基于空气生物学和卫星遥感的小麦条锈病监测预警研究

Wheat stripe rust caused by Puccinia striiformis f. sp. tritici, is the most important wheat disease in China. It is a kind of wind-dispersed pandemic disease, and in most wheat producing regions in China, P. striiformis f. sp. tritici usually oversummers or overwinters in different areas with different geographic climatic conditions and then completes its life cycle by long-distance dispersal of urediniospores. This disease could cause severe yield losses and it is always a critical threat to wheat production in china. It is of great significance for China's wheat production safety to implement monitoring and early warning of wheat stripe rust and to take disease control measures timely. In this study, based on field survey of the disease, aerobiological methods and satellite remote sensing technologies would be used to explore the method for the evaluation of the inoculum amount of P. striiformis f. sp. tritici in oversummering regions and overwintering regions and to explore the method for satellite remote sensing monitoring of wheat stripe rust. Forecasting of wheat stripe rust in deposition locations would be conducted via the simulation of long-distance dispersal of the pathogen from oversummering regions and overwintering regions using the HYSPLIT-4 model developed by NOAA Air Resources Laboratory and Australia's Bureau of Meteorology which is a system for computing trajectory dispersion and deposition simulations. Monitoring of the epidemics of wheat stripe rust would be conducted via the analysis of the satellite remote sensing images using the software ENVI (the Environment for Visualizing Images). A integrated monitoring and warning system for wheat stripe rust would be developed based on aerobiology and satellite remote sensing.Thus the difficult problems of regional epidemic monitoring of wheat stripe rust would be solved. The results of this project should provide technical supports for regional monitoring and warning of wheat stripe rust in China and provide the basis for regional epidemic forecasting of wheat stripe rust and for macroscopic control of this disease. This study could also provide a methodological reference for monitoring and warning of other airborne plant diseases.

小麦条锈病是我国小麦生产中最重要的病害，其是一种大区流行性病害，具有流行频率高、爆发性强、发生范围广的特点，可造成小麦严重的产量损失，一直是我国小麦安全生产的重要威胁。做好小麦条锈病的监测预警工作，对于及时采取防控措施和保障我国小麦安全生产具有重要意义。本研究拟利用空气生物学方法和卫星遥感技术，结合田间病害实地调查，探讨我国小麦条锈病菌越夏区和越冬区的菌源量的监测评估方法以及卫星遥感监测小麦条锈病的技术，通过HYSPLIT-4模式模拟病原菌远程传播和卫星遥感图像分析相结合，监测小麦条锈病菌越夏和越冬后的远程传播情况以及病害发生情况，解决小麦条锈病发生流行区域性监测预警难题。本项目的研究结果将为我国小麦条锈病的区域性监测预警提供技术支持，为小麦条锈病区域发生流行预测提供基础，为从宏观上治理小麦条锈病提供依据，并将为其他植物气传病害的监测预警研究提供方法学参考。

由Puccinia striiformis f. sp. tritici引起的小麦条锈病是世界范围内小麦生产中非常重要的一种病害。在我国，条锈病菌的越夏和越冬对于该病害的发生流行尤为重要。该病害的区域性监测预警对于该病害的可持续、有效治理具有重要意义。本项目研究主要利用空气生物学方法和卫星遥感技术，结合田间病害实地调查和病原菌监测，探讨了我国小麦条锈病菌越夏区和越冬区的菌源量的监测评估方法以及小麦条锈病区域性监测技术，并且进行了基于近红外光谱技术的小麦条锈病潜育期叶片内条锈病菌DNA含量的定量检测、小麦条锈病高光谱遥感监测、无人机遥感监测小麦条锈病研究工作。利用HYSPLIT-4模式对我国小麦条锈病越夏和越冬后的远程传播进行了模拟和验证。利用软件ENVI (the Environment for Visualizing Images)进行了小麦条锈病卫星遥感图像和无人机遥感图像处理。通过项目的执行，初步构建了基于HYSPLIT-4模式的小麦条锈病流行监测模型，开发了2种菌源量估测的方法，构建了基于卫星遥感的病害反演模型。并且，基于近红外光谱技术和高光谱遥感技术实现了小麦条锈病的早期监测，所构建的识别模型效果较好。特别是建立了基于近红外光谱技术进行潜伏侵染小麦条锈病菌菌量定量检测的方法。同时，本研究获得了2013－2017年的全国小麦条锈病发生动态周报数据、小麦条锈病近红外光谱数据以及大量的小麦条锈病、小麦叶锈病和小麦白粉病的叶片和冠层水平的高光谱遥感数据。本研究结果有助于我国小麦条锈病的区域性监测预警和小麦条锈病的宏观治理。