北京市农林科学院机构知识库

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

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文献类型：外文期刊

作者： Zhang, Jingcheng ¹ ; Pu, Ruiliang ² ; Yuan, Lin ³ ; Huang, Wenjiang ⁴ ; Nie, Chenwei ¹ ; Yang, Guijun ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Univ S Florida, Dept Geog Environm & Planning, Tampa, FL 33620 USA

3.Zhejiang Univ, Inst Agr Remote Sensing & Informat Syst Applicat, Hangzhou 310029, Zhejiang, Peoples R China

4.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China

关键词： Diseases;forecasting;meteorology;remote sensing

期刊名称：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING （影响因子：3.784；五年影响因子：3.734 ）

ISSN： 1939-1404

年卷期： 2014 年 7 卷 11 期

页码：

收录情况： SCI

摘要： 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.

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