# Deformation monitoring with Sentinel-1 Wave mode data ††thanks: Email:
<EMAIL_ADDRESS>
Piyush S. Agram , Matthew T. Calef , Kelly M. Olsen , Kimberly Carlson , Scott
Arko
Descartes Labs Inc.
Santa Fe, NM, USA  0000-0003-0711-0264 0000-0003-4701-7224 0000-0002-2709-9237
0000-0001-9154-1248

###### Abstract

We describe the salient characteristics of Sentinel-1 wave (WV) mode
vignettes. We describe our approach for working with WV mode data that enables
vignette-based data access and processing, thereby eliminating the Sentinel-1
Single Look Complex (SLC) data packaging and current archive metadata
conventions as a bottleneck to large scale processing. We discuss the spatial
and temporal coverage of Sentinel-1 WV mode data and show that a large volume
of data has been acquired over land masses in this mode, thus allowing us to
use it for land monitoring applications as well as ocean applications. For
targeted infrastructure monitoring studies, we are able to generate
coregistered, geocoded stacks of WV mode SLCs for any area of interest (AOI)
with sufficient wave mode coverage, in a few minutes. We demonstrate the
applicability of using WV mode data for deformation monitoring applications.
Finally, we discuss the benefits and limitations of working with Sentinel-1 WV
mode data.

_K_ eywords Sentinel-1 $\cdot$ SAR $\cdot$ InSAR

##  1 Introduction

The Sentinel-1 synthetic aperture radar (SAR) constellation [1], part of
Europe’s Copernicus Earth Observation programme, has been operational since
October 2014 and has significantly boosted the scientific community’s ability
to monitor our planet’s surface at a global scale. Data acquired by this
constellation have been freely available for end users, enabling local and
global scale application users to reliably access and analyze SAR imagery over
their areas of interest (AOIs). A number of wide-area land monitoring
applications have been built on top of Sentinel-1’s data archive, e.g., [2].

As described in [1], the operation of the Sentinel-1 satellites follows a pre-
programmed conflict-free observation plan where the imaging modes, i.e.
Interferometric Wide (IW) swath mode and Extended Wide (EW) swath mode, can be
operated for a maximum of 25 minutes per orbit. For the remaining time the
instrument operates over the open ocean in the Wave (WV) mode providing
sampled images of 20 × 20 km area, known as vignettes, every 100 km along the
orbit at a low data rate mode (see Figure 1). While the use of IW mode
Sentinel-1 imagery is well established for various applications using both
radar backscatter and interferometry, e.g., [2, 3], the characteristics of the
WV mode are less well known. In this manuscript, we describe the salient
features of this mode and its applications in detail.

This manuscript is organized as follows. Section 2 provides an overview of
Sentinel-1’s WV mode, including a detailed look at its spatio-temporal
coverage characteristics. We present a short overview of possible applications
using Sentinel-1 WV mode data in Section 3. In Section 4, we delve deeper into
the use of WV mode data for interferometry and deformation monitoring
applications. Finally, in Section 5, we discuss the benefits and limitations
of using Sentinel-1 WV mode data for land monitoring applications.

##  2 Sentinel-1 WV mode

The WV imaging mode [4, 5] was specifically designed to measure ocean wave
spectra and ocean surface winds at a global scale. The Sentinel-1 mission
acquires WV mode vignettes in a "leapfrog" pattern [6], i.e, data is acquired
in a single polarization (VV or HH) over 20 km by 20 km vignettes every 100 km
along the orbit, acquired alternately on two different beams or incidence
angles. Vignettes on the same beam are separated by 200 km. Swaths alternate
beams between near range (WV1) and far range (WV2). The detailed imaging
characteristics of the WV mode beams are enumerated in Table 1. Note that WV
mode has a higher spatial resolution (4 to 5 meters) compared to the
operational IW and EW modes.

Table 1: Sentinel-1 WV mode swath characteristics | WV1 | WV2
---|---|---
Wavelength |  $5.5466$ cm |  $5.5466$ cm
Antenna length |  $12.3$ m |  $12.3$ m
Bandwidth |  $74.5$ MHz |  $48.2$ MHz
Range sampling rate |  $100.1$ MHz |  $54.6$ MHz
Pulse repetition frequency |  $1.65$ KHz |  $1.65$ KHz
Incidence angle at minimum altitude |  $23.47^{\circ}$ \- $25.03^{\circ}$ |  $36.67^{\circ}$ \- $37.92^{\circ}$
Look angle at minimum altitude |  $21.03^{\circ}$ \- $22.40^{\circ}$ |  $32.56^{\circ}$ \- $33.62^{\circ}$
Incidence angle at maximum altitude |  $21.68^{\circ}$ \- $23.22^{\circ}$ |  $34.88^{\circ}$ \- $36.13^{\circ}$
Look angle at maximum altitude |  $19.43^{\circ}$ \- $20.79^{\circ}$ |  $30.96^{\circ}$ \- $32.02^{\circ}$

It is also important to understand that WV mode is a lower "data" rate imaging
mode [1] and hence has a lower signal-to-noise (SNR) ratio than the
operational imaging modes - IW and EW. The Sentinel-1 Mission Performance
Cluster (https://sar-mpc.eu/) consistently tracks the performance of this
imaging mode and reports that the Normalized Radar Cross Section (NRCS) bias
is near zero dB and the standard deviation is 1.6 dB for WV1 and 1.8 dB for
WV2 beams [7]. It should also be noted that the WV2 antenna pattern originally
used the stripmap S4 beam coefficients due to a limit on the number of
possible patterns in the on-board memory resulting in worse performance in
terms of Noise Equivalent Sigma Zero (NESZ). An optimised WV2 antenna pattern
configuration was implemented on the 22nd June, 2021 for Sentinel-1A and on
the 24th June, 2021 for Sentinel-1B allowing a NESZ improvement of about 5 dB
[7].

###  2.1 Data packaging

Figure 1: Geometry associated with a single WV mode SAFE granule (Blue) in the
Copernicus Data Space Ecosystem (CDSE) archive and the actual bounding boxes
of individual vignettes (Red) included in the granule. Note that the SAFE
granule contains no usable data over the most of the geometry associated with
the granules in the distribution archive.

Sentinel-1 WV mode SLC data are distributed in the Standard Archive Format for
Europe (SAFE) format, with each vignette included as a stand-alone TIFF file
within the SAFE granule. All vignettes acquired in a single datatake are
packaged together in a single SAFE granule, resulting in a large variation in
the size of these granules. A single WV mode SLC SAFE granule can contain from
anywhere between 15 to 160 vignettes. The SAFE granules are also tagged with
the geometry of the bounding box of all contained vignettes as shown in Figure
1.

Following [3], we have labelled and indexed every individual vignette in WV
mode SLCs archived at the Alaska Satellite Facility (ASF), and built data
access mechanisms that avoid having to download entire SAFE archives. This
allows us to scale our processing pipelines with WV mode data in a manner
similar to our burst-based IW mode pipelines. Sentinel-1 WV mode acquisitions
are not synchronized [1], and hence do not follow a consistent underlying
tiling system like the IW mode bursts [3, 8]. Nevertheless, knowing the
location of each individual vignette allows us to treat this dataset like
those provided by tasked commercial providers and access them on-demand at
scale.

###  2.2 Acquisition history

Figure 2: Number of vignettes per month over the history of the Sentinel-1
mission through June 1, 2024. These are accessible via the Alaska Satellite
Facility (ASF) archive.

Figure 2 shows the count of vignettes acquired by each satellite per month
over the history of the Sentinel-1 mission, that is available via the ASF
archive. Over nine million individual vignettes have been acquired so far at a
rate of about sixty thousand vignettes per satellite per month. From Figure 2,
it appears that the number of WV mode acquisitions using Sentinel-1A declined
after the Sentinel-1B outage in December 2021, possibly due to increased
demand for coverage in IW mode. We would expect the number of WV mode
acquisitions to recover to nominal levels again after the launch of
Sentinel-1C.

###  2.3 Coverage of land masses

Table 2: Approximate vignette count per continent over the history of the Sentinel-1 mission untill June 1, 2024. Continent | Approximate Count
---|---
Africa | $65000$
Australia | $34000$
North America | $40000$
South America | $45500$
Total | $183500$

The WV mode acquisitions are not synchronized [1]. However, because the
Sentinel-1 mission follows a conflict-free observation scenario that is not
modified often, we fortuitously end up with deep stacks of repeating
vignettes. When both Sentinel-1A and Sentinel-1B satellites were in operation,
more data was acquired over land masses. We present examples of WV mode
coverage over a 12-day cycle in May 2020 over Australia and Africa in Figure
3. Table 2 shows the approximate number of vignettes over different
continental land masses in the archive so far. WV mode coverage over mainland
Asia and Europe is almost zero and negligible over Antarctica. Note that a
vast majority of these vignettes over land masses were acquired repeatedly due
to the mission’s stable observation scenario. Since the Sentinel-1B outage in
December 2021, WV mode coverage over land masses is largely restricted to
Eastern Australia.

Figure 3: Footprints of vignettes acquired between May 1, 2020 to May 13, 2020
over Australia (Left) and Africa (Right), that are accessible via the Alaska
Satellite Facility (ASF) archive.

##  3 Monitoring applications using WV mode

Figure 4: Two 1000m x 1000m chips from a single vignette showing bright
targets in the Arabian sea in March 2018 in 5 meter WV mode backscatter
imagery. This region is not imaged nominally by optical satellites or other
SAR satellites.

In this section, we present an overview of possible applications with
Sentinel-1 WV mode data. The WV mode was designed and implemented to study
ocean winds and waves [4, 5]. The associated Sentinel-1 Level 2 Ocean (OCN)
products [6], their validation and applications are addressed in great detail
in the literature of oceanography and ocean remote sensing communities, e.g.,
[9, 10, 11, 12] and we refer interested readers to
https://oceanwavesremotesensing.ifremer.fr/ for these topics. Instead, we
treat Sentinel-1 WV mode SLCs like SAR imagery acquired by any other sensor
operating in stripmap mode and focus on its applicability to traditional SAR
applications. We provide some simple examples and do not delve into
implementation details, as deformation monitoring (Section 4) is the primary
focus of this manuscript.

###  3.1 Backscatter-based ocean applications

Sentinel-1 WV mode data is, as far as we know, the only public source of
imagery with approximately five meter resolution that is consistently acquired
over open oceans where traditional remote sensing missions like Landsat and
Sentinel-2 usually don’t acquire imagery. Figures 4 and 5 includes examples of
direct and indirect observations of maritime activity over remote regions that
are far away from the coastline to be regularly monitored using other publicly
available datasets. In Figure 4, we include an image in the Arabian sea over a
relatively busy shipping lane from March 2018. In Figure 5, we include a
vignette with no obvious bright targets but showing possible oil-related
streaks from maritime activity on a busy shipping lane in the Pacific ocean
from November 2020.

Figure 5: Possible oil related artifacts in a 6000 meter-by-6000 meter parch
of the Pacific ocean in November 2020. This region is not imaged nominally by
optical satellites or other SAR satellites regularly.

###  3.2 Backscatter-based land applications

We process WV mode vignettes on to a 5 meter grid using the same pipelines
that we have developed for IW bursts [3]. We do observe along-track offsets in
geolocation for WV mode and we address this in Section 4.2. In general, we are
able to use WV mode backscatter data in the same manner as IW mode backscatter
data. We only present one example here. Figure 6 shows a simple RGB composite
of three vignettes from Track 110 acquired on Jan 23/Feb 4/Feb 16 2023 over
Wairoa, New Zealand. This area was impacted by Cyclone Gabrielle on this time
frame and the affected areas are discernible. Though this area was also imaged
in IW modes on Track 8 and Track 175, these acquisitions are only 24 hours
apart resulting in 11-12 day time gap between revisits. WV mode data
complements the IW acquisitions and improves the temporal sampling for
monitoring applications and disaster response.

Figure 6: Simple RGB composite of three WV mode vignettes from Track 110
acquired on Jan 23, Feb 4 and Feb 16 2023 over Wairoa, New Zealand. This area
was impacted by Cyclone Gabrielle in Feb 2023.

###  3.3 Proxy for commercial smallsat data

Sentinel-1 WV mode data appears to have SNR and geolocation accuracy
characteristics that are similar to current smallsat SAR satellites like
Iceye, Capella and Umbra. While only a few hundreds of scenes from these
smallsat sensors are available in the public domain to develop pipelines and
models, the nine million WV mode vignettes acquired so far can help fill the
gap for many applications. While not perfect, the higher resolution of WV mode
data is a better proxy for smallsat data than the lower resolution IW mode.

##  4 Deformation monitoring using WV mode

In this section, we delve deeper into geometric properties of Sentinel-1 WV
mode and our approach to generating coregistered stacks from this data. We
also present an example of deformation monitoring using WV mode data over a
mining area. We observe that while WV mode data has lower SNR than the
operational imaging modes from Sentinel-1, it still benefits from the
mission’s narrow orbit tube [1]. Specifically, if vignettes from the same
track overlap on repeat passes they are almost guaranteed to support InSAR
analysis.

###  4.1 Geometric accuracy

Relative geometric accuracy for the Sentinel-1 IW mode, which is the primary
driver for automating interferometric and change detection analysis, is on the
order of 15-20cm [13]. This is an order of magnitude less than the resolution
of the IW mode SAR imagery, allowing us to geocode SLC data directly and
significantly simplify SAR-InSAR processing workflows [3]. While several
studies have focused on the geometric accuracy of IW and stripmap modes of
Sentinel-1, we did not find any information related to the geometric accuracy
of WV mode in literature.

Since, we do not have access to corner reflectors located within WV mode
vignette footprints for characterizing geometric accuracy, we apply techniques
that rely on amplitude cross-correlation similar to ones used in [14] for
characterizing relative geometric accuracy between repeat passes. Figure 7
shows the relative along-track offsets for two different stacks of WV mode
data. We observe discrepancies up to 5 milliseconds (approximately 35 meters)
in the along track direction. The range offsets are comparable to those
observed in IW mode and have not been shown.

The observed along track shifts possibly indicate uncompensated timing
corrections in the WV mode processor. We also note that WV mode was primarily
designed to operate over open oceans, where relative geometric accuracy
requirements are low and interferometry was not the primary target application
for this mode.

Figure 7: Relative along track time shifts in milliseconds, with respected to
an arbitrarily chosen reference scene in the stack, estimated using image
cross-correlation for two different stacks over (Left) Brisbane, Queensland,
Australia and (Right) Mableton, Georgia, USA.

###  4.2 Stack generation

From Section 4.1, it is evident that geocoding each vignette independently,
like we do in our IW mode burst-based pipelines [3], will not result in sub-
pixel level coregistration necessary for interferometry. Hence, we adopted a
modified workflow to generate coregistered stacks from WV mode vignettes. The
modified approach is as follows:

  1. 1.

Geocode each vignette independently using its metadata and orbit information
to an intermediate product at a posting of 2.5 meters.

  2. 2.

Estimate a bulk shift per image with respect to a reference image in the stack
in map coordinates from the geocoded data using amplitude cross correlation
and a large window of size greater than 1024, using a network-based approach
[15]. The estimated offsets in map coordinates are transformed to along-track
time and slant range offset using a simple rotation matrix computed at the
center of the correlation windows.

  3. 3.

Apply the estimated bulk offsets in along-track time and slant range to the
vignette metadata and geocode them again to produce the final coregistered
stack at a posting of 2.5 meters.

The modified workflow implements the same two-stage coregistration paradigm,
with coarse and fine coregistration steps, as traditional interferometric
workflows [16], except that we perform these steps in map coordinates. Working
directly in map coordinates allows us to eliminate the need to store and share
large intermediate radar geometry grid-related arrays while benefiting from
the simplified data management features within our platform [17]. The
described workflow can be easily adopted to work with data from other SAR
sensors and we have validated this approach by applying it to limited stacks
of SLC data available to us from TerraSAR-X, COSMO SkyMed and Iceye as
reported in [3].

Once WV mode data has been coregistered, geocoded and available on our
platform, we are able to deploy our InSAR analytics pipelines that we have
developed with IW mode [18] on this data.

Figure 8: Scatter plot of points included in an InSAR deformation analysis
based on complex WV mode data. Points are colored by cumulative deformation
and overlaid on Sentinel-2 optical imagery. Deformation measurements are in
millimeters.

###  4.3 Results

In this section, we present the estimated deformation time-series estimated
from a stack of Sentinel-1 WV mode data acquired over a mine in Dalby,
Queensland, Australia from Track 38 from June 2023 to May 2024. We processed
the stack as described in Section 4.2, and we generated deformation estimates
using the second method (Method 2) described in Section 4 of [18]. We analyzed
the geocoded SLC data on a downsampled 5 meter-by-5 meter grid as the high
density of reliable scatterers still yielded over half a million points over
this AOI. The results are shown in Figures 8 and 9. Figure 10 shows the
estimated relative deformation of a single pixel (the red dot in Figure 10).

Figure 9: Cropped view of Fig 8. The thick red dot indicates the location
where the deformation history is reported in Fig. 10.

We make the following observations from comparing results derived using WV
mode data and IW mode data.

  * •

With WV mode data we can generate deformation results on a 5m by 5m regular
grid, while while IW mode data we generate results on a 10m by 10m regular
grid. This factor of four allows us to resolve smaller deformation features
than would be possible to resolve with IW data as input.

  * •

The denser grid also results in increased resource usage and processing costs
for the same physical area.

  * •

The fraction of points selected when using the WV mode data as input was
_higher_ than the fraction of points selected when using the IW mode data as
input. We used the same coherence cutoff in both cases and we used roughly the
same time range – early June through late April 2024. This observation is
consistent with previous studies [19, 20].

  * •

It is worth reiterating the the SNR is lower for WV mode data, nevertheless,
using these data as an input for an InSAR pipeline lead to results that were
quite good compared to the results with the higher SNR IW mode data.

Figure 10: Deformation history in millimeters at the red dot in Fig 9. Olsen
et al. ([18]) describes the method that generated the error bars.

Overall, we are able to use Sentinel-1 WV mode data in our InSAR analytics
pipelines in the same we would data acquired by other SAR sensors.

##  5 Conclusion

In this manuscript, we have described the salient features of WV mode SAR
imagery acquired by the Sentinel-1 mission. We have described our approach of
indexing individual WV mode vignettes, that allows us to work with this type
of SAR data at scale. We have described the potential of WV mode backscatter-
based applications over land and ocean. We have also demonstrated the
interferometric capabilities of Sentinel-1’s WV mode with an example. We also
demonstrated that Sentinel-1 WV mode data are potentially useful sources of
data during emergency response and as a proxy for commercial smallsat SAR data
for model and application development. With the imminent launch of Sentinel-1
C and D satellites, we expect the volume of WV mode acquisitions to increase
again, including over land. While global coverage is not possible by design,
and availability of deep stacks is not guaranteed with WV mode, we will
continue to treat this collection of SAR data similarly to other tasked and
opportunistic data collects from commercial providers for land and ocean
applications.

## Data Availability

The single-look complex (SLC) Sentinel-1 imagery and associated metadata are
available at the Alaska Satellite Facility’s Vertex Portal here:
https://search.asf.alaska.edu/#/ (accessed on 1 June 2024).

## Acknowledgments

The work presented in this manuscript was first conceptualized by Mike Warren,
who also built the preliminary database of wave mode vignette footprints in
2020. This work contains Copernicus Sentinel-1 data (2016-2024) processed by
European Space Agency.

## References

  * [1] Ramon Torres, Paul Snoeij, Dirk Geudtner, David Bibby, Malcolm Davidson, Evert Attema, Pierre Potin, BjÖrn Rommen, Nicolas Floury, Mike Brown, Ignacio Navas Traver, Patrick Deghaye, Berthyl Duesmann, Betlem Rosich, Nuno Miranda, Claudio Bruno, Michelangelo L’Abbate, Renato Croci, Andrea Pietropaolo, Markus Huchler, and Friedhelm Rostan.  Gmes sentinel-1 mission.  Remote Sensing of Environment, 120:9 – 24, 2012.  The Sentinel Missions - New Opportunities for Science.
  * [2] Alessandro Ferretti, Emanuele Passera, and Renalt Capes.  End-to-end implementation and operation of the European Ground Motion Service (EGMS): Algorithm Theoretical Basis Document.  Technical Report EGMS-D3-ALG-SC1-2.0-006, European Environment Agency, dec 2021.
  * [3] Piyush S. Agram, Michael S. Warren, Matthew T. Calef, and Scott A. Arko.  An efficient global scale Sentinel-1 radar backscatter and interferometric processing system.  Remote Sensing, 14(15), 2022.
  * [4] S. Lehner, J. Schulz-Stellenfleth, B. Schattler, H. Breit, and J. Horstmann.  Wind and wave measurements using complex ERS-2 SAR wave mode data.  IEEE Transactions on Geoscience and Remote Sensing, 38(5):2246–2257, 2000.
  * [5] Klaus Hasselmann, B Chapron, L Aouf, F Ardhuin, F Collard, G Engen, Susanne Hasselmann, P Heimbach, H Johnsen, et al.  The ERS SAR wave mode: A breakthrough in global ocean wave observations.  2013\.
  * [6] European Space Agency.  Sentinel-1 User SAR Guide.  https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar, 2023.  Accessed on Jan 1, 2024.
  * [7] Guillaume Hajduch, Matthieu Bourbigot, Harald Johnsen, and Riccardo Piantanida.  Sentinel-1 level 1 detailed algorithm definition.  Technical Report S1-RS-MDA-52-7441, European Space Agency, feb 2022.
  * [8] Sentinel-1 Mission Performance Cluster.  Sentinel-1 burst id map, version 20220530.  https://sar-mpc.eu/test-data-sets/, may 2022.  Generated by Sentinel-1 SAR MPC.
  * [9] Fabrice Ardhuin, Fabrice Collard, Bertrand Chapron, Fanny Girard-Ardhuin, Gilles Guitton, Alexis Mouche, and Justin E. Stopa.  Estimates of ocean wave heights and attenuation in sea ice using the sar wave mode on sentinel-1a.  Geophysical Research Letters, 42(7):2317–2325, 2015.
  * [10] J. E. Stopa and A. Mouche.  Significant wave heights from sentinel-1 sar: Validation and applications.  Journal of Geophysical Research: Oceans, 122(3):1827–1848, 2017\.
  * [11] Huimin Li, Alexis Mouche, Justin E. Stopa, and Bertrand Chapron.  Calibration of the normalized radar cross section for Sentinel-1 wave mode.  IEEE Transactions on Geoscience and Remote Sensing, 57(3):1514–1522, 2019.
  * [12] Chen Wang, Alexis Mouche, Pierre Tandeo, Justin E Stopa, Nicolas Longépé, Guillaume Erhard, Ralph C Foster, Douglas Vandemark, and Bertrand Chapron.  A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel-1 wave mode.  Geoscience Data Journal, 6(2):105–115, 2019.
  * [13] Nuno Miranda, Peter Meadows, Riccardo Piantanida, Andrea Recchia, David Small, Adrian Schubert, Pauline Vincent, Dirk Geudtner, Ignacio Navas-Traver, and Francisco Ceba Vega.  The sentinel-1 constellation mission performance.  In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pages 5541–5544, 2017.
  * [14] Zhang Yunjun, Heresh Fattahi, Xiaoqing Pi, Paul Rosen, Mark Simons, Piyush Agram, and Yosuke Aoki.  Range geolocation accuracy of c-/l-band sar and its implications for operational stack coregistration.  IEEE Transactions on Geoscience and Remote Sensing, 60:1–19, 2022\.
  * [15] Heresh Fattahi, Piyush Agram, and Mark Simons.  A network-based enhanced spectral diversity approach for tops time-series analysis.  IEEE Transactions on Geoscience and Remote Sensing, 55(2):777–786, 2017.
  * [16] Eugenio Sansosti, Paolo Berardino, Michele Manunta, Francesco Serafino, and Gianfranco Fornaro.  Geometrical sar image registration.  IEEE Transactions on Geoscience and Remote Sensing, 44(10):2861–2870, 2006.
  * [17] Carly Marie Beneke, Samuel Skillman, Michael S Warren, Tim Kelton, Steven Patrick Brumby, Rick Chartrand, and Mark Mathis.  A platform for scalable satellite and geospatial data analysis.  In AGU Fall Meeting Abstracts, volume 2017, pages IN32C–04, 2017\.
  * [18] Kelly M Olsen, Matthew T Calef, and Piyush S Agram.  Contextual uncertainty assessments for insar-based deformation retrieval using an ensemble approach.  Remote Sensing of Environment, 287:113456, 2023.
  * [19] Alessandro Ferretti, Andrea Tamburini, Fabrizio Novali, Alfio Fumagalli, Giacomo Falorni, and Alessio Rucci.  Impact of high resolution radar imagery on reservoir monitoring.  Energy Procedia, 4:3465–3471, 2011.  10th International Conference on Greenhouse Gas Control Technologies.
  * [20] Mario Costantini, Alessandro Ferretti, Federico Minati, Salvatore Falco, Francesco Trillo, Davide Colombo, Fabrizio Novali, Fabio Malvarosa, Claudio Mammone, Francesco Vecchioli, Alessio Rucci, Alfio Fumagalli, Jacopo Allievi, Maria Grazia Ciminelli, and Salvatore Costabile.  Analysis of surface deformations over the whole italian territory by interferometric processing of ers, envisat and cosmo-skymed radar data.  Remote Sensing of Environment, 202:250–275, 2017.  Big Remotely Sensed Data: tools, applications and experiences.