Open Access

ARTICLE

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

Qi Sheng¹, Huiqin Ma^1,*, Jingcheng Zhang¹, Zhiqin Gui¹, Wenjiang Huang^2,3, Dongmei Chen¹, Bo Wang¹

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

* Corresponding Author: Huiqin Ma. Email:

Phyton-International Journal of Experimental Botany 2025, 94(2), 421-440. https://doi.org/10.32604/phyton.2025.060152

Received 25 October 2024; Accepted 16 January 2025; Issue published 06 March 2025

Abstract

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 information in multiple stress discrimination in cloudy and rainy areas. It performs well in revealing the damage spatial distribution of the two diseases at a regional scale, providing a basis for detailed disease severity monitoring, and scientific prevention and control.

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