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Mitigation of cross-polar antenna pattern errors in SMOS: simplified approach

Author
Duran, I.; Torres, F.; Corbella, I.; Duffo, N.; Lin, W.; Martín, M.
Type of activity
Presentation of work at congresses
Name of edition
14th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment
Date of publication
2016
Presentation's date
2016-04
Book of congress proceedings
2016 14th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)
First page
131
Last page
134
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/MICRORAD.2016.7530520 Open in new window
Repository
http://hdl.handle.net/2117/99429 Open in new window
URL
http://ieeexplore.ieee.org/document/7530520/ Open in new window
Abstract
This paper analyzes a simplified image reconstruction method that exclusively takes into account the dominant antenna pattern terms to model image inversion artifacts and mitigate SMOS polarimetric spatial bias over the Ocean. This simplified method, that reduces computation time at the cost of small radiometric performance degradation, is useful to the L1 teams to process a large amount of data when full radiometric accuracy is not required (e.g. stability assessments).
Citation
Duran, I., Torres, F., Corbella, I., Duffo, N., Lin, W., Martín, M. Mitigation of cross-polar antenna pattern errors in SMOS: simplified approach. A: Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment. "2016 14th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad)". Espoo: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 131-134.
Keywords
Cross-polar antenna pattern error mitigation, Geophysical image processing, Image reconstruction, Imatge inversion artifact, Ocean, Oceanography, Polarimetry, Radiometric performance degradation, Radiometry, Radiomètric accuracy, Remote sensing, SMOS polarimetric spatial bias, Simplified image reconstruction method
Group of research
RF&MW - Laboratory of RF & microwave systems, devices and materials
RSLAB - Remote Sensing Lab

Participants