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Temporal Characteristics of Stress Signals Using GRU Algorithm for Heavy Metal Detection in Rice Based on Sentinel-2 Images

Author

Listed:
  • Yu Zhang

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

  • Meiling Liu

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

  • Li Kong

    (Urban and Rural Planning and Design Institute Co., Ltd., Anhui Jianzhu University, Hefei 230022, China)

  • Tao Peng

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

  • Dong Xie

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

  • Li Zhang

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

  • Lingwen Tian

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

  • Xinyu Zou

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

Abstract

Heavy metal stress, which is a serious environmental problem, affects both animal and human health through the food chain. However, such subtle stress information is difficult to detect in remote sensing images. Therefore, enhancing the stress signal is key to accurately identifying heavy metal contamination in crops. The aim of this study was to identify heavy metal stress in rice at a regional scale by mining the time-series characteristics of rice growth under heavy metal stress using the gated recurrent unit (GRU) algorithm. The experimental area was located in Zhuzhou City, Hunan Province, China. We collected situ-measured data and Sentinel-2A images corresponding to the 2019–2021 period. First, the spatial distribution of the rice in the study area was extracted using the random forest algorithm based on the Sentinel 2 images. Second, the time-series characteristics were analyzed, sensitive parameters were selected, and a GRU classification model was constructed. Third, the model was used to identify the heavy metals in rice and then assess the accuracy of the classification results using performance metrics such as the accuracy rate, precision, recall rate (recall), and F1-score (F1-score). The results showed that the GRU model based on the time series of the red-edge location feature index has a good classification performance with an overall accuracy of 93.5% and a Kappa coefficient of 85.6%. This study shows that regional heavy metal stress in crops can be accurately detected using the GRU algorithm. A combination of spectrum and temporal information appears to be a promising method for monitoring crops under various types of stress.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:5:p:2567-:d:756353
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    References listed on IDEAS

    as
    1. Huihui Zhao & Peijia Liu & Baojin Qiao & Kening Wu, 2021. "The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China," Land, MDPI, vol. 10(11), pages 1-13, November.
    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. 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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