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A Framework for Rice Heavy Metal Stress Monitoring Based on Phenological Phase Space and Temporal Profile Analysis

Author

Listed:
  • Xinyu Zou

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

  • Xiangnan Liu

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

  • Mengxue Liu

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

  • Meiling Liu

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

  • Biyao Zhang

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

Abstract

Previous studies make it possible to use remote sensing techniques to monitor heavy metal stress of rice synchronously and continuously. However, most studies mainly focus on the analysis of rice’s visual symptoms and physiological functions rather than temporal information during the growth period, which may reflect significant changes of rice under heavy metal stress. In this paper, an enhanced spatial and temporal adaptive reflectance fusion model was used to generate synthetic Landsat time series. A normalized difference water index and an enhanced vegetation index were employed to build phenological phase space. Then, the ratio of the rice growth rate fluctuation (GRFI Ratio) was constructed for discriminating the different heavy metal stress levels on rice. Results suggested that the trajectories of rice growth in phenological phase space can depict the similarities and differences of rice growth under different heavy metal stress levels. The most common phenological parameters in the phase space cannot accurately discriminate the heavy metal stress level. However, the GRFI Ratio that we proposed outperformed in discriminating different levels of heavy metal stress. This study suggests that this framework of detecting the heavy metal pollution in paddy filed based on phenological phase space and temporal profile analysis is promising.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:3:p:350-:d:201044
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    References listed on IDEAS

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    1. 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.
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    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.

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