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arXiv · 2503.11532

Observation-only learning of neural mapping schemes for gappy satellite-derived ocean colour parameters

Clément Dorffer·Frédéric Jourdin·Thi Thuy Nga Nguyen·Rodolphe Devillers·David Mouillot·Ronan Fablet
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Abstract

Monitoring optical properties of coastal and open ocean waters is crucial to assessing the health of marine ecosystems. Deep learning offers a promising approach to address these ecosystem dynamics, especially in scenarios where gap-free ground-truth data is lacking, which poses a challenge for designing effective training frameworks. Using an advanced neural variational data assimilation scheme (called 4DVarNet), we introduce a comprehensive training framework designed to effectively train directly on gappy data sets. Using the Mediterranean Sea as a case study, our experiments not only highlight the high performance of the chosen neural network in reconstructing gap-free images from gappy datasets but also demonstrate its superior performance over state-of-the-art algorithms such as DInEOF and Direct Inversion, whether using CNN or UNet architectures.

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Clément Dorffer, Frédéric Jourdin, Thi Thuy Nga Nguyen, Rodolphe Devillers, David Mouillot, Ronan Fablet. 2025-03-14. Observation-only learning of neural mapping schemes for gappy satellite-derived ocean colour parameters. https://doi.org/10.1109/tgrs.2025.3624465

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