Probabilistic inference method to discriminate closed water from sea ice using SENTINEL-1 Sar signatures

• Published in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
• Main Authors: Herbert, Christoph Josef, Camps Carmona, Adriano José, Vall-Llossera Ferran, Mercedes Magdalena
• Other Authors: Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. RSLAB - Grup de Recerca en Teledetecció
• Format: Other/Unknown Material
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
• Subjects: Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció; Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Radar; Remote sensing; Bayesian statistical decision theory; Synthetic aperture radar; Sea ice; Melt ponds; Bayesian inference; Sentinel-1 Synthetic Aperture Radar (SAR); Teledetecció; Estadística bayesiana; Radar d'obertura sintètica
• Online Access: http://hdl.handle.net/2117/366675; https://doi.org/10.1109/IGARSS47720.2021.9554671

Consistent sea ice monitoring requires accurate estimates of sea ice concentration. Current retrieval algorithms are based on low-resolution microwave radiometry data with limited penetration depth and are unable to resolve surface characteristics of sea ice in sufficient detail which is necessary to discriminate intact sea ice from closed water. Important information about surface roughness conditions are contained in the distribution of radar backscattering images which can be - in principle - used to detect melt ponds and different sea ice types. In this work, a two-step probabilistic approach based on Expectation-Maximization and Bayesian inference considers the spatial and statistical characteristics of medium-resolution daily-available Sentinel-1 SAR images. The presented method segments sea ice into a number of separable classes and enables to discriminate surface water from the remaining sea ice types. The lead author was supported by “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/D118/11660050, and received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673. The project was also funded through the award “Unidad de Excelencia María de Maeztu” MDM-2016-0600, by the Spanish Ministry of Science and Innovation through the project “L-band” ESP2017-89463-C3-2-R, and the project “Sensing with Pioneering Opportunistic Techniques (SPOT)” RTI2018-099008-B-C21/AEI/10.13039/501100011033. Peer Reviewed Postprint (published version)