Observation-only deep learning for gappy satellite-derived ocean color data using 4DVarNet Dorffer, C. Jourdin, F. Nguyen, T.T.N. /Devillers, Rodolphe Mouillot, D. Fablet, R. 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 gapfree 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 end-to-end neural mapping schemes based CNN or UNet architectures. 2025 text https://www.documentation.ird.fr/hor/fdi:010095766 fdi:010095766 Dorffer C., Jourdin F., Nguyen T.T.N., Devillers Rodolphe, Mouillot D., Fablet R.. Observation-only deep learning for gappy satellite-derived ocean color data using 4DVarNet. 2025, 63, 4212512 [12 ] EN MEDITERRANEE