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.

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 1, 2024, to December 31, 2024. => 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 June 30, 2024. - for SWOT KaRIn : the SEA LEVEL products L3_LR_SSH (V2.0.1) distributed by AVISO for Expert SWOT Level-3 SSH KaRin (DOI: https://doi.org/10.24400/527896/A01-2023.018) for the period from March 28, 2023 to December 31, 2024. One mapping algorithm is proposed: the MIOST approach which give the global SSH solutions: the MIOST method is able of accounting for various modes of variability of the ocean surface topography (e.g., geostrophic, barotrope, equatorial waves dynamic, etc.) by constructing several independent components within an assumed covariance model.

These gridded products are produced from the along-track (or Level-3) SEA LEVEL products (DOI: doi.org/10.48670/moi-00147) delivered by the Copernicus Marine Service (CMEMS, marine.copernicus.eu) for satellites SARAL/AltiKa, Cryosat-2, HaiYang-2B, Jason-3, Copernicus Sentinel-3A/B, Sentinel-6 MF, SWOT nadir, and SWOT Level-3 KaRIn sea level products (DOI: https://doi.org/10.24400/527896/A01-2023.018). Three mapping algorithms are proposed: MIOST, 4DvarNET, 4DvarQG: - the MIOST approach which give the global SSH solutions: the MIOST method is able of accounting for various modes of variability of the ocean surface topography (e.g., geostrophic, barotrope, equatorial waves dynamic …) by constructing several independent components within an assumed covariance model. - the 4DvarNET approach for the regional SSH solutions: the 4DvarNET mapping algorithm is a data-driven approach combining a data assimilation scheme associated with a deep learning framework. - the 4DvarQG approach for the regional SSH solutions: the 4DvarQG mapping technique integrates a 4-Dimensional variational (4DVAR) scheme with a Quasi-Geostrophic (QG) model.

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/

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 6A, 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 (CMEMS, http://marine.copernicus.eu/ ). The gridded product is based on NRT L3 Nadir datasets for the period from July 1, 2024, to December 31, 2024. => 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 L3 Nadir datasets for the period from March 28, 2023, to June 30, 2024. - for SWOT KaRIn : the SEA LEVEL products L3_LR_SSH (V2.0.1) delivered by AVISO for Expert SWOT L3 SSH KaRin (DOI: https://doi.org/10.24400/527896/A01-2023.018) for the period from March 28, 2023 to December 31, 2024. One mapping algorithm is proposed: the MIOST approach which give the global SSH solutions: the MIOST method is able of accounting for various modes of variability of the ocean surface topography (e.g., geostrophic, barotrope, equatorial waves dynamic …) by constructing several independent components within an assumed covariance model.

Monomission altimeter satellite along-track sea surface heights computed with respect to a twenty-year mean. Previously distributed by Aviso+, no change in the scientific content. All the missions are homogenized with respect to a reference mission which is currently OSTM/Jason-2. The sla is computed with an optimal and centered computation time window (6 weeks before and after the date). Two kinds of datasets are proposed: filtered (nominal dataset) and unfiltered.

Produit altimétrique combiné (multimissions) régional comprenant les hauteurs de surface (anomalies de hauteurs de mer, topographie dynamique) et variables dérivées (anomalies de courants géostrophiques et courants géostrophiques aboslus) calculées par rapport à une moyenne sur vingt ans.

La prévision saisonnière de sargasses repose sur le système décrit par Jouanno et al. (2023), actuellement opéré par le Laboratoire d’Étude en Géophysique et Océanographie Spatiale (LEGOS) et sa première version a été développée dans le cadre des projets ANR FORESEA et TOSCA SAREDA. Ce système repose sur un modèle mécaniste de population de sargasses, le NEMO-Sarg 1.0 (Jouanno et al. 2021,2023, 2025), qui intègre à la fois des modèles physiologiques et de transport des macro-algues. Le modèle prend en compte des quotas internes de nutriments variables (C, N, P). NEMO-Sarg est basé sur le modèle Nucleus for European Modeling of the Ocean (NEMO), permettant une parallélisation efficace et un interfaçage avec des modèles physico-biogéochimiques. La croissance des sargasses est modélisée comme une fonction des réserves internes de nutriments (quotas), des nutriments inorganiques dissous dans l'eau, du rayonnement et de la température de l'eau. Leur décroissance dépend de la sénescence et de l'état de mer. Jouanno et al. (2021, 2023, 2025) décrit le code et ses évolutions et illustre la capacité du modèle à représenter le cycle saisonnier des sargasses et le changement de régime autour de 2011. La version d'ensemble du modèle de sargasses avec une résolution horizontal à 1/4° a été implémentée et permet la production mensuelle de prévisions à 7 mois, initialisé avec l'estimation de la couverture surfacique des sargasses en quasi temps-réel du Moderate Resolution Imaging Spectroradiometer MODIS (Berline & Descloitres, 2021). La prévision est le résultats d'ensembles de 25 membres pour prendre en compte l'incertitude de la prévisibilité du modèle couplé océan-atmosphère. Pour chaque membre, le vent de surface et le rayonnement solaire sont obtenus d'un membre extrait aléatoirement parmi les 51 membres de la cinquième génération du système de prévision saisonnière SEAS5 du European Centre for Medium-Range Weather Forecasts (ECMWF, Johnson et al., 2019). Les courants de surface, la température et la salinité sont obtenus d'ensembles de 25 prévisions océanographiques physiques régionales basées sur NEMOv4.0 (Nucleus for European Modelling of the Ocean, Madec & The NEMO System Team, 2023) contraint avec les champs météorologiques des membres du SEAS5. Une climatologie basée sur le modèle d'analyse et de prévision biogéochimique BIO4 de Mercator Ocean International est utilisée pour les nutriments car il n'y a pas de prévision saisonnière biogéochimique performante accessible à la communauté. La limite de 7 mois pour la prévision des sargasses est imposée par le longueur du forcast opérationnel de l'ECMWF. Les performances de la prévision pour la période 2010-2022 sont décrites dans Jouanno et al. (2023).

Produits altimétriques monomissions OSTM/Jason-2. Ces produits ont été produits par CLS dans le cadre du projet Pistach, financé par le Cnes.