An Assessment Model for Winter Wheat Crop Water Status Fusing Hyperspectral and Environmental Data

Abstract

Accurate monitoring of the crop water status is of great significance for agricultural water management. To address the limitations of traditional spectral models that neglect the synergistic effects of environmental factors, this study aimed to improve the prediction ability of winter wheat water status by integrating multi-source data and machine learning algorithms. The results demonstrated significant improvements in prediction accuracy when environmental factors were integrated with hyperspectral data. During the jointing, heading, and filling stages, the prediction accuracy of the winter wheat plant water content model based on canopy hyperspectral fusion environmental factors (temperature and soil water content) was significantly higher than that based on the canopy spectral data model. The model performance (R2) increased from 0.74, 0.59, and 0.70 to 0.82, 0.69, and 0.76, respectively. The SVM-based full-growth-stage fusion model exhibited superior performance (R2 = 0.85, RMSE = 5.10%, RE = 7.79%), achieving accuracy improvements of 3.53%, 23.19%, and 11.84% compared to three key growth-period models. This study confirms that integrating canopy hyperspectral data with environmental factors systematically enhances the generalization capability and accuracy of winter wheat water content prediction, providing a reliable technical solution for precision irrigation and innovative agricultural development in the future.

Plant phenotyping relevance

冬小麦の作物水分状態（植物水分含量）を、キャノピー hyperspectral データと環境データから機械学習で推定するモデル開発・性能評価が中心であり、植物生理状態のセンシング手法に該当する。

Code and data availability

The paper's hyperspectral, environmental, and plant water content measurements are not publicly deposited; the Data Availability Statement says they are available only on request from the corresponding author. No author analysis code, models, or public repository is mentioned.

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