Morishita et al.
Progress in Earth and Planetary Science(2023) 10:66
https://doi.org/10.1186/s40645-023-00597-5

Progress in Earth andPlanetary Science

Open Access

PAPER WITH FULL DATA ATTACHED

Nationwide urban ground deformationin Japan for 15 years detected by ALOS
and Sentinel‑1
Yu Morishita1* , Ryu Sugimoto1, Ryosuke Nakamura1, Chiaki Tsutsumi1, Ryo Natsuaki2 andMasanobu Shimada3

Abstract
InSAR time series analysis has become a major tool for nationwide land deformation monitoring. Sentinel-1 SAR
data have enabled us to measure and monitor ground deformation globally with high accuracy and resolution
through InSAR time series analysis, due to its constant and frequent global coverage and open data policy since 2014.
Although several datasets from previous SAR satellites were available before Sentinel-1, such comprehensive deformation monitoring was not performed due to several limitations such as data quality, analysis technique, data policy,
and processing capacity at that time. However, since a large amount of ALOS InSAR products and an open-source
InSAR time series analysis tool LiCSBAS have become openly and freely available, we can easily derive the deformation from 2006 to 2011 by using them. In this study, we detected the deformation time series and velocity in all majorurban areas in Japan from 2006 to 2011 and compared the results with the deformation from 2014 to 2020 detectedby Sentinel-1 data. The two deformation datasets with different time periods revealed various 15-year deformation
histories, such as long-term constant subsidence in Tomakomai and Niigata, changes in deformation areas and/or velocities in Hirosaki, Kujyukuri, Kanazawa, and Matsushiro, and appearance or disappearance of deformationin Joso, Yoyogi, and Kyoto. Future abundant and continuous SAR data acquisitions will reveal more long-term deformation transitions and help to understand the details of the mechanisms.
Keywords ALOS, Ground deformation, InSAR, Japan, Nationwide, Sentinel-1, Subsidence, Time series analysis*Correspondence:
Yu Morishita
asobu.morimori@gmail.com
1
Digital Architecture Research Center, National Institute of Advanced
Industrial Science and Technology, 2‑4‑7 Aomi, Koto‑ku, Tokyo 135‑0064,
Japan
2
Department of Electrical Engineering and Information Systems, The
University of Tokyo, 7‑3‑1 Hongo, Bunkyo‑ku, Tokyo 113‑8656, Japan
3
Division of Architectural, Civil and Environmental Engineering,
School of Science and Engineering, Tokyo Denki University, Ishizaka,
Hatoyama‑machi, Hiki‑gun, Saitama 350‑0394, Japan

1 Introduction
Since the launch of Sentinel-1 in 2014, several activities
to measure or monitor the large-scale ground deformation at high resolution using Synthetic Aperture Radar
Interferometry (InSAR) time series analysis have been
proposed and operationalized (e.g., Crosetto et al. 2020;
Lazecký et al. 2020; Morishita 2021) due to the global,
continuous, frequent, and freely available Sentinel-1 data.
However, before Sentinel-1, such operational large-scale
deformation monitoring was very rare due to several limitations, such as the amount and quality of the available
Synthetic Aperture Radar (SAR) data (e.g., orbit control,
observation frequency, swath width, orbit information
accuracy), the immaturity of the analysis technique, theaccessibility of the data, and the computational power to© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

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process huge amounts of data (Chaussard et al. 2013; Elliott et al. 2016).
In 2022, the National Institute of Advanced Industrial
Science and Technology (AIST) Advanced Land Observing Satellite (ALOS) InSAR products were released as anopen and free dataset (Sugimoto et al. 2021). The products include ~ 500 thousand interferograms from 2006 to2011 covering Japan, the Philippines, Indonesia (except
the islands of Borneo and New Guinea), Nepal, Turkey,
the East African Rift, and the west coast of North America as of July 2023. Since unwrapped data are available
for all interferograms, they can be easily used for InSAR
time series analysis. In addition, an open-source InSAR
time series analysis tool, LiCSBAS, was released in 2020,
which allows us to semi-automatically and easily derive
the deformation time series and velocity from the stackof unwrapped interferograms (Morishita et al. 2020).
Morishita (2021) derived the deformation time series
and velocity in all major urban areas in Japan from 2014
to 2020 using Sentinel-1 interferograms and LiCSBAS.
Further historical deformation information would be
useful for understanding the mechanism and taking
countermeasures if necessary. In this study, we detected
deformation in Japan from 2006 to 2011 using ALOSInSAR products and LiCSBAS. In addition, we compared
the deformations in two different periods and discussed
the 15-year deformation history.

The area of 431 Kumamoto was contracted to fit the DID
in this study because the area in Morishita (2021) was
expanded to detect the broad postseismic deformation
caused by the 2016 Kumamoto earthquake sequence. The
northern area of 131 Kanto, which could not be coveredby two frames of ALOS, was also shortened comparedwith Morishita (2021).

2 Data and methods
2.1 Areas of interest

The same 73 urban areas as in Morishita (2021) wereselected as areas of interest (AOIs) in this study basedon the densely inhabited district (DID; Ministry of Land,
Infrastructure, Transport and Tourism 2015) metric(Table 1, Fig. 1), namely meeting at least one of the following conditions:(1) If the DID has a population of ≥ 100,000.
(2) If the DID has a population of < 100,000 but close to(1).
(3) If the DID is located in one of the 47 prefectural
capitals.
(4) If leveling surveys have been conducted continuously in the DID (Ministry of the Environment2020).
(5) If significant deformation has been detected in the
DID in previous studies (e.g., Morishita et al. 2020).
The three-digit AOI ID is also the same as Morishita(2021) (i.e., two-digit prefectural ID (01–47) and a single-digit sub-number (1–7)). Six sub-AOIs were added
to clearly describe the locations of detected deformation in 131 Kanto, 201 Nagano, and 271 Osaka (Table 1).

2.2 Data

We used the AIST ALOS InSAR products (Sugimoto
et al. 2021). They were published on AI Bridging Cloud
Infrastructure (ABCI) in April 2022 under the Creative
Commons (CC) BY 4.0 license and can be freely downloaded. The products consist of all possible pairs of interferograms using all available data observed by ALOS
from February 2006 to April 2011. The interferometric
processing and phase unwrapping were performed by
SigmaSAR (Shimada 2018) and Statistical-Cost, Network-Flow Algorithm for Phase Unwrapping (SNAPHU; Chen and Zebker 2002), respectively. The digital
elevation model (DEM) used is Advanced Spaceborne
Thermal Emission and Reflection Radiometer (ASTER)
Global Digital Elevation Model (GDEM) ver2 (Tachikawa
et al. 2011). The geocoded image has a pixel spacing of0.0003 degrees (~ 30 m).
Only the frames with the off-nadir angle of 34.3° wereused because there are not enough images with other offnadir angles. The frames covering more than half of each
AOI in both ascending (path ID 394–432) and descending (path ID 052–077) orbits were selected for processing. Since the swath width of each frame is ~ 70 km andthere is an overlap area of ~ 30 km between adjacent
paths, about half of the AOIs are covered by multiple
paths with slightly different incidence angles (Additional
file 1: Table S1). Only the descending path 062 (covering
202 Matsumoto, 231 Nagoya, and 232 Toyohashi) was
excluded from processing because the number of available epochs (5 epochs) and the total period (< 1.3 years)
were insufficient to achieve significant accuracy for timeseries analysis. Consequently, all AOIs except 232 Toyohashi have both ascending and descending data.
Three large AOIs (i.e., 131 Kanto, 213 Nagoya, and271 Osaka) require two consecutive frames to cover theentire area. To mitigate the discontinuity of the timeseries result at the frame boundary, the unwrapped dataof the two frames were concatenated before the timeseries analysis by compensating the biases from the average of the difference of the unwrapped phases in theoverlapping area between frames. Their frame IDs are
modified in this paper (e.g., the concatenated frame IDof 404_0700_343 and 404_0710_343 is 404_0702_343)
to distinguish them from single frames (Additional file 1:
Table S1).

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Table 1 AOI list and detected deformation signals
AOI ID

Main City Name

011

Sapporo012

Hakodate

013

Asahikawa

014

Kushiro015

Obihiro016

Tomakomai

021

Aomori022

Hirosaki

023

Hachinohe

031

Morioka

041

Sendai042

Kesennuma

043

Osaki

051

Akita

061

Yamagata

062

Yonezawa

071

Fukushima

072

Koriyama

131

Kanto

Deformationa

Figures

ALOS (2006–2011)

Sentinel-1 (2014–2020)(S): 5 mm/year(S): 5 mm/year(S): 5 mm/year(S): 10 mm/year(S): 10 mm/year2

S: 5 mm/year(S): 40 mm/year(S): 40 mm/year

4(S): 10 mm/year

S: 5 mm/year

S: 10 mm/year
U: 5 mm/year- 081- Joso(S): 5 mm/year- 121- Kujyukuri

S: 5 mm/year

S: 5 mm/year

8(S): 15 mm/year(S): 20 mm/year(U): 5 mm/year(U): 5 mm/year

5

- 132

- Yoyogi

U: 5 mm/year

9

- 141

- Hakone(S): 10 mm/year

14

151

Niigata

S: 10 mm/year

S: 15 mm/year

152

Nagaoka(S): 5 mm/year(S): 5 mm/year

153

Sanjo

154

Kashiwazaki

155

Ojiya

156

Joetsu157

Minamiuonuma

161

Toyama171

Kanazawa

172

Nanao

181

Fukui

191

Kofu

201

Nagano- 203

Matsushiro

202

Matsumoto

211

Gifu

221

Shizuoka

222

Hamamatsu

3

S: 25 mm/year
P: 5 mm/yearb

S: 5 mm/year

12
15

S: 5 mm/year

S: 5 mm/year(S): 10 mm/year(S): 10 mm/year(S): 5 mm/year(S): 10 mm/year(S): 5 mm/year(S): 15 mm/year

610(S): 5 mm/year

223

Numazu(S): 5 mm/year

224

Fuji

(S): 5 mm/year

231

Nagoya(S): 15 mm/year

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Table 1 (continued)
AOI ID232

Main City Name

Deformationa

Figures

ALOS (2006–2011)

Sentinel-1 (2014–2020)

S: 5 mm/year(S): 5 mm/year

Toyohashi

241

Tsu

242

Yokkaichi(S): 5 mm/year

251

Otsu

261

Kyoto

U: 5 mm/year(S): 15 mm/year(S): 5 mm/year

271

Osaka

S: 30 mm/year(S): 15 mm/year- 272- Arima-Takatsuki

U: 5 mm/year

281

Kobe

(S): 20 mm/year282

Himeji

S: 5 mm/year283

Toyooka291

Nara

U: 5 mm/year

301

Wakayama(S): 5 mm/year311

Tottori

321

Matsue331

Okayama

341

Hiroshima(S): 10 mm/year(S): 10 mm/year342

Fukuyama(S): 10 mm/year(S): 30 mm/year

351

Yamaguchi(S): 10 mm/year(S): 5 mm/year352

Shimonoseki

361

Tokushima

371

Takamatsu

381

Matsuyama

391

Kochi
Fukuoka

402

Kitakyushu(S): 5 mm/year(S): 5 mm/year

403

Kurume(S): 5 mm/year(S): 5 mm/year

411

Saga

(S): 10 mm/year

421

Nagasaki(S): 15 mm/year422

Sasebo(S): 5 mm/year

431

Kumamoto

441

Oita

442

Beppu

451

MiyazakiKagoshima

471

Naha

13

(S): 5 mm/year

401

461

11

P: 25 mm/yearb(S): 5 mm/year

S: 5 mm/year

16

S: 5 mm/year(S): 5 mm/year(S): 10 mm/year(S): 10 mm/yearaDetected deformation. P, postseismic deformation; S, subsidence; U, uplift; (), only outside of DID

bOnly postseismic data were used

Among the 73 AOIs, significant coseismic deformation
occurred in 154 Kashiwazaki by the moment magnitude(Mw) 6.6 Niigata-ken Chuetsu-oki earthquake on July 16,
2007 (Nishimura et al. 2008a). To exclude the coseismic
deformation from the time series analysis result, the data
before the earthquake were excluded.
On March 11, 2011, the Mw 9.0 Tohoku earthquake
occurred. Most of the AOIs in eastern Japan wereaffected by the large coseismic deformation, and someof them have post-earthquake data (Kobayashi et al.
2011). However, the coseismic deformation is dominated by the long-wavelength component and can beremoved by the spatiotemporal filter and deramp during the time series analysis (see Sect. 2.3). Therefore,
the post-earthquake data were included in the following
time series analysis.

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Fig. 1 Footprints of the ALOS frames used in this study. The blue and red rectangles denote the ascending and descending frames, respectively.
The black rectangles and three-digit numbers denote the AOI and its ID, respectively (see Table 1)

2.3 Methods

In the previous study using Sentinel-1 data (Morishita2021), an open-source tool LiCSBAS was used to perform the time series analysis (Morishita et al. 2020). The
workflow of LiCSBAS consists of the following 11 steps:
Step 0.1: Download Looking into Continents fromSpace with Synthetic Aperture Radar (LiCSAR)
products
Step 0.2: Convert file format
Step 0.3: Correct tropospheric noise (optional)
Step 0.4: Mask low-coherence regions in theunwrapped interferograms (optional)
Step 0.5: Crop a specified rectangular region ofinterest (optional)
Step 1.1: Quality check and identification of badinterferograms

Step 1.2: Check loop closure and identify bad interferograms
Step 1.3: Small baseline inversion
Step 1.4: Calculate velocity standard deviations
Step 1.5: Mask time series
Step 1.6: Filter (and deramp) the time series
LiCSBAS is designed to semi-automatically performtime series analysis using Centre for the Observationand Modelling of Earthquakes, Volcanoes and Tectonics (COMET) LiCSAR products derived from Sentinel-1 data (Lazecký et al. 2020). Step 0.1 is dedicatedto the download of the LiCSAR products. However, anyother InSAR products derived from any SAR satellites
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the required data (i.e., unwrapped data, coherence, andother metadata) are prepared in the predefined format.
To download the AIST ALOS InSAR products andconvert them to the LiCSBAS-ready format, a patch
tool LiCSBASpatch_AIST_ALOS is available as areplacement for Step 0.1 in LiCSBAS. This patch tool
allows us to perform all steps of LiCSBAS for AISTALOS InSAR products.
In contrast to Sentinel-1, the baseline condition of
ALOS data must be carefully considered. Since the orbit
of modern space-borne SAR satellites such as Sentinel-1
and ALOS-2 is well controlled, the perpendicular baseline of the interferometric pair is always small and theDEM error component can be ignored in the time series
analysis (Morishita et al. 2020; Morishita 2021). However,the baseline of ALOS is much longer than that of modern SAR satellites and also temporally correlated, which
would introduce a significant false signal proportional
to the DEM error in the displacement time series (Samsonov 2010; Fattahi and Amelung 2013). While LiCSBAS
does not have a function to compensate for the DEM
error component, this function has been implemented
in Step 1.6 in the newly developed LiCSBAS2. The DEM
error component, which is proportional to the perpendicular baseline, is estimated and subtracted from thedisplacement time series after deramping and before thespatiotemporal filtering in Step 1.6. For more details on
the effect of DEM error noise reduction, see Sect. 4.2.
LiCSBAS2 ver1.9.0-alpha was used in this study. Steps
other than Step 1.6 in LiCSBAS2 are the same as in
LiCSBAS.
The number of epochs or interferograms for each frame
is much smaller (max. 30 epochs) than that of Sentinel-1(max. 136 epochs) due to the low observing frequency of
ALOS (Additional file 1: Table S1). However, the spatial
resolution is much higher (~ 30 m) than that of COMET
LiCSAR Sentinel-1 products (~ 100 m), resulting in ~ 10times more data for each epoch or interferogram. Consequently, the total amount of data processed is larger
than Sentinel-1. In particular, Step 1.3 takes a long time
for a large number of pixels, even with parallel processing using multiple Central Processing Units (CPUs). To
reduce the processing time, the Graphics Processing Unit
(GPU) option implemented in LiCSBAS ver 1.5.0 (March
5, 2021) or later was used. For example, Step 1.3 for frame
405_0702_343 in 131 Kanto (29 epochs, 265 interferograms, image size 3712 × 3970) took 131 min to completewith 5 CPU cores (resource type of rt_C.small in ABCI),
but only 29 min with GPU 16 GB memory (rt_G.small).
In the preprocessing steps of LiCSBAS, the tropospheric noise correction using Generic Atmospheric
Correction Online Service (GACOS) data in Step 0.3
was not applied because (1) the target area is flat andtherefore the stratified tropospheric noise is minimal; (2)the turbulent noise is significantly mitigated in Step 1.6
by a spatiotemporal filter (Morishita et al. 2020; also see
Sect. 4.1). Low coherence pixels were masked in Step 0.4
with an average coherence threshold of 0.2. Unwrapped
data were cropped for each AOI in Step 0.5.
To significantly reduce the bad effect of unwrapping
errors and to keep only high-coherence interferogramswith almost full coverage of unwrapped data, the thresholds of the unwrapped pixel rate in Step 1.1 and the root
mean square of the loop phase in Step 1.2 were set to 0.8
and 1.0, respectively, which are stricter than the default
values of 0.3 and 1.5, respectively. As a result, mostinterferograms with long perpendicular baselines wereautomatically rejected. However, in contrast to C-band
Sentinel-1, many interferograms with long temporalbaselines and short perpendicular baselines remained
due to the slow temporal decorrelation of L-band andurban areas (Morishita and Hanssen 2015). In addition,some interferograms or epochs suffering from severe
noise (e.g., due to ionospheric disturbances) were manually removed (Additional file 1: Table S2). The refinedinterferometric network contained no gaps in all AOIs
and frames. The number of remaining interferogramsand epochs varies widely from frame to frame, ranging
from 10 to 338 and 5 to 30, respectively (Additional file 1:
Table S1).
To retain only high-precision pixels, strict masking
thresholds were applied, i.e., the number of gaps (n_gap)
was set to 0 (i.e., no gaps are allowed), the number ofloop errors (n_loop_err) was set to 0 (i.e., no potential
unwrapping errors were allowed), and the number of noloop interferograms (n_ifg_noloop) was set to 0 (i.e., allinterferograms at all pixels must have at least one loop
to check for phase unwrapping errors). Despite thesestrict parameters, a sufficient number of interferograms
and pixels remained due to the low temporal decorrelation characteristic of the L-band (Morishita and Hanssen2015).
While the ionospheric noise in L-band is intense, theionospheric noise correction has not been applied to the
AIST ALOS InSAR products (Sugimoto et al. 2021). To
mitigate the significant ionospheric noise, a deramp in
Step 1.6 was necessary. While the ionospheric noise wasapproximated by a linear ramp for small areas, a quadratic polynomial was used for relatively large AOIs (i.e.,
131 Kanto, 152 Nagoya, 271 Osaka). The temporal andspatial filter widths in Step 1.6 were the default values(i.e., three times the average temporal interval and 2 km,
respectively, Additional file 1: Table S1). The temporal filter width differs from frame to frame and AOI to AOI,
depending on the average temporal interval, and mostly
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that of Sentinel-1 due to the low frequency of ALOS
observations. This reduces the ability to detect nonlinear or seasonal deformation signals. See Sect. 4.1 fordiscussion.
We then computed the velocity for each AOI andframe from the deformation time series. The reference
area was set to the whole area (i.e., average of all pixels), while the reference areas to discuss the time seriesat specific points were selected separately around thepoints of interest in Sect. 3. We also tried calculating
the amplitude and time offset of the seasonal component according to Morishita (2021), but their accuracy
was not high enough to discuss the seasonal component

due to the low observation frequency of ALOS. Therefore, we do not discuss the seasonal component in thefollowing sections. After deriving the line-of-sight(LOS) velocity for each AOI and frame, we decomposed
the ascending and descending LOS velocities from multiple frames into vertical and east–west (EW) velocities
in each AOI (Wright et al. 2004; Morishita 2021).

3 Results and comparison to Sentinel‑1
3.1 Overview

Similar to the Sentinel-1 results reported in Morishita (2021), many deformation signals were detectedin this study (Table 1). Some AOIs show consistentFig. 2 Deformation in 016 Tomakomai, Hokkaido. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively.
The black dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. c The
LOS deformation time series at P0161 derived from ALOS (2006–2011, large circles) and Sentinel-1 (2014–2020, small rectangles) data

Fig. 3 Deformation in 151 Niigata. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The black dashedlines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The gray areas in b
denote the rice paddy fields. c The LOS deformation time series at P1511 derived from ALOS (2006–2011, large circles) and Sentinel-1 (2014–2020,
small rectangles) data

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deformation with Sentinel-1, while others show significantly different deformation signals. For example, linear subsidence was detected in 016 Tomakomai (Fig. 2)
and 151 Niigata (Fig. 3), and its rate (~ 10 mm/year)
and spatial extent are consistent with Sentinel-1, suggesting that the subsidence has been constant for morethan 15 years. In 022 Hirosaki (Fig. 4), 121 Kujyukuri(Fig. 5), 171 Kanazawa (Fig. 6), and 203 Matsushiro(Fig. 7), significant linear subsidence was detected similar to Sentinel-1, but the spatial and temporal pattern
was different. At 081 Joso (Fig. 8), 132 Yoyogi (Fig. 9),152 Nagoya (Fig. 10), and 261 Kyoto (Fig. 11), uplift or
subsidence signals were detected by ALOS, but there
were no or much slower deformation signals in the
Sentinel-1 result, meaning that these deformations
occurred in the past limited period and then stopped
or strongly decelerated. While interesting nonlinear
deformation was detected by Sentinel-1 at 153 Sanjo(Additional file 1: Fig. S1), 291 Nara (Additional file 1:
Fig. S2), and 431 Kumamoto (Additional file 1: Fig. S3),
these areas were very stable in the ALOS period. In thefollowing subsections, we pick up several interesting
deformation signals detected by ALOS and comparethem with the Sentinel-1 result.
As the target areas are flat plains, significant horizontal
deformation is not expected and therefore independent
ascending and descending results should show consistent
results. In fact, all the ascending and descending results
showed consistent deformation and no significant unexpected EW deformation was detected.
In the Sentinel-1 result, most of the rice paddy fields
showed subsidence, presumably due to the systematic bias caused by the fading signal (Ansari et al. 2021;
Morishita 2021). We have masked the rice paddy fields inthe figures of the Sentinel-1 results in the comparison to

Fig. 4 Deformation in 022 Hirosaki, Aomori. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The
black dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The grayareas in b denote the rice paddy fields. The dashed black rectangles are the reference area for the time series. c The LOS deformation time series
at P0211 and P0222 derived from ALOS (2006–2011, large circles) and Sentinel-1 (2014–2020, small rectangles) data. d The land-use and land-covermap

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Fig. 5 Deformation in 121 Kujyukuri, Chiba. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The
black dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The grayareas in b denote the rice paddy fields. The dashed black rectangles are the reference area for the time series. c The LOS deformation time series
at P1211-P1213 derived from ALOS (2006–2011, large circles) and Sentinel-1 (2014–2020, small rectangles) data

Fig. 6 Deformation in 171 Kanazawa, Ishikawa. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively.
The black dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The
gray areas in b denote the rice paddy fields. c The LOS deformation time series at P1711 and P1712 derived from ALOS (2006–2011, large circles)
and Sentinel-1 (2014–2020, small rectangles) datahide the possible effect of the systematic bias and to makethe comparison easy (also see Sect. 4.3).
When plotting the time series at a specific point in awide AOI, the region considered stable near that point
was redefined as the reference region to reduce the effectof spatially long-wavelength residual errors. (Such cases
are explicitly shown in the figure.)
3.2 Subsidence
3.2.1 022 Hirosaki, Aomori

Rapid subsidence (max. ~ 40 mm/year) in two separate
regions was detected in 022 Hirosaki (Fig. 4). AlthoughSentinel-1 also detected the two subsidence signals andthe velocity in the southern region (P0221) is almost thesame, the velocity in the northern region (P0222) of ALOS
is significantly slower (~ 10 mm/year) than that of Sentinel-1 (~ 40 mm/year). In terms of point coverage, ALOS
has much better coverage than Sentinel-1, especially inorchard areas, while no points were obtained in rice paddy
fields, even with ALOS, due to the drastic change in surface conditions (i.e., filled with water in spring and driedin winter). This better coverage of ALOS allows us to know
the spatial extent of subsidence and to detect unknown
subsidence between two major subsidence basins.

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Fig. 7 Deformation in 203 Matsushiro, Nagano. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The
black dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The grayareas in b denote the rice paddy fields. c The LOS deformation time series at P2011 derived from ALOS (2006–2011, large circles) and Sentinel-1(2014–2020, small rectangles) data. d The Matsushiro earthquake swarm area. The red dots indicate the epicenters from August 1965 to May 1967

Fig. 8 Deformation in 081 Joso, Ibaraki. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The black
dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The gray areasin b denote the rice paddy fields. The dashed black rectangles are the reference area for the time series. c The LOS deformation time series at P0811
derived from ALOS (2006–2011, large circles) and Sentinel-1 (2014–2020, small rectangles) data

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Fig. 9 Deformation in 132 Yoyogi, Tokyo. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The black
dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The dashed black
rectangles are the reference area for the time series. c The LOS deformation time series at P1321 and P1322 derived from ALOS (2006–2011, large
circles) and Sentinel-1 (2014–2020, small rectangles) data

Fig. 10 Deformation in 231 Nagoya, Aichi. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The black
dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The gray areasin b denote the rice paddy fields. The dashed black rectangles are the reference area for the time series. c The LOS deformation time series at P2311
and P2312 derived from ALOS (2006–2011, large circles) and Sentinel-1 (2014–2020, small rectangles) data

3.2.2 121 Kujyukuri, Chiba

The Kujyukuri Plain is one of the largest subsidence areasin Japan. This area is rich in natural gas dissolved in waterand has been experiencing subsidence due to natural
gas extraction since the 1960s (Shen et al. 2004; Nonaka
et al. 2020). Since the 1970s, restrictions on natural gas
extraction have been applied to prevent rapid subsidence, but widespread subsidence continues, and evenin the 2020s, subsidence of up to about 2 cm/year has
been observed by leveling and InSAR time series analysis

using Sentinel-1 and ALOS-2 data (Chiba Prefecture2022; Ishitsuka and Matsuoka 2016; Nonaka et al. 2020;
Morishita 2021).
In this study, four ALOS frames are available (i.e., two
ascending and two descending; Fig. 1 and Additional
file 1: Table S1). The small common reference area among
the four frames was set to the northeast of the subsidencearea, rather than using the entire area for each frame asa reference to compare the time series from different
frames.

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Fig. 11 Deformation in 261 Kyoto. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The black dashedlines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The gray areas in b
denote the rice paddy fields. c The LOS deformation time series at P2611 derived from ALOS (2006–2011, large circles) and Sentinel-1 (2014–2020,
small rectangles) data

The result shows three or four distinct areas of subsidence (Fig. 5). The time series show a nearly linear
trend. Although the rough distribution is similar to
the Sentinel-1 result, the peak locations and velocities are significantly different. The northern subsidence
area around P1212 has been extended to the northeast,and the maximum subsidence rate has been increased
from 2006–2011 to 2014–2020. In contrast, the subsidence velocity in the next southern area (between P1212
and P1213) is greater in 2006–2011 than in 2014–2020.
Around P1213, there was a single peak of subsidence in
2006–2011, but two new peaks seem to have appeared to
the north and west in 2014–2020. Interesting uplift has
also been detected in the east of the plain along the coast(P1211, ~ 5 mm/year) by both ALOS and Sentinel-1, suggesting that this uplift has been going on for more than
15 years with a constant velocity, although the mechanism is unknown.
3.2.3 171 Kanazawa, Ishikawa

Significant subsidence (~ 10 mm/year) was detected in
171 Kanazawa, as in the case of Sentinel-1 (Fig. 6). There
are two distinct areas of subsidence in the north andsouth. While the northern subsidence around P1712 isalmost similar between 2006–2011 and 2014–2020, thesouthern one around P1711 is faster in 2014–2020 thanin 2006–2011, implying that the subsidence has been
accelerated. The time series shows almost constant subsidence. The other peak (~ 5 mm/year) of subsidence wasalso detected between the two areas in the ALOS result,although it is not clear in the Sentinel-1 result.

3.2.4 203 Matsushiro, Nagano

Very localized subsidence (~ 500 m diameter, ~ 5 mm/year) was detected at 203 Matsushiro, Nagano (Fig. 7).
The larger and faster subsidence (~ 1 km, 10 mm/year)
was also detected by Sentinel-1 in the later period at thesame location, suggesting that the subsidence has continued and gradually accelerated for at least more than
13 years.
The Matsushiro earthquake swarm started in August
1965, and the subsidence area detected by this study islocated on the northwest side of the earthquake swarm
area with a diameter of ~ 15 km (Fig. 7d; Japan Meteorological Agency 2016). Groundwater and hot springs were
gushed out in association with this earthquake swarm,
and the earthquake swarm is thought to have been causedby the intrusion of deep groundwater into the focal area
(Okusawa and Tsukahara 2001). The largest earthquakeof M5.4 occurred on April 5, 1966, and the peak of seismic activity was around that time. The total number ofearthquakes until the end of 1966 was > 600 thousand.
While the number of earthquakes has decreased significantly since then, seismic activity is still being observed.
More than 90 cm of localized uplift in September 1966and exponential subsidence thereafter were detected byleveling around the recent subsidence area (Okada andTsubokawa 1970). The recent subsidence detected by thisstudy may also be related to the past earthquake swarm
and groundwater activity.
3.2.5 081 Joso, Ibaraki

ALOS detected slow subsidence (~ 5 mm/year) outside
the DID at 081 Joso, Ibaraki, while the Sentinel-1 result

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shows no clear subsidence there (Fig. 8). A consistent subsidence signal was also detected by the stacking
approach using ALOS data (Yamanaka et al. 2013).

In addition, the episodic growth of active folds 15 km
east of the epicenter was also detected by ALOS InSAR(Nishimura et al. 2008a). The deformation in Kashiwazaki
was not given much attention at that time, but the coseismic interferograms clearly show some phase changes
that may represent linear surface ruptures along the subsidiary faults, as seen in the 2016 Kumamoto earthquake(Fujiwara et al. 2016, 2020) (Fig. 12d, e).
In this study, we derived the time series after the earthquake to detect the postseismic deformation. The velocity shows two subsidence regions with linear boundaries(Fig. 12a). The spatial pattern shows some consistency
with the coseismic deformation. In particular, the linear feature with an EW direction (around the middle
between P1541 and P1542) corresponds to the small
hill line with < 10 m relative height (Fig. 12f ), which may
be an unknown subsidiary fault. The time series showsa rapid exponential decay just after the earthquake(Fig. 12c). P1541 shows ~ 2 cm subsidence between July
19 and October 19, 2007, and then almost constant slow
subsidence of ~ 5 mm/year after 2008. Similar exponential decay was also observed around the Kumamotosubsidiary faults after the 2016 Kumamoto earthquake(Morishita 2021).
Interestingly, the Sentinel-1 result shows small subsidence (~ 3 mm/year) in almost the same area (Fig. 12b).
The time series shows that the subsidence is not constant,but episodic in February 2018 (Fig. 12c). At the sametime, episodic subsidence was observed in Sanjo, ~ 50 km
northeast of Kashiwazaki, probably due to unusually
heavy snowfall and associated overpumping of groundwater (Morishita et al. 2020; Morishita 2021). Considering the geographical situation, the cause of episodic
subsidence in Kashiwazaki is probably the same as thatin Sanjo. This implies that the subsidiary faults have controlled the spatial extent of subsidence associated withgroundwater change.

3.3 Uplift
3.3.1 132 Yoyogi, Tokyo

A slow uplift signal (~ 5 mm/year) was detected at 132
Yoyogi, Tokyo (Fig. 9a). Although it is difficult to discuss
the nonlinearity of the time series from these infrequent
temporal sampling observations, the uplift appears tohave occurred or accelerated in 2008 and 2009 (Fig. 9c).
There does not seem to be a clear temporal correlation
with the groundwater level change (Tokyo Metropolitan
Government 2022).
While the Sentinel-1 result shows no uplift at Yoyogi,a different uplift signal was detected at Chofu, 10–15 km
west of Yoyogi (Morishita 2021). Interestingly, the upliftarea shifted from Yoyogi to Chofu in about 10 years,
although the relationship between the two uplifts is
unknown.
3.3.2 152 Nagoya, Aichi

Although no significant deformation (> 5 mm/year) was
detected in 152 Nagoya, slow uplift (~ 4 mm/year) was
detected in two northern areas; P2311 Kasugai City andP2312 Owariasahi City (Fig. 10). These uplift signals were
not detected in the Sentinel-1 result. The time series
show different features; an episodic uplift around P2311
might have occurred in the first half of 2009, while theuplift around P2312 seems to be almost constant, though
the temporal characteristics are not very clear from theinfrequent observations.
3.3.3 261 Kyoto

Significant uplift with almost constant velocity (> 5 mm/year) was detected in 261 Kyoto (Fig. 11). The constant
uplift in almost the same area is also seen in the Sentinel-1 result, but the velocity is lower (~ 3 mm/year) thanin the ALOS result. Hashimoto (2016) also reported theconsistent uplift signal, discussed the onset of the upliftusing older European Remote-Sensing Satellite (ERS) and
Environmental Satellite (Envisat) data, and concluded
that the uplift may have started around 2003–2004 andwas caused by the recovery of the groundwater table.
3.4 Tectonic‑related deformation
3.4.1 154 Kashiwazaki, Niigata

The Mw 6.6 Niigata-ken Chuetsu-oki earthquake occurred on July 16, 2007. The epicenter was
located ~ 20 km north-northeast of Kashiwazaki. The
earthquake caused widespread large deformation aroundthe epicenter due to the reverse fault slip of the main seismogenic fault (Aoki et al. 2008; Nishimura et al. 2008b).

3.4.2 272 Arima‑Takatsuki, Osaka

Hashimoto (2016) detected the transition of the deformation around the Arima-Takatsuki fault zone using
ERS, Envisat, and ALOS from 1992 to 2010. In thisstudy, consistent deformation was detected as expected(Fig. 13). Morishita (2021) revealed the further changeof the deformation style in the later period by Sentinel-1.
For a more detailed discussion, please refer to Hashimoto(2016) and Morishita (2021).
3.4.3 141 Mt. Hakone, Kanagawa

A significant subsidence signal was detected at Mt.
Hakone (Fig. 14), although it is outside the scope of
this study (i.e., non-urban area). Sentinel-1 was unable
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Fig. 12 Deformation in 154 Kashiwazaki, Niigata. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively.
The black dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The
gray areas in b denote the rice paddy fields. c The LOS deformation time series at P1541 and P1542 derived from ALOS (2006–2011, large circles)
and Sentinel-1 (2014–2020, small rectangles) data. d and e are the coseismic interferograms associated with the Mw 6.6 Niigata-ken Chuetsu-oki
earthquake on July 16, 2007, in the ascending (June 14–July 30, 2007) and descending (January 16–July 19, 2007) orbits, respectively. The blackarrows indicate the locations of the edges of the subsidiary faults. The insets show the wider area containing the broad coseismic deformationand the red stars denote the epicenter of the earthquake. f The hill shade map

decorrelation. This example clearly shows the strong
capability of the L-band to detect deformation in nonurban areas.
A small phreatic eruption occurred in 2015 (Doke et al.
2018; Kobayashi et al. 2018), and the subsidence detected
here occurred close to the eruption area. Doke et al.
(2020) also detected consistent subsidence signals using
ALOS data and suggested that the subsidence is related
to the decreased supply of hydrothermal fluids fromdeeper areas. For a more detailed discussion, see Dokeet al. (2020).

4 Discussion
4.1 Advantages and disadvantages over Sentinel‑1

In general, ALOS results have better coverage than Sentinel-1, especially in vegetated areas (see Sects. 3.2.1 and3.4.3). One of the main reasons is the slower temporaldecorrelation of the L-band (Morishita and Hanssen2015). In addition, the ease of phase unwrapping in the
L-band is also an important factor. Since LiCSBAS rejects
interferograms with many unwrapping errors in Step 1.2
and pixels with possible unwrapping errors in Step 1.5,
the success rate of the phase unwrapping has an impact
on the coverage. A phase difference of one-quarter of the
wavelength (i.e., half cycle or π rad; ~ 6 cm in L-band and1.4 cm in C-band) can cause a phase unwrapping error(e.g., Chen and Zebker 2002). In C-band, a few centimeters of atmospheric noise can cause a phase unwrapping
error between separated regions, while in L-band thisrisk is much lower.
However, there is a critical drawback to ALOS, the low
temporal frequency of the observations. In most frames,
the observation frequency is 3–6 times/year (Additional file 1: Table S1), while that of Sentinel-1 is 30–60

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Fig. 13 Deformation in 272 Arima-Takatsuki, Osaka. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively.
The black dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines.
The gray areas in b denote the rice paddy fields. The red lines denote the known active faults. The dashed black rectangles are the reference area
for the time series. c The LOS deformation time series at P2711-P2713 derived from ALOS (2006–2011, large circles) and Sentinel-1 (2014–2020, small
rectangles) data

Fig. 14 Deformation in 141 Mt. Hakone, Kanagawa. a The vertical deformation velocities derived from ALOS. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The dashed black rectangle is the reference area for the time series. b The LOS deformation timeseries at P1411 derived from ALOS datatimes/year. It is almost impossible to capture the detailsof seasonal or nonlinear deformation time series. For
example, in 155 Ojiya, Niigata, large seasonal deformation (i.e., > 5 cm sudden subsidence in winter caused bygroundwater pumping, followed by gradual recovery) was

detected by Sentinel-1 (Morishita et al. 2020; Morishita2021). In contrast, it is quite difficult to find out the seasonal deformation from the ALOS time series with only
3–6 times/year, although the limited time series data areconsistent with the seasonal deformation (Fig. 15).

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Fig. 15 Deformation in 155 Ojiya, Niigata. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The
black dashed lines are the boundaries of the DID. The black solid lines denote the 5 mm/year contour lines without the 0 mm/year lines. The grayareas in b denote the rice paddy fields. c The LOS deformation time series at P1551 derived from ALOS (2006–2011, large circles) and Sentinel-1(2014–2020, small rectangles) data

In addition, spatially correlated nonlinear deformation
is more difficult to distinguish from atmospheric noise. In
Step 1.6 of LiCSBAS, the atmospheric noise is estimatedand subtracted from the time series by a spatiotemporal
filter under the assumption that the atmospheric noise isspatially correlated but temporally uncorrelated (Morishita et al. 2020). The temporal filter width must be sufficiently larger than the observation frequency; otherwise,
there will be no effect of the temporal filter. By default,
the triple mean temporal interval is used. In the case of
ALOS, the temporal filter width is usually 200–400 days,
which means that deformation signals shorter than half ayear with some spatial correlation are considered atmospheric noise and cannot be detected. On the other hand,
Sentinel-1 used a temporal filter width of 55 days, whichallowed the clear detection of the seasonal deformation
signal (Morishita 2021).
Unfortunately, ALOS-2, launched in 2014 as the successor of ALOS, has a similar low frequency of observations as ALOS, and we have almost no chance to detectthe detailed seasonal deformation using L-band data
so far (Morishita 2021). However, the next-generation
L-band SAR satellites, such as ALOS-4 and NASA-ISROSAR (NISAR), will have much higher observation frequency than ALOS and ALOS-2 and will enable us todetect the temporally detailed deformation time series
with high coverage even in vegetated areas (Jet Propulsion Laboratory 2020; Motohka et al. 2020).
4.2 Effect of DEM error

Unlike modern SAR satellites such as Sentinel-1 and
ALOS-2, the perpendicular baseline of ALOS had notbeen kept as short as possible, resulting in significant
DEM error phases as mentioned in Sect. 2.3. The DEM
error component can be described by

d=−

B⊥
dh
r sin θwhere B⊥ is the perpendicular baseline, r is the slant
range distance between the satellite and ground, θ is thelook angle, and dh is the DEM error (Fattahi and Amelung 2013). For example, in frame 422_0650_343 at 411
Oita, the perpendicular baseline ranges from about-2000 m to 2000 m with reference to the single prime
image. Regarding the DEM error, Tachikawa et al. (2011)
reported that the ASTER GDEM version 2 used for the
ALOS InSAR products has an average standard deviation of 12.6 m compared to the Geospatial Information Authority of Japan (GSI) 10 m DEM. Assuming thetypical ALOS parameters (i.e., r = 860 km and θ = 34.3°),
B⊥ = 2000 m and dh = 10 m correspond to ~ 2 cm error in
the displacement.
While the displacement time series without DEM error
correction shows a clear correlation with B⊥, most of thefluctuations are removed by the DEM error correction,i.e., most of the displacement component without DEM
error correction is due to the DEM error component(Fig. 16c). The significant DEM error component also
affects the velocity because B⊥ is correlated with time
(Fig. 16a, b). The amplitude of the estimated DEM error
is mostly within 5 m, but sometimes exceeds 10 m, which
is roughly consistent with the accuracy of the DEM(Fig. 16d). The spatial pattern of the estimated DEM error
is roughly correlated with topography (i.e., highlands

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Fig. 16 DEM error effects in 441 Oita. a and b are the vertical deformation velocities derived from ALOS with and without the DEM error correction,
respectively. The black dashed lines are the boundaries of the DID. c The LOS deformation time series at P4411 and P4412 derived from ALOS datawith and without the DEM error correction and the perpendicular baselines. d The estimated DEM error component. e The hill shade map. f The
optical image

tend to have negative DEM error; Fig. 16d, e, f ). In addition, in the flat areas, districts with tall buildings tend tohave positive DEM errors. This is because many of theactual scatterers are located in high places (e.g., the roof
or wall of the buildings), while the DEM does not reflect
the height of the buildings.
4.3 Effect of systematic bias due to fading signal

In the Sentinel-1 result, most rice paddy fields showedsubsidence signals (Fig. 17c; Morishita 2021). Theseare presumably not true deformations, but systematic
biases due to the fading signal that occurs with multilooked distributed scatterers when only the short timeinterval interferometric pairs are used in the time series
analysis (Ansari et al. 2021). However, in contrast to theSentinel-1 result, almost no measurement points wereobtained on the rice paddy fields in the ALOS result,while L-band can generally maintain high coherence on
vegetated areas (e.g., Fig. 17a, d). This is probably due

to the application of large multilook factors and strong
filters in the LiCSAR products used to derive the Sentinel-1 result, which produced artificially coherent signals
on the rice fields. The ALOS result (i.e., no point in therice fields) is more natural because the seasonal changes
in surface conditions there are too drastic to maintaincoherence.
In other non-urban areas, the ALOS result also showsno signal like the systematic bias, while the effect of thefading signal is expected to be greater in the L-band
than in the C-band (De Zan et al. 2015). To investigate
the effect of the systematic bias, we performed the LiCSBAS analysis using the ALOS dataset with a bandwidth(i.e., the number of shortest temporal baseline interferograms per epoch; Ansari et al. 2021) of two (bw = 2) inthe frames covering 081 Joso (i.e., 057_2902_343 and405_0702_343). The ALOS vertical velocity with bw = 2
shows a similar displacement pattern (i.e., subsidence
around the rice fields) to the Sentinel-1 result with bw = 3

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Fig. 17 Effect of the systematic bias in 081 Joso, Ibaraki. a, b, and c are the vertical deformation velocities derived from ALOS with the fullbandwidth, ALOS with the bandwidth of two, and Sentinel-1, respectively. The black dashed lines are the boundaries of the DID. The dashed black
rectangles are the reference area for the time series. d The land-use and land-cover map. e The interferometric network with the full bandwidth(blue) and bandwidth of two (red)or 4 (Fig. 17b, c). Interestingly, significant uplift signals
appeared in the ALOS result with bw = 2 commonly in
the golf fields, which could also be caused by the effectof the systematic bias with the opposite sign to the rice
paddy field. While the shortest time interval of ALOS
is 46 days, much longer than that of Sentinel-1, the significant systematic bias was generated using only the
relatively short-interval interferograms. Therefore, thereason why the ALOS result in this study does not showa systematic bias is probably because this study used not
only short-interval but also long-interval interferograms(Fig. 17e). The use of the long-interval interferograms
was possible because of the high coherence (i.e., slow
temporal decorrelation) of the L-band (Morishita and
Hanssen 2015).
The number of available ALOS or ALOS-2 data is notvery large due to their infrequent observations (~ 30
acquisitions for 5 years at most) in contrast to Sentinel-1(> 100 for 5 years). Generating all possible interferograms

as in this study for ALOS or ALOS-2 should not be infeasibly burdensome, and the long-interval interferogram,which can maintain sufficient coherence, would play a
critical role in mitigating the systematic bias. Therefore,
using all possible interferometric pairs is a realistic andeffective solution to mitigate the effect of systematic bias
for ALOS or ALOS-2 data.
4.4 Importance of continuous observation

Some areas (e.g., 016 Tomakomai, 151 Niigata, south of
022 Hirosaki) showed similar subsidence in the ALOS
(2006–2011) and Sentinel-1 (2014–2020) results, indicating that subsidence has continued for 15 years. However,many areas showed different deformation (i.e., appearance/disappearance, acceleration/deceleration, enlargement/shrinkage). Because the observation periods do
not overlap, we cannot know when and how the deformation changed between 2011 and 2014. Although someSAR data are available for this period (e.g., TerraSAR-X,

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COSMO-SkyMed, RADARSAT-2), they are not globally
available and accessibility is limited (i.e., costly). Longterm, continuous, and uninterrupted availability of global
SAR data will make it possible to detect the spatial andtemporal evolution of land surface deformation and tounderstand its mechanisms in detail. The successorsof Sentinel-1 A/B (i.e., Sentinel-1 C/D) and many othernext-generation SAR satellites (e.g., NISAR and ALOS4) are planned to be launched soon and will prevent thegeneration of the blank period of the watchful eyes ofSAR from space.

Supplementary Information

5 Conclusions
In this study, we measured the ground surface deformation in all major urban areas in Japan from 2006 to 2011using open and free AIST ALOS InSAR products and theopen-source InSAR time series analysis tool LiCSBAS.
We compared the result with the deformation derived
from open and free COMET LiCSAR products basedon Sentinel-1 data from 2014 to 2020 (Morishita 2021)
and investigated the long-term deformation. As a result,various deformation signals were detected, such as longterm constant subsidence (e.g., 016 Tomakomai and 151
Niigata), changes in deformation areas and/or velocities(e.g., 022 Hirosaki, 121 Kujyukuri, 171 Kanazawa, and203 Matsushiro), and appearance or disappearance of
deformation (e.g., 081 Joso, 132 Yoyogi, and 261 Kyoto).
InSAR time series analysis using SAR data covering the
ground surface with a wide swath and high density allows
us to measure the long-term deformation extensively andefficiently. While this study revealed 15 years of deformation history, more abundant and uninterrupted SAR
data, which will be obtained by future SAR satellites, willachieve even longer and more spatially and temporally
detailed deformation monitoring.

Acknowledgements
The computational resource of ABCI provided by AIST was used. The background images used in all figures are from GSI Tiles (https://​maps.​gsi.​go.​jp/​devel​opment/​ichir​an.​html). PALSAR image data courtesy of the JAXA/METI,
image processed by AIST.

Abbreviations
ABCI	AI bridging cloud infrastructure
ALOS	Advanced Land Observing Satellite
AIST	National Institute of Advanced Industrial Science and Technology
AOI	Area of interest
ASTER	Advanced Spaceborne Thermal Emission and Reflection RadiometerCC	Creative commons
COMET	Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics
CPU	Central processing unit
DEM	Digital elevation model
DID	Densely inhabited district
Envisat	Environmental Satellite
ERS	European Remote-Sensing Satellite
EW	East–west
GACOS	Generic Atmospheric Correction Online Service
GDEM	Global Digital Elevation ModelGPU	Graphics processing unit
GSI	Geospatial Information Authority of JapanInSAR	Synthetic Aperture Radar Interferometry
LiCSAR	
Looking into Continents from Space with Synthetic Aperture
Radar

LOS	Line of sight
Mw	Moment magnitude
NISAR	NASA-ISRO SAR
SAR	Synthetic Aperture Radar
SNAPHU	Statistical-Cost, Network-Flow Algorithm for Phase Unwrapping

The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​023-​00597-5.
Additional file 1: Table S1. AOI list, frames used in this study andtheir statistics. Table S2. Manually removed interferograms. Figure S1.
Deformation in 153 Sanjo, Niigata. a and b are the vertical deformation
velocities derived from ALOS and Sentinel-1, respectively. The black
dashed lines are the boundaries of the DID. The black solid lines denotethe 5 mm/year contour lines without the 0 mm/year lines. The gray areasin b denote the rice paddy fields. Figure S2. Deformation in 291 Nara.
a and b are the vertical deformation velocities derived from ALOS and
Sentinel-1, respectively. The black dashed lines are the boundaries of the
DID. The black solid lines denote the 5 mm/year contour lines without the
0 mm/year lines. The gray areas in b denote the rice paddy fields. Figure
S3. Deformation in 431 Kumamoto. a and b are the vertical deformation velocities derived from ALOS and Sentinel-1, respectively. The black
dashed lines are the boundaries of the DID. The black solid lines denotethe 5 mm/year contour lines without the 0 mm/year lines. The gray areasin b denote the rice paddy fields.

Author contributions
All authors contributed to conceptualization and writing—review and editing;
YM was involved in methodology, validation, formal analysis, investigation,
writing—original draft preparation, and visualization and provided software;
YM and RS contributed to data curation; RNak and CT were involved in project
administration; and RNak contributed to funding acquisition.
Funding
This paper is based on results obtained from a Project, JPNP20006, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Availability of data and materials
All the ALOS LiCSBAS results generated in this study and the figures (LOS
velocities, decomposed vertical and EW velocities, optical images, interferometric network, mask) are available on the ZENODO repository (https://​doi.​
org/​10.​5281/​zenodo.​81638​82). The AIST ALOS InSAR products are available
on the INSAR Browser (https://​gsrt.​digia​rc.​aist.​go.​jp/​insar​brows​er/​index.​html).
LiCSBASpatch_AIST_ALOS is available on GitHub (https://​github.​com/​yumor​
ishita/​LiCSB​ASpat​ch_​AIST_​ALOS). LiCSBAS2 is available on GitHub (https://​github.​com/​yumor​ishita/​LiCSB​AS2) and the ZENODO repository (https://​doi.​
org/​10.​5281/​zenodo.​76546​40). The Sentinel-1 LiCSBAS results are available on
the ZENODO repository (https://​doi.​org/​10.​5281/​zenodo.​40296​16). The GIS
data of the DID and rice paddy fields are available on the Ministry of Land,
Infrastructure, Transport and Tourism website (https://​nlftp.​mlit.​go.​jp/​ksj/​index.​html). The hypocenter data of the Matsushiro earthquake swarm are available
on the Japan Meteorological Agency website (https://​www.​data.​jma.​go.​jp/​eqev/​data/​bulle​tin/​hypo.​html).

Declarations
Competing interests
The authors declare that they have no competing interests.

Morishita et al. Progress in Earth and Planetary Science(2023) 10:66

Received: 31 July 2023 Accepted: 2 November 2023

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