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Winter Wheat Maturity Prediction via Sentinel-2 MSI Images

Author

Listed:
  • Jibo Yue

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Ting Li

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Jianing Shen

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Yihao Wei

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Xin Xu

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Yang Liu

    (Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China)

  • Haikuan Feng

    (College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
    Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Institute of Quantitative Remote Sensing and Smart Agriculture, Henan Polytechnic University, Jiaozuo 454000, China)

  • Xinming Ma

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Changchun Li

    (Institute of Quantitative Remote Sensing and Smart Agriculture, Henan Polytechnic University, Jiaozuo 454000, China)

  • Guijun Yang

    (Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    Institute of Quantitative Remote Sensing and Smart Agriculture, Henan Polytechnic University, Jiaozuo 454000, China)

  • Hongbo Qiao

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

  • Hao Yang

    (Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Qian Liu

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China)

Abstract

A timely and comprehensive understanding of winter wheat maturity is crucial for deploying large-scale harvesters within a region, ensuring timely winter wheat harvesting, and maintaining grain quality. Winter wheat maturity prediction is limited by two key issues: accurate extraction of wheat planting areas and effective maturity prediction methods. The primary aim of this study is to propose a method for predicting winter wheat maturity. The method comprises three parts: (i) winter wheat planting area extraction via phenological characteristics across multiple growth stages; (ii) extraction of winter wheat maturity features via vegetation indices (VIs, such as NDVI, NDRE, NDII1, and NDII2) and box plot analysis; and (iii) winter wheat maturity data prediction via the selected VIs. The key findings of this work are as follows: (i) Combining multispectral remote sensing data from the winter wheat jointing-filling and maturity-harvest stages can provide high-precision extraction of winter wheat planting areas (OA = 95.67%, PA = 91.67%, UA = 99.64%, and Kappa = 0.9133). (ii) The proposed method can offer the highest accuracy in predicting maturity at the winter wheat flowering stage ( R 2 = 0.802, RMSE = 1.56 days), aiding in a timely and comprehensive understanding of winter wheat maturity and in deploying large-scale harvesters within the region. (iii) The study’s validation was only conducted for winter wheat maturity prediction in the North China Plain wheat production area, and the accuracy of harvesting progress information extraction for other regions’ wheat still requires further testing. The method proposed in this study can provide accurate predictions of winter wheat maturity, helping agricultural management departments adopt information-based measures to improve the efficiency of monitoring winter wheat maturation and harvesting, thus promoting the efficiency of precision agricultural operations and informatization efforts.

Suggested Citation

  • Jibo Yue & Ting Li & Jianing Shen & Yihao Wei & Xin Xu & Yang Liu & Haikuan Feng & Xinming Ma & Changchun Li & Guijun Yang & Hongbo Qiao & Hao Yang & Qian Liu, 2024. "Winter Wheat Maturity Prediction via Sentinel-2 MSI Images," Agriculture, MDPI, vol. 14(8), pages 1-22, August.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1368-:d:1456992
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    References listed on IDEAS

    as
    1. Kampstra, Peter, 2008. "Beanplot: A Boxplot Alternative for Visual Comparison of Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(c01).
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