Retrieval of crop parameters of spinach by radial basis neural network approach using X-band scatterometer data1
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- 作者：Abhishek Pandey (1)
Sunil Kr. Jha (2)
R. Prasad (1) - 关键词：spinach ; radial basis neural network ; x ; band scatterometer ; scattering coefficient
- 刊名：Russian Agricultural Sciences
- 出版年：2010
- 出版时间：August 2010
- 年：2010
- 卷：36
- 期：4
- 页码：312-315
- 全文大小：669KB
- 参考文献：1. Steven, M.D., Werker, R., and Milnes, M., Assimilation of Satellite Data in Crop Monitoring and Yield Prediction., Guyot, G. and Phulpin, T., Eds., in / Proc. of the Seventh International Symposium on Physical Measurements and Signature in Remote Sensing, France, Rotterdam, 1997, pp. 853鈥?57.
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14. Simon, H., / Neural Network Test Book, 2003, pp. 256鈥?12. - 作者单位：Abhishek Pandey (1)
Sunil Kr. Jha (2)
R. Prasad (1)
1. Department of Applied Physics, Institute of Technology, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh, India
2. Department of Physics, Banaras Hindu University, Var anasi, 221005, Uttar Pradesh, India
文摘
Mapping of vegetation from remote sensing is an active area of research since past two decades. Neural networks also successfully applied to such fields. In the present work a Radial basis function Network (RBFN) is trained and tested with the experimentally obtain data sets. Vertical transmitted and vertical received scattering coefficient sigma VV and horizontal transmitted and horizontal received scattering coefficients sigma HH and angle of incidence are used as the inputs of the network. Whereas crop parameters Leaf area index (LAI), Biomass (BM), and plant height and soil moisture parameters are used as the target data sets to train the network. It is noted that retrieved parameters are so close to the experimental results that confirm the potential of RBFNs as estimator. The main advantages of RBFN over other theoretical approaches are that it is less time taking and less complex approach.