Bayesian sea ice detection with the ERS scatterometer and sea ice backscatter model at C-band

Ines Otosaka*, Maria Belmonte Rivas, Ad Stoffelen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


This paper describes the adaptation of a Bayesian sea ice detection algorithm for the scatterometer on-board the European Remote Sensing (ERS) satellites (ERS-1 and ERS-2). The algorithm is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs) and its performance is validated against coincident active and passive microwave data. We furthermore propose a new model for sea ice backscatter at the C-band in vertical polarization based on the sea ice GMFs derived from ERS and advanced scatterometer data. The model characterizes the dependence of sea ice backscatter on the incidence angle and the sea ice type, allowing a more precise incidence angle correction than afforded by the usual linear transformation. The resulting agreement between the ERS, QuikSCAT, and special sensor microwave imager sea ice extents during the year 2000 is high during the fall and winter seasons, with an estimated ice edge accuracy of about 20 km, but shows persistent biases between scatterometer and radiometer extents during the melting period, with scatterometers being more sensitive to summer (lower concentration and rotten) sea ice types.

Original languageEnglish
Pages (from-to)2248-2254
Number of pages7
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number4
Early online date18 Dec 2017
Publication statusPublished - Apr 2018
Externally publishedYes

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