Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data

Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data

Abstract Microwave radiometry at L‐band is sensitive to sea ice thickness (SIT) up to ∼ 60 cm. Current methods to infer SIT depend on ice‐physical properties and data provided by the ESA’s Soil Moisture and Ocean Salinity (SMOS) mission. However, retrieval accuracy is limited due to seasonally and r...

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Bibliographic Details
Main Authors: Christoph Herbert, Adriano Camps, Florian Wellmann, Mercedes Vall‐llossera
Format: Article
Language:English
Published: Wiley 2021-03-01
Series:Geophysical Research Letters
Subjects:
Bayesian inference
sea ice
SMOS
unsupervised learning
Online Access:https://doi.org/10.1029/2020GL091285
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