scholarly journals Editorial for the Special Issue “Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions”

2020 ◽  
Vol 12 (4) ◽  
pp. 606 ◽  
Author(s):  
Wolfgang Dierking ◽  
Marko Mäkynen ◽  
Markku Similä
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Satellite Remote Sensing ◽  
Continuous Monitoring ◽  
Temporal Variations ◽  
Data Sources ◽  
Spatial And Temporal Variations ◽  
Satellite Monitoring ◽  
Special Issue ◽  
Ice Conditions

Satellite remote sensing is an important tool for continuous monitoring of sea ice covered ocean regions and spatial and temporal variations of their geophysical characteristics [...]

scholarly journals Variability and Formation Mechanism of Polynyas in Eastern Prydz Bay, Antarctica

2021 ◽  
Vol 13 (24) ◽  
pp. 5089
Author(s):  
Saisai Hou ◽  
Jiuxin Shi
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Satellite Remote Sensing ◽  
Temporal Variations ◽  
Prydz Bay ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Radar Images ◽  
Fast Ice ◽  
Ice Production

Based on satellite remote sensing, several polynyas have been found in Prydz Bay, East Antarctica. Compared with the Mackenzie Bay Polynya, the only polynya in the west, the polynyas in eastern Prydz Bay have a larger area and higher ice production, but have never been studied individually. In this study, four recurrent polynyas were identified in eastern Prydz Bay from sea ice concentration data during 2002–2011. Their areas generally exhibit synchronous temporal variations and have good correlation with wind speed, which indicates that they are primarily wind-driven polynyas that need at least one stationary ice barrier to block the inflow of drifting sea ice. The components of the ice barriers of these four polynyas were identified through comparison of satellite remote sensing visible images and synthetic aperture radar images. All types of fast ice, including landfast ice, offshore fast ice and ice fingers serving as ice barriers for these polynyas are anchored by an assemblage of small icebergs and have an approximately year-round period of variations that also regulates the variability of polynyas. The movement and grounding of giant icebergs near the polynyas significantly affects the development of the polynyas. The results of this study illustrate the important impact of icebergs on Antarctic wind-driven polynyas and the formation of dense shelf water.

Snow particle sizes and their distributions in Dronning Maud Land, Antarctica, at sample, local and regional scales

2016 ◽  
Vol 28 (3) ◽  
pp. 219-231
Author(s):  
Susanne Ingvander ◽  
Peter Jansson ◽  
Ian A. Brown ◽  
Shuji Fujita ◽  
Shin Sugyama ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Coupled Processes ◽  
Sample Type ◽  
Surface Snow ◽  
Dronning Maud Land ◽  
Size Variability ◽  
Snow Particle

AbstractIn this study, snow particle size variability was investigated along a transect in Dronning Maud Land from the coast to the polar plateau. The aim of the study was to better understand the spatial and temporal variations in surface snow properties. Samples were collected twice daily during a traverse in 2007–08 to capture regional variability. Local variability was assessed by sampling in 10×10 m grids (5 m spacing) at selected locations. The particle size and shape distributions for each site were analysed through digital image analysis. Snow particle size variability is complex at different scales, and shows an internal variability of 0.18–3.31 mm depending on the sample type (surface, grid or pit). Relationships were verified between particle size and both elevation and distance to the coast (moisture source). Regional seasonal changes were also identified, particularly on the lower elevations of the polar plateau. This dataset may be used to quantitatively analyse the optical properties of surface snow for remote sensing. The details of the spatial and temporal variations observed in our data provide a basis for further studies of the complex and coupled processes affecting snow particle size and the interpretation of remote sensing of snow covered areas.

Methods of satellite remote sensing of sea ice

Sea Ice ◽  
2016 ◽  
pp. 239-260 ◽  
Author(s):  
Gunnar Spreen ◽  
Stefan Kern
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Satellite Remote Sensing

scholarly journals Integration of Crowdsourced Images, USGS Networks, Remote Sensing, and a Model to Assess Flood Depth during Hurricane Florence

2020 ◽  
Vol 12 (5) ◽  
pp. 834
Author(s):  
Carolynne Hultquist ◽  
Guido Cervone
Keyword(s):  
Remote Sensing ◽  
Sensor Networks ◽  
Cross Sections ◽  
Satellite Remote Sensing ◽  
Environmental Data ◽  
Data Sources ◽  
Remote Sensing Imagery ◽  
Flood Depth ◽  
Crowdsourced Data ◽  
Automated Methods

Crowdsourced environmental data have the potential to augment traditional data sources during disasters. Traditional sensor networks, satellite remote sensing imagery, and models are all faced with limitations in observational inputs, forecasts, and resolution. This study integrates flood depth derived from crowdsourced images with U.S. Geological Survey (USGS) ground-based observation networks, a remote sensing product, and a model during Hurricane Florence. The data sources are compared using cross-sections to assess flood depth in areas impacted by Hurricane Florence. Automated methods can be used for each source to classify flooded regions and fuse the dataset over common grids to identify areas of flooding. Crowdsourced data can play a major role when there are overlaps of sources that can be used for validation as well providing improved coverage and resolution.

scholarly journals Spatial and Temporal Variations of Arctic Sea Ice From 2002 to 2017

2020 ◽  
Vol 7 (9) ◽  
Author(s):  
Zongliang Wang ◽  
Zhen Li ◽  
Jiangyuan Zeng ◽  
Shuang Liang ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
Spatial And Temporal Variations ◽  
Arctic Sea

scholarly journals Sea ice circulation in the Laptev Sea and ice export to the Arctic Ocean: Results from satellite remote sensing and numerical modeling

2000 ◽  
Vol 105 (C7) ◽  
pp. 17143-17159 ◽  
Author(s):  
Vitaly Y. Alexandrov ◽  
Thomas Martin ◽  
Josef Kolatschek ◽  
Hajo Eicken ◽  
Martin Kreyscher ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Numerical Modeling ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Satellite Remote Sensing ◽  
The Arctic ◽  
Laptev Sea ◽  
The Arctic Ocean ◽  
The Laptev Sea
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