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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.
    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.

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