数字农科院2.0

Utilising the Potential of a Robust Three-Band Hyperspectral Vegetation Index for Monitoring Plant Moisture Content in a Summer Maize-Winter Wheat Crop Rotation Farming System

文献类型： 外文期刊

作者： Kanneh, James E.;Li, Caixia;Ma, Yanchuan;Li, Shenglin;Be, Madjebi Collela;Wang, Zuji;Zhong, Daokuan;Han, Zhiguo;Li, Hao;Wang, Jinglei

作者机构：

关键词： plant moisture content;spectroradiometer;tri-band index;machine learning;moisture stress;remote sensing

期刊名称： REMOTE SENSING

ISSN：

年卷期： 2026 年 18 卷 2 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Highlights What are the main findings? Normalised water stress index (), a new three-band index has high potential for better monitoring of drought in winter wheat-summer maize fields. NWSICombining these new indices ( and ) with the traditional moisture stress monitoring indices improves monitoring accuracy. What are the implications of the main findings? NWSINDIThe new three-band indices provide good options for accurate plant moisture stress monitoring in winter wheat-summer maize rotation systems. The NWSI and NDI, combined with traditional moisture stress monitoring indices, lay a scientific basis for precision irrigation.Highlights What are the main findings? Normalised water stress index (), a new three-band index has high potential for better monitoring of drought in winter wheat-summer maize fields. NWSICombining these new indices ( and ) with the traditional moisture stress monitoring indices improves monitoring accuracy. What are the implications of the main findings? NWSINDIThe new three-band indices provide good options for accurate plant moisture stress monitoring in winter wheat-summer maize rotation systems. The NWSI and NDI, combined with traditional moisture stress monitoring indices, lay a scientific basis for precision irrigation.Abstract Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM-WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = -0.8369, while in wheat, it reached R = -0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R2 of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R2 of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R2 across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application.

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