Integrating Remotely Sensed and Meteorological Observations to Forecast Wheat Powdery Mildew at a Regional Scale

The prevalence of powdery mildew (PM) in winter wheat field has a severe impact on crop production. An effective and timely forecast of the disease at a regional scale is necessary to control and prevent it. In this study, both meteorological and remotely sensed observations associated with crop characteristics and habitat traits were integrated for modeling the PM occurrence probability. With an effective feature selection procedure, four meteorological factors, including precipitation, temperature, sun radiation, humidity, and two remotely sensed features including reflectance of red band (R_R) demonstrate that the disease risk maps were able to depict the approximately spatial distribution of PM and its temporal dynamic in the study area. Compared with the model constructed with meteorological data only, the integrated model constructed with both remote sensing and meteorological data has produced a higher accuracy (increasing overall accuracy from 69% to 78%) of forecasting the PM occurrence. This suggests that there would be a great potential for predicting the PM occurrence probability by integrating both meteorological and remote sensing data at a regional scale. In the future, multiple forms of information (e.g., Web sensors networks data) are expected to be incorporated in the disease-forecasting model to further improve its performance for forecasting the disease occurrence (e.g., PM) at a regional scale.