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Forecasting of Powdery Mildew disease with multi-sources of remote sensing information

文献类型： 外文期刊

作者： Zhang, Jingcheng 1 ; Yuan, Lin 1 ; Nie, Chenwei 1 ; Wei, Liguang 1 ; Yang, Guijun 1 ;

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

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Minist Agr, Key Lab Informat Technol Agr, Beijing 100097, Peoples R China

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

关键词： Powdery mildew;Winter wheat;Land surface temperature;Vegetation index;Logistic regression

期刊名称：THIRD INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS (AGRO-GEOINFORMATICS 2014)

ISSN： 2334-3168

年卷期： 2014 年

页码：

收录情况： SCI

摘要： Powdery mildew (PM) is a typical disease in winter wheat which causes severe yield loss in China. To control the disease effectively, it is important to develop a disease forecasting model at a regional scale. In this study, the remotely sensed data that reflect crop vigor and habitat traits were adopted as candidate inputs in model development, including various vegetation indices, land surface temperature and plant's drought index. Based upon a correlation analysis, a total of 9 remotely sensed variables at specific growing stages that had significant response to PM were identified as explanatory variables. To assess the ground truth of PM occurrence, a field campaign was conducted in suburban area of Beijing in 2010. According to the remote sensing data and corresponding ground truth data, the PM forecasting model was established in terms of the logistic regression analysis. The validation result showed that the disease risk map could reflect the general spatial distribution pattern of PM occurrence in the study area, with an overall accuracy of 72%. To facilitate the disease control practices, the map of disease probability was converted to a binary map (presence/absence) using a thresholding method. The potential of remote sensing information in PM forecasting is illustrated in this study.

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