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Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images

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  • Yan Guo

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002, China
    Henan Engineering Research Center of Crop Planting Monitoring and Warning, Zhengzhou 450002, China)

  • Jia He

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002, China)

  • Huifang Zhang

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002, China)

  • Zhou Shi

    (Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China)

  • Panpan Wei

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002, China)

  • Yuhang Jing

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002, China)

  • Xiuzhong Yang

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002, China
    Henan Engineering Research Center of Crop Planting Monitoring and Warning, Zhengzhou 450002, China)

  • Yan Zhang

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002, China
    Henan Engineering Research Center of Crop Planting Monitoring and Warning, Zhengzhou 450002, China)

  • Laigang Wang

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    International School, Huanghe Science & Technology College, Zhengzhou 450016, China)

  • Guoqing Zheng

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou 450002, China
    Henan Engineering Research Center of Crop Planting Monitoring and Warning, Zhengzhou 450002, China)

Abstract

Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (H dsm ) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R 2 , root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the H dsm , are 0.58, 4528.23 kg/hm 2 , and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm 2 ) is slightly smaller than the measured mean AGB (16,960.23 kg/hm 2 ). (2) The R 2 , RMSE, and RPD of the improved AGB estimation model, based on AGB/H dsm , are 0.88, 2291.90 kg/hm 2 , and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm 2 ) is more similar to the measured mean AGB (17,222.59 kg/hm 2 ). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the H dsm . Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

Suggested Citation

  • Yan Guo & Jia He & Huifang Zhang & Zhou Shi & Panpan Wei & Yuhang Jing & Xiuzhong Yang & Yan Zhang & Laigang Wang & Guoqing Zheng, 2024. "Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images," Agriculture, MDPI, vol. 14(3), pages 1-17, February.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:3:p:378-:d:1346855
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    References listed on IDEAS

    as
    1. Jianyong Zhang & Yanling Zhao & Zhenqi Hu & Wu Xiao, 2023. "Unmanned Aerial System-Based Wheat Biomass Estimation Using Multispectral, Structural and Meteorological Data," Agriculture, MDPI, vol. 13(8), pages 1-24, August.
    2. Shilong Piao & Jingyun Fang & Philippe Ciais & Philippe Peylin & Yao Huang & Stephen Sitch & Tao Wang, 2009. "The carbon balance of terrestrial ecosystems in China," Nature, Nature, vol. 458(7241), pages 1009-1013, April.
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