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Rice Phenology Retrieval Based on Growth Curve Simulation and Multi-Temporal Sentinel-1 Data

Author

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  • Bo Wang

    (College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Yu Liu

    (College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Qinghong Sheng

    (College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Jun Li

    (College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Jiahui Tao

    (College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Zhijun Yan

    (College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract

The accurate estimation and monitoring of phenology is necessary for modern agricultural industries. For crops with short phenology occurrence times, such as rice, Sentinel-1 can be used to effectively monitor the growth status in different phenology periods within a short time interval. Therefore, this study proposes a method to monitor rice phenology based on growth curve simulation by constructing a polarized growth index (PGI) and obtaining a polarized growth curve. A recursive neural network is used to realize the classification of phenology and use it as prior knowledge of rice phenology to divide and extract the phenological interval and date of rice in 2021. The experimental results show that the average accuracy of neural network phenological interval division reaches 93.5%, and the average error between the extracted and measured phenological date is 3.08 days, which proves the application potential of the method. This study will contribute to the technical development of planning, management and maintenance of renewable energy infrastructure related to phenology.

Suggested Citation

  • Bo Wang & Yu Liu & Qinghong Sheng & Jun Li & Jiahui Tao & Zhijun Yan, 2022. "Rice Phenology Retrieval Based on Growth Curve Simulation and Multi-Temporal Sentinel-1 Data," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8009-:d:852789
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    References listed on IDEAS

    as
    1. Lausch, Angela & Salbach, Christoph & Schmidt, Andreas & Doktor, Daniel & Merbach, Ines & Pause, Marion, 2015. "Deriving phenology of barley with imaging hyperspectral remote sensing," Ecological Modelling, Elsevier, vol. 295(C), pages 123-135.
    2. Jian Chen & Yiping Liu & Lingjun Wang, 2019. "Research on Coupling Coordination Development for Photovoltaic Agriculture System in China," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    3. Lingjun Wang & Ying Wang & Jian Chen, 2019. "Assessment of the Ecological Niche of Photovoltaic Agriculture in China," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
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