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Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data
Author(s) -
Herbert Christoph,
Camps Adriano,
Wellmann Florian,
Vallllossera Mercedes
Publication year - 2021
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2020gl091285
Subject(s) - sea ice , arctic , radiometer , brightness temperature , remote sensing , radiometry , environmental science , satellite , sea surface temperature , bayesian probability , climatology , computer science , meteorology , geology , artificial intelligence , oceanography , microwave , geography , telecommunications , aerospace engineering , engineering
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 regionally variable surface conditions during the formation and melting of sea ice. In this work, Arctic sea ice is segmented using a Bayesian unsupervised learning algorithm aiming to recognize spatial patterns by harnessing multi‐incidence angle brightness temperature observations. The approach considers both statistical characteristics and spatial correlations of the observations. The temporal stability and separability of classes are analyzed to distinguish ambiguous from well‐determined regions. Model uncertainty is quantified from class membership probabilities using information entropy. The presented approach opens up a new scope to improve current SIT retrieval algorithms, and can be particularly beneficial to investigate merged satellite products.