Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data

International Journal of Applied Earth Observations and Geoinformation

Yanru Huang¹  Yingying Dong¹  Huiqin Ma²  Yun Geng²  Wenjiang Huang³  Xianfeng Zhou³  Yu Ren⁴  Yue Shi⁴  Qiaoyun Xie⁴  Huichun Ye⁴  Quanjun Jiao⁵ 

[1]Corresponding authors at: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

[2]Hainan Key Laboratory of Earth Observation, Hainan Institute of Aerospace Information, Chinese Academy of Sciences, Sanya 572029, China

[3]University of Chinese Academy of Sciences, Beijing 100049, China

[4]Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

[5]School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China

关键词: Yellow rust;    Spectral index;    Hyperspectral remote sensing;    Quantitative identification;    PROSPECT-D model;    Winter wheat;   

DOI  :  

来源: DOAJ

【 摘 要 】

Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited. To fill this gap in the literature, in the current study, we constructed a new spectral index, the yellow rust optimal index (YROI), using the hyperspectral data obtained by ASD field spectrometer to quantitatively estimate yellow rust severity. The index is based on the spectral response of spores, and vegetation biophysical and biochemical parameters (VPCPs); and integrated with the PROSPECT-D model. We evaluated the new index and compared it with 11 commonly used yellow rust detection indices using experimental leaf- and canopy-scale spectral datasets. Results demonstrated the superior accuracy of YROI for both the leaf (R2 = 0.822, RMSE = 0.070) and canopy (R2 = 0.542, RMSE = 0.085) scales. In this research, we quantitatively analyzed the spectral response mechanism of wheat yellow rust, which provided a new idea for the quantitative identification of crop diseases. Moreover, our results can be employed as a reference and theoretical basis for the accurate and timely quantitative identification of crop diseases over the large areas in the future.

【 授权许可】

Unknown