Detection of Arctic Sea Ice Using 89 GHz Microwave Radiometer Channels

Yue Zhao; Xingdong Wang; Changfeng Luo

doi:10.62762/TIOT.2024.528361

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Volume 2, Issue 2, IECE Transactions on Internet of Things

Volume 2, Issue 2, 2024

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Jinchao Chen

School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China

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IECE Transactions on Internet of Things, 2024, Volume 2, Issue 2: 36-43

Free Access | Research Article | 07 April 2024

Detection of Arctic Sea Ice Using 89 GHz Microwave Radiometer Channels

Yue Zhao 1 *

Xingdong Wang 1

Changfeng Luo 1

1 College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China

* Corresponding author: Yue Zhao, email: [email protected]

DOI: 10.62762/TIOT.2024.528361

Received: 09 February 2024, Accepted: 26 March 2024, Published: 07 April 2024

Abstract

Sea ice is a crucial component of the cryosphere, and extensive research has been conducted on sea ice using microwave remote sensing due to its robustness against cloud cover and illumination variations. This paper focuses on classifying Arctic sea ice based on microwave remote sensing data. Leveraging the high stability of microwave radiometers, we analyze the characteristics of different sea ice types across the Arctic region in January 2017 using high-resolution AMSR-E/AMSR2 data at the 89 GHz frequency band. Data at this frequency are less susceptible to cloud and water vapor interference, while lower frequency bands have traditionally been more commonly used in similar studies. However, our study emphasizes the significance of the 89 GHz frequency band, which offers high-resolution data for distinguishing between multi-year ice, first-year ice, and open water. The brightness temperature differences among these sea ice types are analyzed, and a method based on these differences is proposed for classification. The fine tree algorithm in MATLAB’s decision tree toolbox is employed to generate the classification results.

Graphical Abstract

Detection of Arctic Sea Ice Using 89 GHz Microwave Radiometer Channels

Keywords

Sea ice classification

Arctic

Microwave remote sensing

Decision tree

References

[1] Cavalieri, D. J. (1994). A microwave technique for mapping thin sea ice. Journal of Geophysical Research: Oceans, 99(C6), 12561-12572.

[2] Cavalieri, D. J., Gloersen, P., & Campbell, W. J. (1984). Determination of sea ice parameters with the Nimbus 7 SMMR. Journal of Geophysical Research: Atmospheres, 89(D4), 5355-5369.

[3] Cavalieri, D. J., & Parkinson, C. L. (2012). Arctic sea ice variability and trends, 1979–2010. The Cryosphere, 6(4), 881-889.

[4] Comiso, J. C., & Nishio, F. (2008). Trends in the sea ice cover using enhanced and compatible AMSR-E, SSM/I, and SMMR data. Journal of Geophysical Research: Oceans, 113(C2).

[5] Comiso, J. C. (1986). Characteristics of Arctic winter sea ice from satellite multispectral microwave observations. Journal of Geophysical Research: Oceans, 91(C1), 975-994.

[6] Ulaby, F. T., Kouyate, F., Brisco, B., & Williams, T. L. (1986). Textural infornation in SAR images. IEEE Transactions on Geoscience and Remote Sensing, (2), 235-245.

[7] Soh, L. K., Tsatsoulis, C., Gineris, D., & Bertoia, C. (2004). ARKTOS: An intelligent system for SAR sea ice image classification. IEEE Transactions on geoscience and remote sensing, 42(1), 229-248.

[8] Ressel, R., Frost, A., & Lehner, S. (2015). A neural network-based classification for sea ice types on X-band SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7), 3672-3680.

[9] Cavalieri, D. J., Parkinson, C. L., Gloersen, P., Comiso, J. C., & Zwally, H. J. (1999). Deriving long-term time series of sea ice cover from satellite passive-microwave multisensor data sets. Journal of Geophysical Research: Oceans, 104(C7), 15803-15814.

[10] Nghiem, S. V., Steffen, K., Kwok, R., & Tsai, W. Y. (2001). Detection of snowmelt regions on the Greenland ice sheet using diurnal backscatter change. Journal of Glaciology, 47(159), 539-547.

[11] Gough, S. R. (1972). A low temperature dielectric cell and the permittivity of hexagonal ice to 2 K. Canadian Journal of Chemistry, 50(18), 3046-3051.

[12] Li, Y., & Cao, J. (2023). Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm. IECE Transactions on Internet of Things, 1(1), 1-8.

[13] Fang, F. A. N. G., Tan, W., & Liu, J. Z. (2005). Tuning of coordinated controllers for boiler-turbine units. Acta Automatica Sinica, 31(2), 291-296.

[14] Wang, N., Fang, F., & Feng, M. (2014, May). Multi-objective optimal analysis of comfort and energy management for intelligent buildings. In The 26th Chinese control and decision conference (2014 CCDC) (pp. 2783-2788). IEEE.

[15] Fang, F., Jizhen, L., & Wen, T. (2004). Nonlinear internal model control for the boiler-turbine coordinate systems of power unit. PROCEEDINGS-CHINESE SOCIETY OF ELECTRICAL ENGINEERING, 24(4), 195-199.

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APA Style

Zhao, Y., Wang, X., & Luo, C. (2024). Arctic Sea Ice Detection based on Microwave Radiometer in 89 GHz Channels. IECE Transactions on Internet of Things, 1(1), 36–43 https://doi.org/10.62762/TIOT.2024.528361

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