Spatio-temporal characteristics of the sea-ice volume of the Bohai Sea, China, in winter 2009/10

Liu, Chengyu; Gu, Wei; Chao, Jinlong; Li, Lantao; Yuan, Shuai; Yu, Xu

doi:10.3189/2013aog62a305

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…The sea ice area dropped by over 40% from 27 January to 4 February 2013 according to the GOCI data, whereas the MODIS data could not record the detailed changes during this period due to frequent cloud cover. Similarly, the first and last day of the frozen season can be better determined by the GOCI data rather than using MODIS or AVHRR data [5,9]. The proposed approach has more implications for the sea ice hazard risk assessment based on grid computing [1].…”

Section: Discussionmentioning

confidence: 99%

“…Central Wavelengths Satellite remote sensing data have been widely applied to investigate the spatiotemporal distribution and evolution of sea ice. Particularly, polar-orbiting satellites, such as the Earth Observing System (EOS) moderate-resolution imaging spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration (NOAA) advanced very high-resolution radiometer (AVHRR), have been commonly used to study the Bohai Sea ice [5,9,[11][12][13][14][15][16]. Although these satellite data cover the study area with a fairly high spatial resolution (~1 km), they are typically limited in temporal resolution and provide only one image covering the study area per day, which are constrained by the proportion of cloud-free images of the MODIS and the AVHRR data being mostly less than 20% [5,15].…”

Section: Bandmentioning

confidence: 99%

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Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Huang

Shao

et al. 2019

Sustainability

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Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.

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Section: Discussionmentioning

confidence: 99%

Section: Bandmentioning

confidence: 99%

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Huang

Shao

et al. 2019

Sustainability

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“…where the thresholds T 2 and T 4 were set in the range of 0.08∼0.14 and 266.0∼269.0 for band 2 and 4 for different zones (see figure 1 for the zone division), respectively. Detailed description of the method can be found in Xie et al (2003) and Liu et al (2013). The zonal threshold method was found to extract the sea ice area with an accuracy of ∼80% (Guo et al 2008).…”

Section: Methodology 231 Extraction Of Sea Ice Areamentioning

confidence: 99%

Multidecadal anomalies of Bohai Sea ice cover and potential climate driving factors during 1988–2015

Shao

et al. 2017

Environ. Res. Lett.

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Despite the backdrop of continuous global warming, sea ice extent has been found not to consistently decrease across the globe, and instead exhibit heterogeneous variability at middle to high latitudes. However, the existing studies are focused primarily on high latitude frozen seas, while studies on the long-term variability of sea ice cover at middle latitudes are generally lacking. Afforded by continuous satellite imagery, evolution of sea ice cover over nearly three decades from 1988 to 2015 in the Bohai Sea as a peculiar mid-latitude frozen sea area is reported for the first time. An anomalous trend of slight overall increase of 1.38 ± 1.00% yr–1 (R = 1.38, i.e. at a statistical significance of 80%) in Bohai Sea ice extent was observed over the 28 year period. The detrended annual average ice area (AAIA) was further found to correlate with a slight decreasing mean ice-period average temperature (IAT, r = –0.58, p < 0.01) of 11 meteorological stations around the Bohai Sea as well as a mild increasing cumulative freezing degree days (CFDD, r = 0.65, p < 0.01). Correlation with decreasing Arctic Oscillation (AO) index (r = –0.60, p < 0.01) and North Atlantic Oscillation (NAO) index (r = –0.69, p < 0.01) over the study period suggested AO and NAO as the primary large-scale climate factors for Bohai Sea ice. In addition, the seasonal cycle of ice cover showed a single peak with longer freezing phase than melting phase, due to the different temperature change rate during the freezing and melting phases. The results can provide important references for monitoring the recent climate change in the region and beyond.

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“…Using remote sensing technology, it is possible to quickly obtain a wide scale electromagnetic wave reflection, radiation or scattering images of the Bohai sea ice. In recent years, remote sensing is widely applied in extent extraction and the thickness and concentration estimation of the Bohai sea ice [2,[15][16][17][18][19][20][21][22][23][24][25][26] . Nevertheless, there is no research about Bohai sea ice density reversion using remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Sea ice density estimation in the Bohai Sea using the hyperspectral remote sensing technology

et al. 2014

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Sea ice density is one of the significant physical properties of sea ice and the input parameters in the estimation of the engineering mechanical strength and aerodynamic drag coefficients; also it is an important indicator of the ice age. The sea ice in the Bohai Sea is a solid, liquid and gas-phase mixture composed of pure ice, brine pockets and bubbles, the density of which is mainly affected by the amount of brine pockets and bubbles. The more the contained brine pockets, the greater the sea ice density; the more the contained bubbles, the smaller the sea ice density. The reflectance spectrum in 350~2500 nm and density of sea ice of different thickness and ages were measured in the Liaodong Bay of the Bohai Sea during the glacial maximum in the winter of 2012-2013. According to the measured sea ice density and reflectance spectrum, the characteristic bands that can reflect the sea ice density variation were found, and the sea ice density spectrum index (SIDSI) of the sea ice in the Bohai Sea was constructed. The inversion model of sea ice density in the Bohai Sea which refers to the layer from surface to the depth of penetration by the light was proposed at last. The sea ice density in the Bohai Sea was estimated using the proposed model from Hyperion image which is a hyperspectral image. The results show that the error of the sea ice density inversion model is about 0.0004 g•cm -3 . The sea ice density can be estimated through hyperspectral remote sensing images, which provide the data support to the related marine science research and application.

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Spatio-temporal characteristics of the sea-ice volume of the Bohai Sea, China, in winter 2009/10

Cited by 17 publications

References 7 publications

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Multidecadal anomalies of Bohai Sea ice cover and potential climate driving factors during 1988–2015

Sea ice density estimation in the Bohai Sea using the hyperspectral remote sensing technology

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