Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 720.64 KB
Add to list
  1. Search results
  2. Semi-empirical calibration of the int...

Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering

Nicolas Baghdadi, Mehrez Zribi, Simonetta Paloscia, Niko Verhoest (UGent) , Hans Lievens (UGent) , Frederic Baup and Francesco Mattia
(2015) REMOTE SENSING. 7(10). p.13626-13640
Author
Organization
Abstract
The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for Synthetic Aperture Radar (SAR) data at C- and X-bands to SAR data at L-band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. This dataset was collected over numerous agricultural study sites in France, Luxembourg, Belgium, Germany and Italy using various SAR sensors (AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR). Results showed slightly better simulations with exponential autocorrelation function than with Gaussian function and with HH than with VV. Using the exponential autocorrelation function, the mean difference between experimental data and Integral Equation Model (IEM) simulations is +0.4 dB in HH and -1.2 dB in VV with a Root Mean Square Error (RMSE) about 3.5 dB. In order to improve the modeling results of the IEM for a better use in the inversion of SAR data, a semi-empirical calibration of the IEM was performed at L-band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM (RMSE about 2.2 dB).
Keywords
synthetic aperture radar, integral equation model, L-band, bare soil, SOIL-MOISTURE RETRIEVAL, SIR-C/X-SAR, SURFACE-ROUGHNESS, BARE SOILS, FIELD-MEASUREMENTS, NEURAL-NETWORKS, RADAR IMAGES, IEM, PARAMETERS, BEHAVIOR

Downloads

  • Download
    RemoteSensing-Baghdadi et al 2015.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 720.64 KB

Citation

Please use this url to cite or link to this publication:

MLA
Baghdadi, Nicolas, et al. “Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering.” REMOTE SENSING, vol. 7, no. 10, 2015, pp. 13626–40, doi:10.3390/rs71013626.
APA
Baghdadi, N., Zribi, M., Paloscia, S., Verhoest, N., Lievens, H., Baup, F., & Mattia, F. (2015). Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering. REMOTE SENSING, 7(10), 13626–13640. https://doi.org/10.3390/rs71013626
Chicago author-date
Baghdadi, Nicolas, Mehrez Zribi, Simonetta Paloscia, Niko Verhoest, Hans Lievens, Frederic Baup, and Francesco Mattia. 2015. “Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering.” REMOTE SENSING 7 (10): 13626–40. https://doi.org/10.3390/rs71013626.
Chicago author-date (all authors)
Baghdadi, Nicolas, Mehrez Zribi, Simonetta Paloscia, Niko Verhoest, Hans Lievens, Frederic Baup, and Francesco Mattia. 2015. “Semi-Empirical Calibration of the Integral Equation Model for Co-Polarized L-Band Backscattering.” REMOTE SENSING 7 (10): 13626–13640. doi:10.3390/rs71013626.
Vancouver
1.
Baghdadi N, Zribi M, Paloscia S, Verhoest N, Lievens H, Baup F, et al. Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering. REMOTE SENSING. 2015;7(10):13626–40.
IEEE
[1]
N. Baghdadi et al., “Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering,” REMOTE SENSING, vol. 7, no. 10, pp. 13626–13640, 2015.
@article{7002166,
  abstract     = {{The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for Synthetic Aperture Radar (SAR) data at C- and X-bands to SAR data at L-band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. This dataset was collected over numerous agricultural study sites in France, Luxembourg, Belgium, Germany and Italy using various SAR sensors (AIRSAR, SIR-C, JERS-1, PALSAR-1, ESAR). Results showed slightly better simulations with exponential autocorrelation function than with Gaussian function and with HH than with VV. Using the exponential autocorrelation function, the mean difference between experimental data and Integral Equation Model (IEM) simulations is +0.4 dB in HH and -1.2 dB in VV with a Root Mean Square Error (RMSE) about 3.5 dB. In order to improve the modeling results of the IEM for a better use in the inversion of SAR data, a semi-empirical calibration of the IEM was performed at L-band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM (RMSE about 2.2 dB).}},
  author       = {{Baghdadi, Nicolas and Zribi, Mehrez and Paloscia, Simonetta and Verhoest, Niko and Lievens, Hans and Baup, Frederic and Mattia, Francesco}},
  issn         = {{2072-4292}},
  journal      = {{REMOTE SENSING}},
  keywords     = {{synthetic aperture radar,integral equation model,L-band,bare soil,SOIL-MOISTURE RETRIEVAL,SIR-C/X-SAR,SURFACE-ROUGHNESS,BARE SOILS,FIELD-MEASUREMENTS,NEURAL-NETWORKS,RADAR IMAGES,IEM,PARAMETERS,BEHAVIOR}},
  language     = {{eng}},
  number       = {{10}},
  pages        = {{13626--13640}},
  title        = {{Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering}},
  url          = {{http://doi.org/10.3390/rs71013626}},
  volume       = {{7}},
  year         = {{2015}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: