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Article Dans Une Revue IEEE Transactions on Computational Imaging Année : 2017

Data-driven Models for the Spatio-Temporal Interpolation of satellite-derived SST Fields

Résumé

Satellite-derived products are of key importance for the high-resolution monitoring of the ocean surface on a global scale. Due to the sensitivity of spaceborne sensors to the atmospheric conditions as well as the associated spatio-temporal sampling, ocean remote sensing data may be subject to high-missing data rates. The spatio-temporal interpolation of these data remains a key challenge to deliver L4 gridded products to end-users. Whereas operational products mostly rely on model-driven approaches, especially optimal interpolation based on Gaussian process priors, the availability of large-scale observation and simulation datasets calls for the development of novel data-driven models. This study investigates such models. We extend the recently introduced analog data assimilation to high-dimensional spatio-temporal fields using a multi-scale patch-based decomposition. Using an Observing System Simulation Experiment (OSSE) for sea surface temperature, we demonstrate the relevance of the proposed data-driven scheme for the real missing data patterns of the high-resolution infrared METOP sensor. It has resulted in a significant improvement w.r.t. state-of-the-art techniques in terms of interpolation error (about 50\% of relative gain) and spectral characteristics for horizontal scales smaller than 100km. We further discuss the key features and parameterizations of the proposed data-driven approach as well as its relevance with respect to classical interpolation techniques.
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Dates et versions

hal-01656178 , version 1 (05-12-2017)

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Citer

Ronan Fablet, Phi Huynh Viet, Redouane Lguensat. Data-driven Models for the Spatio-Temporal Interpolation of satellite-derived SST Fields. IEEE Transactions on Computational Imaging, 2017, 4, pp.647 - 657. ⟨10.1109/TCI.2017.2749184⟩. ⟨hal-01656178⟩
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