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
2021 ◽  
Vol 15 (6) ◽  
pp. 2803-2818
Author(s):  
Joan Antoni Parera-Portell ◽  
Raquel Ubach ◽  
Charles Gignac

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.


2021 ◽  
Author(s):  
Joan A. Parera-Portell ◽  
Raquel Ubach ◽  
Charles Gignac

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.


2020 ◽  
Vol 12 (14) ◽  
pp. 2262
Author(s):  
Donghyun Jin ◽  
Sung-Rae Chung ◽  
Kyeong-Sang Lee ◽  
Minji Seo ◽  
Sungwon Choi ◽  
...  

Sea ice is an important meteorological factor affecting the global climate system, but it is difficult to observe in sea ice ground truth data because of its location mainly at high latitudes and in polar regions. Accordingly, sea-ice detection research has been actively conducted using satellites, since the 1970s. Polar-orbiting and geostationary satellites are used for this purpose; notably, geostationary satellites are capable of real-time monitoring of specific regions. In this paper, we introduce the Geo-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager (AMI) sea-ice detection algorithm using Japan Meteorological Agency (JMA) Himawari-8/Advanced Himawari Imager (AHI) data as proxy data. The GK-2A/AMI, which is Korea Meteorological Administration (KMA)’s next-generation geostationary satellite launched in December 2018 and Himawari-8/AHI have optically similar channel data, and the observation area includes East Asia and the Western Pacific. The GK-2A/AMI sea-ice detection algorithm produces sea-ice data with a 10-min temporal resolution, a 2-km spatial resolution and sets the Okhotsk Sea and Bohai Sea, where the sea ice is distributed during the winter in the northern hemisphere. It used National Meteorological Satellite Center (NMSC) cloud mask as the preceding data and a dynamic threshold method instead of the static threshold method that is commonly performed in existing sea-ice detection studies. The dynamic threshold methods for sea-ice detection are dynamic wavelength warping (DWW) and IST0 method. The DWW is a method for determining the similarity by comparing the pattern of reflectance change according to the wavelength of two satellite data. The IST0 method detects sea ice by using the correlation between 11.2-μm brightness temperature (BT11.2) and brightness temperature difference (BTD) [BT11.2–BT12.3] according to ice surface temperature (IST). In addition, the GK-2A/AMI sea-ice detection algorithm reclassified the cloud area into sea ice using a simple test. A comparison of the sea-ice data derived the GK-2A/AMI sea-ice detection algorithm with the S-NPP/visible infrared imaging radiometer suite (VIIRS) sea ice characterization product indicates consistency of 99.0% and inconsistency of 0.9%. The overall accuracy (OA) of GK-2A/AMI sea-ice data with the sea ice region of interest (ROI) data, which is constructed by photo-interpretation method from RGB images, is 97.2%.


2019 ◽  
Vol 36 (8) ◽  
pp. 1643-1656
Author(s):  
Li Yi ◽  
King-Fai Li ◽  
Xianyao Chen ◽  
Ka-Kit Tung

AbstractThe rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂T in this work. A fog event is said to be detected if ∂T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.


2020 ◽  
Author(s):  
Matthew Hammond ◽  
Giuseppe Foti ◽  
Christine Gommenginger ◽  
Meric Srokosz ◽  
Martin Unwin ◽  
...  

<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>


2013 ◽  
Vol 51 (1) ◽  
pp. 38-56 ◽  
Author(s):  
Vincent-De-Paul Onana ◽  
Nathan T. Kurtz ◽  
Sinead Louise Farrell ◽  
Lora S. Koenig ◽  
Michael Studinger ◽  
...  

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