The potential of InSAR for quantitative surface parameter estimation

Although the problems of soil moisture and surface roughness estimation of bare surfaces have been extensively analyzed in terms of conventional polarimetric synthetic aperture radar (SAR) data, the information content of polarimetric interferometric SAR (Pol-InSAR) data regarding surface parameters has not yet been fully investigated. We consider this problem and show that the main contribution to roughness and moisture dependence of coherent surface scattering in Pol-InSAR is the signal-to-noise ratio (SNR). For the estimation of surface parameters, a new hybrid surface scattering model has been developed which is based on the polarization dependence of SNR and its impact on the observed coherences. We show that the model is invertible and allows us to recover surface roughness and moisture estimates from the ratios of measured InSAR coherence. Further, the hybrid model is used for a study of sensitivity analysis. Lastly, we show validation results from experimental Pol-InSAR data from the Deutsche Forschungsanstalt fur Luft-und Raumfahrt (German Aerospace Center (DLR)) E-SAR system at L-band. These results indicate that Pol-InSAR has high potential, to be used as an alternative method, for quantitative moisture and roughness estimates of bare surfaces.

[1]  Fabio Rocca,et al.  The wavenumber shift in SAR interferometry , 1994, IEEE Trans. Geosci. Remote. Sens..

[2]  Shane Cloude,et al.  A new parameter for soil moisture estimation , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[3]  Dennis R. Fatland,et al.  DInSAR measurement of soil moisture , 2003, IEEE Trans. Geosci. Remote. Sens..

[4]  Jong-Sen Lee,et al.  Surface roughness and slope measurements using polarimetric SAR data , 2002, IEEE Trans. Geosci. Remote. Sens..

[5]  Irena Hajnsek,et al.  Inversion of surface parameters from polarimetric SAR , 2003, IEEE Trans. Geosci. Remote. Sens..

[6]  S. Usai.  A New Approach for Longterm Monitoring of Deformations by Differential SAR Interferometry , 2001 .

[7]  I. Hajnsek,et al.  An Eigenvector Method for the Extraction of Surface Parameters in Polarmetric SAR , 2000 .

[8]  A. K. Fung,et al.  A numerical study of the regions of validity of the Kirchhoff and small‐perturbation rough surface scattering models , 1988 .

[9]  Jan Askne,et al.  Coherence Characteristics of Radar Signals from Rough Soil , 2001 .

[10]  G. Nesti,et al.  Decorrelation of backscattered signal due to soil moisture changes , 1995, 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications.

[11]  Thuy Le Toan,et al.  The effect of surface roughness on multifrequency polarimetric SAR data , 1997, IEEE Trans. Geosci. Remote. Sens..

[12]  Jakob J. van Zyl,et al.  Change detection techniques for ERS-1 SAR data , 1993, IEEE Trans. Geosci. Remote. Sens..

[13]  Jose Luis Alvarez-Perez.  An extension of the IEM/IEMM surface scattering model , 2001 .

[14]  Irena Hajnsek,et al.  Surface Parameter Estimation Using Interferometric Coherences at Different Polarisations , 2003 .

[15]  Temporal and spatial resolution requirements for a soil moisture mission , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[16]  Giorgio Franceschetti,et al.  The Effect of Surface Scattering On Ifsar Baseline Decorrelation , 1997 .

[17]  佐藤 晃一,et al.  Polarimetric SAR Interferometryによる森林の特徴について , 2000 .

[18]  Fuk K. Li,et al.  Synthetic aperture radar interferometry , 2000, Proceedings of the IEEE.