Comparison Between Radial Basis Function Neural Network and Regression Model for Estimation of Rice Biophysical Parameters Using Remote Sensing

Abstract

Abstract The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance (350 to 2 500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density (45 plants m −2). Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reflectance ( R) and its three different transformations, the first derivative reflectance ( D1), the second derivative reflectance ( D2) and the log-transformed reflectance (LOG). GRNN based on D1 was the best model for the prediction of rice LAI and GLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.