节水灌溉Issue(3)：26-33,41,9.DOI:10.12396/jsgg.2025336
基于多光谱与热红外影像的烤烟地土壤含水率反演
Research on the Inversion of Soil Moisture Content in Tobacco Fields Based on Multispectral and Thermal Infrared Imagery
摘要
Abstract
In order to achieve rapid and precise monitoring of soil moisture content in flue-cured tobacco fields,this study was conducted in nine flue-cured tobacco experimental plots in Tuokeng Village,Menggu Town,Qiaojia County,Zhaotong City,Yunnan Province as the study area.The research integrated multispectral and thermal infrared UAV imagery with machine learning models to retrieve soil moisture content.Data acquisition involved multispectral imagery from a DJI Mavic 3 UAV(four bands,spatial resolution of 0.003 m)and thermal infrared imagery from a DJI Matrice 4T UAV(VOx sensor,spatial resolution of 0.005 m),along with in-situ measurements of soil volumetric water content.The images were preprocessed using ENVI and Pix4D software,followed by the extraction of nine key multispectral features.These were combined with thermal infrared DN values to construct the feature set.A series of machine learning models—including Random Forest(RF),Support Vector Machine(SVM),Extreme Gradient Boosting(XGBoost),and Convolutional Neural Network(CNN)—were applied to develop both single-source and multi-source soil moisture retrieval models.Model performance was evaluated based on root mean square error(RMSE)and the coefficient of determination(R2).The results demonstrated that multi-source models significantly outperformed their single-source counterparts.The RF model,incorporating multispectral features and thermal infrared DN values,yielded the best performance(RMSE=1.30%,R2=0.79),with XGBoost providing comparable results(RMSE=1.31%,R2=0.78).SHAP analysis further revealed that the Normalized Difference Red Edge Index(NREI),NDVI_RVI,and thermal infrared DN values were the key features.This study highlights the potential of combining multispectral and thermal UAV imagery with machine learning for efficient soil moisture retrieval in flue-cured tobacco fields,providing technical support for precision irrigation and sustainable water resource management in agricultural regions.关键词
无人机/机器学习/随机森林/支持向量机/XGBoost/卷积神经网/土壤含水率反演/多光谱/热红外/数据融合Key words
unmanned aerial vehicle/machine learning/random forest/support vector machine/XGBoost/convolutional neural network/inversion soil moisture content/multispectral/thermal infrared/data fusion分类
农业科技引用本文复制引用
查宏波,赵芳,王力,陈嘉航,徐凯,王海东,杨启良,吴立峰..基于多光谱与热红外影像的烤烟地土壤含水率反演[J].节水灌溉,2026,(3):26-33,41,9.基金项目
中国烟草总公司云南省公司科技计划项目(2025530000241020) （2025530000241020）
云南省农业水资源高效利用与智慧管控重点实验室(202449CE340014) （202449CE340014）
云南省智能水肥药一体化技术与装备创新团队(202505AS350025). （202505AS350025）
