Extraction of Bohai Sea ice from MODIS data based on multi-constraint endmembers and linear spectral unmixing

Li, Yawen; Yang, Daiqin

doi:10.1080/01431161.2020.1734248

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…This index can effectively extract sea ice extent from turbid water and is suitable for winter Bohai Sea ice monitoring. Li and Yang (2020) proposed a linear spectral unmixing method based on multiconstraint endmembers (LSU-MCE) for sea ice and sea water discrimination in the Bohai Sea using MODIS images from 2016 to 2017 with an accuracy of 98.25%. This method can eliminate interference caused by suspended sediment.…”

Section: Introductionmentioning

confidence: 99%

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

Yan,

Zhang,

et al. 2024

Front. Mar. Sci.

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The Bohai Sea and its surrounding areas are rich in oil and natural gas and play an important role in industry, agriculture and the economy. However, the Bohai Sea suffers severely from sea ice in the winter. While previous research has predominantly focused on methods for retrieving sea ice parameters in the Bohai Sea, analyses of their long-term statistical patterns have been limited. The Geostationary Ocean Color Imager (GOCI) is the first geostationary satellite for ocean color remote sensing, offering high spatial and temporal resolution, which greatly facilitates the extraction of Bohai Sea ice parameters. Utilizing GOCI data, we systematically extracted relevant sea ice parameters for the Bohai Sea region from 2011 to 2021. These parameters include sea ice concentration, sea ice thickness, and sea ice drift. We conducted a comprehensive statistical analysis of the long-term sea ice changes in the Bohai Sea and found that the development process of winter sea ice area is different from the sea ice thickness, and the direction of sea ice drift is basically unchanged. Then we developed statistical models linking sea ice parameters with ocean dynamic factors such as temperature, wind, and drift currents. Among them, the correlation coefficient between the predicted value and the measured value of the sea ice area model is the highest, reaching 0.8382. Furthermore, we examined the previously unexplored relationship between daily sea ice area, sea ice thickness, and accumulated temperature with their respective starting temperatures and accumulation periods. This study provides critical data to support Bohai Sea ice monitoring and marine environmental research. The results of this study contribute to a better understanding of sea ice change trends in the Bohai Sea and inform the development of disaster prevention and mitigation measures.

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Section: Introductionmentioning

confidence: 99%

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

Yan,

Zhang,

et al. 2024

Front. Mar. Sci.

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“…The digital image processing method is to highlight the sea ice information by processing and transforming the image. For example, Li et al proposed a linear spectral decomposition method based on MODIS images with multiple constrained end members [34]. The pixels are decomposed to extract the range of the sea ice.…”

Section: Introductionmentioning

confidence: 99%

Automatic and Accurate Extraction of Sea Ice in the Turbid Waters of the Yellow River Estuary Based on Image Spectral and Spatial Information

et al. 2022

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Sea ice is an important part of the global cryosphere and an important variable in the global climate system. Sea ice also presents one of the major natural disasters in the world. The automatic and accurate extraction of sea ice extent is of great significance for the study of climate change and disaster prevention. The accuracy of sea ice extraction in the Yellow River Estuary is low due to the large dynamic changes in the suspended particulate matter (SPM). In this study, a set of sea ice automatic extraction method systems combining image spectral information and textural information is developed. First, a sea ice spectral information index that can adapt to sea areas with different turbidity levels is developed to mine the spectral information of different types of sea ice. In addition, the image’s textural feature parameters and edge point density map are extracted to mine the spatial information concerning the sea ice. Then, multi-scale segmentation is performed on the image. Finally, the OTSU algorithm is used to determine the threshold to achieve automatic sea ice extraction. The method was successfully applied to Gaofen-1 (GF1), Sentinel-2, and Landsat 8 images, where the extraction accuracy of sea ice was over 93%, which was more than 5% higher than that of SVM and K-Means. At the same time, the method was applied to the Liaodong Bay area, and the extraction accuracy reached 99%. These findings reveal that the method exhibits good reliability and robustness.

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Variability of Sea Ice from 2008 to 2019 in the Bohai and Northern Huanghai Sea, China and the Relationship with Climatic Factors

Gong

Chen

Zhang

et al. 2022

J. Ocean Univ. China

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Extraction of Bohai Sea ice from MODIS data based on multi-constraint endmembers and linear spectral unmixing

Cited by 7 publications

References 19 publications

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

Automatic and Accurate Extraction of Sea Ice in the Turbid Waters of the Yellow River Estuary Based on Image Spectral and Spatial Information

Variability of Sea Ice from 2008 to 2019 in the Bohai and Northern Huanghai Sea, China and the Relationship with Climatic Factors

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