文献类型： 会议论文

第一作者： Wei-guo Li

作者： Wei-guo Li ¹ ;

作者机构： 1.Jiangsu Academy of Agricultural Sciences, 50 Zhongling str., Xuanwu ditrict, Nanjing 210014, China

关键词： Crop disease;agronomy parameters;multispectral information;spatial variation in county area;data fusion

会议名称： Asian conference on remote sensing

主办单位：

页码： 386-395

摘要： In order to accurately and quickly acquire crop disease information in Yangtze-Huaihe river region in China area county, this paper studies the fusion effect between medium resolution(30mx30m, HJ-l/CCD) and high resolution(2mx2m, GF-l/PMS) remote sensing data. On the basis of screening suitable spatial scale remote sensing images for the distribution characteristics of winter wheat field, we analyzed the interaction between winter wheat growth condition index and scab disease index, and build a winter wheat scab estimation model based on multi-agronomic parameters and monitor the spatial change of winter wheat scab in county area. The results showed that three spatial scale fusion images of 2 m× 2 m, 8 m× 8 m and 16 m× 16 m have little difference, and there are obvious differences in average gradient and standard deviation. The definition of 16 m× 16m fusion image is the best, and the spectral information is abundant, which is beneficial to the identification of winter wheat in the test area. Leaf area index, chlorophyll content and aboveground biomass weight of winter wheat were the main growth indicators affecting the occurrence of scab. The correlation coefficients(r) between them and disease index were 0.688, 0.709 and 0.669, respectively. Based on the main influence indicators of winter wheat scab, the estimation model of disease index of winter wheat scab was constructed with the REMS of 10.5% and the relative error of 14.6% respectively. The method proposed in this study can effectively monitor the spatial change of winter wheat scab in county area in China. Scab is one of the main diseases of winter wheat. It not only affects the yield of winter wheat, but also causes the deterioration of wheat grains, which can cause human and animal poisoning in serious cases, winter wheat scab occurs in all wheat regions, mostly in temperate regions with humid and rainy climate. From seedling to grain filling, seedling withering, stem dry rot and ear rot are the main causes, among which ear rot is the most serious. When the florescence of winter wheat exists in the presence of fungi, it is very easy to occur when the temperature is 16 ～25 ℃ in the case of continuous rains and clouds for 3 to 5 days. If it can not be monitored and prevented in time, once the outbreak occurs, it will often cause a large area of winter wheat yield reduction and grain quality decline, which will cause huge economic losses. Therefore, effective monitoring of field winter wheat scab has always been the focus and hot spot of government departments and academia(Zhao C J et al., 2004; Liu L Y et al., 2004; Cao X R et al., 2013). When crops are stressed by diseases, the appearance or internal structure of crops will change, and there will be some differences in spectral reflectance and radiation characteristics(Li W G. 2013: Yuan L et al., 20014;Wei L G et al., 2014). This is also the basic theoretical basis of using remote sensing spectral technology to identify crop diseases. In recent years, some scholars have begun to consider combining remote sensing spectral information with climate and environmental factors to monitor and forecast crop diseases and insect pests because of the obvious relationship between the occurrence of crop diseases and insect pests and the changes of climatic and environmental conditions. For example, Bhattacharya B K et al (2013) combined meteorological data with multiple remote sensing vegetation indices to monitor mustard rot disease by multi-stage remote sensing tracking. Zhang J C et al (2014)integrated remote sensing information and meteorological data to forecast winter wheat powdery mildew at regional scale in Beijing wheat region. Otuka A et al (2014)studied the feasibility of remote sensing monitoring of brown planthopper in the Mekong Delta of Vietnam based on regional climatic and environmental conditions. Silva JRMD (2015) combined MSG satellite data with surface temperature data to study tomato pest risk zoning. When

分类号： tp7