Validation of artificial neural network techniques in the estimation of nitrogen concentration in rape using canopy hyperspectral reflectance data

• Published in: International journal of remote sensing 2009-01, Vol.30 (17), p.4493-4505
• Main Authors: Wang, Yuan, Wang, Fumin, Huang, Jingfeng, Wang, Xiuzhen, Liu, Zhanyu
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Artificial neural networks; Biological and medical sciences; Canopies; Earth sciences; Earth, ocean, space; Estimating; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Learning theory; Neural networks; Rape; Reflectance; Regression; Teledetection and vegetation maps
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160802577998

A systematic comparison of two types of method for estimating the nitrogen concentration of rape is presented: the traditional statistical method based on linear regression and the emerging computationally powerful technique based on artificial neural networks (ANN). Five optimum bands were selected using stepwise regression. Comparison between the two methods was based primarily on analysis of the statistic parameters. The rms. error for the back-propagation network (BPN) was significantly lower than that for the stepwise regression method, and the T-value was higher for BPN. In particular, for the first-difference of inverse-log spectra (log 1/R)′, T-values performed with a 127.71% success rate using BPN. The results show that the neural network is more robust to training and estimating rape nitrogen concentrations using canopy hyperspectral reflectance data.