Mixture density networks for re-constructing historical ocean-color products over inland and coastal waters: demonstration and validation

Mixture density networks for re-constructing historical ocean-color products over inland and coastal waters: demonstration and validation

Ocean color remote sensing tracks water quality globally, but multispectral ocean color sensors often struggle with complex coastal and inland waters. Traditional models have difficulty capturing detailed relationships between remote sensing reflectance (Rrs), biogeochemical properties (BPs), and in...

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Bibliographic Details
Main Authors: Sundarabalan V. Balasubramanian, Ryan E. O’Shea, Arun M. Saranathan, Christopher C. Begeman, Daniela Gurlin, Caren Binding, Claudia Giardino, Michelle C. Tomlinson, Krista Alikas, Kersti Kangro, Moritz K. Lehmann, Lisa Reed
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Remote Sensing
Subjects:
aquatic remote sensing
neural networks
multispectral
biogeochemical parameters
inland and coastal waters
MODIS
Online Access:https://www.frontiersin.org/articles/10.3389/frsen.2025.1488565/full
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https://www.frontiersin.org/articles/10.3389/frsen.2025.1488565/full

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