Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery

Siitam, Laura; Sipelgas, Liis; Pärn, Ove; Uiboupin, Rivo

doi:10.1080/01431161.2016.1268734

Cited by 12 publications

(10 citation statements)

References 34 publications

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“…All fifteen winter seasons can be divided into three categories based on their D SAR values (listed in Table 2): Short winters with D SAR < 50 days (2006-2007, 2007-2008, 2014-2015, 2015-2016, and 2016-2017), intermediate winters with 50 ≤ D SAR ≤ 100 days (2003-2004, 2004-2005, 2008-2009, 2011-2012, and 2013-2014), and long winters with D SAR > 100 days (2002-2003, 2005-2006, 2009-2010, 2010-2011, and 2012-2013). Notably, the interannual variability of the ice season duration in the Curonian Lagoon correlates very well with similar results obtained for the Gulf of Riga [59].…”

Section: Spatial Properties Of Ice Cover Extent In the Curonian Lagoonsupporting

confidence: 87%

“…In the Baltic Sea, ISD is highly variable and depends on the region, with the longest one observed in the Bothnian Bay and decreasing southward [9]. Studies of the ice regime of the Gulf of Riga and along the coast of Latvia [59,65] gave results very similar to our study. The severity of winters in 2002-2003, 2005-2006, 2009-2010, and 2010-2011, and milder winters observed in 2006-2007 and 2007-2008 over these regions correspond well with our results.…”

Section: Ice Season Durationsupporting

confidence: 86%

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Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)

2019

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A first-ever spatially detailed record of ice cover conditions in the Curonian Lagoon (CL), Europe’s largest coastal lagoon located in the southeastern Baltic Sea, is presented. The multi-mission synthetic aperture radar (SAR) measurements acquired in 2002–2017 by Envisat ASAR, RADARSAT-2, Sentinel-1 A/B, and supplemented by the cloud-free moderate imaging spectroradiometer (MODIS) data, are used to document the ice cover properties in the CL. As shown, satellite observations reveal a better performance over in situ records in defining the key stages of ice formation and decay in the CL. Using advantages of both data sources, an updated ice season duration (ISD) record is obtained to adequately describe the ice cover season in the CL. High-resolution ISD maps provide important spatial details of ice growth and decay in the CL. As found, ice cover resides longest in the south-eastern CL and along the eastern coast, including the Nemunas Delta, while the shortest ice season is observed in the northern CL. During the melting season, the ice melt pattern is clearly shaped by the direction of prevailing winds, and ice drift velocities obtained from a limited number of observations range within 0.03–0.14 m/s. The pronounced shortening of the ice season duration in the CL is observed at a rate of 1.6–2.3 days year‒1 during 2002–2017, which is much higher than reported for the nearby Baltic Sea regions. While the timing of the freeze onset and full freezing has not changed much, the dates of the final melt onset and last observation of ice have a clear decreasing pattern toward an earlier ice break-up and complete melt-off due to an increase of air temperature strongly linked to the North Atlantic Oscillation (NAO). Notably, the correlation between the ISD, air temperature, and winter NAO index is substantially higher when considering the lagoon-averaged ISD values derived from satellite observations compared to those derived from coastal records. The latter clearly demonstrated the richness of the satellite observations that should definitely be exploited in regional ice monitoring programs.

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Section: Spatial Properties Of Ice Cover Extent In the Curonian Lagoonsupporting

confidence: 87%

Section: Ice Season Durationsupporting

confidence: 86%

Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)

2019

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“…Through qualitative image interpretation, CZI will aid coastal zone management by providing key information on reclamation [ 1 ], wetlands [ 2 ], land cover/use [ 3 ], mangroves [ 4 ], coral reef [ 5 ], and sea ice [ 6 ] in the coastal zone and islands, where significant changes are usually expected due to intense human activity. At the same time, the quantitative information inversion from CZI images may also be expected to retrieve key parameters of aquatic environments (including turbidity, suspended sediment concentration, transparency, and water depth) [ 7 , 8 , 9 ], natural resources (such as wetland biomass) [ 10 ], and abnormal ecological phenomena [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Out-of-Band Response for the Coastal Zone Imager (CZI) Onboard China’s Ocean Color Satellite HY-1C: Effect on the Observation Just above the Sea Surface

Cui

Ding

Jia³

et al. 2018

Sensors

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The out-of-band (OOB) response is one of the key specifications for satellite optical sensors, which has important influences on quantitative remote sensing retrieval. In this paper, the effect of OOB response on the radiometric measurements made just above the sea surface is evaluated for the three broad visible bands (i.e., blue, green, and red) of the Coastal Zone Imager (CZI) onboard China’s ocean satellite HY-1C to be launched in September 2018. For the turbid coastal (Case 2) waters whose optical properties are mainly dominated by suspended sediment and colored dissolved organic material, the OOB effect can be neglected (<2%) for all three CZI visible bands. For the phytoplankton-dominated (Case 1) waters which are mainly distributed in the clear open ocean, a significant (>2%) OOB effect was found in the green band over oligotrophic waters (chlorophyll a concentration ≤~0.1 mg/m3), and accordingly a model based on the CZI blue-green band ratio is proposed to correct this effect. The OOB influence on the CZI ocean color retrieval is discussed. This research highlights the importance of the comprehensive pre-launch radiometric characterization and the OOB effect correction for the broad band space-borne sensor, in order to achieve a high-quality quantitative ocean product.

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“…INTRODUCTIONITH climate change, drastic changes in Arctic sea ice have had global impact that cannot be ignored. Sea ice type and thickness is an important parameter used to describe the state of sea ice, and it is of considerable importance in relation to global climate change research, Arctic ecology, and monitoring of navigational safety in polar regions [1][2]. In comparison with in situ surveys, microwave remote sensing has received increasing attention recently owing to its near all-Manuscript received XXX.…”

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confidence: 99%

Assessment of Arctic Sea Ice Classification Ability of Chinese HY-2B Dual-Band Radar Altimeter During Winter to Early Spring Conditions

Zhang¹,

Zhu²,

Zhang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate identification of sea ice type from altimeter data is important for inversion of sea ice freeboard by radar altimeter, as well as for maritime navigation in polar regions. Previous sea ice classification studies have rarely involved multifrequency altimeter data. In this study, benefiting from the dual-frequency observation capability of the Chinese HY-2B altimeter, differences between waveform features of C-and Kuband radar altimeter over different types of sea ice were investigated and analyzed for the first time. To validate and evaluate the sea ice classification capability of different feature combinations of the different bands, we extracted 14 features from the waveforms of C-and Ku-band HY-2B altimeter data acquired between December 2019 and April 2020. Our experiments demonstrated that the C-and Ku-band radar waveforms are sensitive to sea ice type, and that sea ice type classification could be improved by combining Ku-and C-band data. This work also proposed a sea ice classification methodology using C-and Kuband altimeter waveforms. Comparison with results from previous studies indicated that the overall accuracy of the proposed method (up to 93.45%) is much higher than that of existing state-of-the-art methods.Index Terms-dual-band radar altimeter, HY-2B, sea ice classification, waveform features. I. INTRODUCTIONITH climate change, drastic changes in Arctic sea ice have had global impact that cannot be ignored. Sea ice type and thickness is an important parameter used to describe the state of sea ice, and it is of considerable importance in relation to global climate change research, Arctic ecology, and monitoring of navigational safety in polar regions [1][2]. In comparison with in situ surveys, microwave remote sensing has received increasing attention recently owing to its near all-Manuscript received XXX.

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Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery

Cited by 12 publications

References 34 publications

Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)

Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)

Out-of-Band Response for the Coastal Zone Imager (CZI) Onboard China’s Ocean Color Satellite HY-1C: Effect on the Observation Just above the Sea Surface

Assessment of Arctic Sea Ice Classification Ability of Chinese HY-2B Dual-Band Radar Altimeter During Winter to Early Spring Conditions

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