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Inversion of Soil Moisture Content in Silage Corn Root Zones Based on UAV Remote Sensing

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  • Qihong Da

    (College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
    State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China)

  • Jixuan Yan

    (College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
    State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China)

  • Guang Li

    (State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China
    College of Forestry, Gansu Agricultural University, Lanzhou 730070, China)

  • Zichen Guo

    (College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
    State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China)

  • Haolin Li

    (College of Environmental Science and Engineering, Beijing University of Technology, Beijing 100124, China)

  • Wenning Wang

    (College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
    State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China)

  • Jie Li

    (College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
    State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China)

  • Weiwei Ma

    (State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China
    College of Forestry, Gansu Agricultural University, Lanzhou 730070, China)

  • Xuchun Li

    (College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
    State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China)

  • Kejing Cheng

    (College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, No. 1 Yingmen Village, Anning District, Lanzhou 730070, China
    State Key Laboratory of Crop Science in Arid Habitat Co-Constructed by Province and Ministry, Lanzhou 730070, China)

Abstract

Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain multispectral imagery during the seedling, jointing, and tasseling stages. Field experimental data were integrated, and supervised classification was used to remove soil background and image shadows. Canopy reflectance was extracted using masking techniques, while Pearson correlation analysis was conducted to assess the linear relationship strength between spectral indices and SMC. Subsequently, convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and partial least squares regression (PLSR) models were constructed to evaluate the applicability of these models in monitoring SMC before and after removing the soil background and image shadows. The results indicated that: (1) After removing the soil background and image shadows, the inversion accuracy of SMC for CNN, BPNN, and PLSR models improved at all growth stages. (2) Among the different inversion models, the accuracy from high to low was CNN, PLSR, BPNN. (3) From the perspective of different growth stages, the inversion accuracy from high to low was seedling stage, tasseling stage, jointing stage. The findings provide theoretical and technical support for UAV multispectral remote sensing inversion of SMC in silage corn root zones and offer validation for large-scale soil moisture monitoring using remote sensing.

Suggested Citation

  • Qihong Da & Jixuan Yan & Guang Li & Zichen Guo & Haolin Li & Wenning Wang & Jie Li & Weiwei Ma & Xuchun Li & Kejing Cheng, 2025. "Inversion of Soil Moisture Content in Silage Corn Root Zones Based on UAV Remote Sensing," Agriculture, MDPI, vol. 15(3), pages 1-19, February.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:331-:d:1582470
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    References listed on IDEAS

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    1. Gago, J. & Douthe, C. & Coopman, R.E. & Gallego, P.P. & Ribas-Carbo, M. & Flexas, J. & Escalona, J. & Medrano, H., 2015. "UAVs challenge to assess water stress for sustainable agriculture," Agricultural Water Management, Elsevier, vol. 153(C), pages 9-19.
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    Cited by:

    1. Xuchun Li & Jixuan Yan & Caixia Huang & Weiwei Ma & Zichen Guo & Jie Li & Xiangdong Yao & Qihong Da & Kejing Cheng & Hongyan Yang, 2025. "Estimation of Silage Maize Plant Moisture Content Based on UAV Multispectral Data and Ensemble Learning Methods," Agriculture, MDPI, vol. 15(7), pages 1-18, March.

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