基于高光谱小波变换的玉米叶片含水量估测

doi:10.3964/j.issn.1000-0593(2025)10-2875-10

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Abstract In the life activities of plants, water plays a decisive role in crop yield. Rapid detection and acquisition of plant leaf water status is of great significance for understanding the physiological water requirements of field crops and corresponding water management. Hyperspectral indices are an important means of non-destructive, real-time estimation of crop leaf water content. However, the commonly used spectral index is significantly affected by the growth period in estimating leaf water content, and the stability is poor. Meeting production requirements is challenging due to the estimation accuracy. To achieve the accuracy of corn leaf water estimation and realize efficient use of corn, this study conducted field experiments with different moisture gradients in typical corn-growing areas in Inner Mongolia from 2023 to 2024, measured the hyperspectral reflectance of corn leaves at three key growth periods, and established a relationship model between leaf water content (LWC) and wavelet function and spectral index to determine the best performing wavelet function and spectral index, and evaluated their stability and robustness in detecting corn leaf water content. The results showed that the correlation analysis of leaf water content using spectral index and wavelet function found that the MDATT index had the best prediction result (R²=0.52) among the 13 selected water indices. Still, the estimation accuracy was greatly affected by the growth period and layer. In contrast, the continuous wavelet transform improved the estimation accuracy of LWC while overcoming the influence of growth period and layer on the prediction accuracy. Among them, the best performing wavelet function and its characteristics were Coif3 (S₆W₁₇₂₅) (R²=0.83). Compared with the spectral index, the Coif3 function in the wavelet function was more stable in estimating the water content of corn leaves. The determination coefficient R² of the independent verification result of the model was 0.76, and the verification error was the smallest, with RMSE and RE of 3.08% and 3.51%, respectively. The research results enable the accurate assessment of water content during the critical growth period of corn and the precise management of irrigation, thereby contributing to the sustainable development of the integrated water-fertilizer corn planting system in central and western China.

Key words：Corn leaf; Moisture content; Wavelet function; Vegetation index

Received: 2024-12-21 Accepted: 2025-07-15

ZTFLH:

S127

Corresponding Authors: LI Fei E-mail: Lifei@imau.edu.cn

Cite this article:

XIAO Ya-ting,TANG Yu-zhe,BAI Yu-fei, et al. Estimation of Leaf Moisture Content of Maize Based on Spectral Index and Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2875-2884.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)10-2875-10 OR https://www.gpxygpfx.com/EN/Y2025/V45/I10/2875

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