A5000488576- Arctic and Antarctic ice dynamics
- Methane Hydrates and Related Phenomena
- Climate change and permafrost
- Cryospheric studies and observations
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Icing and De-icing Technologies
- Marine and Coastal Research
- Underwater Acoustics Research
- Spacecraft and Cryogenic Technologies
- Climate variability and models
- Oceanographic and Atmospheric Processes
- Marine and environmental studies
- Geophysics and Gravity Measurements
Danish Meteorological Institute
2021-2024
Automatically producing Arctic sea ice charts from Sentinel-1 synthetic aperture radar (SAR) images is challenging for convolutional neural networks (CNNs) due to ambiguous backscattering signatures. The number of pixels viewed by the CNN model in input image used generate an output pixel, or receptive field, important detect large features physical objects such as and correctly classify them. In addition, a noise phenomenon present ESA Instrument Processing Facility (IPF) v2.9 SAR data,...
Sea ice information has traditionally been associated with Manual Ice Charts, however the demand for accurate forecasts is increasing. This study presents an improved operational forecast system Arctic sea focusing on Greenlandic waters. In addition, we present different observational products and conduct inter-comparisons. First, a re-analysis forced by ERA5 from 2000 to 2021 evaluated ensure that stable over time provide statistics users. The output similar initial conditions forecast....
The AutoICE Competition, launched on ESA’s AI4EO platform, brings together AI and Earth Observation practitioners to address the challenge of “automated sea ice mapping” from Sentinel-1 SAR data. Traversing polar waters safely efficiently requires up-to-date maps constantly moving changing conditions showing current extent, local concentration, auxiliary descriptions conditions. For several decades, charts have been manually produced by visually inspecting...
Abstract. Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights necessity of timeliness accuracy charts. In addition, with increased availability satellite imagery, automation becoming more important. The AutoICE Challenge investigates possibility creating deep learning models capable mapping multiple parameters automatically from spaceborne synthetic aperture radar (SAR) imagery assesses current state automatic-sea-ice-mapping scientific...
Sea ice is critical to map for safe and efficient maritime navigation, mitigate ship trapping capsizing. also vital monitor assess the state of changing climate a component in weather models, reflecting sunlight towards space acting as an insulating material between ocean surface atmosphere.Professional sea analysts at national services based on Synthetic Aperture Radar (SAR) images acquired by satellites, such Sentinel-1 (S1) satellite constellation. The manually interpret SAR using their...
Abstract. Arctic sea ice monitoring is a fundamental prerequisite for anticipating and mitigating the impacts of climate change. Satellite-based observations have been subject to intense attention over last few decades, with passive microwave (PMW) radiometers being primary sensors retrieving pan-Arctic concentration, albeit coarse spatial resolutions or even tens kilometers. Space-borne Synthetic Aperture Radar (SAR) missions, such as Sentinel-1, provide dual-polarized C-band images <100...
Abstract. Arctic sea ice monitoring is a fundamental prerequisite for anticipating and mitigating the impacts of climate change. Satellite-based observations have been subject to intense attention over last few decades, with passive microwave (PMW) radiometers being primary sensors retrieving pan-Arctic concentration, albeit coarse spatial resolutions or even tens kilometers. Spaceborne synthetic aperture radar (SAR) missions, such as Sentinel-1, provide dual-polarized C-band images < 100...
Abstract. Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights necessity of timeliness accuracy charts. In addition, with increased availability satellite imagery, automation becoming more important. The aim AutoICE Challenge was to encourage creation models capable mapping automatically from spaceborne Synthetic Aperture Radar (SAR) imagery using deep learning while inspiring participants move towards multiple parameter model retrieval...
The Arctic&#8217;s unprecedented transformation due to anthropogenic warming necessitates close monitoring of sea ice understand and address climate change impacts. As the retreats becomes thinner, increased human activity in region emphasizes urgent need for detailed, near real-time information as well improved forecasts maritime safety planning.Current methods Arctic retrieval relies on passive microwave (PMW) sensors, which offer global coverage but struggle capture fine-scale...