International Journal of Digital Earth Monitoring Nitrogen Concentration of Oilseed Rape from Hyperspectral Data Using Radial Basis Function Monitoring Nitrogen Concentration of Oilseed Rape from Hyperspectral Data Using Radial Basis Function

Remote sensing technology is the important tool of digital earth, it can facilitate nutrient management in sustainable cropping systems. In the study, two types of radial basis function (RBF) neural network approaches, the standard radial basis function (SRBF) neural networks and the modified type of RBF, generalized regression neural networks (GRNN), were investigated in estimating the nitrogen concentrations of oilseed rape canopy using vegetation indices (VIs) and hyperspectral reflectance. Comparison analyses were performed to the spectral variables and the approaches. The Root Mean Square Error (RMSE) and determination coefficients (R2) were used to assess their predictability of nitrogen concentrations. For all spectral variables (VIs and hyperspectral reflectance), the GRNN method produced more accurate estimates of nitrogen concentrations than did the SRBF method at all ranges of nitrogen concentrations, and the better agreements between the measured and the predicted nitrogen concentration were obtained with the GRNN method. This indicated that the GRNN method is prior to the SRBF method in estimation of nitrogen concentrations. Among the VIs, the Modified Chlorophyll Absorption in Reflectance Index (MCARI), MCARI1510, and Transformed Chlorophyll Absorption in Reflectance Index are better than the others in estimating oilseed rape canopy nitrogen concentrations. Compared to the results from VIs, the hyperspectral reflectance data also gave an acceptable estimation. The study showed that nitrogen concentrations of oilseed rape canopy could be monitored using remotely sensed data and the RBF method, especially the GRNN method, is a useful explorative tool for oilseed rape nitrogen concentration monitoring when applied on hyperspectral data.

[1]  Wulf Diepenbrock,et al.  Utilization of canopy reflectance to predict properties of oilseed rape (Brassica napus L.) and barley (Hordeum vulgare L.) during ontogenesis , 2006 .

[2]  A. C. Xavier,et al.  Mapping leaf area index through spectral vegetation indices in a subtropical watershed , 2004 .

[3]  Fumin Wang,et al.  Optimal waveband identification for estimation of leaf area index of paddy rice , 2008, Journal of Zhejiang University SCIENCE B.

[4]  Eileen M. Perry,et al.  Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple , 2007 .

[5]  Urs Schmidhalter,et al.  Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence , 2009 .

[6]  Cao Weixing,et al.  Monitoring leaf nitrogen accumulation in wheat with hyper-spectral remote sensing , 2008 .

[7]  L. D. Miller,et al.  Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado , 1972 .

[8]  A. J. Richardsons,et al.  DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATION , 1977 .

[9]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[10]  J. Kjeldahl,et al.  Neue Methode zur Bestimmung des Stickstoffs in organischen Körpern , 1883 .

[11]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[12]  M. Schlerf,et al.  Remote sensing of forest biophysical variables using HyMap imaging spectrometer data , 2005 .

[13]  Meiling Liu,et al.  Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices , 2010 .

[14]  Elfatih M. Abdel-Rahman,et al.  Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[15]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[16]  S. Prasher,et al.  Application of support vector machine technology for weed and nitrogen stress detection in corn , 2006 .

[17]  Pankaj Singh,et al.  Suitability of different neural networks in daily flow forecasting , 2007, Appl. Soft Comput..

[18]  Andrew K. Skidmore,et al.  Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy , 2009 .

[19]  Martha C. Anderson,et al.  A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery ☆ , 2004 .

[20]  G. F. Sassenrath-Cole,et al.  Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration , 2000 .

[21]  Wilhelm Claupein,et al.  Quantifying nitrogen status of corn (Zea mays L.) in the field by reflectance measurements , 2003 .

[22]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .

[23]  Seongjin Choi,et al.  Prediction of plasma etching using a randomized generalized regression neural network , 2004 .

[24]  Pablo J. Zarco-Tejada,et al.  Assessing Canopy PRI for Water Stress detection with Diurnal Airborne Imagery , 2008 .

[25]  S. Schmidtlein,et al.  Mapping of continuous floristic gradients in grasslands using hyperspectral imagery , 2004 .

[26]  R. Person.  Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Pawnee National Grasslands, Colorado , 1972 .

[27]  Liangyun Liu,et al.  Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR) , 2004 .

[28]  Xu Xuexuan,et al.  Spatial heterogeneity of soil nutrients and aboveground biomass in abandoned old-fields of Loess Hilly region in Northern Shaanxi, China , 2008 .

[29]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[30]  Bas Eickhout,et al.  The role of nitrogen in world food production and environmental sustainability , 2006 .

[31]  Mahmut Firat,et al.  Comparative analysis of neural network techniques for predicting water consumption time series , 2010 .

[32]  Moon S. Kim,et al.  The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (A par) , 1994 .

[33]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[34]  S. Jain,et al.  Radial Basis Function Neural Network for Modeling Rating Curves , 2003 .

[35]  Y. Kosugi,et al.  Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery , 2007 .

[36]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[37]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[38]  Y. Wang,et al.  A modified chlorophyll absorption continuum index for chlorophyll estimation , 2006 .

[39]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[40]  Yuan Wang,et al.  Validation of artificial neural network techniques in the estimation of nitrogen concentration in rape using canopy hyperspectral reflectance data , 2009 .

[41]  Fumin Wang,et al.  Relationship between Narrow Band Normalized Deference Vegetation Index and Rice Agronomic Variables , 2004 .

[42]  Mary E. Martin,et al.  HIGH SPECTRAL RESOLUTION REMOTE SENSING OF FOREST CANOPY LIGNIN, NITROGEN, AND ECOSYSTEM PROCESSES , 1997 .

[43]  TANGYan-Lin,et al.  Relations Between Red Edge Characteristics and Agronomic Parameters of Crops , 2004 .

[44]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

[45]  N. Broge,et al.  Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data , 2002 .

[46]  Yafit Cohen,et al.  SWIR-based spectral indices for assessing nitrogen content in potato fields , 2010 .

[47]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .