OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories

• URL: http://arxiv.org/abs/2508.21570v1
• Date: Fri, 29 Aug 2025 12:25:26 GMT
• Title: OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories
• Authors: Bo Li, Yingqi Feng, Ming Jin, Xin Zheng, Yufei Tang, Laurent Cherubin, Alan Wee-Chung Liew, Can Wang, Qinghua Lu, Jingwei Yao, Shirui Pan, Hong Zhang, Xingquan Zhu,
• Abstract summary: Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy.<n>Traditional approaches rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates.<n>We introduce the OceAn Salinity Imputation System (OASIS), a novel diffusion adversarial framework designed to address these challenges.
• Score: 55.860116803220535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. Traditional approaches, such as remote sensing and optimal interpolation, rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates. While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate physical covariates without specialized sensors. In this paper, we introduce the OceAn Salinity Imputation System (OASIS), a novel diffusion adversarial framework designed to address these challenges.

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