利用多源数据对区域尺度上小麦白粉病的发生状况准确及时地预报能为农业服务和农业植保等部门提供重要信息，实现小麦白粉病的有效预防。研究利用一景2014年5月6日的landsat8遥感影像提取出植被指数、地表温度（land surface temperature，LST）和影像中各波段反射率特征，同时用2014年3月－5月份的站点逐日地面气象资料计算获得各气象特征，并经过GIS空间插值分析得到相应的空间气象特征。通过Relief算法和泊松相关系数相结合的方式进行遥感和气象特征的筛选，最终得出改进的简单比值指数（modified simple ratio index，MSR）、重归一化植被指数（re-normalized difference vegetation index，RDVI）、3月21日－4月20日总日照时数和4月11日－5月10日大于0.1 mm降雨日数。采用相关向量机（relevance vector machine，RVM）的方法分别用筛选出的遥感、气象数据特征及2种数据特征相结合的方式构建了河北省石家庄市藁城、晋州和赵县3地区小麦灌浆期白粉病的发生预测模型，并对3种不同数据模型进行了验证与评估。试验结果表明，遥感气象数据模型的总体精度达到84.2%，优于遥感数据模型的80.0%和气象数据模型的74.7%。进而得出，相比于单站点准确和空间不连续的气象数据和类型单一的遥感数据，遥感气象数据更适合于区域尺度范围内的作物病虫害发生发展状况的预测研究。%Powdery mildew is one of the main serious diseases for winter wheat. An accurate and timely forecasting of the wheat powdery mildew occurrence at the regional scale by using multi-source data can provide important information for crop protection decision making, and achieving effective prevention of wheat powdery mildew. In this study, the Landsat8 remote sensing image was used to extract the land surface temperature (LST), the vegetation indices which included normalized difference vegetation index (NDVI), modified simple ratio index (MSR), re-normalized difference vegetation index (RDVI), triangular vegetation index (TVI), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and the band reflectance features. Then we obtained the parameters of wheat growth environment condition such as air temperature, number of rainy days with more than 0.1 mm rainfall, total sunshine hour, average relative humidity, temperature-rain coefficient (the ratio of total rainfall in a period of time to average temperature of the same period) and rainfall coefficient (the square root of the product of rainfall and number of rainy days) in different time steps (including month, 10 days and sensitive period) with the site daily meteorological data; and then we got the corresponding space meteorological features by using the inverse distance weighted (IDW) method in GIS (geographic information system) spatial interpolation analysis. Next, we implemented screening features with the combination of relief algorithm and Poisson’s correlation coefficient, and finally got the MSR, the RDVI, the total sunshine hour from March 21st to April 20th, and the number of rainy days with more than 0.1 mm rainfall from April 11th to May 10th, which were as optimal explanatory variables for developing the powdery mildew forecasting model. The relevance vector machine (RVM) model was used to improve business decisions, detect disease, and forecast weather. And then we used it to predict the probability of powdery mildew occurrence in filling stage of wheat in Gaocheng, Jinzhou and Zhaoxian County, Shijiazhuang City, Hebei Province through remote sensing and meteorological data. The model combining remote sensing and meteorological data produced a higher Spearman relevance value than the single remote sensing data or the meteorological data model, and moreover, the values of Somers’D, Goodman-Kruskal Gamma, and Kendal’s Tau-c of the remote sensing and meteorological data model were all higher than those of the other 2 models. They all indicated that the remote sensing and meteorological data model had a better performance than the other 2 models. The results showed that: the overall accuracy of the remote sensing and meteorological data model was the highest among the 3 methods, with lower omission and wrong judgement than the other 2 models. Furthermore, the overall accuracy and the kappa coefficient of the remote sensing and meteorological data model were 84.2% and 0.686 respectively, which showed better performance over the remote sensing data model (80.0% and 0.602) and the meteorological data model (74.7% and 0.500). These results reveal that compared with the single meteorological data or remote sensing data, the combination of remote sensing and meteorological data is more suitable for the prediction of crop disease occurrence situation in the regional scale.
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