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Sea surface salinity (SSS) is one of the Essential Climate Variables (ECVs), defined by the Global Climate Observing System (GCOS). Salinity is modified by river discharge, land run-off, precipitation, and evaporation, and it is advected by oceanic currents. In turn, ocean circulation, the water cycle, and biogeochemistry are deeply impacted by salinity variations. The Mediterranean Sea represents a hot spot for the variability of salinity. Despite the ever-increasing number of moorings and floating buoys, in situ SSS estimates have low coverage, hindering the monitoring of SSS patterns. Conversely, satellite sensors provide SSS surface data at high spatial and temporal resolution, complementing the sparseness of in situ datasets. Here, we describe a multidimensional optimal interpolation algorithm, specifically configured to provide a new daily SSS dataset at 1/16° grid resolution, covering the entire Mediterranean Sea (Med L4 SSS). The main improvements in this regional algorithm are: the ingestion of satellite SSS estimates from multiple satellite missions (NASA’s Soil Moisture Active Passive (SMAP), ESA’s Soil Moisture and Ocean Salinity (SMOS) satellites), and a new background (first guess), specifically built to improve coastal reconstructions. The multi-sensor Med L4 SSS fields have been validated against independent in situ SSS samples, collected between 2010–2020. They have also been compared with global weekly Copernicus Marine Environment Monitoring Service (CMEMS) and Barcelona Expert Centre (BEC) regional products, showing an improved performance. Power spectral density analyses demonstrated that the Med L4 SSS field achieves the highest effective spatial resolution, among all the datasets analysed. Even if the time series is relatively short, a clear interannual trend is found, leading to a marked salinification, mostly occurring in the Eastern Mediterranean Sea.

Measuring the ocean surface currents at high spatio-temporal resolutions is crucial for scientific and socio-economic applications. Since the early 1990s, the synoptic and global-scale monitoring of the ocean surface currents has been provided by constellations of radar altimeters. By construction, altimeter constellations provide only the geostrophic component of the marine surface currents. In addition, given the effective spatial-temporal resolution of the altimeter-derived products (O (100 km) and O (10 days), respectively), only the largest ocean mesoscale features can be resolved. In order to enhance the altimeter system capabilities, we propose a synergistic use of high resolution sea surface Chlorophyll observations (Chl) and altimeter-derived currents’ estimates. The study is focused on the Mediterranean Sea, where the most energetic signals are found at spatio-temporal scales up to 10 km and a few days. The proposed method allows for inferring the marine surface currents from the evolution of the Chl field, relying on altimeter-derived currents as a first-guess estimate. The feasibility of this approach is tested through an Observing System Simulation Experiment, starting from biogeochemical model outputs distributed by the European Copernicus Marine Service. Statistical analyses based on the 2017 daily data showed that our approach can improve the altimeter-derived currents accuracy up to 50%, also enhancing their effective spatial resolution up to 30 km. Moreover, the retrieved currents exhibit larger temporal variability than the altimeter estimates over annual to weekly timescales. Our method is mainly limited to areas/time periods where/when Chl gradients are larger and are modulated by the marine currents’ advection. Its application is thus more efficient when the surface Chl evolution is not dominated by the biological activity, mostly occurring in the mid-February to mid-March time window in the Mediterranean Sea. Preliminary tests on the method applicability to satellite-derived data are also presented and discussed.

Air–sea heat fluxes are essential climate variables, required for understanding air–sea interactions, local, regional and global climate, the hydrological cycle and atmospheric and oceanic circulation. In situ measurements of fluxes over the ocean are sparse and model reanalysis and satellite data can provide estimates at different scales. The accuracy of such estimates is therefore essential to obtain a reliable description of the occurring phenomena and changes. In this work, air–sea radiative fluxes derived from the SEVIRI sensor onboard the MSG satellite and from ERA5 reanalysis have been compared to direct high quality measurements performed over a complete annual cycle at the ENEA oceanographic observatory, near the island of Lampedusa in the Central Mediterranean Sea. Our analysis reveals that satellite derived products overestimate in situ direct observations of the downwelling short-wave (bias of 6.1 W/m2) and longwave (bias of 6.6 W/m2) irradiances. ERA5 reanalysis data show a negligible positive bias (+1.0 W/m2) for the shortwave irradiance and a large negative bias (−17 W/m2) for the longwave irradiance with respect to in situ observations. ERA5 meteorological variables, which are needed to calculate the air–sea heat flux using bulk formulae, have been compared with in situ measurements made at the oceanographic observatory. The two meteorological datasets show a very good agreement, with some underestimate of the wind speed by ERA5 for high wind conditions. We investigated the impact of different determinations of heat fluxes on the near surface sea temperature (1 m depth), as determined by calculations with a one-dimensional numerical model, the General Ocean Turbulence Model (GOTM). The sensitivity of the model to the different forcing was measured in terms of differences with respect to in situ temperature measurements made during the period under investigation. All simulations reproduced the true seasonal cycle and all high frequency variabilities. The best results on the overall seasonal cycle were obtained when using meteorological variables in the bulk formulae formulations used by the model itself. The derived overall annual net heat flux values were between +1.6 and 40.4 W/m2, depending on the used dataset. The large variability obtained with different datasets suggests that current determinations of the heat flux components and, in particular, of the longwave irradiance, need to be improved. The ENEA oceanographic observatory provides a complete, long-term, high resolution time series of high quality in situ observations. In the future, more similar sites worldwide will be needed for model and satellite validations and to improve the determination of the air–sea exchange and the understanding of related processes.

Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models.

Measurements of ocean surface topography collected by satellite altimeters provide geostrophic estimates of the sea surface currents at relatively low resolution. The effective spatial and temporal resolution of these velocity estimates can be improved by optimally combining altimeter data with sequences of high resolution interpolated (Level 4) Sea Surface Temperature (SST) data, improving upon present-day values of approximately 100 km and 15 days at mid-latitudes. However, the combined altimeter/SST currents accuracy depends on the area and input SST data considered. Here, we present a comparative study based on three satellite-derived daily SST products: the Remote Sensing Systems (REMSS, 1/10 ∘ resolution), the UK Met Office OSTIA (1/20 ∘ resolution), and the Multiscale Ultra-High resolution SST (1/100 ∘ resolution). The accuracy of the marine currents computed with our synergistic approach is assessed by comparisons with in-situ estimated currents derived from a global network of drifting buoys. Using REMSS SST, the meridional currents improve up to more than 20% compared to simple altimeter estimates. The maximum global improvements for the zonal currents are obtained using OSTIA SST, and reach 6%. Using the OSTIA SST also results in slight improvements (≃1.3%) in the zonal flow estimated in the Southern Ocean (45 ∘ S to 70 ∘ S). The homogeneity of the input SST effective spatial resolution is identified as a crucial requirement for an accurate surface current reconstruction. In our analyses, this condition was best satisfied by the lower resolution SST products considered.

In the coastal environment the optical properties can vary on temporal scales that are shorter than the near-polar orbiting satellite temporal resolution (~1 image per day), which does not allow capturing most of the coastal optical variability. The objective of this work is to fill the gap between the near-polar orbiting and geostationary sensor temporal resolutions, as the latter sensors provide multiple images of the same basin during the same day. To do that, a Level 3 hyper-temporal analysis-ready Ocean Color (OC) dataset, named Virtual Geostationary Ocean Color Sensor (VGOCS), has been created. This dataset contains the observations acquired over the North Adriatic Sea by the currently functioning near-polar orbiting sensors, allowing approaching the geostationary sensor temporal resolution. The problem in using data from different sensors is that they are characterized by different uncertainty sources that can introduce artifacts between different satellite images. Hence, the sensors have different spatial and spectral resolutions, their calibration procedures can have different accuracies, and their Level 2 data can be retrieved using different processing chains. Such differences were reduced here by adjusting the satellite data with a multi-linear regression algorithm that exploits the Fiducial Reference Measurements data stream of the AERONET-OC water-leaving radiance acquired at the Acqua Alta Oceanographic Tower, located in the Gulf of Venice. This work aims to prove the suitability of VGOCS in analyzing the coastal optical variability, presenting the improvement brought by the adjustment on the quality of the satellite data, the VGOCS spatial and temporal coverage, and the inter-sensor differences. Hence, the adjustment will strongly increase the agreement between the satellite and in situ data and between data from different near-polar orbiting OC imagers; moreover, the adjustment will make available data traditionally masked in the standard processing chains, increasing the VGOCS spatial and temporal coverage, fundamental to analyze the coastal optical variability. Finally, the fulfillment by VGOCS of the three conditions for a hyper-temporal dataset will be demonstrated in this work.

In the context of the Copernicus Program, EUMETSAT prioritizes the creation of an ocean color infrastructure for system vicarious calibration (OC-SVC). This work aims to reply to this need by proposing the European Radiometry Buoy and Infrastructure (EURYBIA). EURYBIA is designed as an autonomous European infrastructure operating within the Marine Optical Network (MarONet) established by University of Miami (Miami, FL, USA) based on the Marine Optical Buoy (MOBY) experience and NASA support. MarONet addresses SVC requirements in different sites, consistently and in a traceable way. The selected EURYBIA installation is close to the Lampedusa Island in the central Mediterranean Sea. This area is widely studied and hosts an Atmospheric and Oceanographic Observatory for long-term climate monitoring. The EURYBIA field segment comprises off-shore and on-shore infrastructures to manage the observation system and perform routine sensors calibrations. The ground segment includes the telemetry center for data communication and the processing center to compute data products and uncertainty budgets. The study shows that the overall uncertainty of EURYBIA SVC gains computed for the Sentinel-3 OLCI mission under EUMETSAT protocols is of about 0.05% in the blue-green wavelengths after a decade of measurements, similar to that of the reference site in Hawaii and in compliance with requirements for climate studies.

Estimating long-term modifications of the sea surface temperature (SST) is crucial for evaluating the current state of the oceans and to correctly assess the impact of climate change at regional scales. In this work, we analyze SST variations within the Mediterranean Sea and the adjacent Northeastern Atlantic box (west of the Strait of Gibraltar) over the last 37 years, by using a satellite-based dataset from the Copernicus Marine Environment Monitoring Service (CMEMS). We found a mean warming trend of 0.041 ± 0.006 ∘ C/year over the whole Mediterranean Sea from 1982 to 2018. The trend has an uneven spatial pattern, with values increasing from 0.036 ± 0.006 ∘ C/year in the western basin to 0.048 ± 0.006 ∘ C/year in the Levantine−Aegean basin. The Northeastern Atlantic box and the Mediterranean show a similar trend until the late 1990s. Afterwards, the Mediterranean SST continues to increase, whereas the Northeastern Atlantic box shows no significant trend, until ~2015. The observed change in the Mediterranean Sea affects not only the mean trend but also the amplitude of the Mediterranean seasonal signal, with consistent relative increase and decrease of summer and winter mean values, respectively, over the period considered. The analysis of SST changes occurred during the “satellite era” is further complemented by reconstructions also based on direct in situ SST measurements, i.e., the Extended Reconstructed SST (ERSST) and the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST), which go back to the 19th century. The analysis of these longer time series, covering the last 165 years, indicates that the increasing Mediterranean trend, observed during the CMEMS operational period, is consistent with the Atlantic Multidecadal Oscillation (AMO), as it closely follows the last increasing period of AMO. This coincidence occurs at least until 2007, when the apparent onset of the decreasing phase of AMO is not seen in the Mediterranean SST evolution.

We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'GN/LGN, where L'GN is the MODIS-retrieved normalized sun glint radiance image and LGN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold Rs as a function of L'GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for Rs, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images.

Sediment dynamics is the primary driver of the evolution of the coastal geomorphology and of the underwater shelf clinoforms. In this paper, we focus on mesoscale and sub-mesoscale processes, such as coastal currents and river plumes, and how they shape the sediment dynamics at regional or basin spatial scales. A new methodology is developed that combines observational data with numerical modelling: the aim is to pair satellite measurements of suspended sediment with velocity fields from numerical oceanographic models, to obtain an estimation of the sediment flux. A numerical divergence of this flux is then computed. The divergence field thus obtained shows how the aforementioned mesoscale processes distribute the sediments. The approach was applied and discussed on the Adriatic Sea, for the winter of 2012, using data provided by the ESA Coastcolour project and the output of a run of the MIT General Circulation Model.

We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

We present a method for the remote retrieval of the sea surface currents in the Mediterranean Sea. Combining the altimeter-derived currents with sea-surface temperature information, we created daily, gap-free high resolution maps of sea surface currents for the period 2012–2016. The quality of the new multi-sensor currents has been assessed through comparisons to other surface-currents estimates, as the ones obtained from drifting buoys trajectories (at the basin scale), or HF-Radar platforms and ocean numerical model outputs in the Malta–Sicily Channel. The study yielded that our synergetic approach can improve the present-day derivation of the surface currents in the Mediterranean area up to 30% locally, with better performances for the the meridional component of the motion and in the western section of the basin. The proposed reconstruction method also showed satisfying performances in the retrieval of the ageostrophic circulation in the Sicily Channel. In this area, assuming the High Frequency Radar-derived currents as reference, the merged multi-sensor currents exhibited improvements with respect to the altimeter estimates and numerical model outputs, mainly due to their enhanced spatial and temporal resolution.

Knowledge of the vertical structure of the bio-chemical properties of the ocean is crucial for the estimation of primary production, phytoplankton distribution, and biological modelling. The vertical profiles of chlorophyll-a (Chla) are available via in situ measurements that are usually quite rare and not uniformly distributed in space and time. Therefore, obtaining estimates of the vertical profile of the Chla field from surface observations is a new challenge. In this study, we employed an Artificial Neural Network (ANN) to reconstruct the 3-Dimensional (3D) Chla field in the Mediterranean Sea from surface satellite estimates. This technique is able to reproduce the highly nonlinear nature of the relationship between different input variables. A large in situ dataset of temperature and Chla calibrated fluorescence profiles, covering almost all Mediterranean Sea seasonal conditions, was used for the training and test of the network. To separate sources of errors due to surface Chla and temperature satellite estimates, from errors due to the ANN itself, the method was first applied using in situ surface data and then using satellite data. In both cases, the validation against in situ observations shows comparable statistical results with respect to the training, highlighting the feasibility of applying an ANN to infer the vertical Chla field from surface in situ and satellite estimates. We also analyzed the usefulness of our approach to resolve the Chla prediction at small temporal scales (e.g., day) by comparing it with the most widely used Mediterranean climatology (MEDATLAS). The results demonstrated that, generally, our method is able to reproduce the most reliable profile of Chla from synoptical satellite observations, thus resolving finer spatial and temporal scales with respect to climatology, which can be crucial for several marine applications. We demonstrated that our 3D reconstructed Chla field could represent a valid alternative to overcome the absence or discontinuity of in situ sampling.