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

Microwave radiometry at L-band is sensitive to sea ice thickness (SIT) up to similar 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.

Automated summary

In this study, a Bayesian unsupervised learning algorithm is used to segment Arctic sea ice to recognize spatial patterns and analyze the temporal stability and separability of classes. This method is important for improving current sea ice thickness detection algorithms.