An Adaptive Spatiotemporal Clustering Framework for 3D Ocean Subsurface Temperature Reconstruction

• URL: http://arxiv.org/abs/2605.00860v1
• Date: Tue, 21 Apr 2026 09:32:37 GMT
• Title: An Adaptive Spatiotemporal Clustering Framework for 3D Ocean Subsurface Temperature Reconstruction
• Abstract summary: This study proposes an adaptive framework to capture both structural dependencies and temporal variation patterns of ocean surface temperature (OST)<n>By incorporating this framework with various deep learning models, the OST field can be accurately reconstructed at a global scale.<n> Experimental results demonstrate that multiple deep learning methods using the framework largely outperform their original counterparts.
• Score: 31.24880527218282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reconstruction of ocean subsurface temperature (OST) using satellite remote sensing data holds significant scientific value for advancing the understanding of ocean dynamics and climate variability. However, the scarcity of subsurface observations, combined with the high degree of nonlinearity and spatiotemporal heterogeneity in subsurface processes, poses substantial challenges to the accuracy and generalization capability of traditional reconstruction methods. To address these limitations, this study proposes an adaptive framework that could capture both vertical structural dependencies and temporal variation patterns of OST via spatio-temporal clustering. By incorporating this framework with various deep learning models, e.g., dual-path convolutional neural networks (DP-CNN), Attention U-Net, and Vision Transformer (ViT), the OST field can be accurately reconstructed at a global scale only using surface observations, i.e., sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). Experimental results demonstrate that multiple deep learning methods using the proposed framework largely outperform their original counterparts, yielding improvements in RMSE ranging from 12.4\% to 27.2\%. This study provides a reliable solution for subsurface temperature reconstruction, offering important implications for meteorological modeling and climate change assessment.

Related papers

• OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations [1.3348917151047532]
  We introduce OceanDepths, the first open, global, regridded AI-ready dataset.<n>It 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.<n>The dataset spans 2000-2024 at 0.1 degrees x 0.1 degrees spatial resolution and at weekly temporal resolution.
  arXiv  Detail & Related papers  (2026-08-17T10:29:18Z)
• High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations [0.0]
  Ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements.<n>We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely sparse surface data.
  arXiv  Detail & Related papers  (2026-04-03T08:10:55Z)
• OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation [55.978228064498865]
  We present OceanSAR-2, the second generation of our foundation model for SAR-based ocean observation.<n>Building on our earlier release, which pioneered self-supervised learning on Sentinel-1 Wave Mode data, OceanSAR-2 relies on improved SSL training and dynamic data curation strategies.
  arXiv  Detail & Related papers  (2026-01-12T10:20:43Z)
• Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting [0.0]
  We adapt a deep learning model to predict sea temperature (SST) in the Canary Upwelling System.<n>By fine-tuning this model with high-resolution oceanographic reanalysis data, we demonstrate its ability to capture complex patterns.<n>The model successfully reproduces large-scale SST structures but faces challenges in capturing finer details in coastal regions.
  arXiv  Detail & Related papers  (2025-10-29T14:30:12Z)
• ReconMOST: Multi-Layer Sea Temperature Reconstruction with Observations-Guided Diffusion [48.540756751934836]
  ReconMOST is a data-driven guided diffusion model framework for multi-layer sea temperature reconstruction.<n>Our method extends ML-based SST reconstruction to a global, multi-layer setting, handling over 92.5% missing data.
  arXiv  Detail & Related papers  (2025-06-12T06:27:22Z)
• Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury's exospheric data [77.40388962445168]
  This study explores the feasibility of deriving Mercury's regolith elemental composition from in-situ measurements of its neutral exosphere using deep neural networks (DNNs)<n>We present a supervised feed-forward DNN architecture that predicts the chemical elements of the surface regolith below.<n>It serves as an estimator for the surface-exosphere interaction and the processes leading to exosphere formation.
  arXiv  Detail & Related papers  (2025-05-16T09:52:45Z)
• Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation [67.23953699167274]
  Self-supervised learning (SSL) has enabled the development of vision foundation models for Earth Observation (EO)<n>In EO, this challenge is amplified by the redundancy and heavy-tailed distributions common in satellite imagery.<n>We propose a dynamic dataset pruning strategy designed to improve SSL pre-training by maximizing dataset diversity and balance.
  arXiv  Detail & Related papers  (2025-04-09T15:13:26Z)
• Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion [34.00878406145686]
  We describe several Machine Learning models to fill the cloud-occluded areas starting from MODIS Aqua nighttime L3 images.<n>To tackle this challenge, we employed a type of Convolutional Neural Network model (U-net) to reconstruct cloud-covered portions of satellite imagery.<n>Our best-performing architecture show 50% lower root mean square errors over established gap-filling methods.
  arXiv  Detail & Related papers  (2024-12-04T15:49:49Z)
• OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning [50.365198230613956]
  Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. We propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023.
  arXiv  Detail & Related papers  (2024-05-12T09:32:40Z)
• Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields [46.72819846541652]
  We propose a framework based on Vari Data Assimilation and Deep Learning concepts. This framework is applied to recover rich-in-time, high-resolution information on sea surface wind speed.
  arXiv  Detail & Related papers  (2023-12-14T13:40:39Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction [24.55780949103687]
  Sea subsurface temperature is affected by global warming in climate change. Existing research is commonly based on either physics-based numerical models or data based models. We propose a novel framework based on generative adversarial network (GAN) combined with numerical model to predict sea subsurface temperature.
  arXiv  Detail & Related papers  (2021-11-04T23:46:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.