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- Title
Bayesian Unsupervised Machine Learning Approach to Segment Arctic Sea Ice Using SMOS Data.
- Authors
Herbert, Christoph; Camps, Adriano; Wellmann, Florian; Vall‐llossera, Mercedes
- 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. Plain Language Summary: Remote sensing techniques are commonly used to provide maps of sea ice thickness (SIT). Methods to obtain these maps are based on the sea ice composition and on the signal measured by satellite. Sea ice Composition is spatially complex and changes during its formation and melting. Currently used data from observations of ESA's Soil Moisture and Ocean Salinity (SMOS) mission depend on several sea ice parameters, which hinders good estimation of almost any specific sea ice parameter. In this work, a new method to combine the information contained in SMOS brightness temperature data is investigated, with the aim to divide the Arctic region into a number of smaller areas – so called classes. Useful information about sea ice is contained in the spatial and statistical distribution of SMOS data, which are collected at different incidence angles. The relationship between the observations and the statistical properties of the obtained classes allow an assessment of its degree of separability and uncertainty. How classes change in time is used to estimate their temporal stability. The presented approach can be used to investigate the link between a variety of spatial datasets to improve current SIT products, and it can be applied in other scientific fields. Key Points: Retrieval algorithms to infer ice properties, such as sea ice thickness, exhibit high uncertainty due to limited knowledge of complexityAn unsupervised learning approach provides a synergistic framework which links data with the aim to recognize and analyze spatial patternsBayesian segmentation of Arctic sea ice from Soil Moisture and Ocean Salinity (SMOS) data reveals stable and separable classes while indicating model uncertainty
- Subjects
EUROPEAN Space Agency; SEA ice; MACHINE learning; SEAWATER salinity; MICROWAVE radiometry; BRIGHTNESS temperature
- Publication
Geophysical Research Letters, 2021, Vol 48, Issue 6, p1
- ISSN
0094-8276
- Publication type
Article
- DOI
10.1029/2020GL091285