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Assessment of plant water status in winter wheat (Triticum aestivum L.) based on canopy spectral indices

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  • Hui Sun
  • Meichen Feng
  • Lujie Xiao
  • Wude Yang
  • Chao Wang
  • Xueqin Jia
  • Yu Zhao
  • Chunqi Zhao
  • Saleem Kubar Muhammad
  • Deying Li

Abstract

Rapid and non-destructive estimation of plant water status is essential for adjusting field practices and irrigation schemes of winter wheat. The objective of this study was to find new combination spectral indices based on canopy reflectance for the estimation of plant water status. Two experiments with different irrigation regimes were conducted in 2015–2016 and 2016–2017. The canopy spectra were collected at different growth stages of winter wheat. The raw and derivative reflectance of canopy spectra showed obvious responses to the change of plant water status. Except for equivalent water thickness (EWT), other water metrics had good relationships with new combination spectral indices (R2>0.7). An acceptable model of canopy water content (CWC) was established with the best spectral index (RVI (1605, 1712)). Models of leaf water content (LWC) and plant water content (PWC) had better performances. Optimal spectral index of LWC was FDRVI (687, 531), having R2, RMSE and RPD of 0.77, 2.181 and 2.09; R2, RMSE and RPD of 0.87, 2.652 and 2.34 for calibration and validation, respectively. And PWC could be well estimated with FDDVI (688, 532) (R2, RMSE and RPD of 0.79, 3.136 and 2.21; R2, RMSE and RPD of 0.83, 3.702 and 2.18 for calibration and validation, respectively). Comparing the performances of estimation models, the new combination spectral indices FDRVI (687, 531) based on canopy reflectance improved the accuracy of estimation of plant water status. Besides, based on FDRVI (687, 531), LWC was the optimal water metrics for plant water status estimation.

Suggested Citation

  • Hui Sun & Meichen Feng & Lujie Xiao & Wude Yang & Chao Wang & Xueqin Jia & Yu Zhao & Chunqi Zhao & Saleem Kubar Muhammad & Deying Li, 2019. "Assessment of plant water status in winter wheat (Triticum aestivum L.) based on canopy spectral indices," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    • Handle: RePEc:plo:pone00:0216890
    DOI: 10.1371/journal.pone.0216890
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    References listed on IDEAS

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    1. Corey Lesk & Pedram Rowhani & Navin Ramankutty, 2016. "Influence of extreme weather disasters on global crop production," Nature, Nature, vol. 529(7584), pages 84-87, January.
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    1. Yibo Zhao & Shaogang Lei, 2025. "Research on the Inversion Method of Dust Retention in Grassland Plant Canopies Based on UAV-Borne Hyperspectral Data," Land, MDPI, vol. 14(3), pages 1-16, February.
    2. Solgi, Shahin & Ahmadi, Seyed Hamid & Seidel, Sabine Julia, 2023. "Remote sensing of canopy water status of the irrigated winter wheat fields and the paired anomaly analyses on the spectral vegetation indices and grain yields," Agricultural Water Management, Elsevier, vol. 280(C).
    3. Osama Elsherbiny & Yangyang Fan & Lei Zhou & Zhengjun Qiu, 2021. "Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data," Agriculture, MDPI, vol. 11(1), pages 1-21, January.

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