Mapping of Surface Change by Interferometric Spaceborne Synthetic Aperture Radar using JERS-1 SAR

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1 Mapping of Surface Change by Interferometric Spaceborne Synthetic Aperture Radar using JERS-1 SAR Masaki Murakami 1, Mikio Tobita 2, Satoshi Fujiwara 3, Koh Nitta 4, Hiroyuki Nakagawa 5 1 Geodetic Department, the Geographical Survey Institute, the Ministry of Construction 1-Kitasato, Tsukuba, Ibaraki, 305 Japan Tel : , Fax : , masaki-m@gsi-mc.go.jp 2 Surveying Department, the Construction College, the Ministry of Construction 3 Mizusawa Geodetic Observatory, the Geographical Survey Institute, the Ministry of Construction 4 National Land Survey Division, Land Bureau, the National Land Agency 5 Geography and Crustal Dynamics Research Center, the Geographical Survey Institute, the Ministry of Construction Abstract Differential interferometric SAR (Synthesized Aperture Radar) provides us with two-dimensional distribution of crustal deformations, which is hardly obtained by other survey techniques. The Geographical Survey Institute (GSI) has been conducting a study on applications of differential interferometric SAR using data acquired by JERS-1 satellite in order to detect crustal deformations associated with earthquakes, volcanic activities and other geophysical phenomena since Our final goal is the development of standard method of interferometric SAR to detect a surface change of a few cm level. In this study, we first investigated crustal deformations associated with many geologic events by SAR interferometry. We detected crustal deformation of 1995 Hyogo-ken Nanbu earthquake over areas by more than 40 km from the epicenter. The interferogram shows fringes correspond to the uplift of northern Awaji Island and fringes correspond to right lateral slip of seismic fault at Kobe. In addition, we can unwrap the interferogram and make a crustal displacement map in radar line-of-sight direction. Our next achievement is the detection of the crustal deformation associated with the 1995 Northern Sakhalin (Neftegorsk) earthquake. Our interferogram shows detailed deformation field of the whole area around the seismic faults including a pair of adjacent peaks of uplift on the eastern side of the southern end of the seismic fault, where field workers could not access and no surveys were performed. We also succeeded to detect crustal deformation of March 1997 Kagoshima-ken Hokuseibu earthquake less than several centimeters. This is the smallest movement we have detected. We have detected not only seismic crustal deformation but also that of volcanic origin. We detected surface deformations in Izu-Oshima. It was last erupted in 1986 and it has been calm since then. INSAR reveals the inflation of the island and subsidence of the lava from 1992 to 1995, 6 to 8 years after the eruption. In addition, we carried out a comprehensive study to identify an uplift of the crust in the eastern part of Izu peninsula due to persistent earthquake swarm. In the interferogram, we can see the fringes indicating that uplift. However, there exist other fringes where the ground truth data indicate no crustal deformations, which are attributed to the atmospheric effect. Superimposing two or more interferograms can reduce these atmospheric effects. For the study of the glacier migration, INSAR is a potential tool. In collaboration with the National Institute of Polar Research (NIPR), we obtained SAR interferograms using JERS-1 and detected ice sheet motion over the area around Amundsen bay, Enderby land in Antarctica These results show characteristic feature of the deformation of each target and fully show the advantage of SAR interferometry. We construct seismic fault model of 1995 Hyogo-ken Nanbu earthquake, 1995 Northern Sakhalin (Neftegorsk) earthquake and March 1997 Kagoshima-ken Hokuseibu earthquake using INSAR result. These fault models can reproduce the crustal deformation field, which indicate the validity of the models. To improve the accuracy of INSAR, we must identify error sources and correct them. From early analyses, we found the tropospheric delay is one of the major error sources. At present only way to remove them is to superimpose two or more interferograms obtained for different seasons. One future possibility is to use tropospheric delay data from nationwide GSP array. As a first step, we compare SAR interferogram and GPS tropospheric delay distribution. Our preliminary result shows similar morphology between GPS delay difference map and SAR interferogram. 1. Introduction and Scientific Objectives Differential interferometric SAR (Synthesized Aperture Radar) opened a new frontier in the field of geodesy and crustal dynamics. It provides us with two-dimensional distribution of crustal deformations. Other survey techniques such as GPS surveys, trilatelation and leveling surveys provide us with displacement at spatially discrete points spacing a few kilometers or much more. We might make more dense distribution of GPS observation array, but available resources limit its density. By using SAR interferometry, however, we can monitor ground surface over a wide area of 75 km x 75 km with a spatial resolution of the order of 10 m without installing any ground facilities. The capability of SAR interferometry applied to crustal deformation studies has been fully demonstrated by the detection of crustal deformation due to the 1994 Landers, California, earthquake by Massonnet et al. (1993) and Zebker et al. (1994) using ERS-1 data, and has been in the limelight since then. The Geographical Survey Institute (GSI) has been conducting a study on applications of differential interferometric SAR using data acquired by JERS-1 satellite in order to detect crustal deformations associated with earthquakes, volcanic activities and other geophysical phenomena since The most important advantage of the Differential INSAR as compared with conventional survey techniques is the spatial

2 coverage over a big area. A visualization of crustal deformation plays a key role to understand the mechanism of an earthquake or a volcanic phenomenon. Our final goal is the development of standard method of interferometric SAR to detect a surface change of a few cm level using JERS-1 Synthetic Aperture Radar, and to make Differential INSAR a standard tool to monitor surface changes in many different fields of geoscience. Another application of INSAR is the monitoring of the movement of glaciers. From a continuous monitoring of a number of glaciers that are properly selected, we can trace the change of the speed of this movement and derive upper or lower limits of the mean sea level change due to global warming. For both applications above, we think that accuracy of INSAR must be improved up to 1-cm level. 2. Methods and Research Activities This study is composed by five parts: (1) INSAR Analysis of the Selected Test Sites: We applied INSAR for geologically interesting areas. The test sites are as follows. (a) Kobe and Awaji (b) North Sakhalin (c) Kagoshima (d) Izu Peninsula (e) Izu Oshima island (f) Tokyo (g) Antarctica These test sites cover wide variety of the surface conditions, and the ground truth data of the areas for crustal deformation monitoring such as GPS, leveling, EDM data are available. (2) Development of a Method to Construct a Model of Crustal Deformation from SAR Interferograms: We are developing a method to reconstruct theoretical seismic faults and source models to explain detected crustal deformations by InSAR. We start with INVERSION method, which is widely used in theoretical approach of geophysics. (3) Identification of Error Sources: We identify the error sources of INSAR analysis and estimate its maximum contribution. (4) Modeling of Tropospheric Delay: From early analyses, we found the tropospheric delay is one of the major error sources. We carry out an intensive study to establish a standard method to remove this error. We have to model the temporal and spatial distribution of water vapor. We think we could use the following truth data: (a) water vapor content data retrieved from a dense GPS monitoring network by GSI (typical spacing is about 25 km, and accuracy for the tropospheric delay is about 1 cm level) (b) space-borne meteorological data such as NOAA and GMS (c) 20 km integrated meteorological data computed and compiled by the Meteorological Agency. (5) Modeling of the Other Errors: We also make a standard method to model errors other than the tropospheric delay to achieve 1-cm accuracy. 3. Detection of Crustal Deformation by JERS-1/INSAR In this research, we have investigated crustal deformations associated with the geologic events as follows: 1995 Hyogo-ken Nanbu earthquake 1995 Northern Sakhalin (Neftegorsk) earthquake. Persistent earthquake swarm in Izu Peninsula (Fujiwara et al., 1997). March 1997 Kagoshima-ken Hokuseibu earthquake (Tobita et al., 1997b). We also investigated surface deformations at the volcanic caldera in Izu-Oshima and glacier migration in Antarctica (Ozawa et al., 1997). Some of the SAR interferograms have been used to construct seismic fault models. (a) The 1995 Hyogo-ken Nanbu (Kobe) earthquake We have detected the crustal deformation associated with the 1995 Hyogo-ken Nanbu earthquake by SAR interferometry (Murakami et al., 1995; Ozawa et al., 1997). The earthquake occurred on January 17, 1995 and its magnitude is 6.9. We used JERS-1 SAR imageries around Kobe (D72-242, D72-243) taken on September 9, 1992, and February 6, Figure 1. Crustal deformation associated with the 1995 Hyogo-ken Nanbu (Kobe) earthquake. It was in 1995 when we first analyzed the data for a smaller region around Kobe and Northern Awaji (Murakami et al., 1995). At that time, our hardware and software resources were inadequate. Although we can see fringes correspond to the crustal displacement, the interferogram is noisy and removal of fringes due to topography and baseline length was inadequate. In 1997, we re-analyzed the same SAR data with improved hardware and software. Two adjacent data are concatenated and processed together, and we obtained interferogram of a

3 wider area. Then we estimate the baseline by the simulated fringes from tentative baseline components and topographic fringes of the area far from epicenter, which is thought to have effect of crustal deformation. Using this improved algorithm to estimate baseline, fringes due to topography and baseline length have been removed more intensively than before. (June 11) the mainshock (May 27) were used to detect the crustal deformation. Figure 2. Contour map of radar line-of-sight displacement from unwrapped interferogram. As a result we can map the whole deformation field of the earthquake. We found that it spreads over areas by more than 40 km from the epicenter (Figure 1). The interferogram shows fringes correspond to the uplift of northern Awaji Island and fringes correspond to right lateral slip of seismic fault at Kobe. In addition, we can unwrap the interferogram and make a crustal displacement map in radar line-of-sight direction(figure 2). (b) The 1995 Northern Sakhalin (Neftegorsk) earthquake The 1995 Northern Sakhalin (Neftegorsk) earthquake occurred on May 27, It was of magnitude 7.6 and heavily damaged Neftegorsk, large city at Northern Sakhalin. Field workers reported right-lateral, strike-slip seismic fault as long as 35km, running NNE on the surface with maximum lateral displacement of 8.1m (Shimamoto et al. (1996)). However, traffic condition of the southern part of the fault is bad and it was hard to be accessed, so there is almost no information on crustal deformation there. The entire aspect of crustal deformation due to the earthquake was revealed for the first time using JERS-1 SAR interferogram (Figure 3) (Tobita et al., 1997a). JERS-1 SAR imageries of one month before (April 28) and two weeks after Figure 3. Crustal deformation associated with the 1995 Northern Sakhalin (Neftegorsk) earthquake. (from Tobita(1998)) There is not any DEM of the area with appropriate accuracy, so the fringes due to topographic component were removed from the interferogram by using 4-pass method, which means topographic fringes were removed by using a DEM created from SAR interferogram (Figure 4). Though the area is covered with swamps and forests, we obtained good interferometric correlation with the L-band SAR of JERS-1. This shows the superiority of L-band over C-band in this kind of land cover area. Because the mainshock fault runs nearly parallel to the JERS-1 satellite track and sideslip motion along the fault does not have the component in the radar look direction, the interferogram is not very sensitive to the horizontal displacement. Therefore, we can interpret that the interferogram shows mainly vertical displacement. The interferogram shows that about 1m subsidence on the eastern side of the north end of the fault and 30~45cm uplift all over the western side of the fault, which is consistent with GPS observations by Takahashi et al (1995). A pair of adjacent peaks of uplift clearly appears on the eastern side of the southern end of the seismic fault, where field workers could not access. The northern one reaches more than 70cm.

4 1986 eruption (Figure 5) (Murakami (1998)). We estimate baseline using the Izu peninsula data and detect the largescale movement of Izu-Oshima Island, otherwise the inflation all over the island cannot be detected. The amount of inflation is consistent with that observed by GPS and considered to be volcanic activities underground. On the other hand, the subsidence was not associated with any volcanic activities because the volcano has been quiet during the period of SAR observation. We think that the mass loading of the lava has caused the subsidence. The interferogram shows good coherency in and around the caldera regardless of a long time interval of 29 months between the two observations while it shows poor coherency in areas surrounding the caldera. Because the areas with low coherency are covered with vegetation, we think the growth of vegetation between the two observations has caused the poor coherency. Figure 4. A topographic map of northern part of Sakhalin created from SAR interferogram. (from Tobita(1998)) (c) Deformation after volcanic eruption in Izu-Oshima We have detected surface deformations in Izu-Oshima, which is a volcanic island and erupted in November We use two interferometric pairs of JERS-1 SAR imageries taken on October 15, 1992 and March 14, 1995, and.on September 1, 1992 and August 6, It has been long after the last eruption. (d) Deformation associated with earthquake swarm in Izu Peninsula We carried out a comprehensive study to identify an uplift of the crust in the eastern part of Izu peninsula due to persistent earthquake swarm, which is of a volcanic origin. We also aimed to identify the effects of ground surface and atmospheric characteristics on SAR interferometry (Fujiwara et al. (1998)). Using JERS-1/SAR data acquired from 1993 to 1994, we made eleven independent interferograms. From leveling surveys we knew that the magnitude of the uplift around Ito during that period was about 5cm. In the interferogram, we also can see the fringes indicating that uplift. However, there exist other fringes where the ground truth data indicate no crustal deformations. These fringes seem to have some correlation with topography (Figure 6). The correlation, however, does not necessarily appear in a similar way to topographic fringes. For example, some features can be related to cloud cover. We assume that most of those fringes are caused by a heterogeneous distribution of water vapor in the troposphere. Figure 5. Unwrapped interferograms of Izu-Oshima in different period. Our preliminary result indicates possible inflation of the island and a subsidence along solidified lava flow that spouted from a crater at the center of the caldera in the Figure 6 (Left). SAR interferogram in Izu Peninsula showing the effect of water vapor. Figure 7 (Right). Superimposed interferogram of two pairs 8/93-11/93 and 2/93-2/94. It shows the uplift associated with earthquake swarm clearly.

5 One way to eliminate fringes due to water vapor in SAR interferograms is to superimpose two or more interferograms obtained for different seasons (Figure 7). As a result, we can detect the surface deformation of the amount of about 5cm. (e) March 1997 Kagoshima-ken Hokuseibu earthquake On March 26, 1997, an earthquake of magnitude 6.1 occurred on northwestern part of Kagoshima prefecture. We computed INSAR using the JERS-1 data acquired on 1997/3/2 and 1997/4/15 (Figure 8). The interferogram shows about 6cm movement away from satellite at the northern part of the epicenter and movement of about 2 to 3cm toward satellite at the southern part. Seismic study shows that the epicenters of the aftershocks are distributed around the boundary of the toward-satellite movement and away-from-satellite movement (Figure 11: next page). This is the smallest displacement associated with an earthquake we have ever detected. Figure 9. SAR interferogram around Amundsen bay. Fringes appear on ice sheet that seems to move rather slowly. On the other hand, there is very poor coherency at the region of ice streams, which flow fast. The fringes due to topography are not removed because there is no elevation data available over this region. Although the fringes at the area with bare rocks represent topography, it seems that the fringes appearing on ice sheet areas are mainly due to the ice motion and, with less extent, topography because those fringes has too steep inclination to be attributed to topography. The removal of the topography and the quantitative study of the ice motion are remained future problem. Figure 8. Unwrapped interferogarm of the March 1997 Kagoshima-ken Hokuseibu earthquake. One cycle of color wheel is 3cm dispalcement (f) Monitoring of glacier migration We are intending to monitor glacier migration in Antarctica using SAR interferometry in collaboration with the National Institute of Polar Research (NIPR). The difficulty in making interferograms comes from the fact that ice sheet sometimes moves too long distance during the 44-day recurrence period of JERS-1. Large displacements of glacier between two observations make coherency poor. We, GSI and NIPR, first obtained SAR interferograms using JERS-1 and detected ice sheet motion over the area around Amundsen bay, Enderby land in Antarctica (Ozawa et al. (1997)). The data was acquired on Nov.26, 1996 and Jan.9, 1997, the interval is 44 days (Figure 9). 4. Development of Method to Construct a Model of Crustal Deformation from SAR Interferograms As shown in the last section, we have detected crustal deformation associated with some earthquakes by INSAR. We have developed a method to construct seismic fault model using INSAR results. When we construct seismic fault model, one major problem is inhomogeneous distribution of data. Sometimes, lack of data itself is another problem. Crustal displacement data detected by INSAR can solve these problems. Instead of crustal displacement data conventional observation technique for crustal displacement, we use INSAR data with high spatial density and uniform distribution. We have constructed fault models of 1995 Hyogo-ken Nanbu earthquake, 1995 Northern Sakhalin earthquake, March 1997 Kagoshima-ken Hokuseibu earthquake. Our first achievement of the seismic fault model using INSAR data is that of Hyogo-ken Nanbu earthquake. Crustal displacement data from INSAR as input, we estimate seismic fault parameters by inversion of non-linear least square method. Our model shows that the seismic faults are located close to existing active faults in the region, running from the Nojima fault on Awaji-shima to the Rokko faults in the Kobe area (Ozawa et al., 1997). We then calculate the crustal deformation around the fault from the model and simulate SAR interferogram. It indicates almost same fringes as the actual interferogram, which shows the reliability of our fault model. Now we are trying to re-construct the fault model with improved INSAR data. For the Norhtern Sakhalin earthquake, we constructed a fault model from the entire aspect of crustal deformation which the INSAR only can detect (Tobita et al., 1998). The

6 fault slip distribution of our model is consistent with that of the field surveys and the model from wave form inversion. However, in order to explain the surface change around the southern part of the exposed fault, the rupture area is likely to extend to south from the exposed fault, which suggest the faults under ground surface. This result shows the advantage of the spatially dense data from INSAR. We again simulate SAR interferogram by calculating the crustal deformation from our model (Figure 10). It can reproduce characteristic features of the crustal displacement field, which shows the reliability of our fault model. Figure 11. the location of the fault model of March 1997 Kagoshimaken Hokuseibu earthquake from INSAR and GPS (left) and the location of aftershocks (right). Star indicates the epicenter of the main schock. Figure 12 is the comparison between simulated crustal displacement from model fault and actual one. It indicates that our model can reproduce the feature of the displacement well. However, there is a apparent displacement of 2cm toward satellite on the western coast of the SAR observation, which cannot appear in the model simulation. One possibility is that this fringe is not real crustal displacement but tropospheric delay effect, though there is no conclusive explanation yet. Figure 10. Simulated interferometric SAR fringes of the 1995 Northern Sakhalin (Neftegorsk) earthquake by our fault model around the fault. Figure 12. The comparison between simulated crustal displacement from model fault and actual one from INSAR. Our next achievement is the fault model of the March 1997 Kagoshima-ken Hokuseibu earthquake. This time, we use the crustal deformation data acquired from both INSAR and GPS continuous observation array. Figure 11 shows the location of the fault model, which is almost same place as the distribution of aftershocks decided by Japan Meteorological Agency. The theoretical displacement of GPS stations from the model is also same as actual displacements. Recently, we use INSAR result of Izu-Oshima inflation data to estimate the location and depth of magma source by applying Mogi model. In our preliminary result, the fitting seems considerably good (Murakami et al. (1998)). 5. Identification of Error Sources The precision of measurement of a line-of-sight distance between the sensor and a target using JERS-1 SAR data is estimated to 1 cm or better, in some most favorable scene, 1 mm (Tobita et al., 1997b). This means that if we remove

7 major error sources from the interferogram, we are able to detect crustal deformation with a millimeter precision. Possible error sources in SAR interferometry are being identified from many studies such as Fujiwara et al. (1998). Those errors include: (1) decorrelation due to volumetric scatter and temporal changes of ground surface, (2) tropospheric (water vapor) delay of microwave, (3) baseline estimation error due to orbital inaccuracy of JERS-1, We are working on establishment of a standard method to remove those errors in the interferogram. Some of them are eliminated, others are not because of the difficulty of the formulation. Among them, tropospheric delay is one of the major error sources. From the interferograms over Izu region, one can find that peak-to-peak variations reach to 16 cm along a line of sight, which can not be accounted for surface deformation (Fujiwara et al. (1998)) (Figure 6). Atmospheric water vapor is seemingly attributed to those variations. At present, the only solution is to average out variations that randomly appear in several interferograms obtained for different seasons (Figure 7). Another possibility is correcting the tropospheric delay using very dense data of tropospheric water vapor. This will be discussed in next section. Orbital inaccuracy of JERS-1 is another major error source. We estimate a baseline between two orbits and an error related to the baseline inaccuracy can almost be eliminated, though the baseline estimation still needs a time-requiring and skillful work. The principle is as follows: If the baseline and the height of one point are known, then one can calculate interferometric phase difference of the point. Conversely, if the interferometric phase difference of the point and the height of the point are known, then one can calculate the baseline. We use DEM (digital elevation model) as a number of ground control points and estimate baseline by least-squares method. (e.g. Rosen et al. (1996), Tobita et al. (1998), Fujiwara et al. (1998)). Orbital inaccuracy is also hindrance when we attempt to monitor a large-scale slow deformation because interferometric fringes due to this kind of deformation can be hardly distinguished from those due to orbital inaccuracy. As for the decorrelation due to volumetric scatter and temporal changes of ground surface, it is said that L-band SAR is more robust than C-band (Rosen et al. (1996)). We confirmed that fact. For instance, the SAR interferogram obtained for Northern Sakhalin shows remarkably good correlation though a large part of the area is covered with swamps and forests (Tobita et al. (1998)). Other examples are the interferograms for Kobe (Ozawa et al. (1997)) and Izu-Oshima (Murakami et al. (1998)). The temporal separations of the two observations are 29 months, but these pairs have high correlation. On the other hand, in the interferogram of Izu-Oshima, the area surrounding the caldera shows poor correlation. Fujiwara et al. (1998) calculated the correlation in high relief areas, a forest area, and a city area individually in the Izu region. For the city area and the forest area, the decorrelation is roughly linear with baseline length. In contrast, correlation of high relief areas is high when the baseline is tens of meters but drops rapidly with increasing the baseline. They conclude that in city area, the baseline decorrelation is a dominant source of decorrelation, but temporal decorrelation is stable. On the other hand, the correlation in the forest and high relief areas shows greater variance and lager dependence on the temporal separation than that in the city area. The decorrelation includes temporal effects such as changes in vegetation and soil condition. 6. Modeling of Tropospheric Delay Theoretically, if we had dense meteorological data, the effect of temporal and spatial distribution of water vapor on SAR interferometry could be modeled. At present, however, we cannot get such information and we do not have any immediate measure to eliminate the water vapor effect. The only solution at present is to average out variations that randomly appear in several interferograms obtained for different seasons. Advances in the study of GPS meteorology serve for the elimination of water vapor effect. The effect of water vapor on microwave transmission has been identified in the precise positioning of GPS, which also uses L-band microwave signal. This effect is called tropospheric "wet" delay of microwave. There are four differences in water vapor effect between GPS and INSAR (a) In INSAR, the difference of wet delay between two scenes is important. On the other hand the wet delay amount itself affects the accuracy of positioning in GPS surveys. (b) The SAR data is obtained in less than 10 sec, so the water vapor distribution of the instance is reflected in SAR interferogram. The wet delay of GPS is estimated as an average of some period of five minutes through three hours. (c) Wet delay estimated by GPS is measured in the direction of zenith, which is estimated variable direction of satellites. On the other hand, INSAR is sensitive to the wet delay along radar line-of-sight direction and it is oblique incidence. (d) For INSAR, quadratic component of tropospheric delay is removed by interferogram processing. Fringes in interferogram due to surface curvature have a quadratic nature. To remove this surface phase, baseline must be estimated precisely. Baseline estimation is a fitting of simulated interferogram to the actual fringe, which includes the quadratic component of tropospheric phase delay and the surface phase. So the estimated baseline used to flatten the interferogram removes this tropospheric components. We compare between GPS wet delay and INSAR (Nakagawa et al. (1998)). We used SAR interferogram in Tokyo and Izu peninsula region, where there are a lot of GEONET stations. First, five-minute average of GPS zenith delay are calculated by GIPSY/OASYS. Then, we calculated zenith delay difference between the times when SAR data are obtained. After that, we project the zenith delay difference in the radar line-of-sight assuming homogenious atmosphere around the GPS site. Finally, we remove quadratic component of GPS delay difference to compare GPS delay with INSAR, which does not have quadratic component of the fringe, and plot the residual. Our preliminary result shows similar morphology between GPS delay difference map and SAR interferogram (Figure 13). There still remains several problems: (a) The average interval of the GPS site is about 10~20km, which is too sparse to correct INSAR. The morphology of

8 GPS tropospheric delay difference is strongly depend on how to interpolate the data. (b) The topographic fringe might not be removed completely from SAR interferogram. If we refine baseline estimation algorithm, we might solve this problem. However, this problem is rather complicated. There is possibility that some wet delay correlate with the topography, so the apparent remnant topographic fringe is actual phase delay by water vapor. It is hard to distinguish them and we might remove topography-correlated wet delay in flattening process. (c) INSAR reflects instantaneous water vapor distribution at the instance SAR data are obtained. On the other hand, GPS shows the averaged water vapor distribution of a certain period. (d) Atmosphere is not homogeneous around the GPS site, so the amount of the GPS delay is not mere projection of zenith delay. As for c), we make comparison between five minute interval estimation of GPS zenith delay by GIPSY/OASYS and three hours average value by BERNESE software. The delay distribution is quite different between them (Figure 14). This indicates that tropospheric delay distribution changes fast. To check it, we also plot tropospheric delay difference (in this case, quadratic component is not removed) by GIPSY/OASYS data from 0:00UT to 3:00UT in every 30 minutes (Figure 15). This sequence shows tropospheric delay difference changes a lot even in three hours. Therefore, tropospheric delay data from the GSI s routine analysis of GEONET, which is three hours average, is not suitable for estimating the effect of tropospheric delay in SAR interferogram. Figure 13. Comparison between SAR interferogram (upper) and wet delay map from GEONET (lower). Figure 14. Comparison of GPS tropospheric delay differences between (top) and Bernese 3hrs. average data GIPSY/OASYS 5min. interval data (Bottom). Contour interval is 0.5cm. Numbers imposed in the map is the tropospheric delay at the GPS site.

9 Figure 15. A series of tropospheric delay difference for 3 hours in every 30min. The ZTD data of 5min. average is obtained by GIPSY/OASYS (note that quadratic component is not removed from this data). 7. Modeling of the Other Errors We are now trying to understand each feature of the errors. As seen before, it is complicated and difficult to formulate. Next error source we must consider is the effect of ionosphere. So far we have not had the evidence of ionospheric effects in JERS-1 SAR interferogram, which indicate that it might be smaller than other errors or the wavelength of its spatial variation is long in comparison with the scale of one scene. However, there are some reports of ionospheric delay in SAR interferogram used ERS satellite, which has C-band SAR (Tarayre and Massonnet (1996), Massonnet et al (1994)). Microwave with lower frequency gets more ionospheric delay than higher frequency. So L- band is more likely to be affected ionosphere. We should check this point. We think we can use GPS network data for ionospheric delay study, too. In GPS analysis, ionospheric effect is removed using dual-frequency receiver. The phase delay of the microwave is inverse proportion of the square of its frequency, so we combine two waves to calibrate the phase delay due to the ionosphere. Dual-frequency GPS receiver of GEONET will give us the information of ionospheric delay. 8. References [1] Fujiwara, S., H. Nakagawa, M. Murakami, M. Tobita, P. A. Rosen, The study of interferometric baseline for synthetic aperture radar - an example of Izu-oshima, Abstract of 90 th Meeting of the Geodetic Society of Japan, pp , 1998 (in Japanese) [2] Fujiwara, S., P. A. Rosen, M. Tobita, and M. Murakami, Crustal deformation measurements using repeat-pass JERS 1 synthetic aperture radar interferometry near the Izu Peninsula, Japan, Journal of Geophysical Researches, 103, pp , 1998 [3] Massonnet, D., K. L. Feigl M. Rossi, F. Adragna, Radar interferometric mapping of deformation in the year after the Landers earthquake, Nature, 369, pp , 1994 [4] Massonnet, D., M. Rossi, C. Carmona, F. Adragna, G. Peltzer, K. Feigl, T. Rabaute, The displacement field of the Landers earthquake mapped by radar interferometry, Nature, 364, pp , 1993 [5] Murakami, Mak., S. Fujiwara, H. Nakagawa, M. Tobita, P. A. Rosen, Crustal Deformation of Izu-Oshima Detected by JERS-1 Interferometric SAR, Abstract of 90 th Meeting of the Geodetic Society of Japan, pp , 1998 (in Japanese) [6] Murakami, Mak., S. Fujiwara, and T. Saito, Detection of Crustal Deformations Associated with the 1995 Hyogoken-Nanbu Earthquake by Interferometric SAR, Journal of the Geographical Survey Institute, 83, 24-27, 1995 (in Japanese). [7] Nakagawa, H., Fujiwara, S., S. Miyazaki, M. Murakami, M. Tobita, P. A. Rosen, The Comparison of Tropospheric Delay between SAR Interferometry and GPS Continuous Observation Network, Abstract of 90 th Meeting of the Geodetic Society of Japan, pp , 1998 (in Japanese) [8] Ozawa, S., M. Murakami, S. Fujiwara, and M. Tobita, Synthetic aperture radar interferogram of the 1995 Kobe earthquake and its geodetic inversion, Geophysical. Research Letters, 24, , [9] Ozawa, T., K. Doi, K. Shibuya, H. Nakagawa, S. Fujiwara, and M. Murakami, Detection of Ice Sheet Motion by JERS-1 SAR Interferometry, Proceedings of the Second Workshop on SAR Interferometry, SAR WORKSHOP '97 TSUKUBA, 1997 [10] Rosen, P.A., S.Hensley, H.A.Zebker, F.H.Webb, and E.J.Fielding, Surface deformation and coherence measurements of Kilauea Volcano, Hawaii, from SIR-C radar interferometry, Journal of Geophysical Researches, 101, , [11] Shimamoto, T., M. Watanabe, Y. Suzuki, Surface faults associated with the 1995 Neftegorsk earthquake, Research Report on the 1995 North Sakhalin Earthquake and its Disaster, pp , 1996 [12] Tarayre, H. and D. Massonnet Atmospheric propagation heterogeneities revealed by ERS-1 interferometry, Geophysical Research Letters, 23, pp , 1996 [13] Tobita, M. et al., Crustal Deformation Associated with the March 1997 Kagoshima-ken Hokusei-bu Earthquake Detected by InSAR and GPS, Proceedings of the Second Workshop on SAR Interferometry, SAR WORKSHOP '97 TSUKUBA [14] Tobita, M., S. Fujiwara, S. Ozawa, P. A. Rosen, E. J. Fielding, C. L. Werner, M. Murakami, H. Nakagawa, K. Nitta, and M. Murakami, Deformation of the 1995 North Sakhalin earthquake detected by JERS-1/SAR

10 interferometry, Earth, Planets and Space, 50, , [15] Zebker, H. A., P. A. Rosen, R. M. Goldstein, A. Gabriel, C. L. Werner, On the derivation of coseismic displacement field using differential radar interferometry: The Landers earthquake, Journal of Geophysical Researches, 99, pp , 1994