With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concer...With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.展开更多
The concentration of absorbable particulate matter less than 10 μm termed as PM10 is the most important urban air pollution index for air quality monitoring. This paper presents a space based PM10 monitoring algorith...The concentration of absorbable particulate matter less than 10 μm termed as PM10 is the most important urban air pollution index for air quality monitoring. This paper presents a space based PM10 monitoring algorithm based on QUAC （QUick atmosphere correction） for optical remote sensing data and SVR （support vector regression）. PM 10 concentration measurements from nine ground based stations in Hangzhou, China and the MODIS （moderate-resolution imaging spectroradiometer） images were analyzed. Experimental result indicates that the correlation between CD （correction differences） with actual measured data is better than correlation between AOD （aerosol optical depth） with measured data. In addition, the fitting performance of the SVR model established with CD and measured data is better than traditional regression models.展开更多
基金Supported by the central university basic scientific research fund(XDJK2009C006)from Ministry of Educationthe National Youth Science Fund(41201436)from National Science Counci~~
文摘With the development of precision agriculture, the research that applies Remote Sensing technology, especially hyperspectral remote sensing, to realize crop management, monitoring and yield estimation, has been concerned. Nowadays, the growth-monitoring and yield-estimating methods in rice, wheat and other annual crops develop rapidly with some achievements having already been put into service. But the yield estimation research on perennial economic crops is few. Taking peren- nial citrus trees as the research object, using ASD spectrometer to collect citrus canopy spectral, this article studied and analyzed the citrus of veget＆tion index and its relationship on yield, synthetically considered the influence of the agriculture pa- rameters on crop yield, and finally constructed the citrus yield estimation model based on the spectral data and agronomic parameters. Through the Significance Test and Samples＇ Test, olutained that the model＇s fitting degree was R=0.631, F= 13.201, P〈0.01 and the error rate of estimating accuracy was controlled in the range 3%-16%, proving that the model has statistical signification and reliability. It concluded that hyperspectral acquired from citrus canopy has substantial potential for citrus yield estimation. This study is an application and exploration of Hyperspectral Remote Sensing technology in the citrus yield estimation.
文摘The concentration of absorbable particulate matter less than 10 μm termed as PM10 is the most important urban air pollution index for air quality monitoring. This paper presents a space based PM10 monitoring algorithm based on QUAC （QUick atmosphere correction） for optical remote sensing data and SVR （support vector regression）. PM 10 concentration measurements from nine ground based stations in Hangzhou, China and the MODIS （moderate-resolution imaging spectroradiometer） images were analyzed. Experimental result indicates that the correlation between CD （correction differences） with actual measured data is better than correlation between AOD （aerosol optical depth） with measured data. In addition, the fitting performance of the SVR model established with CD and measured data is better than traditional regression models.
