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Optimization of comprehensive wheat growth index system and monitoring model based on LAI

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Abstract

Wheat growth monitoring plays a vital role in agricultural decision-making and food security. This study aims to develop an accurate and efficient monitoring method for wheat growth by integrating satellite remote sensing and machine learning techniques. Based on preprocessed Sentinel-2 satellite images and measured wheat leaf area index (LAI) data, a set of 11 vegetation indices—such as NDVI, NDRE, and RVI—were selected and ranked through Pearson correlation analysis. A comprehensive index system was then constructed by selecting the top eight indices using a stepwise optimization approach. Three machine learning models—Linear Regression (LR), Backpropagation Neural Network (BPNN), and XGBoost—were applied to evaluate the performance of the index system, with the Particle Swarm Optimization (PSO) algorithm employed to optimize each model. The results demonstrate that the PSO-optimized XGBoost model achieved the highest accuracy (R² = 0.94, MSE = 0.075), exhibiting strong stability and robustness to data fluctuations. These findings suggest that the proposed approach provides a reliable solution for wheat growth monitoring.

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Data availability

This Sentinel-2 remote sensing image data are available at the Copernicus Data Space Ecosystem, https://dataspace.copernicus.eu/. The wheat Leaf Area Index (LAI) dataset originates from the canopy chlorophyll and ground validation dataset of winter wheat in Yucheng, Shandong, https://www.geodoi.ac.cn/edoi.aspx? DOI=10.3974/geodb.2020.08.01.V1.

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Funding

This work was funded by the Visualization and Algorithm Integration Research of High-Asia and Arctic Snow Cover, Glaciers, and Geological Hazard Data under the Sub-theme of National Key Research and Development Program of China (2021YFE0116807).

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Author notes

  1. These authors contributed equally to this work: Shuning Liang and Yong Liu.

Authors and Affiliations

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

    Mingguang Diao, Shuning Liang, Jianing Chen, Chuyan Zhang & Yong Liu

Authors

  1. Mingguang Diao
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  2. Shuning Liang
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  3. Jianing Chen
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  4. Chuyan Zhang
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  5. Yong Liu
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Contributions

Authors: Mingguang Diao (M.D.), Shuning Liang (S.L.), Jianing Chen(J.C.), Chuyan Zhang (C.Z.), Yong Liu (Y.L.)M.D.: Conceptualization, methodology, writing—original draft preparation, project administration, funding acquisition; S.L.: Software, formal analysis, writing—original draft preparation; J.C.: Software, writing—original draft preparation; C.Z.: Writing—review and editing, supervision. Y.L.: Investigation, Data curation.

Corresponding authors

Correspondence to
Mingguang Diao or Chuyan Zhang.

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The authors declare no competing interests.

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Diao, M., Liang, S., Chen, J. et al. Optimization of comprehensive wheat growth index system and monitoring model based on LAI.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-25313-9

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  • DOI: https://doi.org/10.1038/s41598-025-25313-9

Keywords

  • Wheat growth
  • Remote sensing
  • Vegetation index
  • Machine learning
  • Extreme gradient boosting

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