A prediction model of apple leaf nitrogen content based on ground imaging spectroscopy was established to rapidly and nondestructively detect nitrogen content in apple leaves.SOC710VP hyperspectral imager was used to ...A prediction model of apple leaf nitrogen content based on ground imaging spectroscopy was established to rapidly and nondestructively detect nitrogen content in apple leaves.SOC710VP hyperspectral imager was used to obtain the imaging spectral information of apple leaves,and the average spectral curve of interest region was extracted.The study is to analyze the characteristics of imaging spectral curves of apple leaves with different nitrogen content.On the basis of the SG smoothing and first derivative pretreatment of the spectral curve,the maximum sensitive band with nitrogen content is screened and the spectral parameters are constructed.Three modeling methods of BP,SVM and RF were used to establish the prediction model of nitrogen content in apple leaves.The results showed that in the visible range,the nitrogen content of apple leaves was negatively correlated with the reflectance of the spectral curve,and was most obvious in the green range.The R2 of BP,SVM and RF of apple leaf nitrogen content prediction model were 0.7283,0.8128,0.9086,RMSE were 0.9359,0.7365,0.5368,the R2 of test model were 0.6260,0.7294,0.6512,RMSE were 0.9460,0.7350,0.9024.Comparing the prediction results of the three models,the optimal prediction model is SVM model,which can well predict the nitrogen content of apple leaves.展开更多
As the largest sub-basin of the middle reaches of the Yellow River with an obvious decreasing trend in annual runoff in recent years,the Weihe River basin is a significant region with regard to the protection and impr...As the largest sub-basin of the middle reaches of the Yellow River with an obvious decreasing trend in annual runoff in recent years,the Weihe River basin is a significant region with regard to the protection and improvement of the environment in West China.Evapotranspiration(ET)is the loss of water from the Earth’s surface to the atmosphere and plays an important role in the regional water cycle,especially when considering water resource shortages.In this study,through analyzing the grid precipitation data after interpolation from 39 meteorological stations in and around the Weihe River basin from 1981 to 2011,certain periods during 1987,1993,1999,2001,2002 and 2009 with similar precipitation characteristics had been chosen for estimating the ET in the Weihe River basin.To illustrate ET’s influence on the water budget,these estimations are calculated based on an improved Penman-Monteith equation as well as remote sensing data and meteorological data.The results show that:(1)the annual ET in the Weihe River basin ranged from 350mm to 400mm in 1987,1993,1999,2001,2002 and 2009,accounting for more than 70%of the corresponding annual precipitation.There is a definite increasing trend in different decades that is primarily distributed during the summer.(2)The spatial distribution patterns of the ET in the six years mentioned area unique set,and the years are roughly identical with more than 500mm in the middle and lower reaches of the Weihe River in the southeastern region and less than 400mm in upper reaches of the Jinghe River in the northwestern area.(3)At the single-point scale,the coefficient of determination(R2)is 0.618 compared to the eddy correlation measurements in 2009 at the Changwu site,showing good agreement between the estimated ET and the observed ET.At the basin scale,the model-estimated ET is slightly lower than the actual ET with regard to the surface water budget.Additionally,the estimated ET in 2001,2002 and 2009 is close to the MODIS ET product.(4)For similar precipitation conditions,the regional amount of water shows a decreasing tendency with increasing ET,which may result from the rise in NDVI and improvements in vegetation coverage caused by human activities.This research suggests the influence of ET on water change at the basin level,which can also explain the decreasing runoff and can provide necessary information for improved water resource management.展开更多
Estimating an apple tree canopy nitrogen contents using hyperspectral techniques is important in theoretical and realistic significance for fertilization and management.Taking 80 Fuji apple trees at the early fruiting...Estimating an apple tree canopy nitrogen contents using hyperspectral techniques is important in theoretical and realistic significance for fertilization and management.Taking 80 Fuji apple trees at the early fruiting stage as the research objects,the hyperspectral characteristics of the apple canopy were analyzed systematically.The apple canopy hyperspectral and the canopy nitrogen contents were measured respectively.The canopy hyperspectral characteristics under different nitrogen contents were analyzed and selected the sensitive wave bands.The apple canopy nitrogen content monitoring models were established by using multiple regression method,robust regression and BP neural network method.The results showed that the canopy hyperspectral reflectance had obvious differences under different nitrogen contents.The sensitive bands concentrate on 724~1136 nm.Estimation models based on hyperspectral indices are not ideal.Models based on robust regression(M regression)and BP neural network are better than multiple statistical model,and the accuracy of the BP neural network monitoring model is the best.The results of the study provide a certain reference for estimating apple nutrition using hyperspectral technology.展开更多
Imaging spectrometer was used to measure the spectral data of apple leaves.The spectral reflectance of apple leaves was extracted.The nitrogen content of apple leaves was correlated with the spectral reflectance after...Imaging spectrometer was used to measure the spectral data of apple leaves.The spectral reflectance of apple leaves was extracted.The nitrogen content of apple leaves was correlated with the spectral reflectance after SG smoothing first-order differential treatment.The sensitive wavelengths were selected and nitrogen content prediction models were founded.The results showed that the spectral of apple leaves with different concentration gradients were obvious.The higher nitrogen content was,the lower spectral reflectance was.Established estimation models by using the selected SG smooth first-order differential spectral sensitive wavelengths SG-FDR403,SG-FDR469,SG-FDR525,SG-FDR566,SG-FDR650,SG-FDR696,SG-FDR781,SG-FDR851,SG-FDR933.The determined coefficient(R^2)of the partial least squares model was 0.5202.The root mean square error(RMSE)of that was 2.19 and the relative error(RE)of that was 5.89%.The R^2 of the support vector machine(SVM)model was 0.724.The RMSE of that was 1.94,and the RE of that was 5.13%.It is indicated that the SVM model can estimate the nitrogen content of apple leaves effectively.展开更多
Using the PROSAIL radiation transfer model and HJ-1A-HSI data to simulate the canopy reflectivity of apple trees, this study lays the foundation for the inversion of canopy parameters. Taking Qixia City of Yantai City...Using the PROSAIL radiation transfer model and HJ-1A-HSI data to simulate the canopy reflectivity of apple trees, this study lays the foundation for the inversion of canopy parameters. Taking Qixia City of Yantai City, Shandong Province as the research area, the apple tree was taken as the research object, and the hyperspectral reflectance, LAI and sample GPS of apple canopy were measured in the field. The parameters required for the PROSAIL model were obtained by experimental methods. The model simulates the reflectivity;the HSI image data is preprocessed, and the canopy reflectivity is extracted by GPS coordinates. The PROSAIL model and the HSI image simulated reflectance were fitted to the measured apple canopy reflectivity. The decisive factor (R2) of the simulated reflectance and the measured reflectance of the PROSAIL model was 0.9944, and the relative error (RE%)was 0.1845. The HSI data simulated reflectance and measured reflectance. The coefficient of determination is 0.9714 and the relative error is 0.6202. Both have achieved good fitting effects and can be used for inversion studies of apple canopy parameters.展开更多
基金the National Natural Science Foundation of China(41671346)Funds of Shandong“Double Tops”Program(SYL2017XTTD02).
文摘A prediction model of apple leaf nitrogen content based on ground imaging spectroscopy was established to rapidly and nondestructively detect nitrogen content in apple leaves.SOC710VP hyperspectral imager was used to obtain the imaging spectral information of apple leaves,and the average spectral curve of interest region was extracted.The study is to analyze the characteristics of imaging spectral curves of apple leaves with different nitrogen content.On the basis of the SG smoothing and first derivative pretreatment of the spectral curve,the maximum sensitive band with nitrogen content is screened and the spectral parameters are constructed.Three modeling methods of BP,SVM and RF were used to establish the prediction model of nitrogen content in apple leaves.The results showed that in the visible range,the nitrogen content of apple leaves was negatively correlated with the reflectance of the spectral curve,and was most obvious in the green range.The R2 of BP,SVM and RF of apple leaf nitrogen content prediction model were 0.7283,0.8128,0.9086,RMSE were 0.9359,0.7365,0.5368,the R2 of test model were 0.6260,0.7294,0.6512,RMSE were 0.9460,0.7350,0.9024.Comparing the prediction results of the three models,the optimal prediction model is SVM model,which can well predict the nitrogen content of apple leaves.
文摘As the largest sub-basin of the middle reaches of the Yellow River with an obvious decreasing trend in annual runoff in recent years,the Weihe River basin is a significant region with regard to the protection and improvement of the environment in West China.Evapotranspiration(ET)is the loss of water from the Earth’s surface to the atmosphere and plays an important role in the regional water cycle,especially when considering water resource shortages.In this study,through analyzing the grid precipitation data after interpolation from 39 meteorological stations in and around the Weihe River basin from 1981 to 2011,certain periods during 1987,1993,1999,2001,2002 and 2009 with similar precipitation characteristics had been chosen for estimating the ET in the Weihe River basin.To illustrate ET’s influence on the water budget,these estimations are calculated based on an improved Penman-Monteith equation as well as remote sensing data and meteorological data.The results show that:(1)the annual ET in the Weihe River basin ranged from 350mm to 400mm in 1987,1993,1999,2001,2002 and 2009,accounting for more than 70%of the corresponding annual precipitation.There is a definite increasing trend in different decades that is primarily distributed during the summer.(2)The spatial distribution patterns of the ET in the six years mentioned area unique set,and the years are roughly identical with more than 500mm in the middle and lower reaches of the Weihe River in the southeastern region and less than 400mm in upper reaches of the Jinghe River in the northwestern area.(3)At the single-point scale,the coefficient of determination(R2)is 0.618 compared to the eddy correlation measurements in 2009 at the Changwu site,showing good agreement between the estimated ET and the observed ET.At the basin scale,the model-estimated ET is slightly lower than the actual ET with regard to the surface water budget.Additionally,the estimated ET in 2001,2002 and 2009 is close to the MODIS ET product.(4)For similar precipitation conditions,the regional amount of water shows a decreasing tendency with increasing ET,which may result from the rise in NDVI and improvements in vegetation coverage caused by human activities.This research suggests the influence of ET on water change at the basin level,which can also explain the decreasing runoff and can provide necessary information for improved water resource management.
文摘Estimating an apple tree canopy nitrogen contents using hyperspectral techniques is important in theoretical and realistic significance for fertilization and management.Taking 80 Fuji apple trees at the early fruiting stage as the research objects,the hyperspectral characteristics of the apple canopy were analyzed systematically.The apple canopy hyperspectral and the canopy nitrogen contents were measured respectively.The canopy hyperspectral characteristics under different nitrogen contents were analyzed and selected the sensitive wave bands.The apple canopy nitrogen content monitoring models were established by using multiple regression method,robust regression and BP neural network method.The results showed that the canopy hyperspectral reflectance had obvious differences under different nitrogen contents.The sensitive bands concentrate on 724~1136 nm.Estimation models based on hyperspectral indices are not ideal.Models based on robust regression(M regression)and BP neural network are better than multiple statistical model,and the accuracy of the BP neural network monitoring model is the best.The results of the study provide a certain reference for estimating apple nutrition using hyperspectral technology.
基金the National Natural Science Foundation of China(41671346)Funds of Shandong“Double Tops”Program(SYL2017XTTD02)Shandong major scientific and technological innovation project:Research demonstration and extension of orchard irrigation and fertilization in accurate management(2018CXGC0209).
文摘Imaging spectrometer was used to measure the spectral data of apple leaves.The spectral reflectance of apple leaves was extracted.The nitrogen content of apple leaves was correlated with the spectral reflectance after SG smoothing first-order differential treatment.The sensitive wavelengths were selected and nitrogen content prediction models were founded.The results showed that the spectral of apple leaves with different concentration gradients were obvious.The higher nitrogen content was,the lower spectral reflectance was.Established estimation models by using the selected SG smooth first-order differential spectral sensitive wavelengths SG-FDR403,SG-FDR469,SG-FDR525,SG-FDR566,SG-FDR650,SG-FDR696,SG-FDR781,SG-FDR851,SG-FDR933.The determined coefficient(R^2)of the partial least squares model was 0.5202.The root mean square error(RMSE)of that was 2.19 and the relative error(RE)of that was 5.89%.The R^2 of the support vector machine(SVM)model was 0.724.The RMSE of that was 1.94,and the RE of that was 5.13%.It is indicated that the SVM model can estimate the nitrogen content of apple leaves effectively.
基金the National Natural Science Foundation of China(41671346)National Key Research and Development Program of China (2017YFE0122500)+2 种基金the Taishan Scholar Assistance Program from Shandong Provincial GovernmentFunds of Shandong “Double Tops” Program(SYL2017XTTD02)Shandong major scientific and technological innovation project: Research demonstration and extension of orchard irrigation and fertilization in accurate management(2018CXGC0209).
文摘Using the PROSAIL radiation transfer model and HJ-1A-HSI data to simulate the canopy reflectivity of apple trees, this study lays the foundation for the inversion of canopy parameters. Taking Qixia City of Yantai City, Shandong Province as the research area, the apple tree was taken as the research object, and the hyperspectral reflectance, LAI and sample GPS of apple canopy were measured in the field. The parameters required for the PROSAIL model were obtained by experimental methods. The model simulates the reflectivity;the HSI image data is preprocessed, and the canopy reflectivity is extracted by GPS coordinates. The PROSAIL model and the HSI image simulated reflectance were fitted to the measured apple canopy reflectivity. The decisive factor (R2) of the simulated reflectance and the measured reflectance of the PROSAIL model was 0.9944, and the relative error (RE%)was 0.1845. The HSI data simulated reflectance and measured reflectance. The coefficient of determination is 0.9714 and the relative error is 0.6202. Both have achieved good fitting effects and can be used for inversion studies of apple canopy parameters.
基金Yunnan Provincial Department of Education （Grant No.： 2012C109）, Ministry of Education, ＂Chun Hui Plan＂ Foundation （Grant No.： Z2012051） and the National Natural Science Foundation of China （Grant No.： 41361020）, Yunnan University Resource Environment and Earth Science research project （Grant No.： 2013CG006） funded.
