Adaptive Spatial and Multi-Variable Generalization of 4dvarnet in Ocean Colour Remote Sensing

This study presents an enhanced approach to ocean colour L4 product generation through the Adaptive Spatial and Multi-Variable Generalization of 4DVarNet - an innovative integration of deep neural networks with variational data assimilation proposed in [1]. We explore the model's capabilities i...

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Bibliographic Details
Published inIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium pp. 7016 - 7019
Main Authors Dorffer, Clement, Nguyen, Thi Thuy Nga, Fablet, Ronan, Jourdin, Frederic
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2024
Subjects
Adaptation models
Atmospheric modeling
Biological system modeling
Costs
data-driven model
Image color analysis
interpolation
Mediterranean
missing data
North Sea
observing system experiment
ocean colour remote sensing
Oceans
suspended particulate matter
Training
variational data assimilation
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Summary:This study presents an enhanced approach to ocean colour L4 product generation through the Adaptive Spatial and Multi-Variable Generalization of 4DVarNet - an innovative integration of deep neural networks with variational data assimilation proposed in [1]. We explore the model's capabilities in generalizing across various geographical regions and bio-optical variables using datasets of the North Sea and the Mediterranean Sea. Our analysis and visualization show that 4DVarNet demonstrates a notable ability to adapt and scale, reducing significantly training cost thanks to this generalization ability.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10641576