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Life sciences · Preprint
arXiv · August 17, 2026
Early or partial results. Treat as a signal, not a conclusion.
OceanDepths is an open-access, regridded dataset coupling satellite-derived sea surface fields (SST, SSS, SSH) to co-located in situ subsurface temperature and salinity profiles across 24 years at high spatial and temporal resolution. The authors demonstrate subsurface state reconstruction using baseline models and present the resource as a testbed for AI methods, but the work is a resource paper with proof-of-concept examples rather than a hypothesis test or clinical validation study.
Preprint. Global ocean surface and subsurface observations from satellite L4 products (SST, SSS, SSH) and co-located in situ EN4 profiles (temperature and salinity) supplemented with GLORYS12 reanalysis.. Global; entire sea surface coverage.
Dataset spans 2000–2024 at 0.1° × 0.1° spatial resolution and weekly temporal resolution Over 9.5 million paired profiles interpolated to 50 standardized depth levels Extreme sparsity of subsurface observations: ~0.01% per depth level
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This is a dataset paper describing resource development and validation with baseline models, not a clinical trial or observational study with health outcomes; it represents foundational infrastructure work suitable for methodological development.
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Despite comprising over 70\% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere.Understanding the global ocean requires jointly observing its surface and subsurface structure, yet no standardized, high-resolution dataset couples satellite surface fields to co-located \emph{in situ} depth profiles in an AI-ready format.Existing resources either consist of model-reconstructed gridded products rather than observations, cover only a single variable or basin, or operate at resolutions too coarse for mesoscale dynamics.We introduce \textsc{OceanDepths}, the first open, global, regridded AI-ready dataset that pairs satellite-derived sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) L4 products with co-located EN4 subsurface temperature and salinity profiles, complemented by matched GLORYS12 ocean reanalysis data to support comparisons or multi-stage learning.The dataset spans 2000--2024 at \SI{0.1}{\degree}$\times$\SI{0.1}{\degree} spatial resolution and at weekly temporal resolution, covering the entire globe's sea surface and with over 9.5 million paired profiles interpolated to 50 standardized depth levels.We provide a configurable system to split the globe in equally sized spatial patches.The 4D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations (${\sim}$0.01\% per depth level) make \textsc{OceanDepths}a challenging testbed for novel AI methods.We demonstrate subsurface state reconstruction as an example task with simple baseline models, but also envision \textsc{OceanDepths}to support the development of observation-based forecast methods and other related tasks.\added{Available at: https://huggingface.co/datasets/ESA-philab/OceanDepths.}
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