IECE - Institute of Emerging and Computer Engineers

  • Sign Up Log in
    Log in

    • -
    CiteScore
    5.0
    Impact Factor
    1. Home
    2. Journals
    3. IECE Transactions on Internet of Things
    4. Volume 2, Issue 1
    Volume 2, Issue 1, IECE Transactions on Internet of Things
    Volume 2, Issue 1, 2024
    Submit Manuscript Edit a Special Issue
    Academic Editor
    Jinchao Chen
    Jinchao Chen
    School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
    Article QR Code
    Article QR Code
    Scan the QR code for reading
    Popular articles
    Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Visual Feature Extraction and Tracking Method Based on Corner Flow Detection A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Inaugural Editorial of the Chinese Journal of Information Fusion Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
    IECE Transactions on Internet of Things, 2024, Volume 2, Issue 1: 26-35

    Free Access | Research Article | 12 March 2024
    Advancements in Multi-Year Ice Concentration Estimation from SSM/I 91.6GHz Observations
    by
    IECE Author Yue Zhao 1 *
    IECE Author Xingdong Wang 1
    IECE Author Zifan Zhang 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.2024.682080
    Received: 15 January 2024, Accepted: 27 February 2024, Published: 12 March 2024  
    Abstract
    To enhance the LOMAX algorithm for sea ice concentration analysis in the polar regions, SSM/I 91.6GHz data was utilized, addressing the underuse of higher frequency data. The refinement process involved redefining PCT values for one-year and multi-year ice regions through both interpolation and least squares methods. Moreover, band operations were conducted to facilitate Arctic multi-year ice concentration retrieval. Comparative analyses with the NT algorithm indicated that the Arctic sea ice extents determined by both algorithms were similar, affirming the credibility of the modified LOMAX algorithm. When examining the results for March and September, the updated LOMAX algorithm demonstrated improved accuracy over the NT algorithm, especially under summer ice melt conditions, highlighting the enhanced performance and reliability of the refined algorithm in various seasonal contexts.
    Graphical Abstract
    Advancements in Multi-Year Ice Concentration Estimation from SSM/I 91.6GHz Observations
    Keywords
    LOMAX
    Multi-year ice concentration
    91.6 GHz
    References

    [1] Cavalieri D J. (1994). A microwave technique for mapping thin sea ice[U]. 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] Spreen, G., Kaleschke, L., & Heygster, G. (2008). Sea ice remote sensing using AMSR-E 89-GHz channels. Journal of Geophysical Research: Oceans, 113(C2).

    [14] Y. Hua & X. Wang (2023). Forest Fire Assessment and Analysisin Liangshan, Sichuan Province Based on Remote Sensing. IECE Transactions on Internet of Things, 1(1), 15–21.

    [15] Lv, Y., Fang, F. A. N. G., Yang, T., & Romero, C. E. (2020). An early fault detection method for induced draft fans based on MSET with informative memory matrix selection. ISA transactions, 102, 325-334.

    [16] 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.

    [17] 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.

    [18] 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.

    Cite This Article
    APA Style
    Zhao, Y., Wang, X., & Zhang, Z. (2024). Advancements in Multi-Year Ice Concentration Estimation from SSM/I 91.6GHz Observations. IECE Transactions on Internet of Things, 2(1), 26–35. https://doi.org/10.62762/TIOT.2024.682080
    Article Metrics
    Citations:

    Google Scholar
    0

    Crossref

    0

    Scopus

    0

    Web of Science

    0
    Article Access Statistics:
    Views: 449
    PDF Downloads: 19
    Publisher's Note
    IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
    Rights and permissions
    IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
    IECE Transactions on Internet of Things

    IECE Transactions on Internet of Things

    ISSN: 2996-9298 (Online)

    Email: [email protected]

    Portico

    Portico

    All published articles are preserved here permanently:
    https://www.portico.org/publishers/iece/

    Copyright © 2024 Institute of Emerging and Computer Engineers Inc.