CatBoost Improves Inversion Accuracy of Plant Water Status in Winter Wheat Using Ratio Vegetation Index

Abstract

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R2 values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm−2, respectively. The model maintained robust performance in testing (R2 = 0.893 for CWC, and R2 = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP.

Plant phenotyping relevance

UAV画像と機械学習を用いて冬コムギの作物含水量・等価含水厚を推定し、モデル性能を比較検証する手法研究であり、植物水分状態の取得・抽出が中心です。

Code and data availability

The supplied blocks describe UAV multispectral imagery, CWC/EWT field measurements, and CatBoost/RF/DT/LightGBM modeling for winter wheat water status, but contain no data availability statement, no public dataset deposit, no author code/scripts or trained model release, and no supplementary asset links. The only URLs,

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