Sea Ice Detection

2021 ◽  
pp. 163-189
Author(s):  
Kegen Yu
Keyword(s):  
Sea Ice ◽  
Ice Detection

NOC GNSS-R Global Ocean Wind Speed and Sea-Ice Products

2020 ◽  
Author(s):  
Matthew Hammond ◽  
Giuseppe Foti ◽  
Christine Gommenginger ◽  
Meric Srokosz ◽  
Martin Unwin ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Global Ocean ◽  
Retrieval Algorithm ◽  
Navigation Satellite ◽  
Validation Data ◽  
Global Navigation ◽  
Ice Detection ◽  
Global Navigation Satellite ◽  
Ocean Wind

<p>Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems, which have been reflected off the Earth’s surface. Using GNSS-R data collected by the UK TechDemoSat-1 (TDS-1) between 2014 and 2018, the National Oceanography Centre (NOC) has developed a GNSS-R wind speed retrieval algorithm called the Calibrated Bistatic Radar Equation (C-BRE), which now features updated data quality control mechanisms including flagging of radio frequency interference (RFI) and sea-ice detection based on the GNSS-R waveform. Here we present an assessment of the performance of the latest NOC GNSS-R ocean wind speed and sea-ice products (NOC C-BRE v1.0) using validation data from the ECMWF ERA-5 re-analysis model output. Results show the capability of spaceborne GNSS-R sensors for accurate wind speed retrieval and sea-ice detection. Additionally, ground-processed Galileo returns collected by TDS-1 are examined and the geophysical sensitivity of reflected Galileo data to surface parameters is investigated. Preliminary results demonstrate the feasibility of spaceborne GNSS-R instruments receiving a combination of GNSS signals transmitted by multiple navigation systems, which offers the opportunity for frequent, high-quality ocean wind and sea-ice retrievals at low relative cost. Other advancements in GNSS-R technology are represented by future mission concepts such as HydroGNSS, a proposed ESA Scout mission opportunity by SSTL offering support for enhanced retrieval capabilities exploiting dual polarisation, dual frequency, and coherent reflected signal reception.</p>

Sea Ice Detection in the Sea of Okhotsk Using PALSAR and MODIS Data

2013 ◽  
Vol 6 (3) ◽  
pp. 1516-1523 ◽  
Author(s):  
Hiroyuki Wakabayashi ◽  
Yuta Mori ◽  
Kazuki Nakamura
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Modis Data ◽  
The Sea Of Okhotsk ◽  
Ice Detection

Bayesian Sea Ice Detection With the Advanced Scatterometer ASCAT

2012 ◽  
Vol 50 (7) ◽  
pp. 2649-2657 ◽  
Author(s):  
Maria Belmonte Rivas ◽  
Jeroen Verspeek ◽  
Anton Verhoef ◽  
Ad Stoffelen
Keyword(s):  
Sea Ice ◽  
Ice Detection

Development of a water clear of sea ice detection algorithm from enhanced SeaWinds/QuikSCAT and AMSR-E measurements

2010 ◽  
Vol 114 (11) ◽  
pp. 2594-2609 ◽  
Author(s):  
Stephen E.L. Howell ◽  
Chris Derksen ◽  
Adrienne Tivy
Keyword(s):  
Sea Ice ◽  
Detection Algorithm ◽  
Ice Detection

scholarly journals An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator

2021 ◽  
Vol 15 (6) ◽  
pp. 2803-2818
Author(s):  
Joan Antoni Parera-Portell ◽  
Raquel Ubach ◽  
Charles Gignac
Keyword(s):  
Sea Ice ◽  
Regional Scale ◽  
Ice Cover ◽  
Detection Algorithm ◽  
Temporal Variations ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Monitoring Tools ◽  
Spatio Temporal ◽  
Ice Detection

Abstract. The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat to biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved sea ice extent indicator covering the European regional seas, the new IceMap500 algorithm has been developed to classify sea ice and water at a resolution of 500 m at nadir. IceMap500 features a classification strategy built upon previous MODIS sea ice extent algorithms and a new method to reclassify areas affected by resolution-breaking features inherited from the MODIS cloud mask. This approach results in an enlargement of mapped area, a reduction of potential error sources and a better delineation of the sea ice edge, while still systematically achieving accuracies above 90 %, as obtained by manual validation. Swath maps have been aggregated at a monthly scale to obtain sea ice extent with a method that is sensitive to spatio-temporal variations in the sea ice cover and that can be used as an additional error filter. The resulting dataset, covering the months of maximum and minimum sea ice extent (i.e. March and September) over 2 decades (from 2000 to 2019), demonstrates the algorithm's applicability as a monitoring tool and as an indicator, illustrating the sea ice decline at a regional scale. The European sea regions located in the Arctic, NE Atlantic and Barents seas display clear negative trends in both March (−27.98 ± 6.01 × 103 km2yr−1) and September (−16.47 ± 5.66 × 103 km2yr−1). Such trends indicate that the sea ice cover is shrinking at a rate of ∼ 9 % and ∼ 13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼ 70 % greater in March.

scholarly journals An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator

2021 ◽  
Author(s):  
Joan A. Parera-Portell ◽  
Raquel Ubach ◽  
Charles Gignac
Keyword(s):  
Sea Ice ◽  
Synthesis Method ◽  
Ice Cover ◽  
Detection Algorithm ◽  
Arctic Seas ◽  
Sea Ice Extent ◽  
Monitoring Tools ◽  
Ice Detection ◽  
The Baltic ◽  
Cloud Mask

Abstract. The continued loss of sea ice in the Northern Hemisphere due to global warming poses a threat on biota and human activities, evidencing the necessity of efficient sea ice monitoring tools. Aiming at the creation of an improved European sea ice extent indicator, the IceMap250 algorithm has been reworked to generate improved sea ice extent maps at 500 m resolution at nadir. Changes in the classification approach and a new method to correct artefacts arising from the MODIS cloud mask allow the enlargement of the mapped area, the reduction of potential error sources and a qualitative improvement of the resulting maps, while systematically achieving accuracies above 90 %. Monthly sea ice extent maps have been derived using a new synthesis method which acts as an additional error filter. Our results, covering the months of maximum (March) and minimum (September) sea ice extent during two decades (from 2000 to 2019), are a proof of the algorithm's applicability as an indicator, illustrating the sea ice decline in the European regional seas. We observed no significant trends in the Baltic (−2.75 ± 2.05 × 103 km2 yr−1) although, on the contrary, the European Arctic seas display clear negative trends both in March (−27.98 ± 6.01 × 103 km2 yr−1) and September (−16.47 ± 5.66 × 103 km2 yr−1). Such trends indicate that the sea ice cover in March and September is shrinking at a rate of ∼9 % and ∼13 % per decade, respectively, even though the sea ice extent loss is comparatively ∼70 % greater in March. Therefore, according to the trends and without taking into account the variability of the sea ice cover, the loss of sea ice extent over two decades in the study area would be comparable to the area of continental France in the case of the March maximum, and to that of Finland in the case of the September minimum.

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