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arXiv · 2503.11588

Generalization performance of neural mapping schemes for the space-time interpolation of satellite-derived ocean colour datasets

Thi Thuy Nga Nguyen·Clément Dorffer·Frédéric Jourdin·Ronan Fablet
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Abstract

Neural mapping schemes have become appealing approaches to deliver gap-free satellite-derived products for sea surface tracers. The generalization performance of these learning-based approaches naturally arises as a key challenge. This is particularly true for satellite-derived ocean colour products given the variety of bio-optical variables of interest, as well as the diversity of processes and scales involved. Considering region-specific and parameter-specific neural mapping schemes will result in substantial training costs. This study addresses generalization performance of neural mapping schemes to deliver gap-free satellite-derived ocean colour products. We develop a comprehensive experimental framework using real multi-sensor ocean colour datasets for two regions (the Mediterranean Sea and the North Sea) and a representative set of bio-optical parameters (Chlorophyll-a concentration, suspended particulate matter concentration, particulate backscattering coefficient). We consider several neural mapping schemes, and we report excellent generalization performance across regions and bio-optical parameters without any fine-tuning using appropriate dataset-specific normalization procedures. We discuss further how these results provide new insights towards the large-scale deployment of neural schemes for the processing of satellite-derived ocean colour datasets beyond case-study-specific demonstrations.

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Thi Thuy Nga Nguyen, Clément Dorffer, Frédéric Jourdin, Ronan Fablet. 2025-03-14. Generalization performance of neural mapping schemes for the space-time interpolation of satellite-derived ocean colour datasets. https://doi.org/10.1109/jstars.2025.3622311

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