Abstract: Abstract: Scab is one of the main diseases of winter wheat in Yangtze-Huaihe River region in China, whose monitoring and forecasting timely in large area will help to adjust the pesticide spraying measures reasonably and realize the purpose of reducing disaster and increasing yield. In this study, we carried out remote monitoring tests of winter wheat scab in 4 counties (Donghai, Lianshui, Taixing and Dafeng) of Jiangsu Province in Yangtze-Huaihe River region, analyzed the interaction and relationship between winter wheat scab characteristics, climatic factors, growth parameters and spectral information, selected major scab's sensitive factors, and established the remote sensing estimation model of winter wheat scab disease index based on interactions between spectral information and climatic factors. The results showed that: 1) There is a good correlation between the daily mean temperatures in different time scales, of which the correlation between the daily mean temperature of 7 days and the daily mean temperature of 10 days is the highest, and the correlation coefficient is 0.966 5. The correlation coefficient between the daily mean temperature of 5 days and the winter wheat scab disease index is the largest, which is 0.772 6, indicating that the daily average temperature of 5 days in different time scales has the most obvious effect on the occurrence of scab in winter wheat. 2) Similar to the daily mean temperature, there are different degrees of correlation between the daily mean relative humidity at different time scales. The correlation coefficient is the largest between daily mean relative air humidity of 7 days and daily mean relative air humidity of 10 days, and its value is 0.933 7. The daily mean relative air humidity of 5 days has the highest correlation with winter wheat scab disease index, and its correlation coefficient is 0.784 2, higher than the daily mean temperature of 5 days, which shows that the daily mean relative air humidity of 5 days has a higher influence on winter wheat scab than the daily mean temperature of 5 days. 3) There is a positive linear correlation between winter wheat biomass, leaf area index (LAI) and leaf chlorophyll content and scab disease index, and the r values of the linear trend fitting are 0.608 4, 0.584 5 and 0.574 6, respectively, which reach the significant level, and indicate the large population density, high canopy density and over vigorous growth of winter wheat are the main incentive for scab. 4) Remote sensing vegetation index such as NDVI (normalized difference vegetation index), RVI (ratio vegetation index) and DVI (difference vegetation index) has a good correlation with winter wheat LAI, biomass and leaf chlorophyll content respectively, and their correlation coefficients are 0.851 6, 0.854 9 and 0.772 7 respectively. NDVI, RVI and DVI can be used to replace LAI, biomass and leaf chlorophyll content to participate in modeling. 5) Combining 5 sensitive factors i.e. NDVI, RVI, DVI, mean daily temperature of 5 days and average daily relative humidity of 5 days, we establish the remote sensing estimation model of winter wheat scab disease index based on interactions between spectral information and climatic factors. The estimated value of the model is consistent with the measured value, root mean square error (RMSE) is 5.3%, and the estimation accuracy is 90.46%. It shows that the estimation model in this study can effectively estimate winter wheat scab in Yangtze-Huaihe River region in China.