SBAS-DInSAR processing on the ESA Geohazard Exploitation Platform

SBAS-DInSAR processing on the ESA
Geohazard Exploitation Platform
Claudio De Luca
deluca.c@irea.cnr.it
Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA)
Consiglio Nazionale delle Ricerche (CNR),
Via Diocleziano, 328, 80124 Napoli

SBAS-DInSAR processing on the ESA Geohazard Exploitation Platform
Agenda
n  Part 1: Introduction to Differential SAR Interferometry
n  Part 2: ESA Platforms for Automatic Web Processing
n  Part 3: New frontiers in Earth Observation research
n  Open Discussion
n  Part 1: Introduction to Differential SAR Interferometry
n  Part 2: ESA Platforms for Automatic Web Processing
n  Part 3: New frontiers in Earth Observation research
n  Open Discussion

•  Active sensors
•  Microwave sensors
•  Coherent sensors
Key points of Radar (SAR) Imaging from space

•  Passive sensors use solar radiation (light) or the one emitted by the
observed object as source of illumination. Typically they operate in the
optical or infrared.
•  Active sensors have their own source of illumination. Typically they
operate in the microwave.
Capability to "observe" during day and night

Optical image Radar (SAR) image
same moment of acquisition
Capability to "observe" even in presence of clouds

Coherent sensors
SAR image
Synthetic Aperture

Baseline
S(t1)
S(t2)
Interferogram
Differential SAR Interferometry (DInSAR)

Campi Flegrei (3-7/2000)
π -π
2
2 _
λ
πϕ ≈→= losdd l
Centimetric displacements (in
some cases even sub-centimetric)
can be measured in “coherent”
areas!
losdd l _
4
λ
π
ϕ ≈
LOS: Line Of Sight
ERS wavelenght: 5.6 cm

0 2.8
cm
40°30’ N
Astroni
Solfatara
Arco Felice
Agnano
Bagnoli
3/2000-7/2000
φm
Phase Unwrapping
Operation
φ = φm + 2π k
Wrapped Phase (-π, π)

δϕ = −
4π
λ
r2
− r1( )= −
4π
λ
r − r1( )−
4π
λ
r2
− r( )≈ −
4π
λ
bsin !ϑ − β( )−
4π
λ
ld _los
=ϕt
+ϕd
Observations are done at different epochs and from different orbital positions
(real case)
Topographic Phase Deformation Phase
LOS: Line Of Sight

DInSAR
Interferogram
Generation
(Flattening)
Coherence
Map
Topographic
Interferogram
(DEM & orbital
information are
needed)
15042009_20052009_bperp=20m

Small baseline DInSAR interferograms are less affected by noise effects
(decorrelation).
ERS1_01/05/1996 - ERS2_15/08/1996
baseline=950 m
ERS2_31/08/1995 - ERS2_ 15/08/1996
baseline=10 m
Napoli Bay (ERS Multi-look image)
Why Small Baseline?

Spatial Decorrelation Effects depend on the perpendicular baseline of the
used SAR data pairs. To reduce such effects the use of small spatial baseline
couples is required.
Temporal Decorrelation Effects are due to temporal reflectivity changes of
the imaged scene. To reduce such effects the use of small temporal baseline
couples is required.
Different Atmospheric
Conditions from one image to
another lead to artifacts in the
generated interferograms. Such
effects can be reduced by
properly averaging independent
interferograms (stacking).
DInSAR Limitations

VOLCANICSEISMIC
URBAN LANDSLIDES
DInSAR can play a key role in many contexts

L’AQUILA
Monday 06/04/2009,
01:00 UTC, Mw=6.3
L’Aquila 2009 earthquake: scenario

06.04
Timeline
10.04 12.04 15.04 16.0408.04
ENVISAT (ASCENDING)
COSMO-SkyMED
(DESCENDING)
TerraSAR-X
(ASCENDING)
ENVISAT (DESCENDING)
L’ Aquila
Earthquake
Mw=6.3
L’Aquila 2009 earthquake: DInSAR analysis

ENVISAT (Desc.) ENVISAT (Asc.)COSMO (Asc.)
ALOS (Asc.)
Co-seismic deformation analysis

Length=12.2 ±0.4km, Width=14.1 ±0.7km
Top depth= 1.9 ±0.2km , slip 0.56 ±0.02m
rake -103°±2
47° ±1 dip
North
133°±2 strike
Source parameters: Atzori et al. 2009, GRL
Results of co-seismic interferograms modelling
Lanari et al. 2010, GRL

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
ERS-1
JERS
ERS-2
RADARSAT
ENVISAT
ALOS1
COSMO-SkyMed
TerraSAR-X
Sentinel-1A
Sentinel-1B
ALOS2
swath width: ≈ 100 km
revisit 2me: ≈ monthly
revisit 2me: 4 - 11 days
revisit 2me: 12 - 6 days
Temporal evolution of radar satellites for EO

ERS data availability: Napoli bay (Italy) example
Data Set Distribution
PerpendicularBaseline[m]
Time [year]
single deformation events generation of displacement time-series

PS SB
Persistent Scatterers (PS) vs. Small Baseline (SB)

To produce deformation times-series from a SAR data sequence:
Ÿ  using interferograms characterized by a “small baseline” in order to mitigate
noise (decorrelation) phenomena;
Ÿ  properly “linking” possible interferometric SAR data subset separated by large
baselines. In particular, for each coherent pixel, the deformation time-series is
computed by searching for an LS solution with a minimum norm constraint (the
SVD method is applied).
Time
Perpendicular
Baseline
Subset 2
Subset 1
Original SBAS algorithm: key idea

⎥
⎦
⎤
⎢
⎣
⎡
−
=
−
==
21
21
1
01
01
1v
M-M-
M-M-
M-
T
tt
)(t)-(t
v,, ....
tt
)(t)-(t
v
φφφφ
δφ=vB
by solving the linear system
wherein δφ is the interferometric phase vector, B the coefficient matrix
and M the number of SAR images.
To solve the linear system, we apply the SVD-method1.
Following the unwrapping operation we evaluate (for each pixel) the
displacement velocity vector v
Berardino, P., Fornaro, G., Lanari, R., Sansosti, E., 2002. A new algorithm for surface deformation monitoring based on small
baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing 40 (11), 2375–2383.
Mathematical framework of the SBAS algorithm

Interferograms
Mean deformation velocity [cm/yr]
> 0.75<- 0.75
Mt. Vesuvio
Campi Flegrei
Ischia
The SBAS algorithm rationale

LOS Mean Velocity (mm/yr)
> 15<-15
Ascending Orbit
ERS-1/2, T129, F747
Descending Orbit
ERS-1/2, T222, F2853
Lundgren et al. 2004, GRL
Asc.Desc.
Mt. Etna (Italy): 1992-2000 deformation analysis

Vertical East-West
[mm/yr]
> 15<-15
Down Up [mm/yr]
> 20<-20
West East
Mt. Etna (Italy): 1992-2000 deformation analysis
Lundgren et al. 2004, GRL

Meanvelocity[mm/yr]
> 20
<-20
Fernandina
Isabela
Sierra Negra
Wolf
ENVISAT Ascending (2003-2007)
Galápagos

> 30<-30
Mean Velocity (mm/yr)
ENVISAT descending (2002-2009)
σ ~ 1 cm
Δ: SAR
* : GPS
Kilauea
Mauna Loa
Mauna Loa and Kilauea Volcanoes (Hawaii)

Mean velocity (mm/yr)
< -10 0 > 10
Santa Ana
Basin
Pomona
Newport –
Inglewood
fault
GPS Network
(SCIGN)
N
Los Angeles Bay
Lanari et al. 2004, GRL
ERS-1/2 Descending data
(1995-2002)

< -10 0 > 10
c
d
e
Santa Ana Basin: DInSAR vs. GPS

San Francisco Bay
Meanvelocity[mm/yr]
> 6
<-6
ERS-1/2 Descending (1992-2000)

EGU General Assembly 2011
April 03-08, 2011 Vienna, Austria
< -6 0 > 6
Hayward fault

Creep event (Feb 1996)
* = Alignment arrays
Average boxes both sides
Difference to relative
motion
Lanari et al. 2007, RSE
Hayward fault: deformation time series

Ÿ  exploiting interferograms characterized by a “small
baseline” in order to limit the noise (decorrelation)
phenomena, thus maximizing the number of
investigated pixels;
Ÿ  using no a priori model on the investigated
deformation signal.
The SBAS approach allows to produce “long term”
deformation times-series by:
SBAS approach: key points

SBAS vs. Alignment array:
San Francisco Bay
SBAS vs. GPS: Los Angeles
LOS
<-10
> 10
mm/year
SBAS vs. Leveling: Napoli Bay
σdispl ~ 5-10 mm
σvel ~ 1-2 mm/year
SBAS-DInSAR result accuracy
Casu et al. 2006, RSE

The Thematic Exploitation Platforms goals are:
à  Facilitate use & processing of large datasets (including non-space data) by a
large number of users (science and non-science).
à  Processing services, software (e.g. toolboxes, etc.) and computing
resources.
à  Provide an environment for services development, integration and
exploitation.
à  Federate user communities around common scientific & thematic objectives.
à  Promote shared science objectives & better use of satellite EO.
à  Collaboration tools (e.g. knowledge base, open publications, social
networking).
The Thematic Exploitation Platform (TEP)

Data Archives
- ERS
- ENVISAT
- Sentinels
- CSK
- TSX
- ALOS
SBAS, NSBAS, ROI_PAC, StaMPS, DIAPASON, DORIS, …
-  G-POD
-  Cloud
-  Federated
resources
ESA Geohazard Exploitation Platform (GEP)

ESA Geohazard Exploitation Platform (GEP)
G-POD (legacy platform, from SSEP):
-  Tool integration need interaction with “integration” team
-  GRID based, even if could be instantiated on Cloud
GEP (under development/validation, pre-operations start in
2017):
-  Tool integration easier (Cloud sandboxes)
-  Cloud based and cost-effective resource provisioning
-  Contains several Pilot Projects providing Geohazards and InSAR-
related services (e.g. SBAS, DIAPASON, StaMPS, …)
-  Allows results, sharing, publication (e.g. via Zenodo, DOI),
reproducibility, discovery and other collaboration services
-  Interested users (Early Adopters) should submit an User
Request Form (URF) to ESA for evaluation

http://gpod.eo.esa.int - eo-gpod@esa.int
G-POD Home Page

EO-SSO credentials

G-POD Services

G-POD Web Portal of SBAS service

Selection of the area to be processed

Reference Point Selection

Reference Point Selection
Reference point must be on land and possibly in
an expected stable area

Selection of the time span

Archive Querying and Data Selection

G-POD Archive
VA4 Archive
VA4 - Only for Processing

Keep the same illumination geometry:
select the same Track!!!

Some acquisitions could be acquired the same
day but at slightly different times

Processed Area
Area of Interest (AOI)
The algorithm merges adjacent frames of the same track to totally cover
the selected AoI.
Data Selection and Optimization

Processed Area
Large Raw Data are cut around the Area of Interest

Processed Area
Automatic data rejection of acquisitions that do not cover the AoI
Data Rejected

Final settings and Job submission

To be modified by expert users.
We warmly suggest to keep them unchanged!

Processing Parameter Selection
•  Noise Filtering
•  Temporal and Spatial
Baseline
•  Doppler centroid
parameters
•  Time Series Generation
Spatial Network
•  Atmospheric Filtering
•  Multi-temporal/
interferogram analysis

Baseline Constrain Selection
•  Temporal and Spatial
Baselines have as default
values 1500 days and
400 m, respectively.
•  These two parameters
have a strong impact
when multi-temporal
analysis is performed.
•  Too small values have the
consequence to reduce
the SAR dataset, too big
values limit the success
of Phase Unwrapping
procedure.

Interferometric Pair Selection

Interf. Pair Selection: Delaunay Triangulation

Interf. Pair Selection: Large Baseline Removing
Maximum Spatial Baseline: 300 m

Interf. Pair Selection: Large Baseline Removing
Maximum Spatial Baseline: 50 m

G.P.D. and Doppler Selection
•  Ground Pixel dimension is
the value used for the
spatial multilook of the
interferograms generated
with ERS and ENV sensors
•  Max Allowed Delta-Doppler
is the minimum azimuth
b a n d w i d t h o v e r l a p
between master and slave
images .
•  Max Allowed Doppler
Centroid has a strong
impact for ERS-2 images
acquired after February
2000.

Common Band Filtering Selection
Common Band Filtering
f e a t u r e s i s a i m e d a t
increase the interferograms
coherence.
This filter removes the
u n c o r r e l a t e d s p e c t r a l
contributions to perform
the interferogram using the
c o m m o n b a n d w i d t h
between master and slave
images

Coherence threshold
•  This parameter permits to
identify the (spatial)
network of points that are
subsequently unwrapped.
•  It is strongly related to
the algorithm used for
Phase Unwrapping:
Extended Minimum Cost
Flow (EMCF).
•  Default value of 0.7 is
suitable for a large part
of case studies.
•  This in not a threshold to
select the final spatial
distribution of points

SBAS Phase unwrapping: the Extended-MCF
Algorithm
The EMCF algorithm (Pepe at al, 2006, TGRS) allows to unwrap a sequence
of M multi-temporal differential interferograms that generate a triangulation
in the Temporal/Perpendicular Baseline Plane.
Time [year]
PerpendicularBaseline[m]
1992 1999 2007

A Delaunay triangulation involving the coherent points in the Azimuth/Range
domain is subsequently built.
Aziumth
Range
The i-th edge of the triangulation corresponds to the i-th differential
interferogram:
( ) ( ), 1,..., ,i z ga r i M a r A RΦ = ∈ ×
Extended-MCF Algorithm: Rationale
“Temporal” network “Spatial” network

A Delaunay triangulation involving the coherent points in the Azimuth/Range
domain is subsequently built. The spatial network can be defined
through one of the inputs of the GUI.
Aziumth
Range
The i-th edge of the triangulation corresponds to the i-th differential
interferogram:
( ) ( ), 1,..., ,i z ga r i M a r A RΦ = ∈ ×
Extended-MCF Algorithm: Rationale

Coherence Threshold Selection
Coh: 0.7Coh: 0.8

•  APS Smoothing Time
Window represents the
window of the temporal
filter in the APS removing
procedure.
•  Larger values generate
more smoothed time
series.
•  Default value is 200
days; useful values lie in
the range 100 to 400
days.
Displacement Time Series Generation and
APS Filtering

Hawaii T200 case study
>6<-6
cm/yr
Mauna Loa
Kilauea
Est Rift
zone

To be modified by expert users.
We warmly suggest to keep them unchanged!

Submitted Job status check

Job Submission

Advanced Features

Noise Filtering Feature

On a pixel by pixel basis, the SBAS algorithm allows the generation of the
surface deformation time-series
[ ]10, ,..., NΦ ≡ Φ Φ
Subsequently, once the deformation time-series are retrieved, the quality
of the inversion is checked by comparing the original interferograms to the
ones reconstructed from the obtained time-series, by means of the so-
called Temporal Coherence factor:
( )1
exp i i
M
i IM IS
i
M
=
⎡ ⎤ΔΦ − Φ − Φ
⎣ ⎦
Γ =
∑
Small Baseline Subset SBAS algorithm

Temporal Coherence is a quality index of our phase reconstruction; two
different aspects may lead to a decrease of the temporal coherence
values:
Ø Time-Inconsistent Phase Unwrapping Errors;
Ø Time-inconsistencies among the generated multilook (wrapped)
interferograms, due to the fact that multi-look operations are
independently carried out on each single SAR data pair.
INDEED …
Temporal Coherence Factor

April 27,
2008
July 26,
2009
April 12, 2009
ΔΦ 1
ΔΦ 1
Azimuth
Range
ΔΦ 2 ΔΦ 3
ΔΦ 2
ΔΦ 3
Azimuth
Range
Azimuth
Range
Multi-look (Wrapped) Phase Time-Inconsistency

April 27,
2008
July 26,
2009
April 12, 2009
ΔΦ 1
ΔΦ 2
ΔΦ 3
Accordingly, multilook (wrapped) interferograms are not time-consistent. It means
that phase acquisitions are not known while a redundant set of M multi-look
interferograms is generated.
We extend the temporal coherence idea to wrapped interferograms, to have time-
consistent noise filtered SB multilook interferograms before any phase
unwrapping operation.
Azimuth
Range
1 2 3 0.r ≡ ΔΦ + ΔΦ + ΔΦ ≠
π
-π
Multi-look (Wrapped) Phase Time-Inconsistency

We search for the solution of the following maximization problem:
( )
1
1
exp
arg max
k k
M
k k IM IS
k
M
k
k
jξ
ξ
=
Φ
=
⎧ ⎫
⎡ ⎤⎪ ⎪ΔΦ − Φ + Φ
⎣ ⎦⎪ ⎪⎪ ⎪
Φ = ⎨ ⎬
⎪ ⎪
⎪ ⎪
⎪ ⎪⎩ ⎭
∑
∑
)
Basically, we maximize a weighted version of the temporal coherence
factor; the weights, representing our confidence on the phase stability of
the generated M multilook DInSAR phases, are set by using the pixel
spatial coherence
( )( )
( )
2 2
2 2
1
exp , 1,...,
1 1
A R
A R
N N
k k
A R h N p N
j x h y p k M
N N
ξ
=− =−
⎡ ⎤= ΔΦ + + ∀ =⎣ ⎦+ + ∑ ∑
Phase Inversion Algorithm

Improved SBAS Inversion
Ø  Once the phase acquisitions are retrieved, a noise-filtered version of
the original interferograms is generated.
Note that Space Adaptive Multilook operations could be also
preliminarly applied to the original interferograms at the
expenses of the overall computation time

Azimuth
Range
February 1, 2009 – August 15, 2010
Perpendicular Baseline ~300 m
The Abruzzi area case-study

Azimuth
Range
May 8, 2005 – April 12, 2009
Perpendicular Baseline ~800 m
The Abruzzi area case-study

Long Valley Caldera case-study

Long Valley Caldera case-study IMPROVEMENT

Buthan case-study

Buthan case-study IMPROVEMENT

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Outputs of the SBAS-InSAR processing chain
MetaData
Data

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Metadata Field
<Metadata tag> : <Metadata value>

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Metadata Field

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Pixel Identifier
Data Field

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Latitude and Longitude [deg]
Data Field

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Topography [m] = Elevation + Residual Topography
Data Field

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Deformation Velocity [cm/yr]
Data Field

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Temporal Coherence
Data Field

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LOS Unit Vectors
Data Field

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Deformation Time series [cm]
Data Field

Ask for an Account to:
eo-gpod@esa.int
sbas-help@irea.cnr.it

eo-gpod@esa.int
EO-SSO ID: eduusr03
Password: Educational_03
EO-SSO ID: eduusr05
EO-SSO ID: eduusr06
G-POD Educational Account Credentials

SAR Data Scenario: Satellites
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
ERS-1
JERS
ERS-2
RADARSAT
ENVISAT
ALOS
COSMO-SkyMed
TerraSAR-X
Sen2nel-1A
Sen2nel-1B
Swath Width ≈ 100 km
Revisit 2me ≈ monthly
Swath Width≈ 40 km
Revisit Time: 4 - 11 days
Swath Width ≈ 250 km
Revisit Time: 12 - 6 days
TerraSAR-X
COSMO-SkyMed
ALOS-2

SENTINEL1-A
ENVISAT
CSK
40 km
100 km
250 km
SAR Data Scenario: Coverage Comparison

Sen3nel-1: ~ 6 TByte data per day
ERS & ENVISAT data over world tectonic regions: 70+ Tera on line; about 10 days of S1-A acquisi2ons!
Sentinel-1 monthly coverage
Towards a Big Data scenario

Sentinel-1: Small Baseline System
1995 2000 2005 2010 2015
Time [year]
-1.0
-0.5
0.0
0.5
1.0
PerpendicularBaseline[km]
ERS and ENVISAT
Perpendicular baseline distribution

Sentinel-1: Small Baseline System
Perpendicular baseline distribution
ERS and ENVISAT
1995 2000 2005 2010 2015
Time [year]
-1.0
-0.5
0.0
0.5
1.0
PerpendicularBaseline[km]
Sentinel-1

ESA’s GEP web site
https://geohazards-tep.eo.esa.int

TEP Scenario 1 – EO Data Exploitation,which allows a user to discover/select
data and pre-existing processing service; process data; and visualize/analyse or
select and apply data manipulation tools to the result.
TEP Scenario 2 – New EO Service Development, which allows a user to
discover/select a data sample and software components; engineer (or upload)
and validate an application (such as a processor); and deploy the application on
the platform for use also by other users.
TEP Scenario 3 – New EO Product Development, which allows a user to
Authenticate; alternatively upload and deploy a new processor; discover/select
data; process the data; and eventually publish the resulting product. Note that
this scenario implies the implementation of effective, scalable, and cost-
optimized strategies for infrastructure provisioning for processing of very big
datasets.
GEP Pilot Project Scenarios
geohazards-tep@esa.int
If interested in submitting a Pilot Project according to the 3 depicted scenarios, contact:

GEP Geo-portal

GEP Geo-portal: data selection

productType

PlatformName
OrbitDirection
44
OrbitTrack

GEP Geo-portal: saving a Data Package
S1A_T44_Naples

GEP Geo-portal: saving a Data Package

GEP Geo-portal: Processing Services

GEP Geo-portal: InSAR SBAS - data

GEP Geo-portal: InSAR SBAS - parameters

GEP Geo-portal: InSAR SBAS - processing

GEP Geo-portal: InSAR SBAS - Results

GEP Geo-portal: My Jobs

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ESA’s GEP web site

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cm/year
>6<-6
Campi Flegrei Caldera
Systematic
S1 P-SBAS
processing
Sen2nel-1
12/6 days
repeat cycle
Community Geoportal
2015 2016
Time [year]
-2
0
2
4
6
8
10
Displacement[cm]
2015 2016
Time [year]
-2
0
2
4
6
8
10
Displacement[cm]
2015 2016
Time [year]
-2
0
2
4
6
8
10
Displacement[cm]
2015 2016
Time [year]
-2
0
2
4
6
8
10
Displacement[cm]
GEP: Sentinel-1 P-SBAS systematic processing

Sentinel-1 P-SBAS results at large scale

cm/year
>6<-6
Time Interval: October 2014 – March 2016

Campi Flegrei caldera
2015 2016
Time [year]
-2
0
2
4
6
8
10
Displacement[cm]

Gargano Region

Gargano Region
2015 2016
Time [year]
-6
-4
-2
0
2
Displacement[cm]
2015 2016
Time [year]
-6
-4
-2
0
2
Displacement[cm]

cm/year
>6<-6
2015 2016
Time [year]
-2
0
2
4
6
Displacement[cm]
2015 2016
Time [year]
-10
-8
-6
-4
-2
0
2
4
Displacement[cm]
Etna Volcano

Ask for an Account to:
sbas-help@irea.cnr.it

Credits
CNR-IREA
M. Bonano, F. Casu, C. De Luca, R. Lanari, M. Manunta, I. Zinno,
M. Manzo, A. Pepe
ESA RSS
R. Cuccu, J.M. Delgado Blasco, G. Rivolta
ESA
H. Laur, S. Loekken, P. Bally, S. Pinto, A. Marin, A. Cuomo
Terradue
F. Brito, F. Pacini, E. Mathot,, H. Caumont

12-days Interferometric
Coherence map of almost
the en3re Europe

300 slices

June-July 2015

Coverage: 3,200,000 km2.
In coopera2on with ESA G-POD
Thank you!

SBAS-DInSAR processing on the ESA Geohazard Exploitation Platform

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