The mean sea surface (MSS) is an important field in physical oceanography, geophysics, and geodesy. In principle, it corresponds to the time-averaged height of the ocean surface. Auxiliary product : mean sea profile above a reference ellipsoid (T/P or WSG84). This surface is available on a regular grid (1/60°x1/60°, 1 minute). The mean sea surface MSS_CNES_CLS2025 (Charayon et al., in prep) has been computed using a 29-year [1993-2021] period of altimetric data. It was built exploring the use of SWOT KaRIn data to propose a new generation MSS able to better represent small wavelengths gapping state-of-the-art nadir-based MSS. Long wavelengths come from a MSS draft chosen to be the Hybrid 23 MSS (https://doi.org/10.24400/527896/a01-2024.002 ) low pass filtered at 20km since this nadir-based MSS model already captured well long wavelengths. The wavelengths shorter than 100km are improved thanks to a SWOT-KaRIn mean profile anomaly to the MSS draft. This mean profile anomaly is built with: - The SWOT-KaRIn science (L3 v3.0 Expert, cycles 1 to 31 included except cycle 17, https://doi.org/10.24400/527896/a01-2023.018) dataset - The experimental multimission gridded L4 sea level heights and velocities with SWOT using MIOST (https://doi.org/10.24400/527896/a01-2025.001 ). - In SWOT diamonds, since KaRIn data are not available, we used another innovation only based on DT2024 1Hz CryoSat2 and AltiKa SSHA data (https://doi.org/10.48670/moi-00146), period from 2010 to 2025, SSHA data relative to MSS Hybrid 23 low pass filtered at 5km. To build this nadir-based innovation, we used the static part of the mapping from MIOST method.

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.

This dataset provides an estimation of the climate feedback parameter and the climate sensitivity developed in collaboration by Magellium and LEGOS. It is based on the publication of Meyssignac et al., (2023, https://doi.org/10.1038/s43247-023-00887-2) . The product is a crucial study on Effective Climate Sensitivity (ECS), focusing on the time variation of the climate feedback parameter (λ). Understanding λ is fundamental as it quantifies the Earth’s radiative response to changes in global average temperature. A less negative λ directly implies a greater climate sensitivity to greenhouse gas concentrations. The product addresses the high uncertainty in ECS by providing an observational estimate of λ temporal variations since 1970. Estimates are derived from the global energy balance equation, regressing the radiative response against temperature over periods longer than 25 years. Users are primarily interested in the time series of λ and its associated uncertainties. The input data for computing λ is provided in a secondary "extended product" for transparency, allowing users to rebuild the indicators. The final product, available as a NetCDF file, can be downloaded in open access and is licensed for any project or study.

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.

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.

Data record (2004-2012) of level 3 hourly sub-skin Sea Surface Temperature derived from Meteosat at 0° longitude, covering 60S-60N and 60W-60E and re-projected on a 0.05° regular grid, in GHRSST compliant netCDF format. The satellite input data come from the imager SEVIRI on MSG satellites (Meteosat-8 and Meteosat-9). SST is retrieved from SEVIRI infrared channels (10.8 and 12.0 µm) using a multispectral algorithm and the cloud mask from CM SAF. NWP outputs (temperature and humidity profiles), OSTIA Sea Surface Temperature re-analysis and analysis, together with a radiative transfer model (RTTOV), are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The product format is compliant with the GHRSST Data Specification (GDS) version 2. Users are advised to use data only with quality levels 3,4 and 5.

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.

Level 3, four times a day, sub-skin Sea Surface Temperature derived from AVHRR on Metop satellites and VIIRS or AVHRR on NOAA and NPP satellites, over North Atlantic and European Seas and re-projected on a polar stereographic at 2 km resolution, in GHRSST compliant netCDF format. This catalogue entry presents NOAA-19 North Atlantic Regional Sea Surface Temperature. SST is retrieved from infrared channels using a multispectral algorithm and a cloud mask. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, Sea Surface Temperature from an analysis, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The quality of the products is monitored regularly by daily comparison of the satellite estimates against buoy measurements. The product format is compliant with the GHRSST Data Specification (GDS) version 2.Users are advised to use data only with quality levels 3,4 and 5.

Level 3, four times a day, sub-skin Sea Surface Temperature derived from AVHRR on Metop satellites and VIIRS or AVHRR on NOAA and NPP satellites, over North Atlantic and European Seas and re-projected on a polar stereographic at 2 km resolution, in GHRSST compliant netCDF format. This catalogue entry presents NOAA-20 North Atlantic Regional Sea Surface Temperature. SST is retrieved from infrared channels using a multispectral algorithm and a cloud mask. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, Sea Surface Temperature from an analysis, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The quality of the products is monitored regularly by daily comparison of the satellite estimates against buoy measurements. The product format is compliant with the GHRSST Data Specification (GDS) version 2.Users are advised to use data only with quality levels 3,4 and 5.

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/