Estimation of vegetation biophysical parameters by remote sensing using radial basis function neural network  

作　　者：YANG Xiao-hua HUANG Jing-feng WANG Jian-wen WANG Xiu-zhen LIU Zhan-yu 

机构地区：[1]Institute of Agricultural Remote Sensing ＆ Information Application, Zhejiang University, Hangzhou 310029, China [2]Key Laboratory of Agricultural Remote Sensing ＆ Information System, Hangzhou 310029, China [3]Communication Training Base of General Staff Headquarters, Beijing 102400, China [4]Zhejiang Meteorological Institute, Hangzhou 310004, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2007年第6期883-895,共13页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：Project (Nos. 40571115 and 40271078) supported by the National Natural Science Foundation of China

摘　　要：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.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, m^2 green leaf/m^2 soil） and Green Leaf Chlorophyll Density （GLCD, mg chlorophyll/m^2 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.

关 键 词：Artificial neural network （ANN） Radial basis function （RBF） Remote sensing RICE Vegetation index （VI） 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]