Generalization Performance of Neural Mapping Schemes for the Space–Time Interpolation of Satellite-Derived Ocean Color Datasets

Overview & Synopsis

Neural mapping schemes have become appealing approaches to deliver gap-free satellite-derived products for sea surface tracers. The generalization performance of these learningbased approaches naturally arises as a key challenge. This is particularly true for satellite-derived ocean color 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 color products. We develop a comprehensive experimental framework using real multisensor ocean color 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 toward the large-scale deployment of neural schemes for the processing of satellite-derived ocean color datasets beyond case-study-specific demonstrations.