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

Geophysical Research Letters, vol. 48

Abstract

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 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.

Authors 4

1. Christoph Herbert corresponding

   Institut d'Estudis Espacials de Catalunya · Universitat Politècnica de Catalunya

   Affiliation as printed

   Barcelona Expert Center (BEC) Barcelona Spain

   CommSensLab Universitat Politècnica de Catalunya (UPC) and Institut d’Estudis Espacials de Catalunya (IEEC/CTE‐UPC) Barcelona Spain

   CommSensLab Universitat Politècnica de Catalunya (UPC) and Institut d’Estudis Espacials de Catalunya (IEEC/CTE‐UPC) Barcelona Spain

2. Adriano Camps

   Institut d'Estudis Espacials de Catalunya · Universitat Politècnica de Catalunya

   Affiliation as printed

   Barcelona Expert Center (BEC) Barcelona Spain

   CommSensLab Universitat Politècnica de Catalunya (UPC) and Institut d’Estudis Espacials de Catalunya (IEEC/CTE‐UPC) Barcelona Spain

   CommSensLab Universitat Politècnica de Catalunya (UPC) and Institut d’Estudis Espacials de Catalunya (IEEC/CTE‐UPC) Barcelona Spain

3. Florian Wellmann Aachen Institute for Computational Geoscience and Reservoir Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Computational Geoscience and Reservoir Engineering RWTH Aachen University Aachen Germany

   Institute for Computational Geoscience and Reservoir Engineering, RWTH Aachen University, Aachen, Germany

4. Merce Vall-llossera

   Institut d'Estudis Espacials de Catalunya · Universitat Politècnica de Catalunya

   Affiliation as printed

   Barcelona Expert Center (BEC) Barcelona Spain

   CommSensLab Universitat Politècnica de Catalunya (UPC) and Institut d’Estudis Espacials de Catalunya (IEEC/CTE‐UPC) Barcelona Spain

   CommSensLab Universitat Politècnica de Catalunya (UPC) and Institut d’Estudis Espacials de Catalunya (IEEC/CTE‐UPC) Barcelona Spain