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Remote estimation of leaf water concentration in winter wheat under different nitrogen treatments and plant growth stages

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Abstract

Hyperspectral remote sensing can quickly, nondestructively and accurately monitor crop water concentration and provide technical support for winter wheat growth monitoring, drought assessment, and variable irrigation. In this study, canopy spectral reflectance, leaf water concentration (LWC), leaf nitrogen concentration (LNC), leaf area index (LAI), and leaf dry matter (LDM) of four wheat cultivars were measured under different irrigation and nitrogen treatments, and the effects of nitrogen treatment and growth period on spectral reflectance and LWC were analyzed. Canopy spectral reflectance for different growth periods, irrigation, and nitrogen treatments showed significant changes, leading to the phenomena of “nitrogen treatment differentiation” and “growth period differentiation” for the normalized difference spectral index [NDSI (762, 1458, 2301)] and normalized difference infrared index (NDII) monitoring models. To reduce the influence of nitrogen treatment and growth period on the LWC estimation model, a modified normalized difference water index (mNDWI) was constructed by introducing the nitrogen factor (ratio of left and right peak area, RIDA) into the optimized combination of water-sensitive bands [ND (815, 1080), ND (1585, 1740), and ND (2030, 2260)]. Compared with NDSI (762, 1458, 2301), the R2 of mNDWI was improved by 36.2%–41.1% under different nitrogen levels and 18.6%–22.4% in different growth periods; this effectively reduced the impact of nitrogen status on LWC monitoring and realized the unified modeling and accurate inversion of LWC for the entire growth period. The new index mNDWI, especially mNDWI (815, 1080) and mNDWI (2030, 2260), can effectively monitor the LWC status of wheat under different cultivation conditions, which is important for the real-time diagnosis of plant moisture to guide precision field irrigation applications.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by grants from the Key Technologies Research and Development Program of Henan Province, China (Grant No. 212102110041), the National Natural Science Foundation of China (Grant No. 32271991); and the Research Start-up Fund to Young Talents of Henan Agricultural University (Grant No. 30601648).

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  1. Wei Feng & Tian-Cai Guo

    Present address: College of Agronomy/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, #15 Longzihu College District, Zhengzhou, Henan, 450046, People’s Republic of China

Authors and Affiliations

  1. College of Agronomy/State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, #15 Longzihu College District, Zhengzhou, Henan, 450046, People’s Republic of China

    Li He, Meng-Ran Liu, Shao-Hua Zhang, Han-Wen Guan & Chen-Yang Wang

  2. CIMMYT-China Joint Center of Wheat and Maize Improvement, #15 Longzihu College District, Zhengzhou, Henan, 450046, People’s Republic of China

    Li He & Wei Feng

  3. Henan Technology Innovation Centre of Wheat, #15 Longzihu College District, Zhengzhou, Henan, 450046, People’s Republic of China

    Li He, Chen-Yang Wang, Wei Feng & Tian-Cai Guo

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Conceptualization: LH and WF; Methodology: LH; Formal analysis and investigation: M-RL, S-HZ, and H-WG; Writing-original draft preparation: LH; Writing-review and editing: WF and C-YW; Funding acquisition: T-CG.

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He, L., Liu, MR., Zhang, SH. et al. Remote estimation of leaf water concentration in winter wheat under different nitrogen treatments and plant growth stages. Precision Agric 24, 986–1013 (2023). https://doi.org/10.1007/s11119-022-09983-3

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