Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.
YANG Xiao-huaHUANG Jing-fengWANG Jian-wenWANG Xiu-zhenLIU Zhan-yu
There is increasing interest in using hyperspectral data for quantitative characterization of vegetation in spatial and temporal scopes. Many spectral indices are being developed to improve vegetation sensitivity by minimizing the background influence. The chlorophyll absorption continuum index (CACI) is such a measure to calculate the spectral continuum on which the analyses are based on the area of the troughs spanned by the spectral continuum. However, different values of CACI were obtained in this method because different positions of continuums were determined by different users. Furthermore, the sensitivity of CACI to agronomic parameters such as green leaf chlorophyll density (GLCD) has been reduced because the fixed positions of con- tinuums are determined when the red edge shifted with the change in GLCD. A modified chlorophyll absorption continuum index (MCACI) is presented in this article. The red edge inflection point (REIP) replaces the maximum reflectance point (MRP) in near-infrared (NIR) shoulder on the CACI continuum. This MCACI has been proved to increase the sensitivity and predictive power of GLCD.
YANG Xiao-huaHUANG Jing-fengWANG Fu-minWANG Xiu-zhenYI Qiu-xiangWANG Yuan
Hyperspectral reflectance (350-2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application.Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters,comprising leaf area index (LAI;m-2 green leaf area m-2 soil) and green leaf chlorophyll density (GLCD;mg chlorophyll m 2 soil),using stepwise multiple regression (SMR) models and support vector machines (SVMs).Four transformations of the rice canopy data were made,comprising reflectances (R),first-order derivative reflectances (D1),second-order derivative reflectances (D2),and logarithm transformation of reflectances (LOG).The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI,with a root mean square error (RMSE) of 1.0496 LAI units.The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD,with an RMSE of 523.0741 mg m-2.The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters,but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data.
YANG XiaoHuaHUANG JingFengWU YaoPingWANG JianWenWANG PeiWANG XiaoMingAlfredo R. HUETE
