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

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.

Plant phenotyping relevance

作物キャノピーの分光反射から植物含水量を推定する新規三帯域指数と機械学習モデルを開発・比較しており、植物生理状態の取得手法が研究の中心である。

Code and data availability

The supplied blocks describe field spectroradiometer canopy reflectance measurements, plant moisture content data, and machine learning modeling, but contain no data availability statement, no public dataset deposit, and no author code/model release. No paper-specific public asset is identifiable.

No evidence-backed public reproduction asset is currently recorded.