外文摘要：As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv. oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result of the disease's epidemic, making it imperative to monitor RBLB at a large scale. With the development of remote sensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutions offer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapid dispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensor in practice. Therefore, it is necessary to identify or construct features that are effective across different sensors for monitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopy hyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAV sensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-band spectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectral indices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) were developed. An optimal feature set that includes the two novel indices and a classical vegetation index was formed. The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The result demonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors. Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensor generality in monitoring RBLB. The outcome of this research permits disease monitoring with different remote sensing data over a large scale.
外文关键词：Vegetation indices;Rice bacterial leaf blight;analysis of spectral response;multispectral data simulation
作者：Zhang, Jingcheng;Dong, Yingying;Yuan, Lin;Zhou, Xingjian;Shen, Dong;Yu, Qimeng
作者单位：Chinese Acad Sci;Hangzhou Dianzi Univ;Zhejiang Univ Water Resources & Elect Power
期刊名称：PHYTON-INTERNATIONAL JOURNAL OF EXPERIMENTAL BOTANY
期刊影响因子：0.0
出版年份：2024
出版刊次：93（4）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台