A wind-induced delay pattern in SAR interferometry and numerical simulation

A particular delay pattern in differential synthetic aperture radar (SAR) interferometry is investigated by using upper sounding data and a numerical simulation technique. A JERS-1 pair of a humid windy summer scene and a dry autumn scene produces a large topography-correlated delay pattern. This topography-correlated delay shows linearity to topography that is mainly due to a wet delay. In this study, the static component of the atmospheric delay is calculated by using upper sounding data observed at a neighbouring station from the site. The result shows 14cm oneway delay corresponding to 3, 800m height difference, while 23cm in SAR interferogram, indicating a disagreement with SAR and meteorological data. A reason for this disagreement may be a distance between the site and the meteorological station. After removing the topography-correlated static component from the interferogram, there still remains a dynamic component caused by a wind. A numerical model, MRI-NHM, developed by Meteorological Research Institute, simulates atmosphere and the dynamic delay is evaluated. The simulation produces similar delay patterns of large scale and a wave-shaped pattern induced by a mountain wave. The simulation explains mechanisms to make these patterns. Although there are disagreements such as small-scale delay patterns and the wave length of the mountain wave, the study shows this simulation approach is promising for evaluatine the atmospheric delay, especially dynamic delay.

[1]  K. Feigl,et al.  Discrimination of geophysical phenomena in satellite radar interferograms , 1995 .

[2]  R. Goldstein,et al.  Topographic mapping from interferometric synthetic aperture radar observations , 1986 .

[3]  Didier Massonnet,et al.  Atmospheric Propagation heterogeneities revealed by ERS‐1 interferometry , 1996 .

[4]  K. Feigl,et al.  The displacement field of the Landers earthquake mapped by radar interferometry , 1993, Nature.

[5]  M. Shimada,et al.  Correction of the satellite's state vector and the atmospheric excess path delay in SAR interferometry-application to surface deformation detection , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[6]  H. Zebker,et al.  High-Resolution Water Vapor Mapping from Interferometric Radar Measurements. , 1999, Science.

[7]  R. Goldstein,et al.  Mapping small elevation changes over large areas: Differential radar interferometry , 1989 .

[8]  T. Herring,et al.  GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System , 1992 .

[9]  Paul A. Rosen,et al.  Crustal deformation measurements using repeat‐pass JERS 1 synthetic aperture radar interferometry near the Izu Peninsula, Japan , 1998 .

[10]  M. Ikawa,et al.  Description of a nonhydrostatic model developed at the Forecast Research Department of the MRI , 1991 .

[11]  P. Rosen,et al.  Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps , 1997 .

[12]  市川 隆一,et al.  Estimations of Atmospheric Excess Path Delay based on Three-Dimensional, Numerical Prediction Model Data. , 1995 .

[13]  Richard M. Goldstein,et al.  Atmospheric limitations to repeat‐track radar interferometry , 1995 .

[14]  D. Sandwell,et al.  Phase gradient approach to stacking interferograms , 1998 .

[15]  G. D. Thayer,et al.  An improved equation for the radio refractive index of air , 1974 .

[16]  T. Kato.  Hydrostatic and Non-hydrostatic Simulations of the 6 August 1993 Kagoshima Torrential Rain , 1996 .

[17]  A. Otuka,et al.  Static component of atmospheric delay in SAR interferometry and upper sounding data , 2002 .