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Marine pollution caused by oil spills — the release of petroleum hydrocarbons — poses a major threat to marine environments. This has become increasingly true also in the Arctic, where reduced sea ice extent has made Arctic oceans more accessible for industry and tourism-related ship traffic. To minimize the environmental impacts of an oil spill, it is important to identify the source and monitor...
This paper presents a global ionospheric error model that was developed for the upcoming L-band NASA ISRO Synthetic Aperture Radar (NISAR) mission. The approach is combining statistical models with empirical data analysis to arrive at global error predictions. The model enables an assessment of capabilities and performance of the NISAR system by providing ionospheric phase noise estimates globally...
Due to their weather and illumination independence and due to their large area coverage at high spatial resolution, Synthetic Aperture Radar (SAR) images have been recognized as a valuable data source for the mapping and tracking of aufeis flooding events. We modified and utilized the change detection approach of [1], based on wavelet analysis to map aufeis-related flooding on the Sagavanirktok River...
This paper focuses on deriving a realistic statistical model for ionospheric effects in low-frequency Synthetic Aperture Radar (SAR) data. The approach used to develop this statistical model is based on the assumption that, for a certain range of scales, ionospheric plasma turbulence can be considered a scale-invariant process that can be described by power-law functions or fractal statistics. Based...
In recent years, significant progress has been made in developing theory and methods for modeling, detecting, and correcting ionospheric effects in low-frequency synthetic aperture radar (SAR) data. While a large number of correction methods have been developed that differ in sensitivity, data needs, and spatiotemporal accuracy, a lack of performance requirements for ionospheric correction has prevented...
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