSI SAF ocean remote sensing products from meteorological satellites both from the polar and geostationary orbit.  The presentation will focus on the latest developments.

In the next few years, existing Sea Ice, Wind and SST Climate Data records will be improved and extended. It will also be the occasion to discuss the expectations of international colleagues and to describe the plans to exploit the capabilities offered by the future MTG and Metop-SG satellites. 

How to cite: Membrive, O., Hernandez, C., Roquet, H., Saux-Picart, S., Eastwood, S., Stoffelen, A., Verhoef, A., Howe, E., and Piolle, J.-F.: Ocean remote sensing from meteorological satellites at OSI SAF, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14466, https://doi.org/10.5194/egusphere-egu23-14466, 2023.

A series of new passive microwave satellite sensors will offer strongly enhanced capability to measure ocean surface vector winds (OSVW), sea surface temperature (SST) and salinity (SSS) during the next decade and beyond. Our presentation gives an overview of the features of these instruments and how we plan to use them in future Earth observations.

The Weather System Follow on Microwave Imager WSF-M MWI, operated by the US DoD scheduled to be launched in January 2024, is a follow-up of the US Navy’s WindSat. The sensor calibration system will continue the four-point calibration method implemented with GMI employing a combination of internal and external calibration targets. Like GMI, WSF-MWI is expected to reach absolute calibration accuracy.  As WindSat did, it will provide fully polarimetric measurements at X, Ku and Ka-band and thus be able to continue WindSat’s OSVW data record.

COVWR, developed by NASA JPL and US DoD, was launched in December 2022. Its novel cost-effective design consists of a fixed feedhorn bench and has only the antenna dish spinning. It is fully polarimetric at 3 frequencies within Ku, K, and Ka bands and can observe most Earth locations simultaneously using fore and aft looks. The 2-look capability strongly aids the measurement of wind direction as it does not have to rely on any external input from Numerical Weather Prediction Models, as for example scatterometers do.

JAXA’s AMSR3, to be launched in late 2023, continues the series that started with AMSR-E in 2002 and followed with AMSR-2 in 2012. The presence of the C-band channels is vital for globally measuring SST with passive microwave sensors. The global availability of microwave SST is essential for the scientific community, as it provides observations in the presence of clouds and aerosols, where infrared sensors fail. The AMSR3 sensor will observe at two C-band and at two X-band frequencies, which will result in increased capabilities to measure ocean wind speeds through rain, including in strong tropical and extratropical storms where most other passive microwave sensors cannot provide usable retrievals. It is possible to find combinations between the C- and X-band channels that minimize the impact of rain but are still sensitive to wind speed, which enables disentangling passive wind and rain signals.       

The ultimate passive microwave sensor for ocean observations will be ESA’s CIMR, which is anticipated to launch in 2028. CIMR has an antenna with 8-meter diameter, measures at 5 frequencies between L- and Ka-band, is fully polarimetric at each frequency and has fore and aft looks. These combined features will not only provide the capability for measuring global SST, OSVW and wind speeds in rain as mentioned above. The large antenna will allow these observations to occur at a spatial resolution of 15 km. This constitutes a significant enhancement over currently operating sensors, which reach resolutions of about 50-km for SST and 30-km for OSVW. Finally, the presence of L-band enables CIMR to also measure SSS and thus provide continued SSS satellite data after SMOS, Aquarius and SMAP.

How to cite: Meissner, T., Wentz, K., Ricciardulli, L., and Wentz, F.: Passive microwave satellite sensors of the next decade for observing ocean vector winds, temperature, and salinity: WSF-MWI, COWVR, AMSR3, CIMR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3701, https://doi.org/10.5194/egusphere-egu23-3701, 2023.

Estimating diurnal variations of Sea Surface Temperature (SST) is important for studying air-sea heat exchange. Existing operational diurnal SSTs are derived from numerical models incorporating satellite data, and assimilated with in-situ measurements, which are very accurate. However, numerical model-based methods incur significant computational costs for identifying the diurnal cycle of SST from various heat flux sources (i.e., sensible, latent heat). In this study, we first proposed a Generative Adversarial Network (GAN) method to reconstruct high-resolution diurnal SST using satellite observations as an actual diurnal signal from the ocean surface layer. A generator in the GAN model was trained using the diurnal variability-related variables, including the hourly SSTs and shortwave radiation measurements from Himawari-8 geostationary satellite observations, to estimate diurnal SSTs. The discriminator in the GAN model was learned to reduce the difference in spatiotemporal variability of diurnal SSTs between a satellite data-assimilated numerical model product (Global Ocean OSTIA Diurnal Skin Sea Surface Temperature; Copernicus marine service) and estimated SST from the generator. The results showed that the reconstructed SST had a better spatial distribution of ocean phenomena such as front and eddy than compared with the numerical model-derived SST. It implied that the GAN model could simulate a high spatial variability of SSTs using satellite-based data with a spatial resolution of 2km. The proposed GAN model produced high validation accuracy, resulting in the coefficient of determination of 0.99, bias of -0.2℃, and root mean square errors of 0.58℃ when compared with in situ SST Quality Monitor drifting buoy data. Since we use geostationary satellite data, the proposed model can capture real diurnal variability of SST more frequently than existing numerical model data using analysis data. In addition, the proposed deep learning model is much more computationally efficient than the numerical models.

How to cite: Jung, S. and Im, J.: Generative Adversarial Network for Reconstructing Diurnal Sea Surface Temperature using Satellite Data over North-west Pacific, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14473, https://doi.org/10.5194/egusphere-egu23-14473, 2023.

In-situ measurements of extreme winds within hurricanes are challenging and scarce at the global level. They are mostly provided by risky reconnaissance flights, most often in the tropical North Atlantic. In 2021, a novel NOAA project deployed 5 Saildrones (SDs) to monitor the tropical Atlantic storm-track areas. One of these missions, SD-1045, crossed Hurricane Sam (Cat. 4) on September 30, 2021, providing an unprecedented view of ocean surface conditions within a major hurricane, reporting surface winds as high as about 40 m/s.  New SD missions for the 2022 Atlantic hurricane season were also able to intercept the tracks of Hurricane Fiona and Ian.

Here we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in these hurricanes, using the following datasets for comparison: NDBC buoys in the path of the storms, microwave (MW) radiometer wind retrievals and tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers, from the high-resolution Synthetic Aperture Radars, and the HWRF model winds. The methodology for adjusting the SD wind measurements to a 10m reference height and to the different spatial scales of satellite observations will be described in detail. In this presentation, we will address the consistency of the SD observations with the satellite data at all wind speed regimes, with special focus at extreme winds.

This study can serve as foundation for planning and monitoring the quality of wind measurements from SD missions in the tropics and extra-tropics using satellite data.  Additionally, if properly interpreted, future SD missions can provide a unique and much needed reference source of calibration/validation for satellite observations at wind speeds above 20 m/s, for which buoy data are less accurate.

How to cite: Ricciardulli, L., Foltz, G., Manaster, A., and Meissner, T.: Assessment of Saildrone Wind Measurements in Tropical Cyclones using Microwave Satellite Sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3670, https://doi.org/10.5194/egusphere-egu23-3670, 2023.

This study will present how climate change impacts ocean ecosystems using NASA’s historical ocean color data (SeaWiFS-MODIS-VIIRS). The general trend analyses of ocean color data for less than three decades are hard to distinguish between natural and anthropogenic changes in multiple climate-forcing impacts. Alternatively, we bring a new approach for extracting the ocean’s primary physical modes for modulating climate variability to the ocean ecosystem, called Ocean Physical Modes projection to Ocean Color data (OPM-OC) analysis. This will show how the multiple climate-forcing components separately contribute to the satellite observable biological properties, such as Chlorophyll-a concentration, Colored Dissolved Organic Matter (CDOM), and Inherent optical properties. Also, applying the OPM-OC to the Apparent Visible Wavelength (AVW) index enables to detection of a more extensive shift of ocean color remote sensing reflectance spectrum in the tropical ocean gyre circulation by global warming.

How to cite: Park, M.-S., Mannino, A., Vandermeulen, R. A., and Lee, S.: Climate impact on ocean ecosystem based on 25 years of ocean color satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16907, https://doi.org/10.5194/egusphere-egu23-16907, 2023.

Nearshore processes that impact water quality for boating, swimming and aquaculture happen at scales where current satellite data lack spatial or spectral resolution. Upcoming government and commercial satellite missions aim to fill this gap. To prepare to maximize the use of these Earth observations and address this challenge, we have been working closely with stakeholders around the Chesapeake Bay to explore satellite-derived indicators that could assist practitioners, i.e. clarity, harmful algal blooms, bacterial indices. Several key assets have recently been deployed to improve the potential for this effort: namely, a new NASA Aerosol Robotic Network for Ocean Color (AERONET-OC) site in the Chesapeake Bay to support ocean color atmospheric correction and validation, hyperspectral satellite data from DESIS and PRISMA, and in situ sampling for satellite calibration and validation. In coordination with these activities, we developed an artificial intelligence (AI) framework for feature discrimination using a single satellite sensor, starting with Sentinel 3a&b OLCI. We are currently extending our initial methodology to integrate multiple satellite data sets of differing spatial, spectral, and temporal resolution, namely MODIS-Aqua with its long record, and DESIS and PRISMA for their hyperspectral and higher spatial resolution. With this Deep learning for Environmental and Ecological Prediction-eValuation and Insight with Ensembles of Water quality (DEEP-VIEW) framework we hope to improve predictions of estuarine impacts of runoff and pollution from land, changes in water clarity, and other metrics that are needed by resource managers and other stakeholders to safeguard health and safety around the Chesapeake Bay.

How to cite: Schollaert Uz, S., Ames, T. J., Clark, J. B., and Aurin, D.: Integration of satellite and in situ observations into machine learning for coastal water quality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17532, https://doi.org/10.5194/egusphere-egu23-17532, 2023.

The concentrations of parameters such as Chlorophyll-a (Chl-a) and Total Suspended Solids (TSS) in seawaters have already been used as indicators of the water quality, the biogeochemical status of surface waters, and nutrient availability. Unmanned Aerial Vehicles (UAVs) have gained global popularity as a remote-sensing tool as they address the optical challenges of water-quality studies in coastal regions. In this work, we evaluate the applicability of a 5-band multispectral sensor mounted on a UAV to derive scientifically valuable water parameters (Chl-a and TSS). The performance of the OC-2 and OC-3 algorithms for Chl-a estimation, as well as the TSS estimation method by Nechad et al. (2010), are tested in two different sites along the Mediterranean coastline. This study provides water quality details on the centimetre-scale and improves the existing approximations that are available for the region through Sentinel-3 OLCI imagery at a much lower spatial resolution of 300 m. The Chl-a and TSS values derived for the studied regions were within the expected ranges and varied between 0 to 3 mg/m³ and 10 to 20 mg/m³, respectively. In addition, a novel Python workflow for the manual generation of an orthomosaic in aquatic areas based on the sensor’s metadata, without the need to resort to commercial photogrammetric software, is proposed. Linear regressions were also applied to compare the Remote Sensing reflectance (Rrs) retrieval methods tested, suggesting strong R² correlations between 0.83 and 0.91 for the “deglinting” method.

How to cite: Roman, A., Tovar-Sanchez, A., Gauci, A., Deidun, A., Caballero, I., Colica, E., D'Amico, S., Heredia, S., and Navarro, G.: High-resolution UAV multispectral imagery for water-quality monitoring in coastal regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8599, https://doi.org/10.5194/egusphere-egu23-8599, 2023.

Monitoring and mapping of major phytoplankton groups (PG) or functional types (PFT) has been targeted as a relevant topic for the understanding and study of marine ecosystem, especially under the present climate change scenario. Developing of algorithms that determine the structure of the phytoplankton communities is a reality since the last 20 years, but not much advanced has been done in the field of image spectroscopy due to the lack of spaceborne sensors with a systematic high temporal and spatial scales. Some operational sensors are changing the game right now, like PRISMA, ENMAP and, in the future, SGB, CHIME and other developments in the hyperspectral dimension. Most of the approaches for determining PFT or PG are based on phytoplankton abundance, cell size or bio-optical properties that use chlorophyll-a or spectral features (absorption, backscatter, and/or reflectance) on water in the VIS-NIR range as inputs. These and other approaches based on machine learning and deep learning are being tested on ENMAP imagery over the Baltic Sea. We will use reflectance data provided by DLR. Comparison of atmospheric correction approaches seems to be a necessary step, and radiance data will be process with current available algorithms. Since ENMAP has 240 bands, and high spatial resolution (30 m), we will tackle the dimensionality reduction problem adapting well-known machine learning approaches to the sensor characteristics (https://isp.uv.es/soft_feature.html).

How to cite: Ruescas, A. B., Garcia-Jimenez, J., Mueller, D., Brockmann, C., Amoros, J., and Stelzer, K.: Study of ENMAP imagery for the application of methods for Phytoplankton Functional Types determination in coastal waters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8662, https://doi.org/10.5194/egusphere-egu23-8662, 2023.

Phytoplankton is at the base of the marine food web but only 10 to 20 % of the primary production is available to higher trophic levels. Besides chlorophyll-a concentration, chlorophyll-a horizontal gradients were shown to mostly sustain the development of mesozooplankton and the marine food web. The detection of chlorophyll-a gradients from satellite ocean colour data therefore appears to be central to the management of sustainable fisheries.

An algorithm allowing the detection of relevant frontal productivity to fish production has been published in 2021 by the Joint research Centre (JRC). This algorithm was operationally computed and made publicly available at global scale in the JRC Data Catalogue and distributed to national institutes in Africa throughout a software named eStation within the GMES & Africa Program. This algorithm has been developed based on MODIS-Aqua data as this sensor longevity of more than 20 years is an asset for studying the temporal evolution of fish ecological habitat and the impact of fisheries by comparing effective with potential fishing yields. However, MODIS-Aqua observation will likely end soon. It becomes therefore primordial to adapt the analysis from MODIS-Aqua to the recent Sentinel 3 OLCI (Ocean and Land Colour Imager) sensor to ensure the continuity of the fish production monitoring including in real-time. OLCI-S3A, which started in April 2016, is the first sensor of the constellation to observe the chlorophyll-a concentration with the objectives to improve the daily coverage of the ocean surface and increase the accuracy of the gradient retrieval.

Here, we present the methodology used to recalibrate the relevant chlorophyll-a gradients to fish production from MODIS-Aqua to OLCI-S3A data at 4 and 1 km spatial resolution with the objective to enriching the Copernicus Marine Environment Monitoring Service catalogue and pursuing the operational marine activities of the eStation on Potential Fishing Zones (PFZ) within the GMES & Africa Program.

How to cite: Bretagnon, M., Druon, J.-N., Mangin, A., Clerici, M., and Lavaysse, C.: Recalibration of the relevant productivity frontal features to fish production from the MODIS-Aqua to Sentinel 3 OLCI ocean colour sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15931, https://doi.org/10.5194/egusphere-egu23-15931, 2023.

The fine-scale study of the diffuse attenuation coefficient, K_d(l), of the spectral solar downward irradiance  is only feasible by ocean color remote sensing. Several empirical and semi-analytical methods exist. However, most of these models are generally applicable for clear open ocean waters. They show limitations when applied to coastal waters. A new empirical method based on neural networks has been developed using a relationship between the remote-sensing reflectances between 443 and 670 nm and K_d(λ). The architecture of the neural network has been defined using synthetical and in situ dataset. The model has been developed for SeaWiFS, MODIS-AQUA, MERIS, VIIRS, OLCI and PACE space-borne sensors. Validation using in-situ measurements from a wide range of type of waters (from oligotrophic to very turbid waters) shows similar retrievals accuracies for low values of K_d(490)  (i.e. <0.20 m^-1) and better estimates for greater values of and K_d(490). The new model is compared to empirical and semi-empirical methods and is suitable for open water but also for turbid waters.

How to cite: jamet, C., Jorge Schaeffer, D., and Loisel, H.: Estimation of the spectral diffuse attenuation coefficient Kd(λ) from UV to NIR using ocean color images: Application from SeaWiFS to PACE, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13911, https://doi.org/10.5194/egusphere-egu23-13911, 2023.