Work

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

Year: 2025

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app152111363

Authors: Bingyan Dong, Shouchen Ma, Zhenhao Gao, Anzhen Qin

Institutions: Henan Polytechnic University, Chinese Academy of Agricultural Sciences, Farmland Irrigation Research Institute, Ministry of Agriculture and Rural Affairs

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Climate action

Open Access status: gold

Estimated APC paid: $2,896