Estimating maize plant water content based on UAV multispectral-thermal infrared fusion and machine learning
Zhang Hao
Wang Zuji
Li Caixia
Ma Yanchuan
James E.Kanneh
Zhong Daokuan
Li Shenglin
Abstract：[Background&Objective]Crop water content is a critical indicator for characterizing crop water stress and physiological status;its accurate estimation is essential for agricultural water management,drought monitoring,and crop growth assessment.Traditional ground-based crop water monitoring methods are costly and rely on limited point-observations,which cannot support large-scale and real-time drought diagnosis.The advances in unmanned aerial vehicle(UAV)remote sensing provide a non-destructive,large-scale alternative to conventional methods.However,UAV-derived spectral features mainly reflect static crop structural properties and have a response delay to crop water stress.Canopy temperature directly characterizes crop physiological changes.In this context,this study proposed a method that integrates multi-source UAV remote sensing features to construct a high-precision,real-time crop water content estimation model.[Method]The method fused UAV-acquired multispectral and thermal infrared images to construct a multi-source data fusion model.Three machine learning algorithms,including Random Forest(RF),K-Nearest Neighbor regression(KNN),and Support Vector Regression(SVR),were used to estimate the variation in plant water content(PWC)of maize throughout its growing season.[Result]①The KNN algorithm combined with NDVI+GNDVI+RVI was most accurate for estimating PWC,with a coefficient of determination(R2)of 0.713 and a relative root mean square error(rRMSE)of 6.610%.② Based on integrated multi-source spectral-thermal indices,the RF algorithm significantly outperformed SVR and KNN in PWC estimation,with an R2 of 0.887 and an rRMSE of 4.298%.③The combination of spectral features(NDVI+GNDVI+RVI)and thermal feature(LST)coupled with the RF algorithm effectively captured the spatiotemporal dynamics of PWC in the study area.[Conclusion]The fusion of multi-source UAV remote sensing data combined with the RF algorithm can accurately estimate maize plant water content;it has good adaptability for crop water estimation under multidimensional feature conditions and offers a reliable technical support for crop water monitoring and drought assessment.
Keywords：plant water contentmultispectralthermal infraredrandom forestvegetation indices
Publication Date：2026-07-31
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：9( 19-27 )