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ATMOSPHERIC EFFECTS REMOVAL OF ASAR-DERIVED INSAR PRODUCTS USING 
MERIS DATA AND GPS 
S. Adham Khiabani a , M. J. Valadan Zoej a , M. R. Mobasheri a , M. Dehghani a 
Geodesy and Geomatics Engineering Faculty, K.N.Toosi University of Technology, No. 1346, 
Vali_Asr St., Tehran, Iran, Postcode: 1996715433 
sina_adham@yahoo.com , valadanzouj@kntu.ac.ir, mobasheri@kntu.ac.ir, dehghani_rsgsi@yahoo.com. 
WG 1/2: SAR and LiDAR systems 
KEY WORDS: Satellite remote sensing, Calibration, Interferometric SAR (InSAR), Spatial modeling, Atmosphere, Water vapor, 
MERIS 
ABSTRACT: 
As confirmed by many scientists, atmosphere has intensive contaminative role on Interferometric Synthetic Aperture Radar (InSAR) 
measurements. Atmospheric parameters, always influence radar’s phase but the intensity of the atmospheric errors on interferograms 
depend on the difference of the parameters’ values. In this paper, some calibration methods will be considered in order to reduce the 
errors in some scenes acquired in 2005 from Mashhad in North East of Iran which is a semi mountainous area. Therefore, model 
estimations and data acquiring processes were determined to sustain the climate’s requirements. Since we have used Advanced 
synthetic aperture Radar (ASAR) data for interferometry purpose, MERIS seemed to be an appropriate data source due to the exact 
similarity of the acquisition times of MERIS and ASAR. As water vapor products which derived from optical Spacebom sensors are 
significantly sensitive to the clouds, a cloud mask algorithm was issued and an interpolation method was utilized to fill the empty 
pixels of the water vapor product. The air pressure and water vapor differential maps was formed in the next step and the derived 
differential maps was converted to zenith delay and radar phase shift respectively. As interferogram flattening was decided to be 
done after error removal process, the Ionospheric effect was neglected due to its linear influence in the area of interest. Implementing 
the model to the ASAR interferogram showed that the corrected InSAR results agreed to the independent GPS measurements with 
around a centimeter difference. Furthermore, atmospheric artifacts were significantly reduced in the final product. 
1. INTRODUCTION 
Interferometric Synthetic Aperture Radar (InSAR) has 
demonstrated its ability in measuring surface displacements 
(Massonnet et al., 1993; Zebker et al., 1994b) and topographic 
mapping (Zebker et al., 1994a). As confirmed by many 
scientists, atmosphere has an intensive contaminative role on 
Interferometric Synthetic Aperture Radar (InSAR) 
measurements (Zebker et. al, 1997). Atmospheric parameters, 
always influence radar’s phase but, the intensity of the 
atmospheric errors on interferograms depend on the difference 
of the parameters’ values (Hanssen, 2001). In the other words, a 
same atmospheric condition in two radar data times of 
acquisition may cause the minimum atmospheric error on 
derived interferogram due to the omission of the error, during 
the interferogram forming process (Zebker et. Al., 1997). 
In the SAR interferometry, atmosphere parameters may cause a 
phase shift on the sensed signal (Hanssen, 2001). This phase 
shift would results in a mismeasurement in surface deformation 
estimation. A spatially linear changing parameter such as 
Ionospheric electron content (neglecting exceptions) or a 
parameter with a fixed horizontal gradient (Air pressure in 
stable conditions) will cause a fixed or almost fixed drift in 
deformation estimation (Hanssen, 2001). This effect is hard to 
separate with the Baseline effect. Implementing the flattening 
strategy after the atmospheric error reduction process may 
reduce this effect in addition. 
The phase shift of the Radar signal depends on the changes of 
Refractivity in different air stratums. The refractivity could be 
obtained from the Eq.l (Davis et al., 1985). 
N = + ~ + k 3 -^yj-4.028xl0 7 y|- + 1.45vv 0) 
The first term of the equation is dry term, the next two terms in 
the brackets named wet term and the other terms are 
Ionospheric and liquid terms respectively (Hanssen, 2001). 
As it could be seen in the Eq.l, refractivity is mainly under the 
influence of 5 parameters of Temperature, Air pressure, Partial 
pressure of water vapor, Ionospheric total electron content and 
liquid water content. 
Water vapor effects have been introduced as the dominant error 
source of InSAR deformation products among the all kinds of 
atmospheric influences due to its common nonlinear spatial 
changes in a limited area (Hanssen, 2001; Li et. al, 2005). 
Stacking and Calibration methods are two suggested strategies 
of atmospheric correction of InSAR products (Li et. al, 2005). 
Stacking methods include those strategies which are 
implemented to raw data to obtain better qualified derived 
products (Zebker et al., 1997). Whereas, the calibration methods 
are those which are implemented to the products to increase the 
accuracy of derived data (Li et al., 2005).