Detecting advanced stages of winter wheat yellow rust and aphid infection using RapidEye data in North China Plain

• Published in: GIScience and remote sensing Vol. 56; no. 7; pp. 1093 - 1113
• Main Authors: Du, Xin, Li, Qiangzi, Shang, Jiali, Liu, Jiangui, Qian, Budong, Jing, Qi, Dong, Taifeng, Fan, Dongdong, Wang, Hongyan, Zhao, Longcai, Lieff, Sam, Davies, Travis
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.10.2019; Taylor & Francis Group
• Subjects: aphid; classification; mapping; remote sensing; winter wheat; yellow rust
• ISSN: 1548-1603; 1943-7226
• DOI: 10.1080/15481603.2019.1613804

Yellow rust (Puccinia striiformis f. sp. Tritici) and aphid (Sitobion avenae F.) are two major biotic factors threatening winter wheat growth in the main growing region in northern China. The goal of this study was to develop a remote sensing based approach to reliably detect and discriminate yellow rust and aphid infection. The study was conducted in the North China Plain in 2017 based on RapidEye satellite images using three supervised classification algorithms, the maximum-likelihood classifier, the support vector machine, and the random forest. An overall accuracy of above 60% for aphid and above 70% for yellow rust can be achieved using a single image (May 7 or 10, 2017) with any of the three algorithms. With multi-temporal images, the overall accuracies both increased for aphid and yellow rust (above 70% and above 78%). Using the image acquired on 23 May 2017, joint infections by yellow rust and aphid can be detected with satisfaction (>73% overall accuracy), although confusion exists between the two infections. This study demonstrates that winter wheat disease/pest infection can be detected with remote sensing technologies, providing decision support to farmers, insurance companies, and government organizations in the agriculture sector.