Arctic sea ice detection based on microwave radiometer in 89GHz channels

Authors

  • Yue Zhao College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan 450001, China https://orcid.org/0000-0002-2286-2977
  • Xingdong Wang College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan 450001, China
  • Changfeng Luo College of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan 450001, China

DOI

https://doi.org/10.52810/TIOT.2021.100065

Keywords:

Sea ice classification, Arctic, Microwave remote sensing, Decision tree

Abstract

Sea ice is a very important part of the frozen circle. In these years of research, a lot of research on sea ice is realized through microwave remote sensing. Because microwave remote sensing is not easily affected by clouds and light, it is widely used in this field. The design of this paper will classify Arctic sea ice based on microwave remote sensing data. Based on the characteristics of high stability of microwave radiometer, this paper studies the characteristics of sea ice types in the whole Arctic region in January 2017, and uses high-resolution AMSR-E / AMSR2 data in 89Hz frequency band. Data in this frequency band are not easily affected by clouds and water vapor, while data in low frequency band are less affected by clouds and water vapor. Over the years, a lot of research has been done on the use of low frequency band at home and abroad. For this design, the research on the 89GHz frequency band we used is of great significance. We used the high resolution characteristics of multi-year  ice and one-year ice in the 89GHz frequency band, the brightness temperature difference of one-year ice, multi-year ice and sea water is analyzed, and the method of using brightness temperature difference to distinguish sea ice species is determined. The fine tree theory in MATALAB decision tree is used to generate classification results. 

Author Biographies

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

Yue Zhao  Currently majoring in electronic Information in College of Information Science and Engineering, Henan University of Technology. The research direction  is remote sensing in sea ice concentration.

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

Xingdong Wang  Currently working at the School of Information Science and Engineering, Henan University of Technology. His current research interests include remote sensing, GIS(geographic information system).

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

Changfeng Luo  From 2017 to 2021, he graduated from the computer department of Henan University of technology with a bachelor's degree. His current research interests is Spatial information and digital technology.

References

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

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

Cavalieri D J, Parkinson C L. (2012). Arctic sea ice variability and trends 1979-2010[J]. Cryosphere, 6, 881-889, doi: 10.5194/tc-6-881-2012.

Comiso J C, Nishio F. (2008). Trends in the sea ice cover using enhanced and compatible AMSR-E, SSM/A, and SMMR data[J]. Journal of Geophysical Research Oceans, 113(C2): 228-236.

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

Ulaby F T. (1986). Textural information in SAR images[J]. IEEE Trans. Geosci.Remote Sens, 24(2): 235-245.

Soh L K, Tsatsoulis C, Gineris D, et al. (2004). ARKTOS: an intelligent system for SAR sea ice image classification[J]. Geo science & Remote Sensing IEEE Transactions on, 42(1): 229-248.

Ressel R, Frost A, Lehner S. (2015). A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images[U]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(7): 1-9.

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

Nghiem S V, Steffen K, Kwok R, et al. (2001). Detection of snowmelt regions on the Greenland ice sheet using diurnal back scatter change [J]. Journal of Glaciology, 47(159): 539-547.

Gough S. (1972). A Low Temperature Dielectric Cell and the Permittivity of Hexagonal Ice to 2K [J]. Can J Chem. 50: 3046-3051.

Y. Li and J. Cao, “WSN Node Optimal Deployment Algorithm Based on Adaptive Binary Particle Swarm Optimization”, ASP trans. Internet things, vol. 1, no. 1, pp. 1–8, May 2021.

Arctic sea ice detection based on microwave radiometer in 89GHz channels

Downloads

Published

2021-09-04

How to Cite

Zhao, Y., Wang, X., & Luo, C. (2021). Arctic sea ice detection based on microwave radiometer in 89GHz channels. ASP Transactions on Internet of Things, 1(2), 23–29. https://doi.org/10.52810/TIOT.2021.100065

Issue

Section

Regular Paper