IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v14y2017i9p1018-d111007.html
   My bibliography  Save this article

Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition

Author

Listed:
  • Lingwen Tian

    (School of Information Engineering, China University of Geoscience, Beijing 100083, China)

  • Xiangnan Liu

    (School of Information Engineering, China University of Geoscience, Beijing 100083, China)

  • Biyao Zhang

    (School of Information Engineering, China University of Geoscience, Beijing 100083, China)

  • Ming Liu

    (School of Information Engineering, China University of Geoscience, Beijing 100083, China)

  • Ling Wu

    (School of Information Engineering, China University of Geoscience, Beijing 100083, China)

Abstract

The use of remote sensing technology to diagnose heavy metal stress in crops is of great significance for environmental protection and food security. However, in the natural farmland ecosystem, various stressors could have a similar influence on crop growth, therefore making heavy metal stress difficult to identify accurately, so this is still not a well resolved scientific problem and a hot topic in the field of agricultural remote sensing. This study proposes a method that uses Ensemble Empirical Mode Decomposition (EEMD) to obtain the heavy metal stress signal features on a long time scale. The method operates based on the Leaf Area Index (LAI) simulated by the Enhanced World Food Studies (WOFOST) model, assimilated with remotely sensed data. The following results were obtained: (i) the use of EEMD was effective in the extraction of heavy metal stress signals by eliminating the intra-annual and annual components; (ii) LAI df (The first derivative of the sum of the interannual component and residual) can preferably reflect the stable feature responses to rice heavy metal stress. LAI df showed stability with an R 2 of greater than 0.9 in three growing stages, and the stability is optimal in June. This study combines the spectral characteristics of the stress effect with the time characteristics, and confirms the potential of long-term remotely sensed data for improving the accuracy of crop heavy metal stress identification.

Suggested Citation

  • Lingwen Tian & Xiangnan Liu & Biyao Zhang & Ming Liu & Ling Wu, 2017. "Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition," IJERPH, MDPI, vol. 14(9), pages 1-17, September.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:9:p:1018-:d:111007
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/14/9/1018/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/14/9/1018/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Varaprasad Bandaru & Craig S. Daughtry & Eton E. Codling & David J. Hansen & Susan White-Hansen & Carrie E. Green, 2016. "Evaluating Leaf and Canopy Reflectance of Stressed Rice Plants to Monitor Arsenic Contamination," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yibo Tang & Meiling Liu & Xiangnan Liu & Ling Wu & Bingyu Zhao & Chuanyu Wu, 2020. "Spatio-temporal Index Based on Time Series of Leaf Area Index for Identifying Heavy Metal Stress in Rice under Complex Stressors," IJERPH, MDPI, vol. 17(7), pages 1-18, March.
    2. Yu Zhang & Meiling Liu & Li Kong & Tao Peng & Dong Xie & Li Zhang & Lingwen Tian & Xinyu Zou, 2022. "Temporal Characteristics of Stress Signals Using GRU Algorithm for Heavy Metal Detection in Rice Based on Sentinel-2 Images," IJERPH, MDPI, vol. 19(5), pages 1-14, February.
    3. Xinyu Zou & Xiangnan Liu & Mengxue Liu & Meiling Liu & Biyao Zhang, 2019. "A Framework for Rice Heavy Metal Stress Monitoring Based on Phenological Phase Space and Temporal Profile Analysis," IJERPH, MDPI, vol. 16(3), pages 1-16, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:14:y:2017:i:9:p:1018-:d:111007. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.