Pedosphere 19(2): 176--188, 2009
ISSN 1002-0160/CN 32-1315/P
©2009 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Comparison between radial basis function neural network and regression model for estimation of rice biophysical parameters using remote sensing |
YANG Xiao-Hua1,2, WANG Fu-Min 1,2, HUANG Jing-Feng1, WANG Jian-Wen2, WANG Ren-Chao1, SHEN Zhang-Quan1 and WANG Xiu-Zhen3 |
1 Institute of Agricultural Remote Sensing & Information Application, Zhejiang University, Hangzhou 310029 (China). E-mail:dr.xiaohuayang@gmail.com
2 Meteorological and Hydrographic Department of General Staff Headquarters, Beijing 100081 (China)
3 Zhejiang Meteorological Institute, Hangzhou 310004 (China) |
| 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 reffectance (350 to 2500 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 reffectance (R) and its three different transformations, the first derivative reffectance (D1), the second derivative reffectance (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 reffectance 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. |
| Key Words: biophysical parameters, radial basis function, regression model, remote sensing, rice |
| Citation: Yang, X. H., Wang, F. M., Huang, J. F., Wang, J. W., Wang, R. C., Shen, Z. Q. and Wang, X. Z. 2009. Comparison between radial basis function neural network and regression model for estimation of rice biophysical parameters using remote sensing. Pedosphere. 19(2): 176-188. |
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