These gridded products are produced from the following upstream data: - for satellites SARAL/AltiKa, Cryosat-2, HaiYang-2B, Jason-3, Copernicus Sentinel-3A/B, Sentinel-6 MF, SWOT Nadir => NRT (Near-Real-Time) Nadir along-track (or Level-3) SEA LEVEL products (DOI: https://doi.org/10.48670/moi-00147) delivered by the Copernicus Marine Service (http://marine.copernicus.eu/ ). The gridded product is based on near-real-time (NRT) Level-3 Nadir datasets for the period from July 7, 2025, to December 31, 2025. => MY (Multi-Year) Nadir along-track (or Level-3) SEA LEVEL products (DOI: https://doi.org/10.48670/moi-00146 ) delivered by the Copernicus Marine Service (CMEMS, http://marine.copernicus.eu/ ). The gridded product is based on MY Level-3 Nadir datasets for the period from March 28, 2023, to July 6, 2025. - for SWOT KaRIn : the L3_LR_SSH Expert v3.0 product distributed by AVISO (DOI: https://doi.org/10.24400/527896/A01-2023.018) from March 28, 2023 to December 31, 2025. One mapping algorithm is proposed: the MIOST approach which provides which provides global Sea Surface Height (SSH) solutions. The MIOST method is capable of accounting for various modes of ocean surface topography variability (e.g., geostrophic, barotropic, equatorial wave dynamics) by constructing multiple independent components within a predefined covariance model.

Archive de toutes les données de température de surface (SST) satellite produites dans le cadre du projet international GHRSST. Ifremer est un GDAC pour ces données, miroir du GDAC NASA/JPL. Ces données sont utilisées pour la génération de produits multi-capteurs (CMEMS, Medspiration) mais également dans le cadre d'un grand nombre d'études ou projets nécessitant l'utilisation de mesures de SST. L'archive regroupe plusieurs jeux de données provenant de différents satellite ainsi que des données in situ de référence pour leur validation. Elle est mise à jour en temps quasi-réel depuis 10 ans, avec service de diffusion opérationnelle associé (FTP et HTTP). Une fiche sextant (issue du catalogue CERSAT) sera fournie pour chaque dataset dans cette archive.

387 points were surveyed with a SP80 DGPS by Maxime Paschal as part of the La Rochelle Zero Carbon Territory (LRTZC) project on 26/05/23. At each point, the type of vegetation was specified.

This dataset provides detections of fronts derived from high resolution remote sensing SST observations by SEVIRI L3C from OSISAF over Western Europe region. The data are available through HTTP and FTP; access to the data is free and open. In order to be informed about changes and to help us keep track of data usage, we encourage users to register at: https://forms.ifremer.fr/lops-siam/access-to-esa-world-ocean-circulation-project-data/ This dataset was generated by OceanDataLab and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).

Satellite altimeters routinely supply sea surface height (SSH) measurements which are key observations to monitor ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in the signal-to-noise ratio, making it very challenging to fully exploit available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinctive methodology emerged to be systematically applied in operational products. To best cope with this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination in the along-track SSH signals and more innovative and adapted noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. Here demonstrated, a fully data-driven approach is developed and applied to provide robust estimates of noise-free Sea Level Anomaly (SLA) signals. The method combines Empirical Mode Decomposition (EMD), to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by Discrete Wavelet Transform (DWT) decompositions. It is now found to best resolve the distribution of the sea surface height variability in the mesoscale 30-120 km wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local signal to noise ratio, but also for uncertainties in the denoising process, which assumes that SLA variability results in part from a stochastic process. Here, measurements from the Jason-3, Sentinel-3 A and SARAL/AltiKa altimeters are processed and analyzed, and their energy spectral and seasonal distributions characterized in the small mesoscale domain. Anticipating data from the upcoming Surface Water and Ocean Topography (SWOT) mission, these denoised SLA measurements for three reference altimeter missions already yield valuable opportunities to assess global small mesoscale kinetic energy distributions. This dataset was developed within the Ocean Surface Topography Science Team (OSTST) activities. A grant was awarded to the SASSA (Satellite Altimeter Short-scale Signals Analysis) project by the TOSCA board in the framework of the CNES/EUMETSAT call CNES-DSP/OT 12-2118. Altimeter data were provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and by the Sea State Climate Change Initiative (CCI) project.

The Sentinel-6 Level-2P skewness products was developed to estimate the skewness from Sentinel-6 LR (Low Resolution Mode) and HR (High Resolution Mode) acquisitions. That demonstration product is generated by different retracking processes, provides an initial estimation of such a phenomenon and allows a finer description of the sea state.

This Level 2 product provides marine reflectances from the VENµS mission, processed with the Polymer algorithm, on a subset of sites with coastal or inland areas. VENµS (Vegetation and Environment monitoring on a New Micro-Satellite) is a Franco-Israeli satellite launched in 2017, dedicated to the fine and regular monitoring of terrestrial vegetation, in particular cultivated areas, forests, protected natural areas, etc. The images acquired in 12 spectral bands by a camera provided by CNES, on a selection of about one hundred scientific sites spread over the planet, are of high spatial (5 m) and temporal resolution. The lifetime of the VENµS satellite has been divided into two phases: a first phase VM1 at an altitude of 720 km with a 2-day revisit, a native spatial resolution of 5.3 m and a swath of 27.6 km from August 2017 to November 2020, and a second phase VM5 at an altitude of 560 km with a daily revisit, a native spatial resolution of 4.1 m and a swath of 21.3 km from March 2022 to July 2024. VENµS is the first sensor on board an orbiting satellite to combine such revisit frequency and spatial finesse for vegetation monitoring. A subset of sites with coastal areas or inland waters have been identified to generate Level 2 data dedicated to marine reflectance. The geographical areas covered are given through a kmz file, see below to download it. This Level 2 data product has been processed using the Polymer algorithm developed by Hygeos (https://hygeos.com/en/polymer/) and provides marine reflectances for the VENµS bands from 420 to 865 nm. These reflectances, without units, include a bidirectional normalization for the Sun at nadir and the observer at nadir. VENµS data products (Level-1, Level-2 and Level-3) are primarily generated with the MAJA algorithm, further information can be found on THEIA website: https://www.theia-land.fr/en/product/venus/

The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite synthetic aperture radar (SAR) integrated sea state parameters (ISSP) data from ENVISAT (referred to as SAR Wave Mode onboard ENVISAT Level 2P (L2P) ISSP data) with a particular focus for use in climate studies. This dataset contains the ENVISAT Remote Sensing Integrated Sea State Parameter product (version 1.1), which forms part of the ESA Sea State CCI version 3.0 release. This product provides along-track significant wave height (SWH) measurements at 5km resolution every 100km, processed using the Li et al., 2020 empirical model, separated per satellite and pass, including all measurements with flags and uncertainty estimates. These are expert products with rich content and no data loss. The SAR Wave Mode data used in the Sea State CCI SAR WV onboard ENVISAT Level 2P (L2P) ISSP v3 dataset come from the ENVISAT satellite mission spanning from 2002 to 2012.

In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (eg become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (eg a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into ‘regions of interest’, spatiotemporal cylinders consisting of circles on the Earth’s surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is done for each of the other datasets, producing a dataset of matchups. About 35 million in situ datapoints were then matched with data from five satellite sources and five model and re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of 287,000 regions of interest spanning 54 years from 1957 to 2023. Each region of interest is 50 km in diameter and 5 days in duration, improving the spatial and temporal resolution of the previous version (v3.4). The list of sources added in this dataset includes: - sea surface temperature and sea ice concentration from Copernicus Climate Service Multi-satellite L4 (http://dx.doi.org/10.5285/62c0f97b1eac4e0197a674870afe1ee6) - sea surface salinity from the Copernicus Marine service Multi Observation Global Ocean Sea Surface Salinity and Sea Surface Density (https://doi.org/10.48670/moi-00051) - Surface ocean sea-air CO2 fluxes and total alkalinity from ETH Zurich OceanSODA-ETHZ-v2 gridded dataset (https://doi.org/10.5281/zenodo.11206366) - salinity and mixed layer depth from SODA v3.4.2 reanalysis (https://doi.org/10.1175/JCLI-D-18-0149.1) - chlorophyll-A from ESA Ocean Colour CCI v6 (doi:10.3390/s19194285) - wind at 10 m and mean sea level pressure from ERA5 reanalysis - nitrate, silicate, phosphate, oxygen, temperature and salinity from World Ocean Atlas 2018 - sea level anomaly from Global Ocean Gridded L4 Sea Surface Heights And Derived Variables Multi-Year dataset by Copernicus Marine Service Information (https://doi.org/10.48670/moi-00148) - sea surface salinity from ESA Salinity CCI L4 v3.2.1 (https://dx.doi.org/10.5285/5920a2c77e3c45339477acd31ce62c3c) - sea surface salinity from JPL SMAP Level 3 CAP Sea Surface Salinity (https://doi.org/10.5067/SMP40-3SPCS) - temperature and salinity from Coriolis Observation Re-Analysis CORA5.2 by Copernicus Marine Service (https://doi.org/10.17882/46219) - subskin sea surface temperature from NOAA OISST SST (http://doi.org/10.5067/GHAAO-4BC01) - sea surface salinity from the Arctic salinity dataset (https://doi.org/10.20350/digitalCSIC/9065) the Barcelona Expert Center (http://bec.icm.csic.es/) - pH and spCO2 from Copernicus Marine Service Surface Ocean Carbon Dataset (https://doi.org/10.48670/moi-00047) An example application, the reparameterisation of a global total alkalinity algorithm, is shown. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example to enable regional studies. This dataset was funded by ESA OceanHealth / Ocean Acidification project which aims at developing the use of satellite Earth Observation for studying and monitoring marine carbonate chemistry.

This daily High-Resolution (HR) Level 3 gridded wind product is derived from Copernicus Sentinel-1 SAR (Synthetic Aperture Radar) observations, over the Mediterranean Sea ("MED" area). It is based on the European Space Agency (ESA) Level-2 OCN products at the highest available resolution. Although L2-OCN products already contain wind vectors, those are calculated using the CMOD5.n Geophysical Model Function (GMF) applied to the co-polarized (co-pol) VV channel (emitting in Vertical polarization and receiving in Vertical polarization). This VV GMF was mapped from scatterometer sensors (Hersbach et al., 2007) which are only able to use co-pol measurements. However, these co-pol GMF are known to lose sensitivity for wind above 20 m/s. Therefore, wind based on such GMF alone, are known to under-estimate wind speed (Polverari et al., 2022). For the L3 products winds based on SAR, we take advantage of the available cross-polarized (cross-pol) VH channel (emitting in Vertical polarization and receiving in Horizontal polarization) for which GMF were specifically derived based on C-Band SAR (Mouche et al., 2017, Mouche et al., 2019). Winds estimated from the combination of both the co-pol and cross-pol channels are referred to as dual-polarization (or dual-pol) winds. As shown in Mouche et al. (2019), taking advantage of the dual polarization strongly improves the wind estimation for high wind conditions thanks to the much greater VH channel sensitivity compared to VV. These new wind estimations are then gridded with a 0.012 degree resolution (between 0.5 and 1.2 km in zonal direction depending on the latitude and 1.3 km in meridional direction) using a cylindrical equidistant projection, independently for ascending and descending satellite passes and for each satellite (so 4 wind fields are available per day for two satellites). This dataset is generated over all Sentinel-1 mission time series starting from March 2018 and updated in delayed mode with a 4-months delay. It is also produced for 4 other different European areas. This dataset is produced and disseminated in the frame of Copernicus Marine Service.