There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters ...There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters to China’s national economy and people's lives and property in the urban and coastal areas. In this paper, the correlative relationship between the reflectivity of land surface and clouds in different time phases is found, based on the analysis of the radiative and satellite-based spectral characteristics of fog. Through calculation and analyses of the relative variability of the reflectivity in the images, the threshold to identify quasi-fog areas is generated automatically. Furthermore, using the technique of quick image run-length encoding, and in combination with such practical methods as analyzing texture and shape fractures, smoothness, and template characteristics, the automatic identification of fog and fog-cloud separation using meteorological satellite remote sensing images are studied, with good results in application.展开更多
Wheat scab(WS,Fusarium head blight),one of the most severe diseases of winter wheat in Yangtze-Huaihe river region,whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and a...Wheat scab(WS,Fusarium head blight),one of the most severe diseases of winter wheat in Yangtze-Huaihe river region,whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and achieve the purpose of reducing yield loss.In the present study,remote sensing monitoring on WS was conducted in 4 counties in Yangtze-Huaihe river region.Sensitive factors of WS were selected to establish the remote sensing estimation model of winter wheat scab index(WSI)based on interactions between spectral information and meteorological factors.The results showed that:1)Correlations between the daily average temperature(DAT)and daily average relative humidity(DAH)at different time scales and WSI were significant.2)There were positive linear correlations between winter wheat biomass,leaf area index(LAI),leaf chlorophyll content(LCC)and WSI.3)NDVI(normalized difference vegetation index),RVI(ratio vegetation index)and DVI(difference vegetation index)which had a good correlation with LAI,biomass and LCC,respectively,and could be used to replace them in modeling.4)The estimated values of the model were consistent with the measured values(RMSE=5.3%,estimation accuracy=90.46%).Estimation results showed that the model could efficiently estimate WS in Yangtze-Huaihe river region.展开更多
基金Key research project "Research of Shanghai City and Costal Heavy Fog Remote Sensing Detecting and Warning System" of Science and Technology Commission of Shanghai Municipality (075115011)
文摘There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters to China’s national economy and people's lives and property in the urban and coastal areas. In this paper, the correlative relationship between the reflectivity of land surface and clouds in different time phases is found, based on the analysis of the radiative and satellite-based spectral characteristics of fog. Through calculation and analyses of the relative variability of the reflectivity in the images, the threshold to identify quasi-fog areas is generated automatically. Furthermore, using the technique of quick image run-length encoding, and in combination with such practical methods as analyzing texture and shape fractures, smoothness, and template characteristics, the automatic identification of fog and fog-cloud separation using meteorological satellite remote sensing images are studied, with good results in application.
基金supported by the National Natural Science Foundation of China(No.41571323)Key Research&Development Plan of Jiangsu Province(BE2016730)+1 种基金Open Research Fund of Key Laboratory of Digital Earth Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences(No.2016LDE007)the Fund of Jiangsu Academy of Agriculture Sciences(6111647).
文摘Wheat scab(WS,Fusarium head blight),one of the most severe diseases of winter wheat in Yangtze-Huaihe river region,whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and achieve the purpose of reducing yield loss.In the present study,remote sensing monitoring on WS was conducted in 4 counties in Yangtze-Huaihe river region.Sensitive factors of WS were selected to establish the remote sensing estimation model of winter wheat scab index(WSI)based on interactions between spectral information and meteorological factors.The results showed that:1)Correlations between the daily average temperature(DAT)and daily average relative humidity(DAH)at different time scales and WSI were significant.2)There were positive linear correlations between winter wheat biomass,leaf area index(LAI),leaf chlorophyll content(LCC)and WSI.3)NDVI(normalized difference vegetation index),RVI(ratio vegetation index)and DVI(difference vegetation index)which had a good correlation with LAI,biomass and LCC,respectively,and could be used to replace them in modeling.4)The estimated values of the model were consistent with the measured values(RMSE=5.3%,estimation accuracy=90.46%).Estimation results showed that the model could efficiently estimate WS in Yangtze-Huaihe river region.