# Measuring ground subsidence in Ha Noi through the radar interferometry technique using terrasar-x and cosmos skymed data

D.H.T. Minh, Q.C. Tran, Q.N. Pham, T.T. Dang, D.A. Nguyen, I. El

Moussawi, Thuy Le Toan

► To cite this version:

D.H.T. Minh, Q.C. Tran, Q.N. Pham, T.T. Dang, D.A. Nguyen, et al.. Measuring ground subsidence in Ha Noi through the radar interferometry technique using terrasar-x and cosmos skymed data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12 (10), pp.3874-3884. 10.1109/JSTARS.2019.2937398 . hal-02610304

HAL Id: hal-02610304

https://hal.inrae.fr/hal-02610304v1

Submitted on 26 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Measuring Ground Subsidence in Ha Noi Through the Radar Interferometry Technique Using TerraSAR-X and Cosmos SkyMed Data

Dinh Ho Tong Minh, Quoc Cuong Tran, Quy Nhan Pham, Tran Trung Dang, Duc Anh Nguyen, Ibrahim El-Moussawi, and Thuy Le Toan

**Abstract**—Multitemporal synthetic aperture radar (SAR) interferometry (InSAR) is a widely used technique to measure the ground subsidence and has already shown its ability to map such phenomena on a large spatial scale with millimetric accuracy from space. In Vietnam, to have independent SAR data for surface risk applications, a new X-band SAR mission (JV-LOTUSat) has been scheduled for launch for the 2019–2020 timeframe. However, Vietnam is located in tropical regions where their conditions are impacted by strong atmosphere. The aim of this article is to provide a better understanding of the capabilities of the X-band for estimating the ground subsidence under tropical atmospheric conditions. Analysis is carried out on two stacks, TerraSAR-X and Cosmos SkyMed X-band, from 2011 to 2014 in Ha Noi. We show that the results on the ground subsidence from InSAR processing can describe consistently the subsidence area based on ground measurements. This article demonstrates that the InSAR technique can be effective at detecting and estimating the subsidence phenomena even with the X-band and under conditions typical of tropical regions. The displacement results from TerraSAR-X and Cosmos SkyMed datasets are consistent, with a correlation coefficient (R²) of 0.91 for the period during which their coverage overlaps. Groundwater overexploitation is one of the main causes of the ground subsidence in Ha Noi. This study provides strong support for the scientific potential of the X-band SAR space-borne mission in Vietnam and other tropical countries because it demonstrates the feasibility of the ground subsidence estimates by the X-band SAR, even in conditions impacted by strong atmosphere.

**Index Terms**—Atmospheric phase screen (APS), ground subsidence, groundwater, Ha Noi, JV-LOTUSat, multitemporal synthetic aperture radar interferometry (InSAR), permanent scatterers/distributed scatterers (PS/DS) processing, synthetic aperture radar (SAR), tropical region, X-band.

## I. INTRODUCTION

URBAN subsidence is a major hazard affecting our society [1]–[3]. In Vietnam, risk of subsidence has become increasingly frequent, particularly, in Ha Noi and Ho Chi Minh City. Planning responses to ground subsidence requires knowledge about its spatial extent and how it has changed over time [4]. Multitemporal synthetic aperture radar (SAR) interferometry (InSAR) is a widely used technique to measure the urban ground subsidence and has already shown its ability to map such phenomena on a large spatial scale with millimetric accuracy from space, associated with either ground subsidence (e.g., [5]–[7]), railway subsidence (e.g., [8]), landslides (e.g., [9]), etc. However, Vietnam is outside the Copernicus data policy of the European Space Agency (ESA), which is dedicated to support studies in the European zone. Only very few ESA data, e.g., C-band ENVISAT ASAR, exist that are suitable for such applications, which typically require a stack of multitemporal SAR data [10]. This is the reason why many works [4], [11], [12] are based on L-band (~23.6 cm) data of the Japan Aerospace Exploration Agency ALOS PalSAR satellite. Recently, C-band Sentinel-1 systematically acquires data at the global scale in the terrain observation with progressive scan (TOPS) mode [13]. However, TOPS Sentinel-1A/1B phase is very sensitive to geometric errors [14]. In this context, a new X-band SAR mission (JV-LOTUSat) has been scheduled for launch by Vietnam for the 2019–2020 time frame. The new JV-LOTUSat satellite is a collaborative effort between the governments of Vietnam and Japan [15].

X-band SAR data are sensitive to small variations (e.g., [16]–[18]). High-quality data are gathered by the German Space Agency TerraSAR-X (TSX) satellite constellation and by the Italian constellation of small satellites for the Mediterranean basin Observation Cosmos SkyMed (CSK). Thanks to the high resolution (less than 3 m with TSX and CSK) and the short wavelength (~3.1 cm) of the X-band, it is possible to study the signal of single objects such as buildings and bridges [19].

Manuscript received April 25, 2018; revised November 14, 2018 and July 23, 2019; accepted August 12, 2019. This work was supported in part by the Vietnam National Science Project DTDL.2012-T/28; in part by the European Space Agency (ESA), the Centre National d'Etudes Spatiales/Terre, Ocean, Surfaces Continentales, Atmosphere (CNES/TOSCA); and in part by UMR TETIS, Institut national de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture (IRSTEA). (Corresponding author: Dinh Ho Tong Minh.)

D. Ho Tong Minh and I. El-Moussawi are with UMR TETIS, Institut national de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture (IRSTEA), University of Montpellier, 34000 Montpellier, France (e-mail: dinh.ho-tong-minh@irstea.fr; ibrahim.el-moussawi@irstea.fr).

Q. C. Tran and D. A. Nguyen are with the Institute of Geological Sciences, Vietnam Academy of Science and Technology, 100000 Ha Noi, Vietnam (e-mail: tqcuong@igsvn.vast.vn; nguyenducanh237@gmail.com).

Q. N. Pham is with the Hanoi University of Natural Resources & Environment, 100000 Ha Noi, Vietnam (e-mail: pqnhan@hunre.edu.vn).

T. T. Dang is with the National Center for Water Resources Planning and Investigation, 100000 Ha Noi, Vietnam (e-mail: dtrung@gmail.com).

T. Le Toan is with the Centre d'Etudes Spatiales de la Biosphere (CESBIO), 31400 Toulouse, France (e-mail: Thuy.LeToan@cesbio.cnes.fr).

Color versions of one or more of the figures in this article are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2019.2937398
However, the short wavelength of the X-band means that it is impacted by atmospheric phase screens (APSs) more than the longer wavelength bands [20]. For example, if at the L-band, an APS value is 0.2 rad, the APS value will become 1.5 rad at the X-band. Unfortunately, Vietnam is located in a tropical area with cloudy conditions on most days. Up to now, no precise information is available concerning the impact of APSs on the performance of ground subsidence estimates by the X-band SAR in Vietnam.

In Ha Noi area, the InSAR approach has reported its potential ability in mapping the ground deformation using the L-band [11], [12], C-band [21], and X-band [22], [23]. Studies so far have estimated the ground subsidence using the classical InSAR methods (e.g., [11], [12], [21], and [23]) and limited multitemporal SAR data (e.g., [11], [21], and [23]). In [22], which was presented at the International Geoscience and Remote Sensing Symposium 2015, this represents our first attempt using the X-band (Cosmos SkyMed and TerraSAR-X) to provide preliminary results on the subsidence in Ha Noi. In [23], this reports the subsidence of the Historical Centre of Ha Noi from April 2012 to November 2013 using the TerraSAR-X. However, these works have limitation on the small study area (e.g., [23]) and on understanding the capacity of the X-band under the impact of APSs (e.g., [22] and [23]). In this article, we aim to provide a better understanding of the capabilities of the X-band for estimating the ground subsidence under severe atmospheric conditions typical of tropical regions. Specifically, we investigate the ability of the X-band SAR to determine the ground subsidence phenomena in Ha Noi City based on both TerraSAR-X and Cosmos SkyMed data from 2011 to 2014. To estimate the ground subsidence, we apply a maximum likelihood estimator-based approach [4], [24], which exploits not only stable point-like scatterers (so-called permanent scatterers—PS) but also distributed scatterers (DS). The increased number of identified PS/DS measurement points on the ground resulting from this approach provides increased confidence in measurements of the ground's motion, compared to the previous algorithms that were based solely on the PS [10], [25].

This article is structured as follows. In the next section, the study site and dataset are introduced. The multitemporal InSAR methodology is shown in Section III. We then present our results on the ground subsidence and their validation in Section IV. In Section V, we provide our interpretation on the ground subsidence and also the role of the groundwater in the region's Pleistocene aquifer (qp). Finally, conclusions are drawn in Section VI.

## II. STUDY SITE AND DATA

### A. Study Site

Ha Noi city is the capital of Vietnam, located in the Red River delta. It is a megacity with more than 6.9 million people (in 2013) and great potential for developing industry, exports, tourism, and services. Ha Noi is a good example of a quickly urbanized city in Vietnam, where water for domestic and industrial use comes from wells located within and around the city. In this

city, the rapid increase of the ground water use started in the late 2010s (the groundwater abstraction was approximately 25 000 m³/day in 1954, 145 000 m³/day in 1960, 175 000 m³/day in 1970, 300 000 m³/day in 1980, 350 000 m³/day in 1990, and 630 000 m³/day in 2012, whereas the safe level of abstraction is less than 300 000 m³/day [27]). Heavy pumping of the groundwater has produced a serious subsidence problem, which in turn has affected the surface structures in Ha Noi. In fact, the ground subsidence induced by water overexploitation of underground reservoirs can be found in other cities of Vietnam such as Ho Chi Minh City [4] and Mekong Delta [28]. In this article, our study area is covered by the urban region and a part of the suburban region of Ha Noi City. The whole area is approximately 10 km × 20 km (see Fig. 1).

<figure><img src="image_2.png" /><figcaption>Fig. 1. Study area at Ha Noi city covering about 10 km × 20 km. The background image is the SRTM 30 m [26].</figcaption></figure>

### B. Data

1) *Ground Data:* The ground leveling measurements are from the Ha Noi subsidence monitoring project [27], conducted by the Institute of Geological Sciences-VAST in 2012–2015 for monitoring the subsidence of the city and to calibrate/validate the InSAR products. The ground data were collected as shown in Fig. 10 and Table I. Leveling data were surveyed in different periods. Each leveling measurement was carried out by using an optical micrometer to determine the difference in level
between points, allowing the elevation of given points to be computed. The velocity (mm/year) of change in leveling measurements was calculated in 18 stations. The vertical velocity of the ground motion varied from -2 to -17 mm/year. These results are used for comparison with the multitemporal InSAR approach.

2) SAR Data: The SAR stacks are comprised of X-band data from Cosmos SkyMed (3.0 m in azimuth and 3.0 m in the ground range) and TerraSAR-X (2.0 m in azimuth and 2.1 m in the ground range) for the period 2011–2014. Both stacks are imaged in the stripmap mode. It is worth noting that from 2012 onwards, two X-band SAR stacks were available that provided enough data to carry out this study even with the exclusion of rainy days.

The TerraSAR-X dataset is comprised of 19 ascending acquisitions from 10 April, 2012 to 23 November, 2014. Twenty-seven ascending acquisitions observed between 27 May, 2011 and 04 December, 2014 were selected to constitute the Cosmos SkyMed dataset. In each stack, each single look complex (SLC) image was resampled on a common grid (so-called master). Table II reports the detailed information.

### III. PS/DS PROCESSING

The rationale of the multitemporal InSAR is to take advantage of multiple flight tracks to minimize the issues relative to atmospheric, spatial, and temporal decorrelations to estimate the ground deformations robustly. Let $\varphi^n$ represents an interferometric phase of the nth image, which is composed of the phase components related to deformation, residual topography, atmosphere, and noise [25], [30]:

$$ \varphi^n = \varphi_{\text{defo}}^n + \varphi_{\text{topo}}^n + \varphi_{\text{aps}}^n + \varphi_{\text{noise}}^n + 2k\pi \quad (1) $$

where $\varphi_{\text{defo}}^n$ is the deformation phase; $\varphi_{\text{topo}}^n$ is the residual topographic phase; $\varphi_{\text{aps}}^n$ is the APS, representing the delay of the signal due to weather conditions; $\varphi_{\text{noise}}^n$ is the phase noise due to temporal decorrelation, miscoregistration, uncompensated spectral shift decorrelation, orbital errors, and thermal noise; and k is an integer ambiguity number.

The residual topographic phase is given as follows:

$$ \varphi_{\text{topo}}^{n} = \frac{4\pi b_{n}}{\lambda \sin\theta R_{n}} \Delta h \quad (2) $$

where $\lambda$ is the carrier wavelength, $R_n$ is the distance between the target and the nth orbit acquisition, $\Delta h$ is the residual topography, $b_n$ is the normal baseline relative to the nth image with respect to a reference image, and $\theta$ is the local incidence angle.

The goal is to estimate the deformation phase, which can be written as follows (assuming a constant velocity model):

$$ \varphi_{\text{defo}}^{n} = \frac{4\pi t_{n}}{\lambda} v \quad (3) $$

where $v$ is the mean deformation light of the sight velocity of the target, and $t_n$ is the temporal baseline.

Recent advances in the multitemporal InSAR, in [24], [31], and [32], the process of estimating the residual topographic,

<figure><img src="image_3.png" /><figcaption>Fig. 2. HCRB for the standard deviation of the estimate of the subsidence velocity.</figcaption></figure>

APS, and deformation phases not only from PS (Permanent/Persistent Scatterers) but also DS (Distributed Scatterers) targets, is split into two steps. In the first step, the maximum likelihood estimation is used that jointly exploits all the $N(N-1)/2$ interferograms available from N images, in order to squeeze the best estimates of the $N-1$ interferometric phases, so-called linked phases. This step is termed phase linking or phase triangulation [24], in which the DS-linked phases can be modeled in the same way as a PS target. Once the first estimation step has yielded the estimates of the $N-1$ linked phases, the second step is required to separate the contributions of the decorrelation noises from the parameters of interest, as in the PS processing.

In this article, the PS/DS processing chain in [4] is adapted to each SAR stack. The results were processed by the TomoSAR platform, which offers SAR, InSAR PS/DS, and tomography processing [33].

### A. Accuracy Expectation

Hybrid Cramér-Rao bound (HCRB) can be exploited in the case where some of the unknowns are deterministic (e.g., PS) and others are random (e.g., DS) [34], [35]. The HCRB is the lower bound of the accuracy of parameter estimation. In [34], the HCRB of the velocity standard deviation $\sigma_v$ from N SAR images can be expressed by

$$ \sigma_v^2 = \left(\frac{\lambda}{4\pi\delta t}\right)^2 \left(2\sigma_\alpha^2 + \frac{1 - \gamma_t^2}{2\gamma_t^2 L}\right) \frac{6}{N^3 - N} \quad (4) $$

where $\delta t$ is the time interval between two nearby acquisitions, L is the number of independent looks, $\gamma_t$ is the temporal decorrelation, and $\sigma_\alpha$ is the APSs. The dominant contribution is given by APSs. The APS delay is frequency independent but the APS phase will be increasingly affected as wavelength decreases. Fig. 2 shows an expectation of the HCRB from the SAR data used in this article with the assumption that $\gamma_t = 0.5$. The parameters of TerraSAR-X are: $\lambda = 3.1$ cm, $\gamma_t = 0.5$, $t = 11$ days,
<figure><img src="image_4.png" /><figcaption>Fig. 3. Vertical displacement history (cm) in Ha Noi city. The background is the average backscatter TerraSAR-X data. (a) Displacement Cosmos SkyMed. (b) Displacement TerraSAR-X.</figcaption></figure>

$L = 50$, and $N = 19$. The parameters of Cosmos SkyMed are: $\lambda = 3.1$ cm, $\gamma_t = 0.5$, $t = 11$ days, $L = 50$, and $N = 27$.

## IV. RESULTS

The period 2011–2014 using TerraSAR-X and Cosmos SkyMed data was treated by using PS/DS processing. More than 810 000 PS/DS measuring points in TerraSAR-X (LOS angle 34.4°) and 420 000 PS/DS measuring points in Cosmos SkyMed (LOS angle 25.2°) datasets were identified within an area of about 200 km².

First, assuming that most of the measured deformation corresponds to the vertical displacement of the surface due to subsidence, we then can obtain vertical displacement through straightforward geometrical arguments. The dates of 10 April, 2012 and 27 May, 2011 were specified as the starting temporal references in the TerraSAR-X and Cosmos SkyMed stacks, respectively. The reference point was chosen for the interferometric analysis and located at the geological sandstone area in Ba Dinh District, which is one of the most stable units of the study area [27]. Results are reported in Fig. 3.

We assume that there is no obvious seasonal variability so that the subsidence history can be approximated by a linear function. The maps in Fig. 4 show the average vertical velocity (mm/year). Green colors represent stable velocities and red colors represent negative velocities (subsidence). In addition, to test the linear model, we calculated the standard deviation of the velocity (see Fig. 5).

To compare the velocity values obtained by the reference leveling and the estimated PS/DS result, a buffer of a certain diameter centered on each leveling station (see Fig. 10) was

associated with a cluster of PS/DS. All the PS/DS measuring points inside this cluster were used to calculate the average velocity of the cluster. Fig. 6 shows the correlation between the reference leveling and the estimated PS/DS result as a function of buffer diameter. There is a sharp drop from the leveling station in the correlation coefficient with distance (e.g., from 50 to 150 m) because the ground deformation was highly local at these areas. For two stacks, at 50 m, the correlation coefficient $R^2$ was 0.69 (TerraSAR-X) and 0.67 (Cosmos SkyMed). Results of performance with leveling data, reported in Fig. 7, showed a root mean square error (RMSE) of 3.4 mm/year (TerraSAR-X) and 2.9 mm/year (Cosmos SkyMed). The comparison between TerraSAR-X and Cosmos SkyMed datasets is shown in Fig. 8.

Finally, to correlate the subsidence with the groundwater level, all well fields in Ha Noi are used to compute the Pleistocene aquifer (qp) water level through a groundwater flow model (see Appendix A). Fig. 9 illustrates the correlation between the ground subsidence value as estimated by the InSAR and groundwater fluctuation in the Pleistocene aquifer (qp). High correlation coefficients of greater than 0.6 were found in the following four areas: Ha Dong; Phap Van; Linh Dam; and Hoang Mai. All of them are in the southern part of Ha Noi.

## V. DISCUSSION

In this article, we showed that the advanced multitemporal InSAR technique, which jointly manages the estimation of both PS and DS targets, can be used to monitor the ground subsidence phenomena in Ha Noi City. The validation indicates good agreement with leveling data.
<figure><img src="image_5.png" /><figcaption>Fig. 4. Ground subsidence velocity (mm/year) in Ha Noi city. (a) Velocity Cosmos SkyMed. (b) Velocity TerraSAR-X.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5. Standard deviation velocity corresponding with Fig. 4.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6. Correlation between the reference leveling and the estimated PS/DS result as a function of buffer diameter.</figcaption></figure>

Together, these results confirm the suitability of the X-band SAR for urban risk applications in Vietnam and in other tropical countries such as Cambodia, Singapore, etc. A brief discussion of the ground subsidence problem in Ha Noi city is required in order to remind the reader of the expected results of PS/DS processing. The main causes of the ground subsidence are of two main kinds, natural and artificial. Natural causes include

modern faults, sediment compaction, seasonal fluctuation of groundwater, and soft soil layers. Artificial causes include exploitation of the groundwater, increased loading by building, dynamic loading by transportation or construction activities, and suffusion, quicksand negative skin friction [37]. Among artificial
<figure><img src="image_8.png" /><figcaption>Fig. 7. Comparison of the velocity between the reference leveling and the estimated PS/DS result. The locations of the leveling measurement are shown in Fig. 10 and in Table I.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 8. Consistency between Cosmos Skymed and TerraSAR-X in the period 2011–2014.</figcaption></figure>

causes, groundwater exploitation by pumping wells is one of the most important [38].

The results on the ground subsidence from PS/DS processing for the period 2011–2014 in Ha Noi describe consistently the subsidence area based on ground measurements. All results from TerraSAR-X and Cosmos SkyMed identify hotpot subsidence areas (e.g., around pumping wells). We found that the number of PS/DS measurement points of TerraSAR-X was almost double that for Cosmos Skymed. This is due to the constellation of Cosmos Skymed, which is characterized by a large baseline, and hence, high geometric decorrelation. However, the displacement histories of TerraSAR-X and Cosmos Skymed are very similar. Comparing two periods, the displacement from Cosmos Skymed and TerraSAR-X shares the same linear trend and the same

<figure><img src="image_10.png" /><figcaption>Fig. 9. Correlation between the ground subsidence value as estimated by InSAR and groundwater fluctuation in the Pleistocene aquifer (qp).</figcaption></figure>

spatial distribution. As expected, the subsidence can be detected in the southern part of the city, where the ground surface has sunk up to 6 cm if we sum over the whole period 2011–2014.

In Fig. 4, the estimation of the average displacement is –3.0 mm/year with the maximum value up to –25 mm/year. Ground subsidence phenomena were found mostly in particular soil types and around the groundwater pumping stations. Furthermore, if a PS/DS point exhibits a strong nonlinear motion, e.g., a seasonal movement, this would result in a large residual with respect to the linear model, and thus, in a high standard deviation value. In Fig. 5, the values for the standard deviation of the velocity are mostly less than 1 mm/year. Hence, it can be inferred that an almost linear subsidence took place in Ha Noi over the period of 2011–2014.

To minimize the physical difference between the reference leveling points and the estimated PS/DS measurement points, we had to exploit a buffer centered on each leveling point that was associated with a cluster of PS/DS. The diameter of the buffer must be selected in such a way that not only reduces the effects of noise from PS/DS measurements, but also does not affect the estimation of the subsidence pattern. From Fig. 6, we found 50 m to be the most suitable figure, in which the average number of PS/DS points was 40 (TerraSAR-X) and 20 (Cosmos SkyMed). We found that the performance of all datasets was quite close to that of the HCRB (see Fig. 2) and consistent with the leveling data. Therefore, the PS/DS processing is effective at detecting and estimating the subsidence phenomena even with the X-band under the impact of conditions leading to strong APSs (see Fig. 11). Finally, the displacement results from TerraSAR-X
and Cosmos SkyMed datasets are consistent, with a correlation coefficient ($R^2$) of 0.91 for the period in which both measures overlapped (see Fig. 8). This can be considered as a successful self-validation.

Many studies consider the influence of groundwater exploitation on the ground subsidence in Ha Noi [38]–[40]. Nhan [40] investigated and predicted the ground subsidence in the Thanh Cong and Phap Van areas by modeling. These studies concluded that the lack of ground subsidence monitoring stations makes it impossible to map isocontour subsidence for the whole of Ha Noi City. During 2008–2009, Tu [41] studied the ground subsidence by natural factors and human activities. That study concluded that groundwater exploitation at the Ha Dinh well field is the main cause factor. In our results, most subsidences can be found around pumping wells where the average displacement rate is up to –10 mm/year (see Fig. 12). This phenomenon was also confirmed by the previous works on the gradient groundwater level due to the influence of groundwater pumping wells in Ha Noi [38], [39].

The correlation between the ground subsidence as measured by InSAR and ground water level in the Pleistocene aquifer (qp) (see Fig. 9) shows that the areas with high correlation coefficients located in southern Ha Noi and high subsidence values occur in the cone of depression (area influenced by exploited groundwater). In contrast, in Hoan Kiem district in central Ha Noi, including sites such as the Ngo Si Lien and Yen Phu well fields, strong subsidence is not concentrated on the cone of depression and the correlation coefficient is quite low. This again indicates that the cone of depression from the groundwater exploitation is not the only significant cause of ground subsidence in central Ha Noi. In these cases, subsoil characteristics can be another factor. We observed that the risk of ground subsidence in Ha Noi occurs in the south of city areas where thick soft soil layers are present. This is quite similar phenomenon as in Ho Chi Minh city area, Vietnam [4]. Future works should be dedicated to take the subsoil characteristics into account.

## VI. CONCLUSION

In this article, we demonstrated the feasibility of ground subsidence estimates by X-band SAR even in conditions impacted by strong atmosphere in Vietnam. Consequently, our results reinforce the scientific potential for the X-band SAR space-borne mission in Vietnam and other tropical countries. The PS/DS technique applied to InSAR appears to be a promising approach for using X-band SAR data to monitor ground surface risks. By using this technique, we not only obtained subsidence history information but also measured the average velocity of the ground subsidence in Ha Noi. Combining the InSAR technique and *in situ* measurement is a useful way to monitor and predict the ground subsidence in Ha Noi.

The results on ground subsidence 2011–2014 (by X-band Cosmos SkyMed and TerraSAR-X), in Ha Noi described consistently the subsidence area based on ground measurements. The correlation coefficient between the ground subsidence value and

<figure><img src="image_11.png" /><figcaption>Fig. 10. Location of leveling measurements.</figcaption></figure>

groundwater level in the Pleistocene aquifer (qp) illustrated that groundwater overexploitation is one of the main causes of ground subsidence. Future work should be carried out by applying the PS/DS technique to new incoming SAR data, e.g., TOPS Sentinel-1 and ALOS PALSAR 2, to continuously monitor the ground subsidence phenomena.

## APPENDIX

The location of leveling measurements is provided in Fig. 10 and Table I.

The detail information of SAR data is reported in Table II.

The standard deviation of atmospheric phase screens corresponding with Cosmos Skymed and TerraSAR-X data is reported in Fig. 11.

An example of subsidence around groundwater pumping wells is shown in Fig. 12.

### A. Groundwater Modeling

To assess the behavior of groundwater in this study, a groundwater flow model was built in MODFLOW code for the right bank of the Red River. The model grid consists of 160 columns and 160 rows in MODFLOW with grid spacing of 480 and 350 m for row and column, respectively. A schematic view of the model is shown in Fig. 13. The period simulated is from December 1995 to December 2014. The conceptual model is divided into five layers as follows: Layer 1—Holocene aquiclude (LCN1); Layer 2—Holocene aquifer (qh); Layer 3—Holocene and Pleistocene aquiclude (LCN2); Layer 4—Pleistocene aquifer (qp);
<table><caption>TABLE I
DETAILED COORDINATION LOCATIONS OF LEVELING MEASUREMENT SITES</caption><thead><tr><th>Name</th><th>Description</th><th>Latitude (dd-mm-ss)</th><th>Longitude (dd-mm-ss)</th><th>Period</th><th>Velocity (mm/year)</th></tr></thead><tbody><tr><td>Ha Dinh</td><td>Groundwater station</td><td>20-59-17</td><td>105-48-28</td><td>2008-2012</td><td>-7.8</td></tr><tr><td>Tuong Mai</td><td>Groundwater station</td><td>20-59-14</td><td>105-51-13</td><td>2008-2012</td><td>-5.8</td></tr><tr><td>Mai Dich</td><td>Groundwater station</td><td>21-01-57</td><td>105-46-56</td><td>2008-2012</td><td>-1.1</td></tr><tr><td>Phap Van</td><td>Groundwater station</td><td>20-57-52</td><td>105-50-46</td><td>2008-2012</td><td>-16.7</td></tr><tr><td>Ngo Sy Lien</td><td>Groundwater station</td><td>21-01-29</td><td>105-50-16</td><td>2008-2012</td><td>-13.6</td></tr><tr><td>Ngoc Ha</td><td>Groundwater station</td><td>21-02-18</td><td>105-48-34</td><td>2008-2012</td><td>-2.1</td></tr><tr><td>Cao Dinh</td><td>Groundwater station</td><td>20-04-22</td><td>105-47-29</td><td>2010-2012</td><td>-1.3</td></tr><tr><td>NhaNo22</td><td>Building station</td><td>20-57-16</td><td>105-51-03</td><td>2006-2008</td><td>-11.5</td></tr><tr><td>NhaNo20</td><td>Building station</td><td>20-57-20</td><td>105-51-02</td><td>2006-2008</td><td>-9.4</td></tr><tr><td>NhaNo18</td><td>Building station</td><td>20-57-22</td><td>105-50-57</td><td>2006-2008</td><td>-9.9</td></tr><tr><td>CT1 Linh Dam</td><td>Building station</td><td>20-58-16</td><td>105-49-34</td><td>2009-2010</td><td>-4.6</td></tr><tr><td>CC2 Van Quan</td><td>Building station</td><td>20-58-36</td><td>105-47-22</td><td>2013-2014</td><td>-4.5</td></tr><tr><td>CQ01 Van Quan</td><td>Building station</td><td>20-58-29</td><td>105-47-16</td><td>2011-2013</td><td>-9.8</td></tr><tr><td>CT3A Van Quan</td><td>Building station</td><td>20-58-20</td><td>105-47-23</td><td>2006-2008</td><td>-5.1</td></tr><tr><td>CT3B Van Quan</td><td>Building station</td><td>20-58-22</td><td>105-47-21</td><td>2006-2008</td><td>-4.3</td></tr><tr><td>CC7 Linh Dam</td><td>Building station</td><td>20-58-15</td><td>105-51-24</td><td>2010-2011</td><td>-9.7</td></tr><tr><td>A2 Ha Dinh</td><td>Building station</td><td>20-59-28</td><td>105-48-38</td><td>2011-2012</td><td>-2.8</td></tr><tr><td>Lang - DC</td><td>Ground station</td><td>21-01-30</td><td>105-48-18</td><td>2003-2014</td><td>-14.0</td></tr></tbody></table>

<table><caption>TABLE II
SAR DATA INFORMATION</caption><thead><tr><th colspan="3">TerraSAR-X</th><th colspan="3">Cosmos SkyMed</th></tr><tr><th>ID</th><th>Time (dd-mm-yy)</th><th>Bn (m)</th><th>ID</th><th>Time (dd-mm-yy)</th><th>Bn (m)</th></tr></thead><tbody><tr><td>1</td><td>10-04-12</td><td>-42</td><td>1</td><td>27-05-11</td><td>-601</td></tr><tr><td>2</td><td>21-04-12</td><td>-24</td><td>2</td><td>05-07-11</td><td>-213</td></tr><tr><td>3</td><td>26-06-12</td><td>-133</td><td>3</td><td>15-08-11</td><td>644</td></tr><tr><td>4</td><td>11-09-12</td><td>-384</td><td>4</td><td>22-08-11</td><td>1036</td></tr><tr><td>5</td><td>30-04-12</td><td>5</td><td>5</td><td>23-09-11</td><td>1083</td></tr><tr><td>6</td><td>05-07-13</td><td>-138</td><td>6</td><td>10-11-11</td><td>1283</td></tr><tr><td>7</td><td>20-09-13</td><td>60</td><td>7</td><td>21-12-11</td><td>-295</td></tr><tr><td><b>8</b></td><td><b>12-10-13</b></td><td><b>0</b></td><td>8</td><td>22-01-12</td><td>-95</td></tr><tr><td>9</td><td>23-10-13</td><td>-120</td><td>9</td><td>10-03-12</td><td>-19</td></tr><tr><td>10</td><td>25-11-13</td><td>-63</td><td>10</td><td>05-06-12</td><td>-66</td></tr><tr><td>11</td><td>11-06-14</td><td>137</td><td>11</td><td>16-07-12</td><td>810</td></tr><tr><td>12</td><td>22-06-14</td><td>-89</td><td>12</td><td>24-08-12</td><td>294</td></tr><tr><td>13</td><td>25-07-14</td><td>-179</td><td>13</td><td>21-11-12</td><td>758</td></tr><tr><td>14</td><td>07-09-14</td><td>215</td><td>14</td><td>30-12-12</td><td>-247</td></tr><tr><td>15</td><td>29-09-14</td><td>45</td><td>15</td><td>20-03-13</td><td>941</td></tr><tr><td>16</td><td>10-10-14</td><td>90</td><td><b>16</b></td><td><b>08-06-13</b></td><td><b>0</b></td></tr><tr><td>17</td><td>21-10-14</td><td>10</td><td>17</td><td>27-08-13</td><td>108</td></tr><tr><td>18</td><td>01-11-14</td><td>-91</td><td>18</td><td>14-10-13</td><td>-298</td></tr><tr><td>19</td><td>23-11-14</td><td>-54</td><td>19</td><td>15-11-13</td><td>1118</td></tr><tr><td></td><td></td><td></td><td>20</td><td>17-12-13</td><td>767</td></tr><tr><td></td><td></td><td></td><td>21</td><td>19-02-14</td><td>149</td></tr><tr><td></td><td></td><td></td><td>22</td><td>27-06-14</td><td>-301</td></tr><tr><td></td><td></td><td></td><td>23</td><td>29-07-14</td><td>-180</td></tr><tr><td></td><td></td><td></td><td>24</td><td>30-08-14</td><td>846</td></tr><tr><td></td><td></td><td></td><td>25</td><td>01-10-14</td><td>351</td></tr><tr><td></td><td></td><td></td><td>26</td><td>02-11-14</td><td>655</td></tr><tr><td></td><td></td><td></td><td>27</td><td>04-12-14</td><td>242</td></tr></tbody></table>

The master date is presented in boldface type.

and Layer 5—Neogene aquifer (n). The boundaries of the model include several surface water bodies such as the Red River, the Nhue River, and West Lake, and these water bodies have hydraulic relationships with the groundwater. The bedrock is exposed in the eastern part of the model area. The bedrock area and the opposite bank of the Red River (outside the study area) are considered as no-flow boundary, which is assigned as an inactive cell in the model. The recharge by rainfall was assigned as 7.5% to 8.2% of total monthly rainfall (based on the results of isotope techniques [36]), monitored in the Lang meteorological

station. For the groundwater extraction, in the right bank of the Red River area, there are 12 water treatment plants and 12 well fields. The total number of production wells is about 200. Total groundwater extraction is about 580 000 m³/day.

Model calibration was done by steady-state and transient models. The steady-state model was set at the start of the simulated period (December 1995) and the transient model was set from December 1995 to December 2014. The computed Pleistocene aquifer (qp) water levels are compared with the observed levels. The hydrogeological parameters and boundary conditions are then adjusted until the computed and observed groundwater levels match. The model was well calibrated with a root mean square error of 1 m.

<figure><img src="image_12.png" /><figcaption>Fig. 11. Standard deviation of atmospheric phase screens. The average value corresponds to 1.0 and 1.2 rad with Cosmos Skymed and TerraSAR-X data, respectively.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Fig. 12. Example of subsidence around groundwater pumping wells is shown at the center of Hoang Mai district.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 13. Schematic view of the groundwater system simulation model (3-D MODFLOW).</figcaption></figure>

## ACKNOWLEDGMENT

The authors would like to thank ASI/TELESPAZIO for the discount in Cosmos SkyMed data of research project DTDL.2012-T/28 and also the project COSMO-SkyMed-Open ID226 for free access data, and Airbus DS Geo Pte Ltd. for the cooperation for supplying TerraSAR data. The authors would also like to thank D. McKey (CEFE, CNRS Montpellier) for English corrections.

## REFERENCES

[1] A. D. Ziegler, L. H. She, C. Tantasarin, N. R. Jachowski, and R. Wasson, "Floods, false hope, and the future," *Hydrol. Process.*, vol. 26, pp. 1748–1750, 2012.

[2] T. H. Dixon et al., "Subsidence and flooding in New Orleans," *Nature*, vol. 441, pp. 587–588, 2006.

[3] B. Osmanoglu, T. H. Dixon, S. Wdowinski, E. Cabral-Cano, and Y. Jiang, "Mexico City subsidence observed with persistent scatterer InSAR," *Int. J. Appl. Earth Observ. Geoinf.*, vol. 13, pp. 1–12, 2011.

[4] D. Ho Tong Minh, V. T. Le, and T. Le Toan, "Mapping ground subsidence phenomena in Ho Chi Minh City through the radar interferometry technique using ALOS PALSAR data," *Remote Sens.*, vol. 7, pp. 8543–8562, 2015.

[5] E. Chaussard, F. Amelung, H. Abidin, and S. H. Hong, "Sinking cities in Indonesia: ALOS PALSAR detects rapid subsidence due to groundwater and gas extraction," *Remote Sens. Environ.*, vol. 128, pp. 150–161, 2013.

[6] S. Stramondo et al., "Subsidence induced by urbanisation in the city of Rome detected by advanced InSAR technique and geotechnical investigations," *Remote Sens. Environ.*, vol. 112, pp. 3160–3172, 2008.

[7] R. Tomas, G. Herrera, J. Delgado, J. M. Lopez-Sanchez, J. J. Mallorqui, and J. Mulas, "A ground subsidence study based on DInSAR data: Calibration of soil parameters and subsidence prediction in Murcia city (Spain)," *Eng. Geol.*, vol. 112, pp. 19–30, 2010.

[8] Q. Luo, G. Zhou, and D. Perissin, "Monitoring of subsidence along Jingjin inter-city railway with high-resolution TerraSAR-X MT-InSAR analysis," *Remote Sens.*, vol. 9, no. 7, 2017, Art. no. 717.

[9] G. Bru et al., "A DInSAR monitoring of landslide and subsidence activity: A case of urban damage in Arcos de la Frontera, Spain," *Remote Sens.*, vol. 9, no. 8, 2017, Art. no. 787.

[10] A. Ferretti, C. Prati, and F. Rocca, "Permanent scatterers in SAR interferometry," *IEEE Trans. Geosci. Remote Sens.*, vol. 39, no. 1, pp. 8–20, Jan. 2001.

[11] V. A. Tran, S. Masumoto, V. Raghavan, and K. Shiono, "DEM derivation and subsidence detection on Hanoi from ERS SAR interferometry," *Geoinform.*, vol. 1, pp. 3–13, 2007.

[12] V. K. Dang, C. Doubre, C. Weber, F. Masson, and N. Gourmelen, "Recent land subsidence caused by the rapid urban development in the Hanoi urban region (Vietnam) using ALOS InSAR data," *Nat. Hazards Earth Syst. Sci. Discuss.*, vol. 1, pp. 6155–6197, 2013.

[13] F. DeZan and A. M. M. Guarnieri, "TOPSAR: Terrain observation by progressive scans," *IEEE Trans. Geosci. Remote Sens.*, vol. 44, no. 9, pp. 2352–2360, Sep. 2006.

[14] P. Prats-Iraola, R. Scheiber, L. Marotti, S. Wollstadt, and A. Reigber, "TOPS interferometry with TerraSAR-X," *IEEE Trans. Geosci. Remote Sens.*, vol. 50, no. 8, pp. 3179–3188, Aug. 2012.

[15] N. Hoai, "Vietnam will launch a SAR satellite in X-band in 2019-2020," *Tien Phong*, vol. 1, 2016. [Online]. Available: https://www.tienphong.vn/cong-nghe/viet-nam-se-phong-ve-tinh-rada-vao-2019-1005652.tpo

[16] L. Tosi, P. Teatini, and T. Strozzi, "Natural versus anthropogenic subsidence of Venice," *Sci. Rep.*, vol. 3, 2013, Art. no. L2710, doi: 10.1038/srep02710.

[17] L. Solari et al., "Combined use of C- and X-band SAR data for subsidence monitoring in an urban area," *Geosciences*, vol. 7, pp. 1–21, 2017.

[18] L. Bai, L. Jiang, H. Wang, and Q. Sun, "Spatiotemporal characterization of land subsidence and uplift (2009–2010) over Wuhan in central China revealed by Terrasar-X InSAR analysis," *Remote Sens.*, vol. 8, no. 4, 2016, Art. no 350.

[19] M. Crosetto, O. Monserrat, M. Cuevas-González, N. Devanthary, and B. Crippa, "Persistent scatterer interferometry: A review," *ISPRS J. Photogramm. Remote Sens.*, vol. 115, 78–89, 2016.

[20] R. F. Hanssen, T. M. Weckwerth, H. A. Zebker, and R. Klees, "High-resolution water vapor mapping from interferometric radar measurements," *Science*, vol. 283, pp. 1295–1297, 1999.

[21] D. Raucoules and C. Carnec, "DEM derivation and subsidence detection on Hanoi from ERS SAR interferometry," in *Proc. 2nd Int. Workshop ERS SAR Interferometry*, 1999, vol. 1, pp. 1–6

[22] Q. C. Tran, D. Ho Tong Minh, V. T. Le, and T. Le Toan, "Ground subsidence monitoring in Vietnam by multi-temporal InSAR technique," in *Proc. IEEE Int. Geosci. Remote Sens. Symp.*, 2015, vol. 1, pp. 3540–3543.

[23] T. S. Le, C.-P. Chang, X. T. Nguyen, and A. Yhokha, "TerraSAR-X data for high-precision land subsidence monitoring: A case study in the historical centre of Hanoi, Vietnam," *Remote Sens.*, vol. 8, no. 4, 2016, Art. no. 338.

[24] A. Ferretti, A. Fumagalli, F. Novali, C. Prati, F. Rocca, and A. Rucci, "A new algorithm for processing interferometric data-stacks: SqueeSAR," *IEEE Trans. Geosci. Remote Sens.*, vol. 49, no. 9, pp. 3460–3470, Sep. 2011.

[25] A. Hooper, H. Zebker, P. Segall, and B. Kampes, "A new method for measuring deformation on volcanoes and other non-urban areas using InSAR persistent scatterers," *Geophys. Res. Lett.*, vol. 31, pp. 1–5, 2004, doi: 10.1029/2004GL021737.
[26] B. Rabus, M. Eineder, A. Roth, and R. Bamler, “The shuttle radar tomography mission—A new class of digital elevation models acquired by spaceborne radar,” *ISPRS J. Photogramm. Remote Sens.*, vol. 57, 241–261, 2003.

[27] T. Q. Cuong, “Subsidence monitoring in Ha Noi by differential InSAR,” Inst. Geological Sci.—VAST, Hanoi, Vietnam, Vietnam National Science Project Code DTDL.2012-T/28, vol. 1, 2016.

[28] L. Erban, S. Gorelick, and H. Zebker, “Groundwater extraction, land subsidence, and sea-level rise in the Mekong Delta, Vietnam,” *Environ. Res. Lett.*, vol. 9, pp. 1–7, 2014, doi: 10.1088/1748-9326/9/8/084010.

[29] N. H. Phuong, “Collecting, verifying, studying and supplementing the soft land map of Hanoi for distribution sustainable development,” Univ. Mining Geol., Hanoi Science Project Rep. TC-DT/06-02-3, 2004, vol. 1.

[30] R. Bamler and P. Hartl, “Synthetic aperture radar interferometry,” *Inverse Problems*, vol. 14, pp. R1–R54, 1998.

[31] F. Rocca, “Modeling interferogram stacks,” *IEEE Trans. Geosci. Remote Sens.*, vol. 45, no. 10, pp. 3289–3299, Oct. 2007.

[32] F. Lombardini, “Optimum absolute phase retrieval in three-element SAR interferometer,” *Electron. Lett.*, vol. 34, pp. 1522–1524, Jul. 1998.

[33] D. H. T. Minh, N. Yen-Nhi, N. Baghdadi, and P. Maurel, “TomoSAR platform: A new IRSTEA service as demand for SAR, interferometry, polarimetry and tomography,” in *Proc. ESA Living Planet Symp.*, 2016, pp. 1–8.

[34] S. Tebaldini and A. M. Guarnieri, “Hybrid Cramér Rao bounds for crustal displacement field estimators in SAR interferometry,” *IEEE Signal Process. Lett.*, vol. 14, no. 12, pp. 1012–1015, Dec. 2007.

[35] M. Pardini, F. Lombardini, and F. Gini, “The hybrid Cramér Rao bounds on broadside DOA estimation of extended sources in presence of array errors,” *IEEE Trans. Signal Process.*, vol. 56, no. 4, pp. 1726–1730, Apr. 2008.

[36] T. T. Le, “Determining the recharge rates from percolation and the relationship between the aquifer in the quaternary sediments Thach—Dan Phuong, Hanoi by the nuclear isotope techniques,” Master’s thesis, Dept. hydrogeology, Hanoi Univ. Mining Geol., Hanoi, Vietnam, 2012.

[37] T. M. Lieu, “Hazard in engineering geology, urban environment of Ha Noi and fundamental for mitigation solutions,” in *Proc. Int. Conf. Anniversary 1000 Years Thang Long—Ha Noi, Sustain. Develop. Hanoi—City Culture, HeroesPeace*, 2010, pp. 1049–1062.

[38] S. Jüsseret, C. Baeteman, and A. Dassargues, “The stratigraphical architecture of the quaternary deposits as support for hydrogeological modelling of the central zone of Hanoi,” *Geologica Belgica*, vol. 13, pp. 77–90, 2010.

[39] T. Thu and D. Fredlund, “Modelling subsidence in the Hanoi city area,” *Can. Geotech. J.*, vol. 37, pp. 621–636, 2000.

[40] P. Q. Nhan, “Research potential groundwater of Hanoi area, retrograde reserves and quality groundwater, built-up oriental strategy exploitation, protection environment for sustainable development capital,” Hanoi Research Project Code 010-04/09-2008-2, 2008.

[41] T. V. Tu, “Assessment ground deformation and building by natural activities and economy of Hadong city and surround—Hatay province,” Research Project, Government of Hatay Province, 2009.

<figure><img src="image_15.png" /></figure>

**Dinh Ho Tong Minh** received the Ph.D. degree in signal processing and telecommunications from Politecnico di Milano, Milan, Italy, in February 2013, and the Habilitation degree from the University of Montpellier, Montpellier, France, in May 2019, on the topic “SAR imaging: From Intensity to Interferometry and Tomography.”

He is a Permanent Scientist with the UMR TETIS, Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture, Montpellier, since April 2015. He is the inventor of a new radar tomographic technology to measure the dense tropical forest biomass from the space. He is the creator of TomoSAR platform, which offers synthetic aperture radar (SAR), interferometry, and tomography processing. His current research interests include deep learning, SAR signal processing, and estimation techniques for the joint exploitation of multiple SAR images, including permanent scatterer InSAR and polarimetric SAR tomography.

<figure><img src="image_16.png" /><figcaption>sensing technique.</figcaption></figure>

**Quoc Cuong Tran** received the Ph.D. degree from BOKU University, Vienna, Austria, in 2009.

He is a Senior Researcher with the Center for Remote Sensing and Geomatic (VTGEO), Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, Vietnam. He is a Team Leader with VTGEO for researching GIS, remote sensing (especially synthetic aperture radar) for geohazards (landsubsidence, sinkhole, and landslide), and quaternary geology. His main research interests include geohazards early warning system by remote sensing technique.

<figure><img src="image_17.png" /></figure>

**Quy Nhan Pham** received the Ph.D. degree in hydrogeology from the Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam, in 2000.

He joined HUMG as an Associate Professor in 2004. He has almost 23 years of experience at teaching on ground water with the HUMG and the Hanoi University of Sciences, Hanoi. He moved to the National Center for Water Resources Planning and Investigation, Vietnam Ministry of Natural Resources and Environment, as the Vice General Director, in 2009. Since 2013, he has been a Vice Rector with the Hanoi University of Natural Resources and Environment, Hanoi. He is an expert on ground water modeling and has contributed a lot to research projects on Arsenic mobilization, ground water salt intrusion, ground water potential, and sustainable ground water development in Vietnam.

Dr. Pham is a Co-Founder of the Asian Pacific Coastal Aquifer Management Meeting, which is a hub for scientists and policy makers sharing the experiences on ground water in coastal aquifers in the region.

<figure><img src="image_18.png" /></figure>

**Tran Trung Dang** received the M.Sc. degree in hydrogeology in 2011 from the Hanoi University of Mining and Geology, Hanoi, Vietnam, where he is currently working toward the Ph.D. degree on the topic “The influence of geological processes, geological age controlling Arsenic mobilization in ground water in Red River Delta.”

He has almost 14 years of experience working in management of water resources monitoring network and groundwater forecasting. He worked with the Northern Division for Water Resources Planning and Investigation as a Hydro-geological Engineer in 2005. From 2013 up to now, he is the Vice Director with the Centre for Water Resources Warning and Forecasting, Hanoi.

<figure><img src="image_19.png" /></figure>

**Duc Anh Nguyen** received the M.Sc. degree in geography from the Hanoi University of Science, Hanoi, Vietnam, in 2013.

He is a Researcher with the Institute of Geological Sciences—The Vietnam Academy of Science and Technology, Hanoi. He has more than six years of experience in researching and applying InSAR technique to study geological hazards in Vietnam (focus on land subsidence, and land slide). In addition, he has experience in using, and processing optical image, slope stability analysis methods, site monitoring/modeling data analysis, regional assessments, and probabilistic approach for hazard mapping.
<figure><img src="image_20.png" /></figure>

Ibrahim El-Moussawi received the master's degree in microwave engineering from Lebanese University, Beirut, Lebanon, in 2015. He is currently working toward the Ph.D. degree with Lebanese University and Montpellier University, Montpellier, France, focusing on synthetic aperture radar algorithms and applications in forests and urban areas.

<figure><img src="image_21.png" /></figure>

Thuy Le Toan received the Ph.D. degree in atomic and nuclear physics from the University of Toulouse, Toulouse, France.

She is a Senior Researcher leading a remote sensing research team with the Centre d'Etudes Spatiales de la Biosphère (CESBIO), Toulouse, and is currently a Co-Chair of the ESA BIOMASS Mission Advisory Group. Her research interests have been in the area of microwave remote sensing for land applications, including experimental and modeling studies, and SAR image analysis, with focus on forest and agriculture.

She has been involved in numerous projects for the EU, ESA, NASA, JAXA, and other national organizations. In these projects, she has gained experience on the use of EO data to characterize the land processes and to quantify their impacts on the carbon cycle. Her current research interests include the use of SAR data to monitor changes in the land surfaces under climatic and human impacts.