Coupling Multi-Source Satellite Remote Sensing and Meteorological Data to Discriminate Yellow Rust and Fusarium Head Blight in Winter Wheat  

作　　者：Qi Sheng Huiqin Ma Jingcheng Zhang Zhiqin Gui Wenjiang Huang Dongmei Chen Bo Wang 

机构地区：[1]School of Automation,Hangzhou Dianzi University,Hangzhou,310018,China [2]Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,100094,China [3]Key Laboratory of Earth Observation of Hainan Province,Hainan Research Institute,Aerospace Information Research Institute,Chinese Academy of Sciences,Sanya,572029,China

出　　处：《Phyton-International Journal of Experimental Botany》2025年第2期421-440,共20页国际实验植物学杂志(英文)

基　　金：supported by National Key R&D Program of China(2022YFD2000100);National Natural Science Foundation of China(42401400);Zhejiang Provincial Key Research and Development Program(2023C02018).

摘　　要：Yellow rust(Puccinia striiformis f.sp.Tritici,YR)and fusarium head blight(Fusarium graminearum,FHB)are the two main diseases affecting wheat in the main grain-producing areas of East China,which is common for the two diseases to appear simultaneously in some main production areas.It is necessary to discriminate wheat YR and FHB at the regional scale to accurately locate the disease in space,conduct detailed disease severity monitoring,and scientific control.Four images on different dates were acquired from Sentinel-2,Landsat-8,and Gaofen-1 during the critical period of winter wheat,and 22 remote sensing features that characterize the wheat growth status were then calculated.Meanwhile,6 meteorological parameters that reflect the wheat phenological information were also obtained by combining the site meteorological data and spatial interpolation technology.Then,the principal components(PCs)of comprehensive remote sensing and meteorological features were extracted with principal component analysis(PCA).The PCs-based discrimination models were established to map YR and FHB damage using the random forest(RF)and backpropagation neural network(BPNN).The models’performance was verified based on the disease field truth data(57 plots during the filling period)and 5-fold cross-validation.The results revealed that the PCs obtained after PCA dimensionality reduction outperformed the initial features(IFs)from remote sensing and meteorology in discriminating between the two diseases.Compared to the IFs,the average area under the curve for both micro-average and macro-average ROC curves increased by 0.07 in the PCs-based RF models and increased by 0.16 and 0.13,respectively,in the PCs-based BPNN models.Notably,the PCs-based BPNN discrimination model emerged as the most effective,achieving an overall accuracy of 83.9%.Our proposed discrimination model for wheat YR and FHB,coupled with multi-source remote sensing images and meteorological data,overcomes the limitations of a single-sensor and single-phase remote sensing informati

关 键 词：Winter wheat yellow rust(YR) fusarium head blight(FHB) DISCRIMINATION remote sensing and meteorology 

分 类 号：S51[农业科学—作物学]