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Sensors and Materials, Vol. 35, No. 10 (2023) 3791–3806
MYU Tokyo
S & M 3451

Land Subsidence Monitoring and Analysis in Heifei
Based on Sentinel-1A Data
Kai Xu,1,2* Siyuan Liu,1 and Chengcheng Fan3,4**
1School of Internet, Anhui University, Heifei 230039, China

2Earth Observation Research and Development Center, 38th Institute of China Electronics Science Corporation,

Heifei 230031, China

3Shanghai Engineering Center for Microsatellites, Shanghai 201210, China
4Innovation Academy for Microsatellites of CAS, Shanghai 201210, China(Received June 1, 2023; accepted October 5, 2023)

Keywords: PSInSAR, Sentinel-1A, SBAS, urban deformation, metro protection zone

Urban land subsidence is one of the important limiting factors for urban development, andstrengthening the monitoring of urban surface subsidence contributes to long-term urban
planning and construction needs. As a subcentral city in the Yangtze River Delta region ofChina, it is particularly important for Heifei to enhance the monitoring of surface subsidence. In
this study, the persistent scatterer interferometric synthetic aperture radar (PS-InSAR) andsmall baseline subset (SBAS) techniques were used to process 139 scenes of Sentinel-1A SAR
satellite ascending orbit data covering the central urban area of Hefei from July 2015 to July
2020, obtaining subsidence information for the region. The results show that the overall urban
area of Heifei is relatively stable, but there are certain deformation trends in local areas. Four
regions with obvious deformation were selected for the in-depth analysis of their deformation
time series. The experimental study in this paper demonstrates that the PS-InSAR and SBAS
techniques can achieve the accurate monitoring of large-scale ground subsidence in urban areas,
ensuring the safety of buildings and their surrounding environments, and providing information
support for urban security maintenance monitoring.

1.

Introduction

With the rapid development of China’s social economy and the continuous acceleration ofurban modernization, land subsidence has become the biggest threat to the rapid development of
cities and the lives and properties of residents.(1,2) Currently, more than 150 countries around theworld are facing the risk of urban land subsidence, as urban infrastructures such as buildings,
highways, airports, and subways are often damaged by changes in the surface space of thesecities.(3,4) The comprehensive effect of human activities and geological factors is the main causeof land subsidence.(5) Land subsidence caused by geological factors is the result of geologicalstructures, earthquakes, or the consolidation and compression of loose surface layers under the
influence of gravity. Urban land subsidence is mainly caused by human activities, including the*Corresponding author: e-mail: kaixu@ahu.edu.cn**Corresponding author: e-mail: fancc@microsate.com

https://doi.org/10.18494/SAM4546

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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excessive exploitation of underground resources such as groundwater, minerals, and natural gas,
as well as the construction of high-rise buildings and underground engineering. To ensure thestability of the surface and prevent the occurrence of ground collapse, it has become importantand practical to conduct the large-scale, high-precision, and long-term ground detection of theurban surface.(6–8)
Traditional methods for detecting land subsidence, such as the use of the global positioning
system, precise leveling measurements, and total station surveys, are affected by various factors
such as terrain and equipment installation. Measurement stations are usually sparsely
distributed, making the long-term maintenance of observation points difficult, time-consuming,labor-intensive, and costly; thus, they are unable to meet the needs of large-scale, real-time, andhigh-precision surface deformation detection. In recent years, the Interferometric Synthetic
Aperture Radar (InSAR) has been widely applied in terrain mapping, surface deformation
monitoring, and other fields owing to its advantages of all-weather, all-time coverage, wide area
coverage, and high spatial resolution. It has also found extensive application in urban land
subsidence monitoring. InSAR-based measurements, relying on the phase information of SAR
images, as well as a series of other improved time series InSAR methods, including Differential
InSAR, Persistent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBASInSAR),(9) and Multiple Aperture Interferometry (MAI) techniques, are based on the principleof time-to-distance imaging. They utilize the complex conjugate multiplication of SAR complex
images to obtain interferograms and calculate the path difference between two radar waves, thus
monitoring one-dimensional small deformations along the line-of-sight (LOS) direction of theradar wave.(10) Theoretically, the measurement accuracy can reach the millimeter level. Applying
InSAR technology to the study of urban land subsidence detection in China is of greatsignificance for urban planning and governance and for ensuring urban economic development.
In this study, we focus on the main urban area of Heifei as the research area and employ the
PS-InSAR technique along with the SBAS processing method. We utilize a dataset consisting of
139 Sentinel-1A single-looking complex (SLC) images covering the period from July 2015 toJuly 2020 to conduct a comprehensive five-year deformation monitoring experiment in the main
urban area of Heifei. The main objective of this research is to assess and demonstrate thepotential application of InSAR technology in the field of urban land subsidence monitoring.

2.

Principle of PSInSAR Technique

The PSInSAR technique utilizes a time series of synthetic aperture radar (SAR) images
acquired at different time intervals in the same area. Assuming there are M + 1 SAR radar
images from different time periods in the study area, one image is selected as the master image
based on experimental requirements, while the remaining M SAR images serve as slave images.
These images undergo registration and interferometric processing, with an external Digital
Elevation Model (DEM)(11) introduced to remove terrain phase effects. This process results in M
pairs of interferometric image sets, yielding M sets of differential data. By constructing a model
based on the obtained Permanent Scatterer (PS) points, the subsidence results for the study area
are calculated.(12–16) In this equation, the phase ∆φ_i for the ith phase can be represented as

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∆=
ϕi ϕdef _ i + ϕtopo _ i + ϕatm _ i + ϕnoise _ i .

(1)Here, φdef _i represents the deformation phase in the LOS direction caused by surface deformation
during the imaging period; φtopo_i accounts for the terrain phase introduced by external DEM
errors; φatm_i corresponds to the phase delay caused by atmospheric disturbances during theimaging period; φnoise_i denotes the noise phase, including spatial decorrelation noise, temporaldecorrelation noise, system noise, and other types of noise, following a Gaussian distribution.
The processing flow is illustrated in Fig. 1.

3.

Principle of SBAS Technique

The SBAS-InSAR technique employs a time-series analysis approach using a combination ofinterferograms derived from multiple image pairs with short baselines.(17) High-quality
interferograms are selected by applying a temporal baseline threshold (TBT) and a perpendicularbaseline threshold (PBT),(18) which effectively filter out interferograms with poor quality. To
optimize spatial decorrelation, the temporal and spatial baselines between differential
interferograms should be minimized, enabling the connection of all pairs with short baselines

Fig. 1.

PS-InSAR processing flow.

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within the set. Isolated SAR data with large spatial baselines are also connected, accelerating thesampling rate of observation data. Ultimately, several smaller subsets can be formed within theexisting SAR image dataset, where both temporal and spatial baselines fall within the designatedthreshold range, all linked through a master image.(19)Assuming there are M + 1 SAR images covering the study area, acquired at times t0, t1, ..., tm,
N sets of interferometric pairs can be obtained through freely combined image pairing:
M +1
M +1
≤N≤M
.
2
2

(2)

For a pair of SAR images acquired at times ta and tb (tb > ta), the interferogram generated
from these images is the ith interferogram. The interferometric phase ϕ(i, β) of a specificinterferometric pixel β in this interferogram is calculated asφ( i , β ) = φ( tb , β ) − φ(ta , β ) ≈4π

λ[ d (tb , β ) − d (ta , β )].

(3)Here, d(tb, β) and d(ta, β) are the deformation components along the radar LOS direction at times
tb and ta, respectively. d(tb, β) (i = 1, ···, M) represents the accumulated deformation time series,Ф(ti, β) is the corresponding phase, and the unwrapped phase is calculated asφ ( ti , β ) ≈4Πd ( ti , β )

λ

.

(4)

Assuming ФT = [Ф(t1), Ф(t2), ..., Ф(tM)] is a vector set composed of phases corresponding to a
specific pixel in different moments of SAR images,(20) the corresponding interferometric phasevector set is ϕ ( t1 ) , ∆ϕ ( t2 )，，∆ϕ ( tM )  .
∆φ T =∆(5)

The time series of the master and slave images are represented as IE = [IE1, IE2, ..., IEN] andIS = [IS1, IS2, ..., ISM], respectively. If IEi > ISi (i = 1, ···, N), then the interferometric phase of the
ith interferogram isφi =φtIE − φtIS ( i =
1 N).
i

i

(6)

From the above equation, the corresponding matrix expression isAφ = σφ ,(7)

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where AN*M is the coefficient matrix, with each row corresponding to each interferogram andeach column corresponding to each SAR image. When N ≥ M, the least-squares solution can bedirectly obtained:(φˆ = AT A) A σφ .
−1

T

(8)

When N < M, ATA is a non-singular matrix.(21) In this case, the solution of Ф in the leastnorm sense can be found, leading toφ −φ
φ2 − φ1
φM − φM −1 T=V=
V1 1 0 ,V, ,V=
=.
2M
t1 − t0
t2 − t1
tM − tM −1 (9)Substituting this into the previous equation yields
IEi∑ Vk = σφi (i = 1, 2,, N ).

=k ISi +1

(10)

Consequently, the matrix budget expression can be transformed into
BV = σφ ,(11)

where BN*M is the coefficient matrix. When ISi+1 < k < IEi, B(i, k) = tk+1 − tk (i = 1,2, ..., N);
otherwise, B(i, k) = 0. By utilizing the SVD decomposition method, the least-norm solution of
the velocity vector can be obtained. Finally, integrating the velocities within each time interval
yields the corresponding deformation components.(22,23) The SBAS processing workflow is
depicted in Fig. 2.

4.

Study Area Overview

4.1

Study area overview

The study area is the central urban area of Heifei City, with a coverage area of approximately
12 × 20 km2. Most areas have an altitude between 15 and 80 m, with an average altitude of 20–
40 m. Heifei, as a subcentral city in the Yangtze River Delta urban agglomeration, hasexperienced rapid socioeconomic development in recent years. Monitoring and analyzing theland subsidence caused by urban construction in Heifei City are of great significance.
4.2

Data sources

In this study, 139 scenes of ascending orbit interferometric wide swath (IW) SLC data
covering the central urban area of Heifei were obtained from Sentinel-1A. The data span from

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Fig. 2.

SBAS processing flow.

July 2015 to July 2020. The slant range resolution of the data is approximately 2.3 × 13.3 m2(range × azimuth), and the average incidence angle is around 39.3°. Precise Orbit Ephemerides
data, with an accuracy higher than 5 cm, were used as the orbit information, which meets therequirements for InSAR data processing. The DEM data used in this study is the Shuttle Radar
Topographic Mission Global 1, with a resolution of 30 m, obtained from airborne radar terrain
mapping. The detailed research data parameters are shown in Table 1.

5.

Experimental Results and Discussion

5.1

PS-InSAR processing results

Using the data from January 15, 2018 as the master image, we performed the PS-InSAR
processing with a spatial baseline threshold of 180 m and a temporal threshold of 840 days,
resulting in the spatio-temporal baseline distribution of the interferometric pairs as shownin Fig. 3.
In this study, the PS point selection method combining the amplitude dispersion thresholdand spectral coherence coefficient was employed. A total of 180381 PS measurement points wereobtained within the 240 square kilometers of the central urban area of Heifei City, with anaverage PS point density of 751.6 points per square kilometer. From the point density distribution,
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Table 1
Research data parameters.
Sentinel-1A Satellite Parameters
Acquisition Time Range
Data Volume/Scene
Ascending/Descending
Imaging Mode
Polarization Mode
Incidence Angle (°)
Resolution/m
Repeat Cycle/dFig. 3.

Value
2015.7–2020.7
139
AscendingInterferometric Wide (IW)VV
39.3
2.3 × 13.3 (Range × Azimuth)
12(Color online) Spatio-temporal baseline of Sentinel-1 data interference.

region. This can be attributed to the dense urban construction in the northern part, which has
more high-coherence point targets. In contrast, the southern region has a relatively higher
vegetation coverage, especially in the southeast where there is less urban development, resulting
in a lack of valid measurement signals in certain localized areas. (Comprehensive coverage canbe achieved by combining the SBAS technique with PSinSAR.) The specific distribution of
deformation rates is shown in Fig. 4.
According to the deformation rate distribution in Fig. 4, the overall stability of the urban area
in Heifei can be observed, with LOS annual average deformation rates ranging from −5 to 5
mm/year. In particular, the northeastern region of the study area shows minimal deformationand is considered the most stable. Localized areas in the central-southern part of the study area
exhibit certain deformation trends, represented by regions A, B, C, and D in the figure. These
regions will be further analyzed in subsequent sections. The study area includes several
important transportation lines, including parts of Metro Line 1, parts of Metro Line 2, the central
section of Metro Line 3, and certain high-speed railway lines within Heifei. Among them, MetroLines 2 and 3 exhibit overall stability without passing through significant deformation areas,
while a portion of Metro Line 1 in the southern segment shows deformation, as exemplified byregion D. Additionally, there are indications of deformation along the railway lines within Heifei,
including Heifei South Station. The deformation velocity distribution map clearly shows a linear
distribution of PS points along the railway lines, which align well with the railway tracks,

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Fig. 4.

(Color online) Distribution of deformation rates in Hefei urban area from July 2015 to July 2020.

indicating that the PSInSAR processing of Sentinel data can provide monitoring results withrelatively high spatial resolution, meeting the requirements for subsidence monitoring in urban
areas.
5.2

SBAS processing results

The data acquired on July 9, 2019 was selected as the master image. The spatial baseline
threshold was set at 2% of the critical baseline, and the temporal baseline threshold was set at
120 days. On the basis of these criteria, a total of 139 pairs of interferometric images weregenerated, as shown in Fig. 5.
After phase unwrapping and removing the effects of flat-earth, DEM errors, orbit errors, andatmospheric effects, the surface deformation of the study area was computed. The LOS averagedeformation in the urban area ranged mostly from −10 to 10 mm/year, as depicted in Fig. 6. In
the deformation map, colder colors represent upward deformation along the LOS direction, while
warmer colors represent downward subsidence, which aligns with the deformation trend
obtained through the PS-InSAR processing.
Overall, the annual average deformation rate results obtained from both techniques show
good agreement, indicating a high level of consistency between the two methods. Although there
are slight differences observed in the image results, both methods are considered reliable formonitoring land subsidence in urban areas. The use of small baseline distances in the SBAS
approach helps to avoid spatial decorrelation and reduces the effect of topography on theinterferograms. The resulting deformation map shows a greater continuity in spatial coveragethan does the PS-InSAR method.

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Fig. 5.

(Color online) Interferogram connectivity graph and spatiotemporal baseline distribution.

Fig. 6.

5.3

3799(Color online) Annual mean deformation rate of SBAS LOS.

Spatiotemporal analysis of key deformation areas

To further analyze the distribution of subsidence velocities in the urban area of Heifei from
July 2015 to July 2020, we selected four representative deformation areas (highlighted by whiteboxes A, B, C, and D in Fig. 4) for comparative analysis. The aim was to validate the accuracy of
the results obtained from both PS-InSAR and SBAS with small baselines and to investigate thecauses of deformation in these areas.

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5.3.1. Anhui Jiatong Tire Factory area
From Fig. 4, it can be observed that Area A exhibits positive deformation signals, and opticalimage comparison reveals that this area corresponds to the Jiatong Tire Factory in Anhui. To
further analyze the deformation characteristics and evolution of this area, a specific point within
Area A was selected to analyze its deformation time series, as shown in Fig. 7. The deformation
time series reveals that the LOS direction in this area demonstrates a linear deformation trendwith a pronounced periodic deformation pattern. It shows an upward expansion (positive
deformation) during the summer and a downward contraction (negative deformation) during thewinter, with vibration amplitudes ranging from −20 to +20 mm. This phenomenon is attributed
to the thermal expansion and contraction of the steel structure in the tire factory, causing cyclic
deformation in response to seasonal temperature variations. This observation confirms the highprecision of InSAR technology in urban areas, which is capable of accurately capturing
deformations caused by temperature-induced changes in steel frame structures.
The consistency between the subsidence trends obtained from SBAS and PS-InSAR
technologies confirms their accuracy to a certain extent. Trend analysis further verifies theseasonal temperature variations in the steel frame structure of the tire factory. Considering thecontamination of periodic signals in the linear deformation trend, we performed a simple
modeling using a sine function to estimate the effects of thermal expansion and contraction. The
resulting linear deformation trend time series after removing this component is shown in Fig. 8,
indicating a linear deformation trend of approximately 1.6 mm/year in the tire factory area.
However, since the simple trigonometric function fitting does not precisely capture the changes
in the thermal expansion and contraction of the steel frame structure, the accuracy of theFig. 7. (Color online) Deformation time series from July 2015 to July 2020 in Jiatong Tire Factory area of Anhui
Province.

Fig. 8.

(Color online) Time series of regional linear deformation trend of Jiatong Tire Factory in Anhui Province.

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deformation trend time series is affected. Therefore, in future studies, it is necessary to establish
a rigorous deformation model for the thermal expansion and contraction of the steel frame
structure on the basis of temperature data, in order to more accurately remove the periodic
signal.
5.3.2. Heifei South Station
From Fig. 4, it can be observed that Area B exhibits positive deformation signals, and opticalmapping reveals that this area corresponds to Heifei South Station, a high-speed railway station.
Similarly, to further analyze the deformation characteristics and evolution of this area, a specificpoint within Area B was selected to analyze its deformation time series, as shown in Fig. 9. The
results indicate that Heifei South Station has also experienced thermal expansion and contraction
due to temperature variations in its steel frame structure, with a LOS amplitude range ofapproximately ±25 mm. It can be observed that there is a significant fluctuation during thesummer, and the subsidence trend shown by PS-InSAR and SBAS in the LOS direction is
generally consistent. Furthermore, note that starting from 2017, there is a slight uplift during thewinter and summer compared with the same period in the previous year. Since there are no newbuildings observed in the vicinity, it can be inferred that this is due to the construction of MetroLine 5. During the construction and operation of the subway, the initial stage of subsidence issmall, but as concrete is poured, the base rebounds and undergoes uplift deformation, resulting
in ground uplift phenomena. By using the trigonometric function fitting to remove the periodic
signal, the linear deformation trend time series of this area was obtained, as shown in Fig. 10.
The results indicate a stable deformation trend of approximately 2.5 cm/year in this area, which
requires further observation of its deformation trend.

Fig. 9.

(Color online) Deformation time series of Hefei South Railway Station from July 2015 to July 2020.

Fig. 10. (Color online) Time series of linear deformation trend in Hefei South Railway Station.

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5.3.3. University college
Area C exhibits negative deformation signals, mainly corresponding to the university college.
Figure 11 shows the deformation time series of this area from July 2015 to July 2020. From thetime series, it can be observed that the deformation in the LOS direction fluctuated around zerofrom 2015 to the first half of 2018, indicating a relatively stable state during that period.
However, a rapid subsidence signal was obtained from June 2018 to September 2019, with thedeformation quickly changing from zero to −20 mm, followed by a relatively stable state up to
the present.
Using optical imagery from Tianditu, as shown in Fig. 12, we observed that there weresignificant changes in the buildings on the university campus during the period from July 2018
to September 2019. This corresponds precisely to the rapid deformation period revealed by
InSAR monitoring. It is preliminarily inferred that this deformation is due to geologicalsubsidence caused by changes in the campus planning. This experiment further demonstrates
the capability of InSAR technology to accurately capture surface deformation information.
5.3.4. Anhui Geological Information Museum area
Area D is located near the No. 1 subway line, with the main building being the Anhui
Geological Information Museum (Fig. 13). The results indicate a LOS deformation trend of
approximately 4 mm/year in this area. As shown in Fig. 14, through the time series analysis, it isobserved that this area also exhibits a certain periodic deformation trend. However, comparedwith the previous cases A and B, the signal in this area is more complex. The periodic signal was
more pronounced before 2019 but disappeared afterward. Before 2019, the subsidence remained
around ±10 mm/year, and afterward, it showed a fluctuating pattern and eventually stabilized.
According to investigations, the Anhui Geological Information Museum is located on Jinxiu
Avenue, near the Shiwuli River, with mostly farmland in the vicinity and the ongoing
construction of Luzhou Avenue. It is speculated that the subsidence may be caused by theirrigation of farmland and the extraction of groundwater. Further analysis is required to combine
these results with actual conditions.
5.4

Cross-validation

Randomly selecting 10000 validation points within the study area, we expanded each point
outward with a radius of 30 m to find the nearest PS points within the radius. The LOS annual
average subsidence rates obtained from both methods were cross-validated and analyzed. If no
PS points were found within the radius, the validation point was considered invalid. In the end,
3488 PS and 4277 SBAS validation points were obtained.(24) After statistical analysis, a normal
distribution curve was obtained, as shown in Fig. 15. The relative error between the two
inversion results was 1.5 mm/year, which falls within the accuracy range of 5 mm/year. To
demonstrate the consistency of the results from both methods more intuitively, a linear fit was
performed on the valid validation points, resulting in a linear relationship of 0.95, as shown inSensors and Materials, Vol. 35, No. 10 (2023)

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Fig. 11. (Color online) Deformation time series of college area from July 2015 to July 2020.

(a)

(b)

(c)

Fig. 12. (Color online) Optical remote sensing image of university college: (a) July 2018, (b) September 2018, and(c) February 2019.

Fig. 13. (Color online) Satellite map of Anhui Geological Information Museum.

Fig. 14. (Color online) Deformation time series of Anhui Geological Archive from July 2015 to July 2020.

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Fig. 15. (Color online) Average annual subsidence
rate and normal distribution curve of PS-INSAR and
SBAS.

Fig. 16. (Color online) Linear relationship between
PS and SBAS annual average subsidence rates.

Fig. 16. This indicates a high degree of consistency between the two methods regarding annual
average subsidence rates and verifies the reliability of both methods for monitoring urban land
subsidence.(25)
In conclusion, the four selected regions in the Sentinel-1A data exhibit a consistent linear
deformation trend, and the results obtained from both the PSInSAR and SBAS techniques are inagreement. Since the leveling data for the period 2015–2020 in the Heifei area was not available,
the accuracy of the land subsidence results obtained using SBAS and PSInSAR techniques
cannot be directly verified. However, by comparing and analyzing the results with existing
research findings, the reliability of these two temporal InSAR techniques for the accuratemonitoring of urban land subsidence in the Heifei area can be indirectly validated. Furthermore,
the linear relationship between the annual average subsidence rates calculated by the PSInSAR
and SBAS methods provides further confirmation of the accuracy of both methods.

6.

Conclusion

In this study, we utilized PSInSAR and SBAS techniques to conduct LOS-based land
subsidence monitoring experiments using the near five-year ascending orbit data of Heifei City
acquired by Sentinel-1A SAR. The research obtained LOS-based land subsidence information in
the study area. The monitoring experiment results revealed that the overall Heifei urban area
remains relatively stable, but certain localized regions exhibit deformation trends. Typical areas
showing such trends include the Anhui Jiatong Tire Factory, the northern and eastern sections of
Heifei South Station’s building and nearby railway tracks, university colleges, and the Anhui
Provincial Geological Data Museum. The analysis of these four representative regions
demonstrates that both PSInSAR and SBAS techniques exhibit characteristics such as highmeasurement accuracy, wide coverage, and high spatiotemporal resolution. They can provide
effective means for the accurate monitoring of large-scale land subsidence in urban areas,
offering significant potential for various applications.
On the other hand, there are still some areas for improvement in the current study, including
the following: (1) The resolution of Sentinel-1A data is 20 × 5 m, which allows for obtaining thedeformation distribution over a large area. However, higher-resolution SAR data is required forSensors and Materials, Vol. 35, No. 10 (2023)

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fine-scale monitoring. (2) Simple sinusoidal function fitting is insufficient to accurately remove
periodic deformation signals observed in localized areas. It is necessary to incorporate external
data such as temperature and groundwater levels to further optimize the model and achieve theprecise removal of periodic signals. (3) The PSInSAR technique primarily measures signals
from stable reflectors such as buildings and bridges, making it challenging to capture surface
deformation signals in bare land and vegetated areas. This limitation results in the inability toobtain effective measurement signals in certain critical local areas in cities, such as along
subway lines. To achieve comprehensive coverage, it is necessary to complement themeasurements using the SBAS technique, which can be combined with PSInSAR for monitoring.
(4) Although a considerable number of images were used in the experiment, only the ascending
orbit data from Sentinel-1A was utilized. Interpreting the results from a single orbit is relatively
difficult, and it is necessary to further supplement and process descending orbit data to integrate
and interpret both ascending and descending results.
The experiments in this study demonstrate that time-series InSAR monitoring technology
performs well in high-precision deformation monitoring. However, it is only aimed at the
deformation in the LOS direction. In future applications, it is recommended to use Earthcurvature correction and elevation data, and use triangulation to convert the deformation in the
LOS direction into vertical displacement. The results should be integrated with the groundmeasurement data of the Beidou(1) Navigation Satellite System (BDS) to improve the accuracy ofmonitoring results.

Acknowledgments
This work was supported by the Anhui Provincial Natural Science Foundation under Grant
2108085QD154, the China Postdoctoral Science Foundation under Grant 2020M681993, theNational Natural Science Foundation of China under Grant 42001408, and the Natural Science
Foundation of Anhui Provincial Education Department under Grant KJ2021A0022.

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About the Authors
Siyuan Liu graduated from Heifei University in 2020 with a bachelor's degree. Since 2021, hehas been pursuing a bachelor's degree at Anhui University. His research interests include remotesensing geodesy, machine learning, and remote sensing target detection.
Kai Xu received his Ph.D. degree from Wuhan University (WHU), Wuhan, China in 2017. He iscurrently an associate professor in Anhui University. His research interests include geometriccalibration and image processing using deep learning. (kaixu@ahu.edu.cn)
Chengcheng Fan received his Ph.D. degree from Wuhan University (WHU), Wuhan, China in2017. He is currently an associate researcher in Innovation Academy for Microsatellites of CAS(IAMCAS). His research interests include remote sensing satellite design and on-orbit intelligent
processing.