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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/4538
Title: Estimation of water cloud model vegetation parameters using a genetic algorithm
Authors: Kumar K.
Hari Prasad, Kanchan S.
Arora M.K.
Published in: Hydrological Sciences Journal
Abstract: The water cloud model is used to account for the effect of vegetation water content on radar backscatter data. The model generally comprises two parameters that characterize the vegetated terrain, A and B, and two bare soil parameters, C and D. In the present study, parameters A and B were estimated using a genetic algorithm (GA) optimization technique and compared with estimates obtained by the sequential unconstrained minimization technique (SUMT) from measured backscatter data. The parameter estimation was formulated as a least squares optimization problem by minimizing the deviations between the backscatter coefficients retrieved from the ENVISAT ASAR image and those predicted by the water cloud model. The bias induced by three different objective functions was statistically analysed by generating synthetic backscatter data. It was observed that, when the backscatter coefficient data contain no errors, the objective functions do not induce any bias in the parameter estimation and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the cases considered, the objective function based on the sum of squares of normalized deviations with respect to the computed backscatter coefficient resulted in the best possible estimates. A comparison of the GA technique with the SUMT was undertaken in estimating the water cloud model parameters. For the case considered, the GA technique performed better than the SUMT in parameter estimation, where the root mean squared error obtained from the GA was about half of that obtained by the SUMT. ¬© 2012 IAHS Press.
Citation: Hydrological Sciences Journal(2012), 57(4): 776-789
URI: https://doi.org/10.1080/02626667.2012.678583
http://repository.iitr.ac.in/handle/123456789/4538
Issue Date: 2012
Keywords: Backscatter coefficient
Bias
Data errors
Genetic algorithm
Inverse problem
Root mean squared error
Sequential unconstrained minimization technique
Vegetation parameter
ISSN: 2626667
Author Scopus IDs: 57198856305
6506799688
7103319791
Author Affiliations: Kumar, K., Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
Hari Prasad, K.S., Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
Arora, M.K., Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
Corresponding Author: Kumar, K.; Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India; email: chandel_kk@yahoo.com
Appears in Collections:Journal Publications [CE]

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