Volcanology: Lessons learned from Synthetic Aperture

Radar imagery

V. Pinela, M. P. Polandb, A. Hooperc

aISTerre, Universite de Savoie, IRD, CNRS, F73376 Le Bourget du Lac, FrancebU.S. Geological Survey Hawai‘ian Volcano Observatory, PO Box 51, Hawai‘i National

Park, HI 97818-0051, USAcCOMET, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT

Abstract

Twenty years of continuous Earth observation by satellite SAR has re-

sulted in numerous new insights into active volcanism, including a better

understanding of subsurface magma storage and transport, deposition of vol-

canic materials on the surface, and the structure and development of volcanic

edifices. This massive archive of data has resulted in fundamental leaps in

our understanding of how volcanoes work–for example, identifying magma

accumulation at supposedly quiescent volcanoes, even in remote areas or in

the absence of ground-based data. In addition, global compilations of vol-

canic activity facilitate comparison of deformation behavior between different

volcanic arcs and statistical evaluation of the strong link between deforma-

tion and eruption. SAR data are also increasingly used in timely hazards

evaluation thanks to decreases in data latency and growth in processing and

analysis techniques. The existing archive of SAR imagery is on the cusp of

being enhanced by a new generation of satellite SAR missions, in addition

Email address: Virginie.Pinel@univ-savoie.fr (V. Pinel)

Preprint submitted to Journal of Volcanology and Geothermal Research October 7, 2014

to ground-based and airborne SAR systems, which will provide enhanced

temporal and spatial resolution, broader geographic coverage, and improved

availability of data to the scientific community. Now is therefore an oppor-

tune time to review the contributions of SAR imagery to volcano science,

monitoring, and hazard mitigation, and to explore the future potential for

SAR in volcanology. Provided that the ever-growing volume of SAR data

can be managed effectively, we expect the future application of SAR data to

expand from being a research tool for analyzing volcanic activity after the

fact, to being a monitoring and research tool capable of imaging a wide va-

riety of processes on different temporal and spatial scales as those processes

are occurring. These data can then be used to develop new models of how

volcanoes work and to improve quantitative forecasts of volcanic activity as

a means of mitigating risk from future eruptions.

Keywords:

SAR, volcanoes, deformation, eruptive deposits, DEM

Contents

1 Introduction 4

2 Synthetic Aperture Radar analysis techniques and available

data 8

2.1 Synthetic Aperture Radar principles . . . . . . . . . . . . . . . 8

2.2 Surface change detection . . . . . . . . . . . . . . . . . . . . . 10

2.3 Retrieval of topography through InSAR . . . . . . . . . . . . . 11

2.4 Retrieval of displacement . . . . . . . . . . . . . . . . . . . . . 13

2

2.4.1 Displacement from InSAR . . . . . . . . . . . . . . . . 14

2.4.2 Pixel offset tracking . . . . . . . . . . . . . . . . . . . . 15

2.4.3 Multiple-aperture interferometry (MAI) . . . . . . . . 16

2.4.4 Precise positioning . . . . . . . . . . . . . . . . . . . . 17

2.5 Time series processing . . . . . . . . . . . . . . . . . . . . . . 18

2.5.1 Persistent scatterer InSAR . . . . . . . . . . . . . . . . 19

2.5.2 Small baseline InSAR . . . . . . . . . . . . . . . . . . . 20

2.5.3 Combined time series InSAR . . . . . . . . . . . . . . . 21

2.6 SAR platforms and available data . . . . . . . . . . . . . . . . 22

3 Mapping surface characteristics with SAR 26

3.1 Amplitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.2 Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3 Phase used for topographic measurement . . . . . . . . . . . . 31

4 Insights from SAR into volcano deformation 35

4.1 Sources of deformation around volcanoes . . . . . . . . . . . . 36

4.2 Overview of volcano deformation studies based on SAR data . 41

4.2.1 Magma storage . . . . . . . . . . . . . . . . . . . . . . 42

4.2.2 Magma transport . . . . . . . . . . . . . . . . . . . . . 46

4.2.3 Temporal evolution of magmatic deformation . . . . . 49

4.2.4 Subsidence of volcanic deposits . . . . . . . . . . . . . 52

4.3 Main InSAR limitations for deformation measurements . . . . 55

5 Key constraints on volcanic edifice growth and stability 58

6 Discussion 63

3

6.1 Looking back: advances made possible from SAR studies of

volcanoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

6.2 Looking forward: better understanding of volcanoes and fore-

casts of eruptions . . . . . . . . . . . . . . . . . . . . . . . . . 67

7 Conclusion 70

8 Acknowledgments 71

1. Introduction1

The advent of satellite remote sensing brought about a revolution in the2

field of volcanology. Before the availability of space-based observations, vol-3

cano monitoring and research relied on painstaking field work to measure4

such parameters as ground deformation, gas emissions, and deposit charac-5

teristics. While valuable and, in many cases, groundbreaking, such as the6

efforts that led to successful predictions of dome-building eruptions at Mount7

St. Helens in the 1980s (Swanson et al., 1983), this work was necessarily lim-8

ited in scope (both in terms of spatial coverage and temporal sampling), and9

relatively few volcanoes were intensely studied. Satellite data have made10

possible a new spectrum of measurements on a global scale that can com-11

plement more focused ground-based studies and can also reveal insights into12

remote or poorly understood volcanoes (Sparks et al., 2012; Pyle et al., 2013).13

Passive measurements in the visible, ultra-violet, and infrared parts of the14

spectrum have been used to detect eruptions, quantify the compositions and15

distributions of deposits, characterize structures, and monitor thermal, ash,16

and gas emissions in near-real time–a critical capability for such applications17

4

as eruption alerts (Wright et al., 2004a) and aviation safety (Hooper et al.,18

2012b). Active measurements from Synthetic Aperture Radar (SAR) have19

further transformed the field of volcano remote sensing, especially since the20

early 1990s. While useful for mapping structures and deposits, SAR offers21

the additional capabilities of quantifying topography and tracking surface de-22

formation, and it is not impacted by time of day or atmospheric conditions.23

Deformation studies, in particular, have exploded thanks to interferomet-24

ric SAR (InSAR). Biggs et al. (2014) reported that 198 volcanoes had been25

systematically observed by InSAR since the launch of the ERS-1 satellite,26

which is more than four times the number of volcanoes where deformation27

studies had been performed by the late 1990s (Dvorak and Dzurisin, 1997).28

Among these 198 volcanoes studied by InSAR over the course of 1992–2010,29

54 volcanoes displayed some indication of deformation, and 25 of those vol-30

canoes erupted during the same interval (Biggs et al., 2014). Including volca-31

noes that have not been systematically monitored on decadal timescales, In-32

SAR results have been reported from 620 volcanoes (out of a total of over 150033

subaerial Holocene volcanoes worldwide see http://www.volcano.si.edu/list volcano.cfm34

), with 161 observed to deform (Biggs et al., 2014). Dzurisin (2003) pointed35

out that aseismic inflation, which is readily detected by InSAR, might be an36

indicator of potential volcanic activity on intermediate timescales of years37

to months – a problematic forecasting window in volcanology – since such38

deformation would indicate magma accumulation that had not yet stressed39

the surrounding rocks to the point of breaking. His foresight has largely been40

borne out by InSAR surveys of entire volcanic arcs (e.g., Pritchard and Si-41

mons, 2002, 2004; Chaussard and Amelung, 2012; Ebmeier et al., 2013a; Lu42

5

and Dzurisin, 2014) that have detected deformation at a number of sup-43

posedly quiescent volcanoes, some of which subsequently erupted. Such44

applications demonstrate the power of InSAR – comprehensive, repeated45

deformation monitoring over broad regions that requires no ground-based46

instrumentation or field personnel.47

The utility of SAR extends beyond deformation monitoring. Variations48

in InSAR coherence over time, which provide a measure of the correlation49

in the scattering properties of the surface, can be used to map, for example,50

lava flow evolution (e.g., Dietterich et al., 2012). InSAR can also quantify51

topography – a critical base for general mapping, and an essential input to52

models of hazardous processes like lava flows (Harris and Rowland, 2001;53

Favalli et al., 2011), pyroclastic deposits (Kelfoun et al., 2009), lahars (Iver-54

son et al., 1998) and debris avalanches (Kelfoun et al., 2008). SAR amplitude55

data have proven their value by detecting structural changes at volcanoes,56

as emphatically demonstrated during the 2010 eruption of Merapi, Indonesia57

(Pallister et al., 2013; Surono et al., 2012). The ability of SAR imagery to see58

through dense cloud cover and the delivery of those data in near-real time59

facilitated hazards mitigation efforts that probably prevented extensive loss60

of life during that eruption (Pallister et al., 2013).61

Since the 1991 launch of the European Space Agency’s (ESA) ERS-1,62

there has always been at least one SAR satellite, and frequently more, in orbit63

around Earth. The first application of these data to assess volcano deforma-64

tion was monitoring subsidence of Mount Etna during 1992-1993 (Massonnet65

et al., 1995). In the 20 years since that landmark result, SAR satellites have66

evolved from mostly C-band sensors with ground-pixel resolutions of tens of67

6

meters to C-, X- and L-band systems that can have resolutions better than68

1 m, with some satellites flying in formation or as part of a constellation.69

The scientific community stands at the cusp of a “golden age” for SAR, with70

existing space-based missions about to be joined by a new generation of satel-71

lites in the forthcoming years, including the ESA’s Sentinel-1–the first SAR72

mission that is operational in nature rather than purely scientific, with data73

available within minutes of acquisition. These new satellites will provide74

data that volcanologists have identified as critical to detecting, tracking and75

understanding eruptive activity, as highlighted by a number of international76

programs. For example, the Geohazard Supersites and Natural Laboratories77

(GSNL) initiative ( http://supersites.earthobservations.org/) is designed to78

focus attention on areas prone to natural hazards by integrating ground-,79

air-, and space-based observations and making these data openly available80

to all researchers at no cost. SAR data are a particular focus of the GSNL81

program, and several volcanic regions have been identified as permanent Su-82

persites (including, as of 2014, Hawai‘i, Iceland, and Italy), with several83

candidate and event Supersites being established as well. Similarly, the 201284

International Forum on Satellite Earth Observations for Geohazards (also85

known as the Santorini conference) specifically advocated that SAR have an86

expanded role in volcano monitoring and research (Bally, 2012). Airborne87

and ground-based SAR are also seeing greater use at volcanoes around the88

world, adding a new dimension to volcanological investigations (e.g., Lund-89

gren et al., 2013; Intrieri et al., 2013).90

The imminent availability of satellite imagery from new SAR systems,91

greater use of ground and airborne SAR, and recent community efforts to92

7

ensure greater access to SAR data provide an opportune occasion to review93

20 years of progress in the volcanological applications of SAR and to an-94

ticipate future advances. Previous reviews, for example by Massonnet and95

Sigmundsson (2000) and Zebker et al. (2000) or also by Stevens and Wadge96

(2004) and d’Oreye et al. (2008), supply an important foundation upon which97

to build, and a reference to which we can relate results achieved since the turn98

of the current century. We begin our review by discussing those principles of99

SAR that make the technique valuable for volcano monitoring and research.100

We then describe application of SAR to volcanoes, including mapping of101

structures and deposits, quantifying topography, and especially tracking sur-102

face deformation, before concluding with an examination of how integrating103

results from SAR can elucidate large-scale dynamic processes and offering104

our perspective on the future of SAR in volcanology.105

2. Synthetic Aperture Radar analysis techniques and available data106

2.1. Synthetic Aperture Radar principles107

The SAR technique allows the formation of high-resolution radar images108

from data acquired by side-looking instruments installed on aircraft or space-109

craft, or even from the ground. The fundamentals underlying SAR image110

processing are presented in e.g., Curlander and McDonough (1992). Each111

pixel of an image corresponds to a resolution element on the ground, which112

receives and scatters back an electromagnetic signal emitted by the radar. A113

pixel is characterized by two values: the amplitude and the phase. The ampli-114

tude can be interpreted in terms of backscattering properties of the ground.115

The phase is not informative on its own because it is a pseudo-random contri-116

8

bution from the configuration of all scatterers within the resolution element.117

However, providing the scattering properties of the element remain stable118

between two acquisitions, the difference in phase between two images can be119

interpreted in terms of the difference in range between the radar instrument120

and the target, which is the principle of radar interferometry (see Section 2.3).121

122

SAR imaging geometry is characterized by two directions: the “azimuth”123

direction being the direction of satellite motion and the “range” direction124

corresponding to the look direction of the radar, which is approximately125

perpendicular to azimuth. Resolution elements in the range direction are126

distinguished by their distance from the satellite, which differs from the case127

of optical images where resolution elements are differentiated by viewing an-128

gle. The combination of the side-looking nature of the sensor and topography129

on the ground mean that the ground surface is often not completely imaged130

by the SAR. Surfaces oriented on ground sloping away from the sensor can131

be in a shadow zone not reached by the radar beam–an effect called ”shadow-132

ing”. When the ground slope is greater than the incidence angle (measured133

from vertical) of the radar signal, upslope becomes closer to the sensor than134

downslope, and the order of pixels in the image becomes reversed–the so-135

called “layover” effect (Figure 1).136

Two images acquired at the same time can be used to image the static137

topography, but to detect and quantify surface changes with time, at least138

two images acquired at different times need to be compared. Before proceed-139

ing with the processing, there is a need to put both images into the same140

geometry, as the sensor never acquires successive images from exactly the141

9

same position. The geometry of one image is chosen as the “master” geom-142

etry and the second “slave” image is resampled into the “master” geometry,143

such that corresponding pixels in both images correspond to the same area144

of ground. This co-registration is achieved based on orbit knowledge and re-145

fined using amplitude image correlation (see section 5.2.1 of Dzurisin (2007)146

for a detailed description of the processing).147

2.2. Surface change detection148

The radar echo for a given pixel depends on the coherent sum of the149

echo from all scatterers within the corresponding resolution element; thus,150

the amplitude and phase of the echo are sensitive to any change in the dis-151

tribution of scatterers within the element. It follows that if scatterers move152

with respect to each other or, as in case of new emplacement of surface lava153

flows, are replaced by a new set of scatterers, this can be detected in a series154

of SAR images. Detection methods are based either on the evolution of the155

reflectivity, that is to say, the amplitude of the radar images–or on changes156

in the correlation of the signal, which is a measure of the phase change.157

Variations in radar amplitude are most often quantified by differencing the158

amplitude between two successive acquisitions (e.g., Wadge et al., 2011) or by159

calculating the ratio between amplitudes (e.g., Wadge et al., 2002a). Decor-160

relation of the signal is estimated by calculation of the “coherence” between161

two acquisitions, which is a complex entity defined as (Zebker et al., 1996):162

ρ =E [z1z

∗2 ]√

E [|z1|2] E [|z2|2], (1)

where z1 and z2 are the signal values from the two images, represented as163

complex numbers, and E[x] refers to the expected value of x– in other words,164

10

the mean value of an infinite number of realisations of x. The expected values165

are usually estimated by spatial averaging over a finite region. A reduction in166

the magnitude of the coherence, which ranges between zero and one, indicates167

decorrelation. Various sources of decorrelation include: thermal decorrela-168

tion, which is due to the influence of thermal noise on the sensor and can be169

estimated theoretically by deriving the signal-to-noise ratio of a specific sys-170

tem; geometric and Doppler centroid decorrelation, which are, respectively,171

due to differences in the incidence angle and in Doppler centroid frequencies172

between two acquisitions; temporal decorrelation, which is caused by any173

change in the distribution of wavelength-scale scatterers within a resolution174

cell, or of their electrical characteristics; and volume decorrelation, which175

is related to the penetration of the radar waves and is dependent on the176

radar wavelength and the scattering medium. For a more complete review of177

decorrelation causes, see Lu and Dzurisin (2014) and references cited therein.178

179

2.3. Retrieval of topography through InSAR180

As mentioned above, while the phase of an individual SAR image cannot181

be easily interpreted, the phase difference between two coregistered images182

relates to the difference in range between the two images. This difference in183

range can in turn be related to the elevation of the ground. SAR interfer-184

ometry (InSAR) involves computing the product of a master image and the185

complex conjugate of a coregistered slave image. The phase of the resultant186

“interferogram” is equal to the difference in phase between the master and187

slave images (the InSAR technique is described in detail by Massonnet and188

Feigl, 1998; Burgmann et al., 2000; Dzurisin, 2007; Massonnet and Souyris,189

11

2008).190

There is a contribution to the interferometric phase from the differing191

viewing geometries of master and slave images, which can be divided into192

the phase expected if the surface of the Earth followed a reference ellipsoid193

(the so-called “flat Earth phase”, φFE) and the phase due to deviations of194

the real Earth surface from the reference surface due to topography (φtopo).195

In addition, there is a contribution from the displacement of the pixel in196

the satellite line-of-sight (LOS) direction (φdef ) and a contribution from the197

difference in the phase delay during propagation of the signal through the198

atmosphere between acquisitions (φatm). Thus, the interferometric phase for199

each pixel can be described as200

φ = W{φFE + φdef + φtopo + φatm + φN}, (2)

where φN is a phase noise term and W{·} is an operator that drops whole201

phase cycles (known as “wrapping”), as only the fractional part of the phase202

can actually be measured. The phase noise term includes thermal noise, but203

is usually dominated by decorrelation due both to the relative movement204

of scatterers (mentioned above) which typically increases with time, and205

differences in viewing angle between the two acquisitions, which also cause206

the scatterer echoes to sum differently. The difference in viewing angle is207

usually expressed as the “perpendicular baseline”, (B⊥) between the two208

acquisitions, which is the component of the baseline perpendicular to the209

line of sight.210

Given an accurate description of the satellite orbits, the flat Earth phase211

can be easily calculated. In the case where φdef and φatm can be considered212

negligible, e.g., for two images acquired at the same time, the interferometric213

12

phase difference can then be interpreted as being due to topography only to214

produce a digital elevation model (DEM):215

φtopo ≈ W

{−4πB⊥h

λR sin θ

}, (3)

where R is the distance between the surface and satellite, h is the elevation216

of the surface above the reference surface, λ is the radar wavelength and θ is217

the angle of incidence. Topography in InSAR phase can also be expressed in218

terms of the “altitude of ambiguity”, ha, which is defined as the change in219

elevation that results in one complete phase cycle, i.e., a topographic fringe,220

Equation 3 becoming221

φtopo ≈ W

{−2πh

ha

}. (4)

The accuracy of topographic measurement therefore improves with increased222

perpendicular baseline, which corresponds to a smaller altitude of ambiguity,223

although if the perpendicular baseline is too large the interferogram will not224

be coherent.225

InSAR does not provide absolute heights, as the phase only records the226

the fractional part of each phase cycle; however, the relative elevation be-227

tween two pixels in an interferogram can be estimated by integrating the228

phase gradient between them, a process known as “phase unwrapping” (Chen229

and Zebker, 2001).230

231

2.4. Retrieval of displacement232

Displacements can be retrieved from SAR data using a variety of methods,233

the most accurate of which is the InSAR technique, although this only gives234

displacement in the line-of-sight direction. Two other techniques can be used235

13

to retrieve displacement in the azimuth direction, but they are less accurate236

than InSAR (see below). When the line-of-sight deformation is very large,237

the InSAR technique may fail and the less accurate techniques are then also238

useful in the range direction. While the aforementioned techniques provide239

measurements of relative displacement between image pixels, with modern240

high-resolution sensors it is also possible to obtain absolute measurements of241

displacement for artificial scatterers using ranging.242

Combining the displacement information in azimuth and range directions243

for both ascending and descending acquisitions, it is then possible to invert244

for the 3D displacement field (Wright et al., 2004b). For a complete review245

on retrieval of the 3D displacement field using only SAR measurements or246

integrating SAR data with GPS, see Hu et al. (2014).247

2.4.1. Displacement from InSAR248

If an interferogram is created from two images acquired at different times,249

the component of displacement in the line of sight, l, can be retrieved from250

the interferometric phase,251

φdef = W

{−4πl

λ

}(5)

As the interferometric phase also contains other terms (Equation 2), these252

must be reduced as much as possible in order to retrieve the displacement.253

Orbit data can be used to calculate φFE and a DEM can be used to estimate254

φtopo. φN is usually reduced, at the cost of resolution, by summing many255

neighboring pixels in space (“multilooking”); however, the atmospheric term256

φatm can be significant and difficult to reduce. Although in principle the257

accuracy of displacement should be much smaller than the wavelength (λ),258

14

in practice it is limited by the atmospheric term (see Section 4.3).259

Similar to topography estimation (Section 2.3), InSAR does not provide260

an absolute value for l, as the phase only records the fractional part of each261

phase cycle; however, the relative line-of-sight displacement between any two262

pixels in an interferogram can be estimated by unwrapping the phase between263

them.264

In summary, InSAR allows to us quantify the projection of the ground265

displacement along the line-of-sight direction with an accuracy on the order266

of a cm or two. To quantify small displacement rates over a long period of267

time, specific algorithms for times series of SAR data processing have been268

developed (see Section 2.5).269

2.4.2. Pixel offset tracking270

Displacements in volcanic areas can be estimated from optical imagery271

by calculating pixel offsets, and the same principle can be applied to SAR272

imagery. Orbit information supplemented by amplitude image correlation273

is used to put all images in a common geometry by performing a global274

geometrical transformation. Pixels affected by large displacements between275

two acquisitions are characterized by residual offsets between the coregis-276

tered images. Their determination by finer amplitude correlation provides277

measurements of surface displacements in both azimuth and range directions.278

The accuracy of the technique depends on coherence (De Zan, 2014) but279

is on the order of one tenth of the spatial resolution, i.e., a few decimeters280

to a few meters. The troposphere has a limited effect on the accuracy pixel281

offsets, but the influence of ionospheric disturbances can be strong (e.g., Gray282

et al., 2000; Oyen et al., in prep). This method has been applied to quantify283

15

large displacement fields in volcanic areas (e.g., Wright et al., 2006; Grandin284

et al., 2009).285

2.4.3. Multiple-aperture interferometry (MAI)286

An alternative method for estimating pixel offsets between images relies287

on splitting the frequency spectrum into two or more sub-apertures. This288

idea was first developed by Scheiber and Moreira (2000) to estimate misreg-289

istration between images in range and azimuth and is referred to as “spectral290

diversity”. The specific application of this technique to measure displacement291

in the azimuth direction was termed “multiple-aperture interferometry” by292

Bechor and Zebker (2006), and its application has been further improved293

by e.g., Jung et al. (2009). The method consists of using sub-aperture pro-294

cessing techniques to form one forward-looking and one backward-looking295

image from one SAR image. Each of these images is then combined with the296

corresponding forward and backward looking images obtained from a second297

SAR acquisition to form one forward-looking and one backward looking inter-298

ferogram. The phase difference between the forward-looking and backward-299

looking interferograms is called an MAI image and contains the displacement300

between the two acquisition times in the azimuthal (along-track) direction.301

MAI measurements are independent of the radar wavelength but require co-302

herence between the images. Like pixel offset tracking, the accuracy of the303

MAI technique depends on coherence, but it is better than offset tracking by304

a factor of 3 for a coherence of 0.5 (De Zan, 2014), and by a higher factor for305

lower coherence. However, these theoretical factors apply only when the am-306

plitude variation is due to speckle. In the case of amplitude contrast caused307

by variations in actual radar reflectivity, the performance of offset tracking308

16

improves relative to MAI.309

Any tropospheric variations with wavelengths larger than 5 meters should310

have the same effect on both the forward and backward-looking interfer-311

ograms such that the displacement measurement is, in principle, not sig-312

nificantly limited by tropospheric contributions (Bechor and Zebker, 2006).313

However, in their study of the 2010 Merapi, Indonesia, eruption, De Michele314

et al. (2013) have shown that the presence of a volcanic ash plume may af-315

fect the backward and forward-looking images differently. As is the case for316

pixel offset tracking, the influence of ionospheric disturbances can also have317

a strong effect on MAI images (e.g., Jung et al., 2013; Oyen et al., in prep).318

2.4.4. Precise positioning319

The techniques for measuring displacement described above provide only320

the relative displacement between any two pixels within the acquisition area.321

With more recent high-resolution satellites, which also have better orbital322

tracking than earlier satellites, it is now possible to position individual point323

scatterers in a global reference frame with an accuracy of a few cm (Eineder324

et al., 2011; Schuber et al., 2012). This is achieved through ranging, and325

opens the door to measurement of displacement using individual artificial326

reflectors on the ground, by repeated point positioning. As with relative327

displacement techniques, a single acquisition geometry can only provide dis-328

placements in 2-D (azimuth and range), but a combination of ascending and329

descending images allows for 3D displacement measurement.330

17

2.5. Time series processing331

Displacements can be estimated more accurately by processing many im-332

ages together, rather than the two-image approaches described above. Most333

multi-image algorithms have concentrated on retrieving displacement from334

interferometric phase (Section 2.4.1), although in one approach, pixel offsets335

(Section 2.4.2) have also been incorporated (Casu et al., 2011). For a review336

on SAR interferometry time series analysis, see Hooper et al. (2012a).337

The simplest approach for combining many images is to sum or “stack”338

the unwrapped phase of many conventionally formed interferograms (e.g.,339

Zebker et al., 1997). Persistent deformation is highlighted in interferometric340

stacks, whereas other random signals, like atmospheric anomalies, are sup-341

pressed. This approach, however, is only appropriate when the deformation342

is episodic (with no change in source parameters over time) or steady-state,343

with no seasonal deformation. Another limitation comes from the fact that344

the non-deformation signals are reduced only by averaging and cannot be345

explicitly estimated. Algorithms for time series analysis of SAR data have346

therefore been developed to better address these issues facing conventional347

InSAR; decorrelation is addressed by using phase behavior over time to se-348

lect pixels for which decorrelation noise is minimized, and non-deformation349

signals are estimated by a combination of modeling and filtering of the time350

series. These time series algorithms fall into two categories, the first being351

persistent scatterer InSAR, which targets pixels whose scattering properties352

remain consistent both in time and from variable look directions, and the353

second being the more general small baseline approach.354

18

2.5.1. Persistent scatterer InSAR355

Decorrelation is caused by the contributions from scatterers within a res-356

olution element summing differently between SAR acquisitions. This can be357

due to relative movement of the scatterers, a change in the looking direction358

of the radar platform, or the appearance or disappearance of scatterers, as359

in the case of snow cover. If one scatterer returns significantly more energy360

than other scatterers within a resolution element, however, decorrelation is361

reduced. This is the principle behind a “persistent scatterer” (PS) pixel362

(sometimes referred to as a “permanent scatterer”). In urban environments,363

the dominant scatterers can be roofs oriented such that they reflect energy364

directly back to the radar, like a mirror, or the result of a “double-bounce”,365

where energy is reflected once from the ground, and once from a perpendic-366

ular structure, returning directly to the radar (Perissin and Ferretti, 2007).367

Dominant scatterers can also occur in areas without manmade structures368

(e.g., appropriately oriented rocks or blocks in lava fields), but there are369

fewer of them, and they tend to be less dominant.370

PS algorithms operate on a time series of interferograms all formed with371

respect to a single “master” SAR image. Phase unwrapping is achieved either372

using a temporal evolution model or algorithms that only assume that the373

temporal evolution should be generally smooth (Hooper, 2010). In both ap-374

proaches, deformation phase is separated from atmospheric phase and noise375

by filtering in time and space, the assumption being that deformation is cor-376

related in time, atmosphere is correlated in space but not in time, and noise377

is uncorrelated in space and time. In comparative studies between the two378

approaches, estimates for the deformation agree quite well, but the second379

19

approach tends to result in better coverage, particularly in rural areas where380

most volcanoes are located (Doin et al., 2010; Sousa et al., 2011).381

The result of PS processing is a time series of displacement for each PS382

pixel, with much reduced noise terms (figure 2). The technique also has the383

advantage of being able to associate the deformation with a specific scatterer,384

rather than a resolution element that has dimensions dictated by the radar385

system– usually on the order of many meters. For volcano deformation stud-386

ies this level of detail is generally not required, although it can be useful in387

separating broader deformation from the local displacements associated with388

specific structures (e.g., faults, small hydrothermal features, and localized389

subsidence features).390

2.5.2. Small baseline InSAR391

A drawback of the PS technique for volcanic applications is that the392

number of PS pixels in a volcanic environment may be limited. However,393

by forming interferograms only between images separated by a short time394

interval and with a small difference in look direction (i.e., a small baseline),395

decorrelation is minimized and for some resolution elements can be small396

enough that the underlying deformation signal is still detectable. Pixels for397

which the phase decorrelates little over short time intervals are the targets398

of small baseline methods.399

Interferograms are formed between SAR images with a small difference400

in time and look angle. In many small baseline algorithms, the interfer-401

ograms are multilooked to further decrease decorrelation noise (Berardino402

et al., 2002; Fornaro et al., 2009), however, there may be isolated ground res-403

olution elements with low decorrelation that are surrounded by elements with404

20

high decorrelation, such as a small clearing in a forest, for which multilooking405

will increase the noise. Other algorithms have therefore been developed that406

operate at full resolution (Lanari et al., 2004; Hooper, 2008), with the option407

to reduce resolution later in the processing chain by “smart” multilooking.408

Pixels are selected based on their estimated spatial coherence in each of the409

interferograms, using either standard coherence estimation, or enhanced tech-410

niques in the case of full-resolution algorithms. The phase is then unwrapped411

either spatially in two dimensions (e.g., Chen and Zebker, 2001), or using the412

additional dimension of time in 3-D approaches (e.g., Pepe and Lanari, 2006;413

Hooper, 2010). At this point, the phase can be inverted to give the phase414

at each acquisition time with respect to a single image, using least-squares415

(Schmidt and Burgmann, 2003), singular value decomposition (Berardino416

et al., 2002), or minimization of the L1-norm (Lauknes et al., 2011). Separa-417

tion of deformation and atmospheric signals can be achieved by filtering the418

resulting time series in time and space, as in the PS approach. Alternatively,419

if an appropriate model for the evolution of deformation in time is known,420

the different components can be directly estimated from the small baseline421

interferograms (Biggs et al., 2007).422

2.5.3. Combined time series InSAR423

Because persistent scatterer and small baseline approaches are optimized424

for resolution elements with different scattering characteristics, they are com-425

plimentary, and techniques that combine both approaches are able to extract426

the signal with greater coverage than either method alone (Hooper, 2008; Fer-427

retti et al., 2011). Depending on the data set, some pixels can be selected428

by both approaches, but some pixels are only selected by one method or the429

21

other (Figure 3).430

2.6. SAR platforms and available data431

Application of SAR to volcanoes began with the first deployments of432

orbital radar instruments in the 1970s and accelerated rapidly starting in433

the 1990s (Table 1). SEASAT was the first satellite SAR to orbit Earth (L-434

band, launched in 1978) but was only active for a matter of months before the435

satellite malfunctioned, although those data can be used for interferometry,436

including over volcanic terrains (Zebker and Villasenor, 1992). Short-term437

orbital radar experiments were also conducted in the 1980s and 1990s using438

NASA’s Space Shuttle as part of the Shuttle Imaging Radar (SIR) A, B,439

and C missions, which included a variety of wavelengths and good cover-440

age of volcanic targets (Gaddis et al., 1989; Zebker et al., 1996), although441

only SIR-C had the ability to measure topography and deformation. The442

first long-term, repeated SAR observations that could be applied to vol-443

cano research and monitoring began with the launch of the European Space444

Agency’s C-band ERS-1 SAR satellite in 1991, which subsequently led to445

the first published application of InSAR to volcano deformation, at Mount446

Etna (Massonnet et al., 1995). This mission was later joined by the ERS-2447

and ENVISAT satellites, which continued European Space Agency C-band448

monitoring of volcanoes for 20 years and formed the foundation of most vol-449

cano InSAR studies during the 1990s and 2000s (Figure 4). The launch450

of RADARSAT-1 in 1995 expanded this C-band catalog, with the excellent451

longevity of the satellite providing an especially valuable archive of data over452

the world’s volcanoes that could be exploited for studies of volcano defor-453

mation over decadal timescales (Baker and Amelung, 2012). RADARSAT-2,454

22

active since 2007, has continued this legacy of C-band observations through455

to the present.456

The launch of the Japanese Aerospace Exploration Agency’s JERS-1457

satellite in 1992 contributed to the realization of the importance of L-band458

SAR data for volcano deformation monitoring, given the much greater coher-459

ence of longer wavelengths in vegetated areas (Lu et al., 2005b) (Figure 5).460

Unfortunately, JERS-1 suffered mechanical failures that limited the num-461

ber of potential interferograms that could be generated. It was not until462

the launch of ALOS-1 in 2006 that comprehensive L-band studies of volca-463

noes located in tropical and other heavily-vegetated environments could be464

attempted. Such work revealed many deforming volcanoes that might not465

have been detected using C-band data (e.g., Chaussard and Amelung, 2012;466

Ebmeier et al., 2013b; Biggs et al., 2014).467

Since 2007, X-band data have been available to study volcanoes thanks468

to the TerraSAR-X, TanDEM-X, and COSMO-SkyMED missions. These469

data typically have a higher spatial resolution than those from C- and L-470

band–sometimes better than 1 m–and more frequent repeat times (especially471

in the case of COSMO-SkyMed, which is made up of a constellation of 4472

satellites that follow the same orbital path around Earth) (Table 1). The473

improved spatial and temporal resolution of these SAR systems has enhanced474

the potential of SAR imagery for change detection (e.g., Richter et al., 2013)475

and places these sensors on par with optical imagery in terms of ground-pixel476

size (Sansosti et al., 2014). The next generation of SAR satellites, beginning477

with the C-band Sentinel-1 and L-band ALOS-2 platforms, will complement478

the existing X- and C-band sensors, offering a range of wavelengths and479

23

repeat intervals that will prove invaluable for future SAR studies of active480

volcanism. As demonstrated by Lundgren et al. (2013) for a 4.5-day-long481

fissure eruption at Kılauea Volcano in 2011, a diversity of wavelengths and482

satellites, combined with frequent image acquisitions, is key to capturing483

rapidly evolving, dynamic volcanic processes–a capability that is now within484

reach thanks to the growing number of orbital SAR platforms.485

As the number of satellite SAR missions has increased, numbers of pub-486

lished volcano SAR studies have seen a commensurate rise with a step-like487

increase in 2010 (Figure 4). The same step is apparent in other fields (like488

tectonics, landslides, and subsidence), but occurs one year earlier, in 2009.489

While it is difficult to identify whether or not the delay of the step in volcanol-490

ogy is significant and, if so, what might have caused the delay, we speculate491

that it might indicate a need for additional efforts within the volcanologi-492

cal community to promote SAR volcano applications and educate volcano493

scientists in its use.494

Airborne SAR systems have provided a valuable complement to space-495

borne platforms, with NASA instruments supplying observations of volcanic496

landforms and eruptive activity starting in the 1980s (Zebker et al., 1987).497

Since the 1990s, airborne SARs that have seen extensive use are AIRSAR498

(e.g., Gaddis, 1992), TOPSAR (e.g., Rowland, 1996), and UAVSAR (e.g.,499

Lundgren et al., 2013). Only the latter was designed for repeat-pass interfer-500

ometry, and its flexibility in terms of rapid deployment and ability to remain501

on station for extended periods of time to monitor an evolving volcanic crisis502

make it a valuable tool for volcano surveillance. Many of these instruments,503

however, are cost-prohibitive for use in volcano research, and therefore have504

24

had limited operational uses. Future developments in airborne SAR instru-505

mentation would benefit from close collaboration between scientists an engi-506

neers, who can build on these past successes to design instrumentation that is507

both capable and cost-effective, thus ensuring its broad application for years508

to come. Perhaps the ultimate volcano-monitoring radar system in terms509

of ability to make near-continuous observations of topographic and surface510

change is ground-based SAR. Although expensive and not widely deployed at511

present, ground-based radars have contributed to an improved understand-512

ing of activity at several volcanoes, especially Soufriere Hills Volcano (Wadge513

et al., 2005, 2008) and Stromboli (e.g., Casagli et al., 2009; Di Traglia et al.,514

2013; Intrieri et al., 2013; Nolesini et al., 2013). Future improvements in515

instrument design and reduction in cost will increase the applications of this516

implementation of SAR volcano monitoring.517

The diversity of wavelengths and repeat intervals that are currently, or518

soon will be, available from a variety of satellite SARs, coupled with air-519

borne UAVSAR and ground-based radar measurements, provides a suite of520

resources for all-weather, near-daily (and sometimes continuous) measure-521

ment of volcano deformation at sites around the world. The initial appli-522

cations of InSAR to volcanology in the 1990s required weeks to months for523

processing and interpretation. By the 2010s, SAR data from many systems524

are available with a latency of just a few hours in some cases (e.g., SAR data525

are commonly delivered to customers in under 90 minutes once they have526

been received at the Alaska Satellite Facility ground station (Meyer et al.,527

2014)), and data processing can be completed in minutes. SAR has there-528

fore grown from a purely research tool appropriate for retrospective analysis529

25

of volcanic events to a research and monitoring tool that can contribute key530

insights during a volcanic crisis. In the sections that follow, we explore appli-531

cation of SAR data–especially space-based observations–for tracking volcanic532

activity both above and below ground.533

3. Mapping surface characteristics with SAR534

As detailed in the previous section, SAR signals have two components:535

amplitude, which measures the strength of the reflected signal, and phase,536

which includes information about the distance between the radar and the537

target. When the target is Earth’s surface, both components, as well as the538

coherence (see equation 1) between data acquired at different times, contain539

valuable information about the ground and have the ability to determine540

a range of attributes, including surface roughness, surface geometry, scat-541

tering properties, surface topography, electrical properties, and changes in542

these parameters. While SAR data are perhaps best known for mapping543

surface deformation using interferometric methods (discussed in section 4),544

the ability to quantify other surface characteristics is of equal importance,545

as these datasets constitute critical resources for assessment and monitoring546

of volcanic hazards.547

3.1. Amplitude548

The most important surface characteristics that control the strength of549

SAR backscatter are moisture content, roughness, and slope. Surfaces that550

are oriented towards the radar, rough on the scale of the radar wavelength,551

and/or moist will generally have stronger reflected returns that those that552

are not. On volcanoes, roughness and slope tend to be the most important553

26

of these factors, and they define much of the variation in amplitude within a554

radar image (Gaddis et al., 1989). Changes in these parameters may provide555

evidence of volcanic activity, including emplacement of new deposits and de-556

struction of existing landforms. Because radar images can be acquired at557

night and during cloudy conditions, SAR has a decisive advantage over opti-558

cal and infrared sensors for monitoring volcanism. This application is perhaps559

best demonstrated by recent activity at Soufriere Hills Volcano (Montserrat),560

Eyjafjallajokull (Iceland), and Merapi (Indonesia).561

The 1995 - present eruption of Soufriere Hills Volcano has been char-562

acterized by the extrusion and destruction of a series of silicic lava domes563

(Wadge et al., 2010). Tracking changes in SAR amplitude over time has564

been a valuable tool for mapping pyroclastic deposits and the evolution of565

the lava dome. Comparison of high-resolution (∼2-m pixel size) TerraSAR-X566

images acquired before and just after an explosion in July 2008 revealed that567

the dome remained stable and was not in danger of collapse–key information568

for civil defense officials that would not have otherwise been available in the569

days following the explosion due to dense cloud cover during that time period570

(Wadge et al., 2011). Pyroclastic deposits were also mapped using changes571

in SAR amplitude over time, with differences in radar shadows in valleys572

between pre- and post-eruptive imagery used to calculate the thicknesses of573

pyroclastic material that had been emplaced in those valleys (Wadge et al.,574

2011). These data were crucial for improving estimations of the eruption rate575

and its temporal evolution.576

At Eyjafjallajokull, the evolution of eruptive vents and ice cauldrons dur-577

ing the initial stage of the summit explosive eruption in 2010, when cloud578

27

and ash cover prevented visual observations, was tracked by a sequence of579

air- and space-borne SAR images (Hooper et al., 2012). The data were used580

to characterize meltwater generation that ultimately led to flooding far from581

the eruption site (Magnusson et al., 2012).582

Merapi experienced a “100-year” eruption in 2010 that threatened hun-583

dreds of thousands of residents on the flanks of the volcano (Surono et al.,584

2012). Interpretation of satellite SAR amplitude data (e.g., Figure 6) in585

near-real time allowed those observations to be combined with ground-based586

geological and geophysical results. The data made possible quantifications of587

dome growth rates and were essential in prompting warnings issued by local588

authorities that ultimately saved thousands of lives (Pallister et al., 2013).589

The amplitude of SAR images was also used to map pyroclastic deposits as-590

sociated with this event. The amplitude of co-polarized (HH) L-band radar591

data decreased where the valley-confined and overbank pyroclastic flow de-592

posits were emplaced (red area on figure 7 b). Reworked PDC deposits,593

the surge zone, and thick tephra deposits are characterized by an increase594

in ground-backscattering (blue area on figure 7 b). These patterns are not595

similar to those observed with X-band data from Montserrat (Wadge et al.,596

2011), demonstrating the importance of wavelength in backscattering prop-597

erties of volcanic (and probably other) deposits. Several airborne synthetic598

aperture radar systems and the Spaceborne Imaging Radar-C/X-Band Syn-599

thetic Aperture Radar (SIR-C/X-SAR) acquired data in L, C and X bands600

over a few active volcanoes. These data have shown that L band images give601

the best results for mapping lava flows and distinguishing multiple flow units602

(Schaber et al., 1980; Gaddis, 1992; MacKay and Mouginis-Mark, 1997).603

28

To date, studies of surface characteristics in volcanic areas using ampli-604

tude measurements from satellite SAR have been mostly restricted to co-605

polarized data, in which the polarizations (either horizontal-H or vertical-606

V) of the transmitted and received data are the same (HH or VV). Stud-607

ies using airborne SAR sensors indicate that cross-polarized data (HV or608

VH) are a more effective discriminator of lava flows having different tex-609

tures and surface roughness (Zebker et al., 1987; Gaddis, 1992). Cross-610

polarization is not available on most satellite SARs with the exception of611

RADARSAT-2 (Table 1). That satellite offers a wide range of beam modes,612

resolutions, and polarizations, including fully polarimetric (HH, HV, VH,613

and VV). RADARSAT-2 images from Kılauea Volcano, Hawai‘i demonstrate614

the utility of cross-polarized data in the study of volcanic regions. For exam-615

ple, distinguishing ‘a‘a from pahoehoe lava flows is relatively straightforward616

in cross-polarized data, but the distinction is less clear in co-polarized im-617

agery (figure 8). Likewise, identification of active lava flows can be aided by618

cross-polarized data. Mapping the extent of lava flows at Kılauea requires619

costly and time-consuming field visits or cloud-free optical/thermal satellite620

imagery. Cross-polarized RADARSAT-2 data, however, are able to easily621

distinguish the active flows from the surrounding forest–a distinction that is622

not clear from co-polarized data (figure 9)–and are not constrained by time623

of day or weather. Future use of cross-polarized SAR to map volcanic and624

other surface features and how they change over time will be facilitated not625

only by continued RADARSAT-2 acquisitions, but also cross-polarization626

modes available on SAR systems carried by both the Sentinel-1 and ALOS-2627

satellites.628

29

3.2. Coherence629

The reflected signal received by a SAR sensor is a function of the char-630

acteristics of the scatterers within a resolution cell on the ground. If the631

geometry of the scatterers changes between the times of two SAR acquisi-632

tions, the reflection from that resolution cell will not be correlated between633

the two images (low value of coherence as defined by equation 1). As an ex-634

ample, vegetated areas are commonly incoherent in SAR interferograms that635

span a given time period owing to rapid changes in the orientation of leaves636

and branches over time (although this can be mitigated to some extent by637

the use of longer wavelengths – particularly L-band; (Zebker and Villasenor,638

1992)). This lack of coherence in interferograms is typically regarded as a639

noise source in InSAR studies, since phase-difference information for defor-640

mation cannot be retrieved from incoherent regions, but there are important641

applications of coherence mapping in Earth science (Zebker and Villasenor,642

1992).643

In volcanology, incoherence can be caused by deposition of pyroclastic644

material, lahars, and lava, and by extreme deformation of the ground sur-645

face. As a result, maps of coherence that span eruptive activity may indicate646

areas covered by volcanic deposits or severely deformed (see Figure 7 c). At647

Unzen volcano, Japan, Terunuma et al. (2005) demonstrated the utility of648

coherence maps for delineating pyroclastic flows and lahars, and McAlpin649

and Meyer (2013) utilized coherence to map lahar deposits emplaced dur-650

ing the 2009 eruption of Redoubt volcano, Alaska. Similarly, lava-flow area651

can be mapped by means of SAR coherence, as demonstrated in Hawai‘i652

(Zebker et al., 1996) and the Galapagos (Rowland et al., 2003) as long as653

30

the lava flows are confined to previously coherent areas (in other words, not654

traversing vegetated areas). By combining lava-flow area determined from655

coherence with estimated flow thickness, it is possible to calculate the aver-656

age effusion rate of lava over the time spanned (Zebker et al., 1996; Poland,657

2014). Dietterich et al. (2012) extended the application of lava-flow mapping658

with coherence by developing a method for combining results from different659

satellites and look angles–datasets that are typically treated independently660

in deformation studies. Using 211 scenes from 6 ENVISAT tracks, they were661

able to map lava-flow activity at Kılauea with a temporal separation between662

coherence imagery of as little as 1 day, and on average less than 2 weeks (fig-663

ure 10A). The exceptional all-weather, day/night ability to map active areas664

over the entirety of Kılauea’s > 100 km2 lava flow field–all with high temporal665

and spatial resolution but without requiring ground-based equipment or field666

personnel–represents a quantum leap in tracking of lava flow emplacement.667

Not only are such data critical to tracking the hazard due to lava flows, they668

can also be used to constrain flow thickness (based on the time after emplace-669

ment for a new lava flow to become coherent: Dietterich et al. (2012))(figure670

10B) and therefore effusion rate–variables of obvious importance to volcano671

monitoring.672

3.3. Phase used for topographic measurement673

While phase differences in SAR scenes are most commonly used to de-674

termine surface deformation, the data can also be used to calculate surface675

elevations (see section 2.3)–one of the most important datasets in volcanol-676

ogy. For example, topographic information is critical input for models that677

forecast flow paths, particularly for lava (Harris and Rowland, 2001; Favalli678

31

et al., 2011) and lahars (Iverson et al., 1998), and it forms the base for most679

geologic mapping. Especially in flow-path applications, topographic data680

should have resolution that is sufficient to accurately forecast paths, and it681

should be updated frequently to account for changes that may impact the682

flow direction of lava and lahars as volcanic activity progresses. In addition,683

up-to-date topographic information is required for mapping surface deforma-684

tion using SAR (section 2.4.1).685

Topographic information is easily obtained from SAR data and, in fact,686

is the source of much current global topographic information (see Lu et al.687

(2012), for a review). Three methods are commonly used to extract ele-688

vation data from SAR phase: 1) repeat-pass measurements using a single689

radar instrument, 2) examination of topographic artifacts in deformation in-690

terferograms, and 3) single-pass measurements using two radar instruments691

concurrently.692

Repeat pass methods utilize data collected of the same point on the693

ground from about the same place in the air or space at two different times.694

Repeat-pass ERS-1/2 data produced some of the first space-based DEMs of695

volcanoes, including at Okmok, Alaska (Lu et al., 2003), and in the Galapagos696

(Rowland et al., 2003). Comparison between SAR-derived DEMs acquired697

before and after eruptions at those volcanoes revealed the volume of sub-698

aerial lava accumulation. The repeat-pass method requires knowledge of the699

baseline between the SAR systems at the times of image acquisition, since700

sensitivity to topography is directly proportional to baseline length (see equa-701

tion 3). Uncertainty is introduced by imprecisely known baselines (Zebker702

and Goldstein, 1986; Farr et al., 2007) and atmospheric conditions varying703

32

between acquisitions (Zebker et al., 1997), and no topographic information704

can be retrieved in areas that are incoherent between SAR acquisitions (Ze-705

bker and Villasenor, 1992). The coherence problem was mitigated somewhat706

by the tandem ERS-1/2 mission, when the orbits of the two satellites were707

configured such that acquisitions of the same area on Earth could be made708

with a temporal separation of 1 day, thus reducing the effects of temporal709

decorrelation.710

A variation on the repeat-pass method is to determine elevation from711

an examination of topographic artifacts in interferometric phase data. When712

processing interferograms to characterize surface deformation, the phase con-713

tribution from topography is removed using a preexisting DEM (see section714

2.4.1); any topographic change that occurred since the acquisition of the715

DEM will be manifested as residual phase. Using the baseline length, this716

phase can be converted to elevation change (through equation 3) and added717

to the preexisting DEM to derive an updated topographic map of the region.718

The use of a large number of interferograms can mitigate potential errors719

due to atmospheric artifacts. Ebmeier et al. (2012) were able to estimate720

lava flow thickness with an uncertainty of around 9 m using a minimum of 5721

interferograms that had large baselines (and, therefore, improved sensitivity722

to topography). As with repeat-pass DEM generation, however, topographic723

information in the interferogram is only retrievable where the interferogram724

is coherent. Nevertheless, this method provides a useful means of not only as-725

sessing deformation, but also solving for surface elevation changes over time726

that may not be represented in the initial DEM.727

Single-pass interferometry is by far the most efficient method for utilizing728

33

SAR to map surface elevations (Zebker and Goldstein, 1986). The technique729

uses two SAR sensors separated by an appropriate distance to simultaneously730

record the radar signal reflected from the surface, thus eliminating incoher-731

ence due to changes in the scattering properties of the surface over time and732

artifacts due to temporal variations in atmospheric conditions. The airborne733

TOPSAR (TOPographic SAR) system is one example that has been used734

to establish both pre- and post-eruption topography in volcanic areas (Lu735

et al., 2003; Rowland et al., 2003). Perhaps the most well-known use of736

the single-pass technique is the Shuttle Radar Topography Mission (SRTM),737

which was flown on the Space Shuttle in 2000. The mission recorded reflected738

radar signals on two SAR antennas on either end of a 60-m-long mast, the739

fixed distance of which helped to reduce uncertainty in the derived elevation740

data due to improper knowledge of the baseline (Farr et al., 2007). SRTM741

data are commonly used to remove topographic phase from deformation in-742

terferograms and have been an invaluable contribution to Earth science in743

general, but users should beware that topographic change since 2000 will744

not be represented in SRTM DEMs and will be manifested as residual phase745

that might be incorrectly interpreted as deformation. In such cases, users746

should attempt to obtain a more current DEM for their study area or employ747

a means of simultaneously solving for deformation and topographic change748

over time.749

Following the SRTM mission, it was over a decade before another satellite750

system was able to acquire SAR data in single-pass mode. The TanDEM-X751

mission of the German Space Agency consists of two nearly identical X-band752

SAR satellites that orbit in close proximity, separated only by about 200753

34

m. In bistatic mode, one satellite transmits a radar pulse to the surface754

and both receive the reflected signal. Applied to volcanoes, topographic755

information derived from TanDEM-X data has been used to construct post-756

eruptive DEMs that have documented volumes of both lava accumulation (Xu757

and Jonsson, 2014) and dome collapse (Kubanek et al., 2014a,b). At Kılauea,758

where lava effusion has been nearly continuous from vents on the volcano’s759

East Rift Zone since 1983, a time series of DEMs derived from TanDEM-X760

data was used to map the 4-dimensional evolution of the lava flow field (figure761

11a ) (Poland, 2014). Such data can be used to calculate the subaerial effusion762

rate of lava over time (figure 11b)–a parameter that may only be poorly763

estimated using other ground-based or remote-sensing techniques. SRTM764

and TanDEM-X data demonstrate the utility and importance of single-pass765

satellite InSAR for deriving Earth topography–a foundation for many Earth766

science datasets and especially important in volcanology. Future satellite767

SAR missions should attempt to incorporate single-pass InSAR to address the768

need for up-to-date high-quality topographic information, which is necessary769

when trying to retrieve displacement evolution through time on a volcano770

marked by significant topographic changes.771

4. Insights from SAR into volcano deformation772

In the previous section, we detailed how SAR imagery can distinguish773

changes in the surface characteristics of volcanoes over time by quantifying774

eruptive deposits and topographic evolution. The most common application775

of SAR to volcanology, however, remains deformation measurement. In this776

section, we first review the various phenomena that induce surface displace-777

35

ments in volcanic areas together with the models developed to interpret this778

deformation. We then discuss how InSAR has helped to understand vol-779

canic processes, like magma storage and transport, subsidence of volcanic780

deposits, and evolution of volcano deformation over time, before discussing781

the limitations of InSAR for volcano geodesy.782

4.1. Sources of deformation around volcanoes783

There are several potential sources of deformation in volcanic areas. Most784

of these sources are related to magmatic activity, but volcanoes are often also785

subject to tectonic deformation, and volcanic edifices can be subject to land-786

slides. In this section we focus on deformation associated with magmatism787

and volcanic eruptions. For additional information on volcano deformation788

modeling see, e.g., Dzurisin (2007) or Segall (2010).789

790

In volcanic areas, the first deformation source to be identified and in-791

terpreted though modeling was inflation/deflation induced by a localized792

magmatic storage zone at depth. Inflation is due to pressure increase by793

magma inflow or crystallization (e.g., Tait et al., 1989), while deflation can794

be caused by magma withdrawal (either to deeper levels or to feed a nearby795

eruption), thermal contraction or gas loss. The simplest model to interpret796

such a signal was proposed by Mogi (1958), who applied it to explain leveling797

and triangulation data collected at Kılauea, Hawai‘i, and Sakurajima, Japan.798

The “Mogi” model is still often applied and gives the surface displacement799

induced by an overpressurized point-source embedded in an elastic homoge-800

36

nous and isotropic half-space. Vertical displacement is expressed by:801

Uz(z = O, r) =∆PcR

3c

G(1− ν)

Hc

(H2c + r2)3/2

, (6)

where Hc and Rc are, respectively, the depth and the radius of the magma802

chamber, G and ν characterize the elastic crustal behavior (respectively, shear803

modulus and Poisson’s ratio), ∆Pc is the overpressure, and r is the radial804

distance at the surface from the axis of symmetry (a vertical axis though the805

center of the magma reservoir). The induced vertical displacement is thus806

at a maximum directly over the centre of the magma chamber and decreases807

monotonically in all directions. This formulation is valid only for reservoirs808

that are small in size compared to their depth, and does not permit determi-809

nation of the reservoir size. McTigue (1987) showed that, even considering a810

finite source, it was almost impossible to distinguish between a highly pres-811

surized source of small extent and a marginally pressurized larger source. To812

get around this ambiguity, the source amplitude is often described in terms813

of a volume change, ∆Vin, where814

∆Vsurf∆Vin

=2(1− ν)

1 + 4G3K

, (7)

with K being the effective bulk modulus for the stored magma. Note that the815

compressibility of magma can accommodate a certain degree of magma ac-816

cumulation or withdrawal without resulting in surface deformation (Johnson817

et al., 2000; Rivalta and Segall, 2008).818

Other analytical solutions have been proposed to account for an ellipsoidal819

reservoir (Yang et al., 1988; Newman et al., 2006) or a horizontal crack that is820

circular in plan view (Fialko et al., 2001). Improved analytical solutions also821

allow for the effects of topography (McTigue and Mei, 1981; Williams and822

37

Wadge, 2000) and viscous behaviour around the magmatic reservoir (e.g.,823

Dragoni and Magnanensi, 1989) to be taken into account. The use of nu-824

merical methods makes possible more complex source geometries, material825

rheologies and properties, considering, for instance, crustal layering (Currenti826

et al., 2010; Pearse and Fialko, 2010; Got et al., 2013).827

Magma storage at shallow depths can persist for months to years (e.g.,828

Sturkell et al., 2006; Elsworth et al., 2008), with any associated inflation rep-829

resenting a potential eruption precursor (Dzurisin, 2003). The rate of pres-830

surization often decreases exponentially, which can be interpreted as magma831

replenishment from a deep constant pressure source (e.g., Lengline et al.,832

2008) or reservoir (e.g., Reverso et al., 2014). At some point, the overpres-833

sure within a shallow reservoir may reach a critical state, leading to rupture834

of the reservoir walls and magma migration, which may eventually feed an835

eruption.836

837

When magma starts migrating, it propagates though the crust as pla-838

nar or curviplanar features called dikes or sills. Sills are usually horizontal839

structures, whereas dykes are steeply inclined or vertical. Intrusion path840

and velocity depend on the driving overpressure of the magma and the local841

stress field, as well as the physical properties of both the magma (primarily842

density and viscosity) and the surrounding crust (primarily density, elastic843

properties and fracture toughness) (Lister and Kerr, 1991; Maccaferri et al.,844

2011). This migration is usually a short-term phenomenon, lasting for hours845

to days. Intrusions can reach the surface or remain stalled at depth. Even in846

the case of eruption, a given amount of magma may remain trapped at depth,847

38

thus inducing a co-eruptive displacement field. The model most commonly848

used to interpret the deformation field resulting from a magmatic intrusion849

is the so-called “Okada” model (Okada, 1985), which gives the deformation850

field produced at the surface by a finite displacement applied on a rectan-851

gular dislocation in an elastic, homogeneous and isotropic half-space. This852

model can be discretized into a number of smaller rectangular elements to853

obtain a distribution of displacement over the plane. Numerical modeling854

can also be applied to constrain the stress distribution along the dislocation855

surface.856

857

In the case of andesitic volcanoes, curviplanar magmatic intrusions of-858

ten link at shallow levels to open conduits, which feed effusive and explosive859

summit eruptions (e.g., Costa et al., 2007). Viscous magma flow through860

open conduits induces both pressurization and shear forces causing near-861

field displacements in the vicinity of the summit (Beauducel et al., 2000;862

Green et al., 2006). Analytical solutions have been proposed to account for863

both pressurization (Bonaccorso and Davis, 1999) and shear stress (Anderson864

et al., 2010), and numerical models allow for investigation of the full coupling865

between magma flow and surface deformation (Albino et al., 2011; Anderson866

and Segall, 2011).867

868

Emplacement of magmatic or volcanic material, either as intrusions or869

eruptive deposits at the surface, contributes to volcanic edifice construction.870

Under the influence of magmatic forcing, local tectonic stresses, gravity and871

climatic effects, a volcanic edifice undergoes surface deformation. Summit872

39

extension in association with compressive structures at the base of an ed-873

ifice have been interpreted as spreading of a volcano under its own weight874

(Borgia, 1994). Large-scale flank sliding has also been identified as an im-875

portant feature of large volcanic edifices, especially for oceanic volcanoes,876

and has been addressed by stability studies taking into account the effect877

of magma accumulation at depth (e.g., Iverson, 1995; Apuani et al., 2005;878

Chaput et al., 2014). On a smaller spatial scale, eruptive deposits are sub-879

ject to compaction and also act as a surface load, inducing local subsidence880

(Beauducel et al., 2000). These loads can be analytically quantified by sum-881

mation of the Green’s function for the response of a point load on an elastic882

half-space (Grapenthin et al., 2010).883

884

At many volcanic areas, the interplay between shallow meteoric water885

and magma inflow leads to the development of active hydrothermal systems886

that are regularly perturbed by the injection of hot fluids. The evolution of887

hydrothermal systems can be manifested in deformation at volcanoes, and888

microgravity monitoring (e.g., Battaglia et al., 1999) and numerical models889

can help to distinguish between magmatic and hydrothermal activity (Hur-890

witz et al., 2007; Hutnak et al., 2009; Fournier and Chardot, 2012).891

892

As detailed above, magmatic activity can produce deformation in many893

different ways. Deformation certainly does not occur only during eruptive cri-894

sis, but may last for years before and after an eruption. From the perspective895

of hazard assessment, a key requirement is the ability to distinguish deforma-896

tion signals induced by magmatic activity from those caused by external phe-897

40

nomena, such as uplift induced by ice retreat for subglacial volcanoes (e.g.,898

Pinel et al., 2007). A further issue is to determine to what extent magma899

storage or migration is occurring, and whether magma migration will lead to900

eruption. In addition to other useful observations like gravity measurements,901

gas flux, seismicity or ground-based geodetic records, high-spatial-resolution902

displacement data from InSAR can make an obvious contribution to these903

problems. Another important goal of deformation studies, in addition to904

eruption forecasting, is to improve knowledge of the geometry and behaviour905

of magma plumbing systems, including changes in the associated stress field.906

Here, too, the contribution from InSAR can be of great benefit.907

4.2. Overview of volcano deformation studies based on SAR data908

As of 2014, InSAR has revealed deformation at more than 160 volcanoes909

around the world (Biggs et al., 2014), including all types of source processes910

discussed in section 4.1 (Figure 12; near-field deformation related to viscous911

magma flow in conduits was a potential exception but recently Salzer et al.912

(2014), taking advantage of the high spatial and temporal resolution provided913

by TerraSAR-X data, observed transient deformation induced at very shallow914

depth beneath the summit dome of Colima volcano that may be related to915

conduit flow). The resulting databases of volcano deformation (e.g., Fournier916

et al., 2010; Biggs et al., 2014) have demonstrated the strong link between917

deformation and eruption, although – just as importantly – not all volcanoes918

that erupt show signs of precursory, or even co-eruptive, deformation (e.g.,919

Moran et al., 2006; Chaussard et al., 2013; Ebmeier et al., 2013b; Biggs et al.,920

2014). Over 20 years of satellite SAR data have facilitated construction of921

time series to investigate the temporal evolution of volcano deformation and922

41

have even enabled observation of multiple eruption cycles at some volcanoes923

(e.g., Bagnardi et al., 2013; Wauthier et al., 2013). In addition, InSAR924

provides an opportunity to image exceptional volcanic events – for example,925

the caldera collapse, voluminous eruption, and associated flank slip at Piton926

de la Fournaise volcano, Reunion Island, in April 2007 (Froger et al., 2010;927

Clarke et al., 2013). Even more striking is the 2005 rifting event in Afar,928

Africa, which was associated with emplacement of a 65-km-long dike with a929

volume in excess of 1 km3 (Wright et al., 2006; Grandin et al., 2009) (see930

figure 13) and was followed in subsequent years by 13 additional intrusive931

events (Grandin et al., 2010; Hamling et al., 2010).932

A complete review of the numerous studies that have utilized InSAR to933

investigate volcano deformation is impossible owing to the ever-growing ap-934

plication of the technology (Figure 4) – a sure sign of its maturation. Instead,935

we discuss here the primary contributions of InSAR to volcano dynamics –936

specifically magma storage (section 4.2.1), magma transport (section 4.2.2),937

deposit subsidence (section 4.2.4) and temporal evolution of deformation938

(section 4.2.3) –while highlighting a few, but by no means all, examples939

of these processes as investigated by InSAR..940

4.2.1. Magma storage941

As documented previously, one of the great advantages of InSAR over942

other deformation-monitoring methods is the ability to scan entire volcanic943

arcs for localizations of cm-scale displacements – especially valuable in ar-944

eas where in-situ monitoring is limited or nonexistent (e.g., Amelung et al.,945

2000; Pritchard and Simons, 2002, 2004; Biggs et al., 2011; Henderson and946

Pritchard, 2013; Lu and Dzurisin, 2014). For the 1990s and most of the947

42

2000s, only C-band data were available, limiting arc-wide surveys to poorly-948

vegetated regions (Pritchard and Simons, 2002, 2004). L-band studies using949

the ALOS satellite starting in 2006 expanded this capability to tropical ar-950

eas (e.g., Fournier et al., 2010; Ebmeier et al., 2011; Philibosian and Simons,951

2012; Chaussard and Amelung, 2012; Chaussard et al., 2013; Ebmeier et al.,952

2013a,b), allowing for near-global remote mapping of volcano deformation.953

Such regional studies have recognized unrest at supposedly quiescent volca-954

noes and aided the construction and refinement of databases and catalogs of955

volcano characteristics. For example, based on the tables provided by Biggs956

et al. (2014), it is possible to ascertain global patterns of magma storage957

depth, which reveal that most subvolcanic magma storage is within 10 km958

of the surface (Figure 14), although this observation might be partly biased959

by the difficulty in imaging subtle deformation from deeper sources. Such960

catalogs facilitate investigations of the parameters that control magma stor-961

age. Chaussard and Amelung (2014) analyzed magma storage depth (based,962

in part, on InSAR results) at 70 volcanoes worldwide with respect to both963

crustal structure and stress regime, finding that magma reservoirs tend to be964

deeper where crust is older, thicker, and subject to compressive stress.965

The broad coverage of SAR provides an opportunity to image large-scale966

deformation due to deep magma storage (Figure 14) – a perspective that967

is not easily achievable using ground-based point measurements. Leveling968

data over the Socorro magma body in New Mexico were used to identify969

several mm/yr uplift associated with a low-seismic-velocity region at a depth970

of 20 km, but only vertical displacements were available, and only from971

a few transects over the deforming zone (Reilinger et al., 1980). InSAR,972

43

however, provided the improved temporal and spatial resolution needed to973

refine models of deformation (Fialko and Simons, 2001) and interpret the974

displacements in terms of heat transfer from a stalled magmatic intrusion975

(Pearse and Fialko, 2010). Areas of deep magma accumulation have also976

been identified using InSAR in South America and Iceland – in some cases,977

deformation that would not otherwise have been discovered. The pattern978

of uplift at Uturuncu, in Bolivia, is consistent with magma accumulation979

at 20-km depth within the Altiplano-Puna province – an area of intense980

silicic volcanism over the past 10 million years that is apparently still active981

(Pritchard and Simons, 2002, 2004; Sparks et al., 2008; Fialko and Pearse,982

2012; Henderson and Pritchard, 2013; Walter and Motagh, 2014). In Iceland,983

deep magma accumulation at 20-km depth has been recognized at Krafla984

(de Zeeuw-van Dalfsen et al., 2004), Hekla (Ofeigsson et al., 2011), and even985

as part of a dipping dike in the Northern Volcanic Zone (Hooper et al., 2011).986

InSAR has also helped to identify and refine the geometrical character-987

istics of magma storage, especially given that ground-based data are some-988

times insufficient to distinguish between spherical and more complex source989

shapes. Taking the Socorro example, InSAR data not only provided evi-990

dence the magma storage zone had a sill-like geometry (Fialko and Simons,991

2001), but also motivated the development of a new analytical solution for992

a circular penny-shaped crack (Fialko et al., 2001) that has seen extensive993

use in volcano deformation modeling (e.g., Baker and Amelung, 2012). In994

Hawai‘i at Mauna Loa – the largest active volcano by volume on Earth –995

inflation and deflation measured by tilt, Electronic Distance Measurement,996

and campaign GPS during the 1970s through 1990s suggested a spherical997

44

magma storage area at 3-4 km beneath the south part of the caldera (Lock-998

wood et al., 1987). InSAR measurements and expanded application of GPS,999

however, demonstrated that magma storage occurred not only beneath the1000

south caldera, but also in a dike-like structure that underlies and follows1001

the trend of the elongated caldera (Amelung et al., 2007). Deformation at1002

Yellowstone caldera, Wyoming, was mostly characterized by leveling mea-1003

surements for decades, which yielded an exceptional record of the ups and1004

downs of the active caldera system (Dzurisin et al., 1994) but was restricted1005

to, generally, a single transect across the eastern part of the caldera. InSAR1006

results demonstrated, for the first time, the outstanding complexity of de-1007

formation, which included multiple sources within and outside of the caldera1008

(Wicks et al., 1998, 2006; Chang et al., 2007, 2010). Finally, the number of1009

magma reservoirs, while possible to infer from GPS, leveling, tilt, and strain1010

monitoring, can be determined unequivocally by InSAR, as experience at1011

Kılauea demonstrates. While multiple storage areas have been hinted at1012

for decades by ground-based displacement data (Fiske and Kinoshita, 1969;1013

Miklius and Cervelli, 2003), InSAR results clearly delineated multiple, dis-1014

crete magma storage areas beneath the volcano’s summit region (Baker and1015

Amelung, 2012).1016

Even where only a single magma storage area is present, ground-based1017

monitoring may not be positioned in the best location to detect deformation1018

due to magma accumulation or withdrawal. Near South Sister, Oregon, for1019

example, InSAR detected inflation centered 5 km west of the volcano’s sum-1020

mit (Figure 15 ); it is not clear if the deformation would have been recognized1021

solely from in-situ measurements, which are traditionally centered on volcano1022

45

summits, even though the center of deformation may be far removed (Wicks1023

et al., 2002; Pritchard and Simons, 2004; Dzurisin et al., 2006). These exam-1024

ples provide clear evidence of the power of InSAR to not only detect magma1025

storage at a variety of locations and depth ranges, but also to elucidate the1026

geometry of magma storage, both in terms of reservoir shapes and numbers.1027

4.2.2. Magma transport1028

From storage areas, how does magma get to the surface? Seismicity pro-1029

vides obvious constraints on the process (e.g., Taisne et al., 2011), and defor-1030

mation data bracketing, for instance, a dike intrusion event can place bounds1031

on the volume of magma transport and also reveal the geometry of a volcano’s1032

magma plumbing system. The relatively poor temporal resolution of SAR1033

data (see section 4.3) limits its ability to catch magma transport ”in the act”,1034

which is usually much better imaged by high-rate GPS or tilt data (e.g., Aoki1035

et al., 1999). However, the literature is replete with exceptional examples of1036

InSAR as a tool for mapping magma transport pathways at volcanoes of1037

all tectonic settings and compositions (see Table 2). At Yellowstone, Wicks1038

et al. (2006) used InSAR data to reveal a complex pattern of surface displace-1039

ments over time, which they used to infer how magma enters, traverses, and1040

leaves the caldera system. InSAR-derived surface displacements associated1041

with the rhyolitic eruption of Chaiten volcano, Chile, beginning in 2008, not1042

only indicated the dike-like pathway that magma used to get to the surface,1043

but also showed that storage and segregation of low-density liquid were con-1044

trolled by existing faults (Wicks et al., 2011). On the lower-silica end of the1045

compositional spectrum, repeated intrusions detected by InSAR at Eyjaf-1046

jallajokull, Iceland (Pedersen and Sigmundsson, 2004, 2006), culminated in1047

46

2010 with an effusive basaltic eruption from flank vents followed about three1048

weeks later by an explosive andesitic eruption from the summit (Sigmunds-1049

son et al., 2010). Deformation from InSAR and, to a lesser extent, GPS1050

over the two decades before the 2010 eruptive activity revealed the plumbing1051

system of the volcano, which allowed for interpretation of other datasets,1052

such as seismicity and petrology (Sigmundsson et al., 2010). At Fernandina,1053

in the Galapagos, intrusion of sills several km beneath the flanks of the vol-1054

cano would not have been detected without InSAR. The recognition that sills1055

can intrude from subvolcanic storage areas may explain several exceptional1056

episodes of rapid uplift of coastal areas in the archipelago that occurred in1057

the early-mid 20th century (Bagnardi and Amelung, 2012). Thanks to the1058

improved understanding of the geometry and volume of magmatic intrusions1059

provided, in part, by models of InSAR data, it is now possible to quantify1060

the stress-field perturbation induced by magma emplacement at depth (e.g.,1061

Amelung et al., 2007; Hamling et al., 2009; Grandin et al., 2010; Bagnardi1062

et al., 2013; Biggs et al., 2013). The existing stress field may also affect1063

intruding dikes. A study of the Upptyppingar intrusion in northern Iceland1064

demonstrated that the dike was at an angle to the least compressive stress,1065

which induced a shear component to the dike opening (Hooper et al., 2011).1066

In this case, inversion of the deformation field using numerical methods pro-1067

vided a means to constrain the background stress field at the time of magma1068

emplacement (Figure 16).1069

Characterizing active magma transport via deformation measurements is1070

typically the domain of continuous, ground-based sensors, which can track1071

magma migration on the same time scales over which it occurs – generally1072

47

minutes to days (e.g., Montgomery-Brown et al., 2011). Catching the process1073

of magma transport “in the act” with InSAR, rather than bracketing an1074

event, requires serendipitous data acquisitions or long-lived transient activity,1075

but can yield exceptional results. For example, a SAR image acquired two1076

hours prior to the onset of the 2009 eruption of Fernandina revealed the1077

initial stages of sill propagation away from the subvolcanic magma reservoir1078

towards the surface (Figure 17). Those data not only aided interpretation1079

of that eruption, but also suggested the mechanism behind the pattern of1080

circumferential and radial eruptive fissures common to all western Galapagos1081

volcanoes – a problem that had vexed geologists for several decades (Bagnardi1082

et al., 2013). The March 5-9, 2011, Kamoamoa fissure eruption at Kılauea1083

Volcano lasted sufficiently long to be imaged by several SAR satellites over1084

the course of the activity, and models of derived interferograms revealed1085

progressive dike opening and volume increase over time (Lundgren et al.,1086

2013). Multiple SAR satellites were also used to track deformation associated1087

with lateral magma migration over the course of several months prior to1088

the 2011 offshore eruption of El Hierro, Canary Islands, demonstrating that1089

InSAR can even provide constraints on magma plumbing associated with1090

eruptive activity that cannot be observed directly (Gonzalez et al., 2013).1091

The capability of InSAR to image deformation associated magma migra-1092

tion that culminates in both eruptions and non-eruptive intrusions will only1093

grow with the launch of new satellites and continued operation of existing1094

systems. As of mid-2014, five orbital SAR systems were available for sci-1095

entific applications (Table 1) – TerraSAR-X/TanDEM-X, COSMO-SkyMed,1096

RADARSAT-2, Sentinel-1a, and ALOS-2. Given that some of these systems1097

48

include multiple satellites and have short repeat times (on the order of days),1098

and that they can be complemented in a few locations by repeated airborne1099

SAR acquisitions, the potential for imaging magma transport ”in the act”1100

with high spatial resolution is better than ever. Such data will provide new1101

input for models of magma transport that will aid in the development of mon-1102

itoring strategies and modeling approaches (Segall, 2013) and may reveal new1103

insights into the structure and dynamics of active volcanism (Bagnardi et al.,1104

2013).1105

4.2.3. Temporal evolution of magmatic deformation1106

More than two decades of InSAR have enabled not only detection of1107

volcano deformation at locations around the world, but also compilation of1108

deformation time series that have made possible an abundance of modeling1109

studies and revealed feedback patterns between various types of volcanic and1110

tectonic activity. Fernandina provides a spectacular example, with InSAR1111

data extending back to 1992. Although there were few acquisitions in the1112

1990s, frequent collects in the 2000s captured deflation due to multiple in-1113

trusions and eruptions superimposed on a background trend of inflation that1114

is measured in terms of meters (Figure 20). Dense time series have also been1115

used to characterize the onset and evolution of deformation at volcanoes1116

that had been quiescent, like South Sister, which began inflating in 19961117

(Dzurisin et al., 2006, 2009; Riddick and Schmidt, 2011), and Laguna del1118

Maule, Chile, which experienced high-rates of deformation during 2007-20121119

that may be a consequence of processes within a large silicic magma chamber1120

(Feigl et al., 2014). Further, high-spatial-resolution deformation time series1121

provide critical input to models of magma ascent and accumulation that go1122

49

beyond simple elastic approximations like those of Mogi (1958) and Okada1123

(1985). For example, deformation time series from Socorro indicate steady1124

uplift, but modeling argues against constant magma overpressure (the con-1125

clusion based on elastic models) and instead in favor of heat transfer and1126

ductile deformation above a giant, previously emplaced sill intrusion (Pearse1127

and Fialko, 2010). Tracking post-eruptive volcano inflation has provided an1128

indication of the replenishment of shallow reservoirs (Lu et al., 2010), which1129

places constraints on magma viscosity and connections with deep magma1130

sources.1131

Perhaps most importantly, temporally dense, high-spatial-resolution In-1132

SAR data supply information regarding interactions between different vol-1133

canic process, and between volcanic and tectonic activity. An outstanding ex-1134

ample is the response of volcanoes in Japan and South America to large earth-1135

quakes. Within days of the 2010 Mw8.8 Maule earthquake off the coast of1136

Chile, subsidence of up to 15 cm was detected at 5 volcanoes in the southern1137

Andes, attributed to coseismic hydrothermal fluid release (Pritchard et al.,1138

2013). Similarly, subsidence was detected at several volcanoes in Japan fol-1139

lowing the 2011 Mw9.0 Tohoku earthquake, possibly indicating deformation1140

of thermally-weakened areas around large magma chambers due to coseismic1141

stress changes (Ozawa and Fujita, 2013; Takada and Fukushima, 2013). It is1142

doubtful that the full extent of either of these earthquake-triggered deforma-1143

tion episodes would have been discerned without InSAR results. As always,1144

the information provided by InSAR should be interpreted together with other1145

geophysical datasets, especially seismic noise studies, which reveal a seismic1146

velocity drop localized below volcanic regions after the Tohoku earthquake1147

50

(Brenguier et al., 2014). InSAR data have also documented flank displace-1148

ments that have occurred as a consequence of eruptive and intrusive activity.1149

Etna has experienced multiple such episodes, with InSAR time series sug-1150

gesting that dike emplacement in 2001-2002 triggered asymmetric motion of1151

the volcano’s eastern flank (Solaro et al., 2011), while an episode of flank1152

motion in 2008 might have provided the motivation for a subsequent dike1153

intrusion (Bonforte et al., 2013). At Piton de la Fournaise, InSAR recorded1154

a complex interplay between co-eruptive deflation, dike intrusion, and flank1155

motion associated with caldera collapse in 2007 – the first well-documented1156

evidence for flank instability at that volcano (Clarke et al., 2013).1157

Ultimately, the importance of temporally dense, high-spatial-resolution1158

deformation maps from InSAR is the ability to combine those data with1159

other observations to better understand how volcanoes work – especially1160

relevant for investigating the mechanisms of large-scale eruptions that can1161

have global impact. Santorini, for instance, is well known as the source of1162

an eruption that might have played a major role in the devastation of the1163

Minoan civilization ∼3600 years ago, and uplift that began there in 20111164

served as a reminder that the volcano is still active. Considering the infla-1165

tion in light of the petrology of past eruptive products, however, argues that1166

the shallow, geodetically imaged storage area is less important in terms of1167

controlling eruptive activity than the deeper magmatic system (Parks et al.,1168

2011). InSAR-derived deformation also provides outstanding constraints on1169

magma supply to a given volcano (Poland et al., 2012), which is the dominant1170

control on the volumes and timings of surface eruptive activity. Improvement1171

of the temporal resolution of geodetic observations, showing for instance a1172

51

period of transient subsidence not associated with an eruptive crisis, has also1173

revealed the need to consider the petrological effects related to degassing and1174

crystallization in order to correctly interpret temporal evolution of displace-1175

ments recorded at the surface above a magmatic reservoir (Caricchi et al.,1176

2014). As the number of SAR satellites and their ability to image defor-1177

mation globally continues to grow, so too will the incorporation of InSAR1178

time series in models of volcano evolution and eruptive activity. These data1179

will eventually feed in to dynamic models that account for magma migra-1180

tion through a volcanic plumbing system, as proposed by Melnik and Costa1181

(2013), which have potential for eruption forecasting, especially when they1182

incorporate deformation data.1183

4.2.4. Subsidence of volcanic deposits1184

While SAR interferometry is rightly known for its use in measuring surface1185

displacements associated with magma accumulation/withdrawal, magma mi-1186

gration, and fault slip at volcanoes, an underappreciated capability is charac-1187

terization of post-emplacement behavior in lava flows and pyroclastic units.1188

That such deposits deform after they are erupted is well known. For exam-1189

ple, both subsidence and uplift –attributed to lava cooling/crystallization and1190

vesiculation, respectively – have been measured on the surfaces of solidifying1191

lava lakes at Kılauea Volcano (Wright et al., 1976; Wright and Okamura,1192

1977; Peck, 1978). These dominantly vertical displacements, at Kılauea and1193

elsewhere, were initially documented by time- and personnel-intensive lev-1194

eling surveys (Murray and Guest, 1982). InSAR offers better spatial and1195

temporal resolution (assuming data are acquired during regular orbital re-1196

peats), and therefore provides improved insights into deformation patterns1197

52

and source mechanisms.1198

The first InSAR observations of post-emplacement lava-flow deformation1199

were reported at Etna, where subsidence of tens of cm per year was measured1200

on lava that had erupted several years previously. Because the deformation1201

extended beyond the margins of these flows, the subsidence was attributed1202

to compaction of deposits and relaxation of the substrate due to loading1203

(Briole et al., 1997). A subsequent analysis by Stevens et al. (2001) on1204

a larger lava flow field at Etna corroborated the earlier results. Since the1205

initial report from Etna, lava flow subsidence, generally attributed to thermal1206

contraction, mechanical compaction, and loading over various timescales,1207

has been documented by InSAR at volcanoes around the world, including1208

Nyamulagira, Democratic Republic of the Congo (Wauthier et al., 2013);1209

Hekla, Iceland (Grapenthin et al., 2010; Ofeigsson et al., 2011); and Okmok,1210

Alaska (Lu et al., 2005a) (for a more complete list see Ebmeier et al. (2012)).1211

The subsidence rates measured by InSAR range from a few mm/yr to tens of1212

cm/year, the larger rate being observed in the months following the lava flow1213

emplacement. Indeed, even the now-solidified lava lakes of Alae, Makaopuhi,1214

and Kılauea Iki on Kılauea Volcano – sites of some of the original work on1215

post-eruptive deformation of lava – continue to deform decades after their1216

emplacement (see Figure 18).1217

Post-emplacement subsidence of pyroclastic flows and lahars has also1218

been documented by InSAR, for instance, at Mount St. Helens, Washing-1219

ton, and at Augustine and Redoubt volcanoes, Alaska. The 18 May 19801220

debris avalanche at Mount St. Helens emplaced approximately 2.5 km3 of1221

cold volcanic edifice, hot cryptodome, water, and ice in the upper part of the1222

53

North Fork Toutle river valley, with a maximum deposit thickness of ∼200 m1223

(Voight, 1981; Glicken, 1996). InSAR data spanning periods beginning more1224

than 12 years after the volcano collapsed revealed three distinct patches of1225

subsidence in the deposit (see Figure 19). Poland and Lu (2008)) attributed1226

the subsidence to a combination of substrate compaction, deposit consoli-1227

dation, and melting of buried ice – thermal contraction was not considered1228

likely because the emplacement temperature of the deposit was probably not1229

sufficiently high to be causing over 2 cm/yr of subsidence after 20 years, and1230

the subsidence did not correspond to the thickest areas of the avalanche.1231

Subsidence of up to 20 cm/yr in patches of lahar deposits associated with1232

the 2009 Redoubt eruption is also thought to be caused by compaction due1233

to melting of buried snow and ice (McAlpin and Meyer, 2013). In contrast,1234

pyroclastic flow deposits from the 1986 eruption of Augustine volcano were1235

found to be subsiding more than 13 years later at a roughly steady rate of up1236

to ∼3 cm/yr, with maximum subsidence corresponding to maximum deposit1237

thickness. Masterlark et al. (2006) modeled the subsidence as due entirely1238

to thermoelastic contraction of an initially hot deposit, and, using a finite1239

element analysis, they were able to constrain the maximum emplacement1240

temperature of the deposit, as well as the deposit volume and thickness1241

distribution to a higher degree of accuracy than possible from combining1242

poor-resolution pre- and post-eruption DEMs. For a more complete list of1243

post-depositional processes affecting ignimbrites, see Whelley et al. (2012).1244

The recognition of post-emplacement subsidence of lava, lahars, and py-1245

roclastic flows in InSAR data provides both a warning and an opportunity for1246

volcanological studies. Scientists must be cautious when employing InSAR or1247

54

other deformation data to interpret volcanic activity, as displacements of re-1248

cent surficial deposits may impact the manifestation of subsurface magmatic1249

activity (Stevens et al., 2001; Lu et al., 2005a). As an example, subsidence in1250

the north part of Kılauea’s summit caldera, possibly due to lava-flow cooling1251

and compaction, distorts deformation due to magma accumulation beneath1252

the surface (see Figure 18) and impacts the results of modeling of subsur-1253

face pressure change. To avoid bias in models of deep magma accumulation1254

at Hekla, Ofeigsson et al. (2011) masked areas of subsidence due to loading1255

and thermal contraction of recent lava flows near the summit of the volcano.1256

While a nuisance when modeling subsurface magmatic sources, deposit sub-1257

sidence can reveal important insights into the thermal, mechanical, and rhe-1258

ological behavior of volcanic materials. For example, deformation of lava and1259

pyroclastic flows can be used to estimate their thickness and emplacement1260

temperature, as well as the mechanical properties of the substrate (Master-1261

lark et al., 2006; Grapenthin et al., 2010) – parameters that might not be1262

discernible by other means but are important to geological and geochemical1263

investigations of eruptive activity.1264

4.3. Main InSAR limitations for deformation measurements1265

The applicability of SAR interferometry for measuring surface displace-1266

ments is not equal everywhere on Earth. Vegetated areas are usually prone1267

to temporal decorrelation (Zebker and Villasenor, 1992), which makes the1268

use of InSAR difficult in tropical zones, unless using L-band data. Other1269

phenomena, such as ice or snow cover and ash deposits, also lead to tem-1270

poral decorrelation, reducing the potential of InSAR (figure 21a). Where1271

scattering characteristics of the ground do remain stable, the geometry of1272

55

acquisition can affect the success of InSAR, with reduced spatial resolution1273

on slopes facing the sensor (Figure 21b) and the potential for slopes facing1274

away to be in shadow (Figure 1). In addition, the approximately polar orbits1275

of SAR satellites mean that InSAR measurements are mostly sensitive to1276

vertical and east-west displacements., and not to north-south movement.1277

Another strong limitation of InSAR results from the variation in atmo-1278

spheric conditions between acquisitions, inducing changes in the phase delay1279

that may be misinterpreted as surface displacement (Zebker et al., 1997;1280

Hanssen, 2001). Ionospheric effects can be an error source particularly in L-1281

band data but can be mitigated in a number of ways (Meyer, 2011). However1282

the main atmospheric noise contribution in SAR data comes from the tro-1283

posphere. Tropospheric properties vary on two characteristic lateral scales:1284

short (few km), induced by turbulent troposphere dynamics, and long (10s1285

of km), due to variations in temperature and humidity profiles within the1286

atmosphere.1287

The turbulent variation still remains difficult to model but can be re-1288

duced by temporal filtering (e.g., Schmidt and Burgmann, 2003; Hooper1289

et al., 2012a) (see section 2.5). The long-scale variation induces not only1290

long-wavelength artifacts, but also interferometric fringes that are strongly1291

correlated with topography, as phase delay depends on how far through the1292

troposphere the signal has traveled (figure 21c,d). Temporal filtering meth-1293

ods are not always sufficient to reduce the topographically correlated delay1294

because SAR data sets typically do not evenly sample seasonal atmospheric1295

fluctuations, resulting in biased estimates (Doin et al., 2009). Furthermore,1296

temporal filtering methods are never able to completely separate tropospheric1297

56

delay from deformation that is not steady-state.1298

Topographically correlated tropospheric artifacts can be partially cor-1299

rected using the information contained within the SAR data, based on the1300

correlation between phase and elevation in non-deforming areas (e.g., Cava-1301

lie et al., 2007; Remy et al., 2003; Lin et al., 2010; Shirzaei and Burgmann,1302

2012). In addition, the long-wavelength artifacts can be estimated over a1303

whole image by considering the variation of this correlation (Bekaert et al.,1304

in revision). Complementary data sets, such as dense GNSS or meteorological1305

measurements acquired at the same time as the SAR images, or meteorologi-1306

cal models, provide an alternative method for estimating the topographically1307

correlated and long-wavelength tropospheric artifacts (e.g., Webley et al.,1308

2002; Foster et al., 2006; Wadge et al., 2006; Doin et al., 2009; Jolivet et al.,1309

2011; Gong et al., 2011). As emphasized in section 4.2.2, the poor tempo-1310

ral sampling of SAR data has been an important limiting factor in tracking1311

magma migration. Transient deformation with a duration of hours, as some-1312

times occurs around the craters of andesitic volcanoes (Voight et al., 1998),1313

is also far below the temporal aliasing threshold of SAR data. The relatively1314

short period of satellite observations – twenty years for C-band data and even1315

less for L-band data that are more suitable for tropical areas – also limits1316

how representative probabilistic studies based on SAR data can be (Biggs1317

et al., 2014).1318

All of the difficulties described above are compounded for andesitic stra-1319

tovolcanoes. These edifices are often characterized by steep slopes, which1320

are highly vegetated due to long repose periods between eruptions, can be1321

partially covered by ice or snow, and undergo short-duration deformation1322

57

related to shallow conduit and dome activity. They are thus particularly1323

challenging to study with InSAR (Pinel et al., 2011).1324

5. Key constraints on volcanic edifice growth and stability1325

In the preceding sections we have reviewed the numerous applications1326

of SAR data, from mapping eruptive deposits (section 3) to constraining1327

various styles and mechanisms of deformation (section 4). While all of these1328

applications are of tremendous value individually, the real power of SAR lies1329

in the ability to integrate its uses to better constrain large-scale dynamic1330

processes related to active volcanism. An example of this capability is the1331

investigation of volcano growth and stability.1332

Volcano growth can occur both endogenously, via intrusions and cumulate1333

formation beneath the surface, and exogenously, through the addition of lava1334

and pyroclastic material to the surface. Assessing the dominant mode of1335

volcano growth at any given time is far from an academic exercise – in lava1336

domes, for example, endogenous growth may lead to over steepening, which1337

promotes dome collapse (Kaneko et al., 2002). Endogenous growth of an1338

entire volcanic edifice can similarly lead to flank instability, at both silicic1339

stratovolcanoes (Lipman et al., 1981; Moore and Albee, 1981) and basaltic1340

shield volcanoes (Clague and Denlinger, 1994). Changes over time in the1341

proportion of extruded to intruded material provide insights into the behavior1342

of a volcano, indicating, for example, that the propensity to erupt might1343

have decreased after a strong earthquake created more open space within the1344

magma plumbing system (Dzurisin et al., 1984). General quantification of1345

intruded plus erupted material can also help to constrain the magma supply1346

58

to the volcano – one of the primary controls on eruptive activity (Poland1347

et al., 2012) – as well as the ability of the volcanic edifice itself to modulate1348

magma storage (Got et al., 2013).1349

Exogenous volcano growth can be measured directly by a variety of tech-1350

niques, from geologic mapping of the thickness and distribution of lava or1351

pyroclastic material to remotely sensed changes in topography. Endogenous1352

growth, on the other hand, necessarily relies upon inferences from monitoring1353

data, including models of surface deformation (Biggs et al., 2010), thermal1354

emissions (Francis et al., 1993; Kaneko et al., 2002), gravity data (Johnson1355

et al., 2010; Flinders et al., 2010), and gas emissions (Allard, 1997). Defor-1356

mation monitoring with InSAR is uniquely suited to constrain edifice growth1357

caused by subsurface magma accumulation, as detailed in section 4.2.1. In-1358

SAR has detected large flank displacements associated with magmatic intru-1359

sions on several volcanoes, among them Etna (Solaro et al., 2010; Bonforte1360

et al., 2011; Ruch et al., 2012) and Piton de la Fournaise (Clarke et al., 2013).1361

Combining models of subsurface volume change from InSAR with measure-1362

ments of erupted volume can constrain the intrusive and extrusive growth1363

of an edifice. For example, Pritchard and Simons (2004), in an arc-wide1364

study over the Central Andes, constrained the intrusive growth rate to be1365

one to ten times larger than the extrusive rate. Biggs et al. (2010) found that1366

the volume of intrusion determined from InSAR data beneath Tungarahua1367

volcano, Ecuador, was roughly equivalent to the volume of extrusion over1368

the same time span, and the combined intrusive-extrusive volume was about1369

the same as the magma flux of the volcano over the past 2300 years. Sim-1370

ilarly, Fukushima et al. (2010) used InSAR data spanning activity of Piton1371

59

de la Fournaise volcano between 1998 and 2000 to show that only 17% of1372

the magma supplied to the volcano during that time was stored, with the1373

rest erupting during 5 discrete episodes of activity. Such estimates are nec-1374

essarily rough, since they do not account for magma compressibility in their1375

volume-change modeling (Rivalta and Segall, 2008); nevertheless, they pro-1376

vide important bounds on processes that would otherwise be unconstrained.1377

Understanding whether volcano growth is dominantly intrusive or extrusive1378

can even help to explain edifice morphology. For example, models to explain1379

the shapes of Galapagos shield volcanoes, which have steep middle slopes1380

but gentle upper and lower slopes, included both construction by effusion1381

(Simkin, 1981) and magmatic tumescence (Cullen et al., 1987). Only when1382

InSAR data that spanned multiple episodes of inflation, deflation, and erup-1383

tion became available could tumescence could be ruled out as a mechanism1384

(Bagnardi et al., 2013).1385

As detailed previously, SAR data can also be used to calculate the volume1386

of surfacial deposits through amplitude imagery (Wadge et al., 2011), phase1387

differences indicating topographic change over time (Ebmeier et al., 2012),1388

and direct determinations of topography and calculations of topographic dif-1389

ferences over time (Poland, 2014). Thus SAR can simultaneously quantify1390

the volumes of both intrusion and extrusion over a given time period. Kılauea1391

provides an excellent example of both the application of this capability and1392

the potential implications for evaluating the magma budget of a volcano, as1393

demonstrated by Poland (2014). During September 2011 through June 2013,1394

TanDEM-X data acquired over Kılauea were used to construct a time series of1395

DEMs, from which it was possible to calculate the erupted volume over that1396

60

span. The derived subaerial effusive volume over the ∼ 2 years was about1397

123 ∗ 106 m3 (Figure 11), but this volume is a minimum because some lava1398

entered the ocean. During periods of no ocean entry, the TanDEM-X-derived1399

lava discharge rate represents the entirety of Kılauea’s effusive output. SAR1400

data covering the same time period can be used to assess volumes of magma1401

storage as well. Deformation of Kılauea during the same time spanned by the1402

TanDEM-X time series indicates relatively minor deformation (Figure 22).1403

The pattern of displacements across the volcano is complex owing to interac-1404

tions among numerous processes, including subsidence of the Southwest Rift1405

Zone, uplift around the flanks of the East Rift Zone, subsidence of cooling1406

lava, faulting, and summit inflation due to magma accumulation. Although1407

obscured by long-term subsidence of the northern part of the caldera due to1408

lava flow cooling, summit deformation is otherwise consistent with inflation1409

of a magma reservoir at about 1.5 km beneath the caldera center (Lund-1410

gren et al., 2013). The modeled volume increase in this reservoir, however,1411

is probably less than 1 million m3 based on analogy with past inflation and1412

deflation of this source (Baker and Amelung, 2012; Lundgren et al., 2013).1413

Even accounting for magma compressibility, which could increase the volume1414

of stored magma by several orders, summit volume change is a very small1415

percentage (< 5%) of the effusive output. Magma may also be stored within1416

the East Rift Zone-especially in an area of uplift that is a result of transient1417

deformation following a dike intrusion and fissure eruption in March 2011.1418

This uplift, however, is not sufficient to account for the large discrepancy1419

between the erupted and intruded volume – even if the intruded volume is1420

increased by several times to account for magma compressibility. It there-1421

61

fore seems that most magma that was fed to Kılauea during mid-2011 to1422

mid-2013 was erupted.1423

By combining the erupted and intruded volumes, the total volume of1424

magma supply to the volcano during this period can be inferred. Since the1425

start of quasi-continuous eruptive activity in 1983, magma supply to Kılauea1426

has generally fallen within the range of 4−8 m3/s (Poland et al., 2012). Since1427

most magma supplied to Kılauea also erupted during mid-2011 to mid-2013,1428

and the eruption rate, determined from TanDEM-X-derived DEMs, is about1429

2 m3/s (Figure 11b), it seems probable that the magma supply to Kılauea1430

during this period was lower than at other times during the 1983-present1431

eruption. This valuable constraint on magma supply, which is consistent with1432

sluggish lava flow activity on Kılauea’s East Rift Zone, would not otherwise1433

be measurable, especially since changes in the pattern of degassing since1434

the start of summit eruptive activity in 2008 have introduced substantial1435

uncertainty in gas-based methods for measuring magma supply and lava1436

discharge rate (Poland, 2014). In addition to long-term supply (measured1437

over years), frequent repeats by SAR satellites over Kılauea provide evidence1438

for short-term changes in the rates of intrusion and effusion (measured over1439

weeks to months). In mid-2012, the effusion rate plummeted at the same time1440

as summit deformation, as indicated by InSAR time series and continuous1441

GPS data plateaued (Figure 11b,c). Because no lava was entering the ocean1442

at this time, the decreased lava discharge and lack of inflation may indicate1443

a short-term (2-month-long) decrease in magma supply to the volcano.1444

The Kılauea case cited above provides an illustrative example of the in-1445

sights that are possible thanks to the high temporal and spatial resolution of1446

62

SAR data, coupled with the capability of those data to provide information1447

on not only subsurface magma accumulation and withdrawal, but also vol-1448

ume change at the surface. No other data source currently in use offers such a1449

diversity of potential products, which range from tracking short-term, small-1450

scale changes in intrusive and eruptive activity to providing information that1451

constrains rates of magma supply to a given volcano.1452

6. Discussion1453

6.1. Looking back: advances made possible from SAR studies of volcanoes1454

For the past 20 years, satellite SAR imagery has provided repeat views1455

of Earth’s surface without regard to weather conditions or time of day, en-1456

abling detection and quantification of changes on the ground. In particular,1457

quantification of surface displacements on the order of a few millimeters is1458

possible over areas of thousands of square kilometers, and surface-change1459

data can be easily obtained from remote areas where ground-based observa-1460

tions are not practical. Volcanology and other Earth science disciplines have1461

derived outstanding benefits from this remote sensing technique by using the1462

imagery to address critical questions about how volcanoes work.1463

These capabilities provide more than just an additional dataset with1464

which to study a particular volcano. Instead, they have led to fundamen-1465

tal new insights into our understanding of volcano behavior. For example,1466

studies of the Galapagos archipelago were largely geological and petrologi-1467

cal in nature until InSAR revealed the dynamic nature of those volcanoes –1468

some of the fastest deforming in the world (Amelung et al., 2000). InSAR1469

results also demonstrated the mechanism for the pattern of eruptive vents1470

63

on western Galapagos volcanoes, where fissures are circumferential near the1471

summit calderas and radial on the flanks. Decades of geological field work1472

and modeling suggested a range of possibilities for that geometry, ranging1473

from stress reorientations near summit calderas to magma chamber shapes,1474

but it was pre- and co-eruptive interferograms that revealed that the under-1475

lying assumption of such studies – that all fissures were underlain by vertical1476

dikes – was incorrect, and that sill emplacement and rotation was the source1477

of the fissure pattern (Bagnardi et al., 2013). Without InSAR, it would1478

be doubtful if the range of deformation behavior of all Galapagos volcanoes1479

would be known, and the structure and growth patterns of the volcanoes1480

would certainly be unconstrained.1481

SAR data have also provided information on volcanoes that are deform-1482

ing and should be considered ”active” with respect to potential future erup-1483

tive activity. Such studies have come in the form of focused investigations1484

on individual volcanoes, like South Sister (Wicks et al., 2002), as well as1485

regional investigations of volcanic arcs (e.g., Pritchard and Simons, 2002,1486

2004; Chaussard and Amelung, 2012; Lu and Dzurisin, 2014). InSAR thus1487

demonstrates the potential to detect volcanoes under which magma is ac-1488

cumulating and that may become active within months to years (Dzurisin,1489

2003) – a capability borne out by the statistical correlation between defor-1490

mation and eruption (Biggs et al., 2014). Just as importantly, InSAR results1491

have demonstrated that some eruptions are not preceded, nor accompanied1492

by, measureable deformation (e.g., Zebker et al., 2000; Pritchard and Simons,1493

2002; Lu and Masterlark, 2003; Moran et al., 2006; Chaussard et al., 2013;1494

Ebmeier et al., 2013b). In some cases, frequently active volcanoes, like Mer-1495

64

api (Chaussard et al., 2013) and Shishaldin, Alaska (Moran et al., 2006)1496

have no deformation detected by InSAR despite multiple eruptions. These1497

observations have led to several hypotheses, including that magma ascends1498

so quickly that it effectively evades detection (since volumes of intrusion and1499

withdrawal would roughly balance one another, meaning that the net volume1500

change was zero during the time spanned by a several-week interferogram),1501

and that frequently erupting volcanoes are sufficiently “open” that pressure1502

changes are not sufficient to cause surface deformation. The growing volume1503

of SAR acquisitions over such volcanoes, with the time between observations1504

moving from ∼monthly to ∼daily, will help to address this critical question.1505

Indeed, the large numbers of studies that have documented no deformation1506

before or during eruptions are a valuable first step towards understanding1507

how these volcanoes work, and how they should be monitored for future1508

changes in eruptive activity.1509

In addition to furthering research into magmatic and volcanic processes,1510

SAR studies of volcanoes have prompted advances in data processing tech-1511

niques that have had an impact far beyond the field of volcanology. An1512

example is the problem of atmospheric artifacts in interferograms, which is1513

a major limitation of InSAR (Zebker et al., 1997; Hanssen, 2001). Tropo-1514

spheric artifacts that correlate with topography are particularly problematic1515

for volcanoes, since deformation due to magmatic processes is also likely to1516

be correlated with topography. In fact, in the first study that applied satellite1517

SAR to volcano deformation (Massonnet et al., 1995), part of the signal that1518

was interpreted to reflect a magmatic process was later found to be a result1519

of atmospheric conditions (Delacourt et al., 1998). To combat this issue at1520

65

volcanoes, a variety of mitigation strategies have been explored, all of which1521

have applications to other uses of InSAR (e.g., Beauducel et al., 2000; Dela-1522

court et al., 1998; Wadge et al., 2002b; Webley et al., 2002; Remy et al., 2003;1523

Foster et al., 2006; Pinel et al., 2011), and work on the problem is ongoing.1524

Volcanoes are also often characterized by low coherence due to vegetation,1525

steep topography, ash deposition or lava emplacement, rapid deformation,1526

and ice and snow cover; new processing strategies have been developed to1527

overcome these challenges. To recover deformation associated with individ-1528

ual volcanic events, improved registration of phase data between SAR im-1529

ages (Yun et al., 2007) and pixel-offset tracking methods (Casu et al., 2011)1530

have yielded excellent results. Likewise, the non-linear nature of volcano1531

deformation over time, coupled with poor coherence, has been addressed by1532

time-series approaches, like Persistent Scatterer InSAR and the small base-1533

line approach (Hooper et al., 2004; Hooper, 2008). Modeling strategies have1534

also advanced in response to volcano deformation data provided by InSAR.1535

Most inverse models of displacement data from volcanoes are kinematic, but1536

newly developed dynamic inversions are capable of resolving the pressure1537

distribution within a magmatic body, thereby constraining the force respon-1538

sible for the measured displacements (e.g., Fukushima et al., 2005, 2010; Yun1539

et al., 2006; Hooper et al., 2011). Modeling methods that allow for increased1540

source complexity are also a direct consequence of high-spatial-resolution In-1541

SAR measurements of volcano deformation (Masterlark and Lu, 2004) and1542

the first attempt to use time-dependent optimization techniques on InSAR1543

data was at a volcanic field (Shirzaei and Walter, 2010).1544

66

6.2. Looking forward: better understanding of volcanoes and forecasts of1545

eruptions1546

Given that the SAR community is on the cusp of a ”golden age”, with1547

the recent or imminent arrival of new satellites and capabilities, how will1548

volcano SAR evolve over the next 20 years? And will these new data and1549

methods bring the community any closer to a better understanding of volcano1550

behavior and to the main expectation of society with regards to volcanology1551

– forecasting (or, dare we say, predicting) eruptive activity?1552

The main impact of SAR studies on risk assessment has so far been its1553

ability to detect unrest at volcanoes where no manifestations of activity had1554

previously been reported (e.g., Wicks et al., 2002; Pritchard and Simons,1555

2002). However the role of SAR data during an eruptive crisis has remained1556

subdued. Thus far, volcano SAR studies have, in general, been used to char-1557

acterize processes after they have occurred, which is valuable for advancing1558

understanding of volcanic behavior but is less useful for hazard mitigation1559

during volcanic crises. A noteworthy exception is the aforementioned case1560

of the 2010 Merapi eruption, in which timely SAR data, especially ampli-1561

tude imagery, provided much of the basis for maintaining evacuation zones1562

and preventing loss of life during explosive eruptions of the volcano (Pallis-1563

ter et al., 2013). In Iceland too, SAR data were used in hazard assessment1564

during the 2010 eruptions of Eyjafjallajkull (Sigmundsson et al., 2010). This1565

success was made possible by rapid availability of imagery to scientists, and1566

it provides an example of the potential value of SAR to volcano monitoring1567

and risk management. Efforts are currently in progress to integrate SAR data1568

into operational volcano monitoring systems (Meyer et al., 2014), and new1569

67

satellite missions will address this need – for example, the capability of near-1570

real-time data transmission from Sentinel-1 – so both amplitude imagery and1571

interferometric SAR should play a greater role in timely volcano monitoring1572

in the future. Additional satellites and the shorter repeat times of those mis-1573

sions will also help to address the need for better temporal resolution, since1574

many volcanic processes – like dike intrusions and dome growth – occur over1575

timescales of hours to days. In this new age for SAR, the community must1576

also take care to develop new processing and archiving strategies to deal1577

with the ever-increasing volume of data and to integrate SAR information1578

with ground-based observations. Only with such support systems can the1579

potential for InSAR as a near-real-time monitoring system be realized.1580

Another step that must be taken is the integration of the continuous flux1581

of SAR data with other observations into dynamic physics-based models of1582

volcanic processes. Such models have demonstrated outstanding potential1583

for investigating parameters like magma compressibility, magma reservoir1584

volume, and melt volatile content – factors that cannot be determined from1585

kinematic inversions (Anderson and Segall, 2011, 2013). Physics-based mod-1586

els also have the potential to transform our ability to forecast future activity1587

by coupling deformation, effusion rate, gas emissions, and other monitor-1588

ing data with a physically realistic model of a volcano (i.e., a model that1589

follows physics-based principles, like conservation of mass and momentum).1590

The great advantage of such an approach is that the probabilities of a spe-1591

cific outcome can not only be calculated, but also updated as new data are1592

acquired (Segall, 2013). Basic physics-based models of the evolution of an1593

eruption rely most heavily upon surface deformation and effusive volume –1594

68

two variables that can be constrained by SAR measurements. Those parame-1595

ters, derived from frequent SAR acquisitions by ground-based, airborne, and1596

satellite sensors, can therefore be fed directly into models that provide prob-1597

abilistic forecasts of, for example, the potential duration of eruptive activity.1598

As physics-based models of volcanoes continue to develop, broader forecasts1599

of parameters, like eruption onset and potential volume, will become pos-1600

sible. While these models will also need to incorporate other geological,1601

geophysical, and geochemical observations where they are available, remote1602

measurements from SAR will provide the foundation upon which to build.1603

At present, the knowledge needed to develop physics-based models is avail-1604

able from only a few well-studied volcanoes, like Kılauea, Etna, Mount St.1605

Helens, and Soufriere Hills. We expect more volcanoes to join these ranks,1606

however, as the record of space-based observations grows, even for remote1607

volcanoes that are not well monitored by terrestrial methods. Within a few1608

years, it is not unrealistic to think that volcano forecasts will be based not1609

on past experience, but instead on quantitative models that use near-real-1610

time SAR and other monitoring data, to dynamically update probabilistic1611

forecasts of activity.1612

In 20 years, SAR has gone from a specialized research tool that required1613

significant expertise and computing power and was available to only a few1614

researchers, to a broadly accessible technique that can be exploited using a1615

variety of software packages and that is close to becoming a vital volcano-1616

monitoring strategy. With the launch of new satellites, development of ad-1617

ditional ground-based and airborne systems, greater availability of data, and1618

continued refinement of data manipulation methods, we expect that barriers1619

69

to the use of SAR will continue to crumble, and that the technology will1620

evolve toward its logical place as a globally accessible volcano research and1621

monitoring tool.1622

7. Conclusion1623

Volcanology has derived outstanding benefits from SAR imagery by using1624

this remote sensing technique to address critical questions about how volca-1625

noes work, such as the timing of magma transfer, mechanisms of magma1626

ascent, construction of volcanic edifices, and volcano-volcano and volcano-1627

tectonic interactions. SAR amplitude data have documented eruptive de-1628

posits and surface change and have the advantage over optical data of being1629

able to operate at all times of day and in all weather conditions. Interfero-1630

metric data have been used to map deposits (from coherence), topography,1631

and deformation. Surface displacement fields derived from SAR benefit from1632

a large spatial coverage and unrivaled spatial resolution. They have thus1633

been key to assessing the shapes and volume changes of subvolcanic magma1634

plumbing systems, the impacts of magma storage and transport on local1635

stress field and future eruptive activity, and the stability of volcanic flanks.1636

SAR data should not, however, be considered independently of other vol-1637

canological datasets, but instead be integrated with these additional sources1638

of information, including both remote and ground-based data, to fully ex-1639

ploit the benefits of all. Thus far, the primary limitations in the use of SAR1640

for volcano monitoring have been the latency in data access and difficulty1641

in data processing, as well as poor temporal sampling. With the greater1642

availability of data made possible by the launch of new satellites, coupled1643

70

with recent computational and technical advances that facilitate processing1644

of large datasets, SAR imagery has become a common research and monitor-1645

ing tool across the Earth sciences. The current challenge for the volcanolog-1646

ical community is the effective use of these new SAR resources for better1647

understanding volcano behavior and addressing societys need for accurate1648

forecasts of hazardous activity. The key to overcoming this challenge rests1649

on expansion of efforts to educate volcano scientists around the world in SAR1650

utilization and interpretation, and the development of new modeling tools1651

that can more fully exploit the richness of insights provided by SAR data1652

from volcanoes.1653

8. Acknowledgments1654

We are grateful to Dan Dzurisin for his helpful comments on the manuscript1655

and we also thank reviewers Matt Pritchard and Valerio Acocella for their1656

suggestions, which greatly improved the overall quality of the paper.1657

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Table 1: Past and present side-looking orbital SAR missions used in vol-

canology (as of June 1, 2014). Additional data from Indian and Korean SAR

missions are also potentially available. For polarization description, ”single”

is for satellites providing SAR data acquired in only one given polarization,

”dual” is for satellites able to provide data acquired with two different po-

larizations (VV+VH or HH+HV) and ”quad” is for satellites able to provide

data in the fully polarimetric mode (HH,VV,HV,VH).

Table 2: Studies of magmatic intrusions though InSAR. If the event is clas-

sified as ”with eruption”, it means that at least one eruptive event associated

with the intrusion emplacement is observed during the period of activity. For

temporal evolution description, ’No’ means that the temporal evolution of

magma intrusion emplacement has not been described based on SAR data.

List of references can be found in Supplementary Material.

114

Figure 1: Imaging geometry. The lower colored line represents a 2-D slice

through a ground surface and the angled black line above represents a single

line in a SAR image acquired by a sensor located above, to the left, and flying

into the page. The black arrow shows the direction of signal propagation from

the SAR sensor and the dashed lines represent perpendicular wave fronts

separated by the spatial resolution of the sensor. The different colors indicate

how the ground surface maps to pixels in the range direction of the SAR

image. The blue slope facing the sensor is “foreshortened” in the SAR image,

whereas the red slope facing away from the sensor is lengthened, and appears

in more pixels despite both slopes having the same length on the ground. The

green section of the steeper mountain is “laid over”, appearing in the same

pixel as the ground surface well to the left, and the yellow lower section of

the mountain appears to the right of the upper section in the SAR image,

despite being located to the left of it on the ground. The steep gray slope

facing away from the sensor is not illuminated by the sensor at all – it is in

“shadow”– and does not appear in the SAR image.

115

Figure 2: Cumulative line-of-sight displacement over Eyjafjallajokull vol-

cano, Iceland, from 18 June 2009 to 01 September 2010 (track 132 ascending

mode). The co-eruptive interval includes the flank eruption, during which

there was very little deformation, and the later summit eruption, during

which the volcano deflated. Background is shaded topography. The hole in

the data is due to ice and snow cover. Black dots are earthquake epicen-

ters for each epoch (Icelandic Meteorological Office). Modified from Martins

et al. (in prep).

Figure 3: Comparison of pixels selected by a PS method and a small base-

line method from ERS data acquired over Eyjafjallajokull volcano, Iceland,

modified from Hooper (2008). The volcano is undergoing inflation due to the

intrusion of a sill at 5-6 km beneath the southern flank. Left, pixels selected

by the PS method of Hooper et al. (2007) and right, pixels selected by the SB

method described in Hooper (2008). The pixels are plotted on topography in

shaded relief, with white representing the approximate area of permanent ice

cover. The location of the area analyzed is shown left inset. 27 images were

used in the analysis although only one interferogram is shown here, which

covers 27 June 1997 to 10 October 1999. Each color fringe represents 2.8 cm

of displacement in the line-of-sight.

116

Figure 4: Temporal evolution of the number of peer reviewed scientific papers

based on satellite SAR data and applied to the study of volcanoes published

before 2014. We make a distinction between studies using only data provided

by ESA (ERS-1, ERS-2 and ENVISAT). A significant increase is observed

since 2010 due to the broad exploitation of L- and C-band data. The list of

scientific paper is from ISI Web Of Science. (See supplementary material for

references.) In insert, for comparison, the list of scientific papers obtained a)

with the two topics “volcanoes” and “deformation” and b) with the topics

“SAR” and (“earthquake” or “landslide” or “subsidence”) using ISI Web Of

Science.)

Figure 5: C-band (top) and L-band (bottom) interferograms spanning June

17–19, 2007, East Rift Zone intrusion and eruption at Kılauea Volcano,

Hawai‘i. Data from the L-band ALOS satellite are more coherent than the

ENVISAT C-band satellite, which is important for interpreting deformation

patterns that extend into forested regions. Deformation patterns indicate

deflation of Kılauea Caldera and widening and uplift in its middle East Rift

Zone as magma drained from beneath the summit to feed a growing dike

within the rift zone (Poland et al., 2008). Both interferograms have the same

scale for line-of-sight surface displacements–5 cm per color cycle. Satellite

flight directions and look angles from vertical are given by the arrows in the

upper left of each image.

117

Figure 6: Amplitudes images (TerraSAR-X) of the summit area of Merapi

volcano during the October-November 2010 eruption. Large changes in the

dome were observed over the course of the eruption. A) Image acquired 26

October at 22:21 UTC (Descending Track 134, incidence angle: 36◦). The

first explosive event (26 October 10:00 UTC) has just removed the 2006 lava

dome, enlarging and deepening the crater. B) Image acquired 4 November

11:00 UTC (Ascending Track 96, incidence angle: 47◦) showing a large (≈

5∗106m3, (Pallister et al., 2013)) lava dome. C) Image acquired 6 November

22:21 UTC (Descending Track 134., incidence angle: 36◦). A new lava dome

has grown to a volume of approximately 1.5 ∗ 106m3 (Pallister et al., 2013)

after the total destruction of the former one by the explosion on 4 November

17:05 UTC. See Pallister et al. (2013) for a complete overview.

118

Figure 7: Deposits of the 2010 eruption of Merapi volcano observed by

SAR imagery. a) Location of Merapi volcano in the central part of Java

island (the white box corresponds to the area covered in parts b and c). b)

ALOS-PALSAR amplitude-change image of Merapi’s southern flank. The

false-color composite (R: earlier image; G: later image; B: ratio of the sec-

ond image divided by the first image) is obtained using pairs of amplitude

images acquired in HH polarization before the eruption (on 16 September

2010) and after the event (on 1 February 2011). Deposit characteristics are

known from field observations and optical imagery (Charbonnier et al., 2013)

c) Coherence image obtained when forming the interferogram between two

descending ALOS/PALSAR images acquired before the main eruptive phase

(on 1 November 2010) and after (on 17 December 2010). The area covered by

deposits from the eruptive activity is characterized by a strong decorrelation

and appears dark. Figure is modified from Solikhin et al. (in revision).

Figure 8: Co- (A) and Cross-polarized (B) RADARSAT-2 amplitude images

acquired on July 7, 2010, depicting the summit, south flank, and upper East

and Southwest Rift Zones of Kılauea Volcano, Hawai‘i. Variations in surface

roughness, particularly between recent ‘a‘a and pahoehoe lava flows (like

those erupted in December 1974 from the Southwest Rift Zone and during the

1969-1974 Mauna Ulu eruption on the East Rift Zone), appear as differences

in shading (light areas indicate high backscatter and dark regions, low) and

are best distinguished in the cross-polarized image.

119

Figure 9: Co- (A) and Cross-polarized (B) RADARSAT-2 amplitude images

acquired on January 23, 2014, depicting the 1983-2014 lava flow field from

the Pu‘u ‘O‘o eruption. At the time of image acquisition, the northern-most

portion of the pahoehoe flow field was the only area with active lava, which

was extending into forested areas. Co-polarized imagery is not useful for

discriminating lava from forest, but the cross-polarized data allow for easy

mapping of flow margins given the backscatter contrast between lava (dark)

and forest (light).

120

Figure 10: Example application of coherence to map lava flows, modified

from Figures 10-12 from Dietterich et al. (2012). (A) Extent of a lava break-

out from an eruptive vent located ∼2 km east of Pu‘u ‘O‘o crater based

on coherence mapping from Envisat interferograms. Breakout started on

November 21, 2007, and colors indicate lava extent over the subsequent ∼110

days. Advance rate (inset; green is western branch of flow indicated by green

arrow on map, and blue is eastern branch indicated by blue arrow on map)

can be calculated based on the position of the flow front over time. Gap

in flow is due to the presence of vegetation, which, like lava, is incoherent.

Gray dashed box indicates area covered by part B. (B) Length of time from

emplacement until lava, which erupted during July-November 2007, becomes

coherent (colors) compared to flow thickness as measured on the ground

in October 2007 (gray contours). Distal extent of flow was in the forest,

and therefore the length of decorrelation is not measurable, while vent area

remained incoherent for longer than other parts of the flow because it re-

mained active long after the main flow area ceased activity in November

2007. Decorrelation time and thickness correspond strongly, indicating that

the time from emplacement that is required for a lava flow to become coher-

ent can be a good indicator of flow thickness. Inset gives thickness versus

time until coherence for the mean length of time a pixel is decorrelated in the

areas between each of the thickness contours. Error bars show the standard

deviation, red line is the best fit, and shaded region indicates the time during

which the flow was active.

121

Figure 11: Four-dimensional growth of the East Rift Zone lava flow field at

Kılauea Volcano, Hawai‘i (modified from Poland (2014)). (A) Map showing

thickness of lava emplaced between August 2011 and June 2013. Colors cor-

respond to flow thickness. Map is calculated from the cumulative thicknesses

determined from 16 time-sequential TanDEM-X DEM difference maps, which

individually show topographic change during discrete time intervals within

the overall time spanned. (B) Time-averaged discharge rate of lava (left

axis = bulk; right axis = dense-rock equivalent assuming 25% vesicularity)

from Kılauea’s East Rift Zone during 2011-2013 based on a time series of

TanDEM-X DEMs. The volume of lava for each time interval was calculated

by multiplying the thickness over the area covered. Bar thickness is greater

than measurement uncertainty. Gray shaded areas indicate times when lava

entered the ocean. Discharge rates determined during these periods are min-

imum values, since lava that flows into the ocean is not accounted for by

changes in topography. (C) Deformation of Kılauea’s summit during mid-

2011 to mid-2013 measured at a GPS station near the center of the caldera

(gray line, left axis) and a nearby pixel located at the point of maximum LOS

deformation (black circles with error bars, right axis). InSAR time series is

from COSMO-SkyMed ascending data. GPS and InSAR measurement points

are given in Figure 22. Red box shows time period when the discharge rate

of lava decreased drastically, coupled with a plateau in long-term summit

inflation; these indicators suggest a short-term decrease in magma supply

rate to the volcano.

122

Figure 12: Various type of deformation related to volcanic activity and ob-

served by InSAR. Deformation may be due to magma storage/emplacement

at depth either as reservoirs or intrusions. Data used for illustration are

modified from Vadon and Sigmundsson (1997); Froger et al. (2004); Lu et al.

(2010); Bonforte et al. (2011); Pinel et al. (2011).

Figure 13: Vertical and horizontal surface displacements induced by the

September 2005 Manda Hararo-Dabbahu rifting event, Afar (Ethiopia) de-

duced from a pixel-by-pixel inversion of displacements provided by InSAR

and subpixel correlations of synthetic aperture radar and SPOT images.

Courtesy of R. Grandin and modified from Grandin et al. (2009).

Figure 14: Inferred depth of deformation sources beneath volcanoes that

have been systematically observed by InSAR, as listed by Biggs et al. (2014)

(their supplementary material, tables 3b, 4b, 4c and 5). Volcanoes showing

only subsidence related to eruptive deposits are excluded.

Figure 15: Perspective view of an interferogram, which spans 1996-2000,

draped over a 30-m DEM of South Sister volcano, central Oregon. Center of

inflation is located about 5 km west of the volcano’s summit. Modified from

Wicks et al. (2002).

123

Figure 16: A deep dike intruded beneath the Upptyppingar region of N.

Iceland in 2007. The orientation was not perpendicular to the direction of

least compressive stress, and slip was therefore induced across the dike. Top

panels show interferograms spanning the intrusion (left, ascending and right,

descending) with each colour fringe representing 28 mm of displacement. Also

shown are horizontal GPS velocities with 95% confidence ellipses, surface

projections of the model source patches (white rectangles) and catalogue

earthquake epicentres for the entire intrusion period (black circles). Bottom

left panel shows the surface projection of the modelled mean opening and slip

from the posterior probability distribution. The right panel shows the dike

position in profile, with the maximum posterior probability solution plotted

in red and other solutions from the probability distribution plotted in grey.

Modified from Hooper et al. (2011).

124

Figure 17: Deformation of Fernandina volcano, Galapagos, associated with

its 2009 eruption, modified from Bagnardi et al. (2013). All interferograms

acquired by ENVISAT, and arrows in lower right corners indicate satellite

flight direction (small arrow) and look direction (thick arrow). (A) Typi-

cal inter-eruptive inflation pattern, with deformation mostly confined to the

summit caldera. This particular interferogram spans January 16-August 14,

2010. (B) Interferogram spanning January 31-April 10, 2009, with the end

time just ∼ 2 hours before the onset of eruption. Deformation is consistent

with intrusion of a sill from the 1-2-km-depth subcaldera magma reservoir.

(C) Interferogram spanning March 31-May 5, 2009, which covers the entirety

of Fernandina’s 2009 eruption. Surface displacements are a consequence of

subcaldera reservoir deflation, sill intrusion, and rotation of that sill towards

the vertical on the flank, where it intersected the surface and erupted lava

flows (yellow area).

Figure 18: COSMO-SkyMed ascending interferogram covering the summit

caldera and upper East Rift Zone of Kılauea Volcano and spanning 2 April

2012 to 19 March 2014. While a number of deformation processes are rep-

resented, several stand out, including line-of-sight subsidence in the north

part of the caldera, as well as at the former Alae and Makaopuhi lava lakes.

These three areas are sites of thick accumulations of lava that, decades after

their emplacement, are still cooling and compacting.

125

Figure 19: Average line-of-sight displacement velocity at Mount St. Helens,

Washington, from a stack of 36 interferograms acquired by ERS-1/2 on track

156 during 1992-2001. With the exception of the debris avalanche deposit

on the north side of the volcano and extending down the North Fork Toutle

River valley, most of the area is incoherent owing to vegetation and snow

cover. Three patches of subsidence are apparent in the stack; these patches

also exist in data from other tracks and satellites, indicating that they are

not atmospheric artifacts and that they are long-term deformation sources.

Modified from Poland and Lu (2008).

Figure 20: Time series of vertical displacement at the caldera center of

Fernandina volcano, Galapagos, using data from a variety of SAR satellites.

Vertical deformation is derived by combining descending and ascending inter-

ferograms from each satellite. Note breaks in time scale (between 1992 and

1998) and vertical displacement scale (between 0.0 and 0.5 m). Throughout

the 19-year span, overall uplift is interrupted by episodes of deflation asso-

ciated with eruptive activity (Chadwick et al., 2011; Bagnardi et al., 2013)

and sill emplacement (Bagnardi and Amelung, 2012). 1995 eruption is not

indicated. Modified from Baker (2012) and Bagnardi (2014).

126

Figure 21: Illustration of some limitations of InSAR using the example of

a typical andesitic stratovolcano, Colima Volcano, Mexico (NCV: Nevado de

Colima Volcano, CV: Colima Volcano, GC: Guzman city, CC: Colima city).

a) Mean coherence obtained from 54 descending interferograms from 2003 to

2006. Good coherence is restricted to towns or villages located at the base of

the volcano and to recent lava flows covering the upper part of the volcano;

other areas are highly vegetated. Another cause of decorrelation is due to

ash fall and unconsolidated explosive deposits in the vicinity of the volcano’s

summit. b) Areas not well imaged on SAR images due to the acquisition

geometry, are superimposed in grey on the contour lines (200 m) derived from

the SRTM Digital Elevation Model. Data are acquired by a satellite traveling

from north to south (descending track) with a look angle of about 22 degrees.

The satellite motion is shown by the black arrow and the look direction is

shown by the gray one. 3.6 % of the scene represented is not well imaged,

mostly (98 %) because of layover effects on slopes which are oriented towards

the radar beam. c) Interferogram corrected for topographic errors obtained

from an ascending track and characterize by a perpendicular baseline of 5 m

and a temporal baseline of 385 days. Each color fringe represents a phase

difference of 2π. Fringes are strongly correlated to topography as shown by

comparison to the SRTM Digital Elevation Model (d). This signal is mostly

produced by tropospheric artifacts, which correlate with topography. a) and

b) are modified from Pinel et al. (2011); c) and d) are modified from Yan

et al. (2014).

127

Figure 22: Ascending-mode COSMO-SkyMed interferogram of Kılauea Vol-

cano overlain on a shaded relief map and spanning September 7, 2011 to

June 20, 2013 – about the same time spanned by the TanDEM-X-derived

DEM differences that constrain the subaerial eruption volume (Figure 11a).

A variety of deformation sources are interacting to form a complex pattern

of LOS displacements, including: Southwest Rift Zone subsidence; faulting

in June 2012; subsidence of lava accumulations along the East Rift Zone and

in the north part of the caldera; inflation of the summit; and LOS uplift of

the East Rift Zone flank, which appears to be a transient related to a dike

intrusion (thick red line) that fed a fissure eruption in March 2011. Black

dot gives location of GPS station from Figure 11c; white dot is location of

pixel from which Figure 11c time series is extracted.

128

Foreshortening

Lengthening

Layover

SAR image

Shadow

Ground surface

Angle of incidence

SAR sensor

Signalpropagation

0

5

10

15

20

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

only ESA data

not only ESA data

25

30

3519

94

a) b)1995

2000

2005

2010

1995

2000

2005

2010

0

100

200

0

100

200

0 5 km

range change

0 5 cm

34°

C-band

39°

L-band

April 12 - June 21, 2007

May 5 - June 20, 2007

forested area

Kīlauea Caldera

East Rift Zone

26 oct. 2010

4 nov. 2010

6 nov. 2010

A)

B)

C)

~1 km ~1 km

N

~0.5 km

AA

114° E113° E112° E111° E110° E109° E108° E107° E106° E

6° S

7° S

8° S MERAPI

J a v a S e a

I n d i a n O c e a nN0

100200 km

J A V A

MERAPI

J a v a S e a

I n d i a n O c e a n

Bandung SemarangSurabaya

Yogyakarta

Banten Jakarta

Coherence01-11-2010 - 17-12-2010Coherence01-11-2010 - 17-12-2010

9150

000

9152

500

9155

000

9157

500

9160

000

9162

500

9165

000

9167

500

9170

000442500435000 440000437500

NN

Merapi

R: 16-09-2010 HHG: 17-12-2010 HHB: Band ratio

R: 16-09-2010 HHG: 17-12-2010 HHB: Band ratio

442500435000 440000437500

B

Valley confined and overbank PDCs

Surge and singe zone

Thick tephra

reworked PDCs

C

1

0

2 km

0 5 km

Kīlauea Caldera

East Rift Zone

Southwest Rift

Zone

Hilina Pali Fault System

Pacific Ocean

December 1974 ‘a‘ā

December 1974 pāhoehoe Mauna Ulu pāhoehoe

Mauna Ulu ‘a‘ā

HH HVA B

Nāpau Crater

Makaopuhi CraterPu‘u ‘Ō‘ō crater

1983–2014 lava-flow field

Forest

Pacific Ocean 0 5 km

active lava flow

Hilina Pali Fault System

HH HVA B

0 1 2 km

Flow thickness (m)

Duration of decorrelation

930 days

13 days

2

2

22

2

2

5

5

55

5

10

10

10

10

15

15

15

20

20

25

30

35

eruptive vent

0 5 10 15 20 25 30 35 400

100

200

300

400

500

Flow thickness (m)

Day

s dec

orre

late

d r2 = 0.97

155.1°W 155°W

19.3°N

19.4°NPu‘u ‘Ō‘ō crater

Flow extent on:Nov. 25, 2007Nov. 28, 2007Dec. 10, 2007Dec. 11, 2007Dec. 26, 2007Jan. 14, 2008Jan. 30, 2008Feb. 3, 2008Feb. 18, 2008Mar. 5, 2008Mar. 12, 2008

eruptive vent

0 20 40 60 80 100 1200

2

4

6

8

10

12

14

Days since Nov. 21, 2007

Dis

tanc

e (k

m)

13.6

m/h

r

2.4 m/hr

Flow advance rate

Flow throughvegetation

0 2 km

Pacific Ocean

A

B

0

0.5

1.0

1.5

2.0

2.5

3.0

Tim

e-av

erag

ed d

isch

arge

rate

(m3 /

s)

B

0

0.375

0.75

1.125

1.5

1.875

2.25

Tim

e-av

erag

ed d

isch

arge

rate

(den

se-r

ock

equi

vale

nt)

(m3 /

s)

0

10

20

30

40

Thic

knes

s (m

)

0 4 kmHawai‘i Island

A

-8

-4

-2

0

2

4

8

GP

S v

ertic

al c

hang

e (c

m)

Jul ‘11 Oct ‘11 Jan ‘12 Apr ‘12 Jul ‘12 Oct ‘12 Jan ‘13 Apr ‘13 Jul ‘13 Oct ‘13

B6

-6

GPS (vertical)

-3

-2

-1

0

1

2

3

4

5

6

InS

AR

LO

S d

efor

mat

ion

(cm

)InSAR (LOS)7

Hydrothermal system

Eruptive depositsload and compaction

Flank sliding

Magma storage

Intrusion propagation,emplacement

Etna,from Bonforte et al., 2011

Piton de la Fournaise,from Froger et al. 2004

Okmok,from Lu et al. 2010

Colima,from Pinel et al. 2011

64°.00’ N

63°.50’ N

64°.10’ N

64.0° N

22°.40’ W 21°.20’ W22°.20’ W 21°.40’ W22°.20’ W Reykjanes,from Vadon & Sigmundsson, 1997

-40 mm/yr 15 mm/yr

21 mm/yr

-32 mm/yr

Range Change

Satellite flight direction

Radar look direction

Reunion Island21°00’

21°10’

21°20’ 55°20’ 55°40’

SICILY

Tyrrhenian Sea

Ionian Sea

40.3

˚

40.4

˚

40.4

˚

40.5

˚

40.6

˚

40.6

˚

40.7

˚

40.8

˚

12.2˚

12.3˚

12.3˚

12.4˚

12.4˚

12.5˚

12.5˚

12.6˚

12.6˚

12.7˚

12.8˚

−5−4−3−2−1

012345

5 mHorizontal

Vertical (m)

10 km

3-10 km

<3 km

>15 km

10-15 km

Range Change

0 28.3 mm

South Sister

0

10 km

N

Typical inter-eruption inflation

2009 pre-eruption

2009 co-eruption

0 5 km

subsidence of1995 lava flow

A

B

C

2009 lava flow

0

2.8

1.4

LOS

(lin

e-of

-sig

ht)

disp

lace

men

t [cm

]

Pacific

Ocean

0 1.55 cm

Range change

2 km

Kīlauea Caldera

East Rift Zone

KīlaueaIki

Makaopuhi

Alae

−15 −10 −5 0 5 10

LOS displacement (mm/yr)

Elk Rock

JohnstonRidge

122˚ 30’ 122˚ 20’ 122˚ 10’

46˚ 10’

46˚ 20’

Figure 2

North Fork Toutle River Coldwater Lake

Castle Lake

Mount St. Helens

Washington

Oregon

Area of map South Fork Toutle River

Spirit

Lake

5 km

Subsidence

May

200

5 er

uptio

n

Dec.

200

6 in

trusio

n

Aug.

200

7 in

trusio

n

April

200

9 er

uptio

n

0.0

0.6

0.8

1.0

1.2

1.4

1.6

0.5

Verti

cal d

ispl

acem

ent (

m)

ERS-1/2Radarsat-1EnvisatALOS

1992 1998 2000 2002 2004 2006 20102008

Year

a) b)

c)

b)

19.8

-103.8

19.2

19.4

19.6

-103.2-103.4-103.6

1

0

0.2

0.4

0.6

0.8

c) d)-104 -103-103.6

20

19

19.2

19.6

19.8

19.4

-103.4 -103.2-103.8 -104 -103-103.6 -103.4 -103.2-103.8

4500 m

100 m

19.6

-103.7

19.4

19.5

-103.5-103.6

20

19

19.2

19.6

19.8

19.4

2000m

1000m

Decorrelation due to forest

PACIFIC OCEAN

subsidence due to cooling lava

SOUTHWEST RIFT ZONE

March 2011 dike

Decorrelation due to lava flows

June 2012faulting event

LOS uplift

LOS subsidence

KĪLAUEA CALDERA

Pu‘u ‘Ō‘ō

0 5 km0 5 cm

Range change

EAST RIFT ZONE

1From November 2010, Envisat operated in a new 30 day orbit, which was not optimal for interferometry at high latititudes.2In late 2015 a second Sentinel-1 satellite will be launched reducing the repeat time to 6 days

Mission Period of operation Wavelength Orbit repeat time PolarizationSEASAT Jun-Oct 1978 23.5 cm 17 days SingleERS-1 Jul 1991 to Mar 2000 5.66 cm 3 or 35 days SingleERS-2 Apr 1995 to Sep 2011 5.66 cm 3 or 35 days SingleJERS-1 Feb 1992 to Oct 1998 23.5 cm 44 days SingleSIR-C/X-SAR 9 to 20 Apr 1994 23.5, 5.8 and 3 cm N/A Quad

and 30 Sep to 11 Oct 1994RADARSAT-1 Nov 1995 to March 2013 5.6 cm 24 days SingleSRTM 11-22 Feb 2000 5.8 and 3.1 cm N/A SingleEnvisat Mar 2002 to Apr 2012 5.63 cm 35 days1 DualALOS Jan 2006 to Apr 2011 23.5 cm 46 days QuadCOSMO-SkyMed Jun 2007 to present 3.1 cm 16 days Dual(constellation of Dec 2007 to present 3.1 cm 16 days Dual4 satellites) Oct 2008 to present 3.1 cm 16 days Dual

Nov 2010 to present 3.1 cm 16 days DualTerraSAR-X Jun 2007 to present 3.1 cm 11 days DualTanDEM-X Jun 2010 to present 3.1 cm 11 days DualRADARSAT-2 Dec 2007 to present 5.6 cm 24 days QuadSentinel-1 2014 to present 5.6 cm 12 days2 DualALOS-2 2014 to present 23.5 cm 14 days Quad

Table 1: Past and present side-looking orbital SAR missions used in volcanology (as of June 1, 2014). Additional data from Indian and KoreanSAR missions are also potentially available. For polarization description, ”single” is for satellites providing SAR data acquired in only one givenpolarization, ”dual” is for satellites able to provide data acquired with two different polarizations (VV+VH or HH+HV) and ”quad” is for satellitesable to provide data in the fully polarimetric mode (HH,VV,HV,VH).

Type of event Location Date Duration Temporal Referencesevolution

Rifting eventsWith eruption Gelai 2007 2 months Yes Baer et al. (2008); Biggs et al. (2009)With no eruption Harrat Lunayyir 2009 4 months Yes Baer and Hamiel (2010)With eruption Manda Hararo-Dabbahu 2005 3 weeks No Wright et al. (2006),

(Main event) Grandin et al. (2009)With eruptions Manda Hararo-Dabbahu 2005-2009 5 years Yes Grandin et al. (2010a,b)

(All rifting episode) Hamling et al. (2010)With no eruption Upptyppingar 2007-2008 14 months No Hooper et al. (2011)

Volcanoes in a rift zoneWith 8 eruptions Nyamuragira 1996-2010 4-60 days No Toombs and Wadge (2012); Wauthier et al. (2013)With eruption Nyiragongo 2002 12-48 hours No Wauthier et al. (2012)

Other VolcanoesWith eruption Chaiten 2008 few hours No Wicks et al. (2011)With eruption El Hierro 2011-2012 8 months Yes Gonzalez et al. (2013)With eruption Etna 2001 23 days No Lundgren and Rosen (2003)With no eruption Eyjafjallajokull 1994 1 month No Pedersen and Sigmundsson (2004)With no eruption Eyjafjallajokull 1999 9 months Yes Pedersen and Sigmundsson (2006)With eruption Eyjafjallajokull 2010 3 months Yes Sigmundsson et al. (2010)With eruption Fernandina 1995 4 months No Jonsson et al. (1999); Amelung et al. (2000)With eruption Fernandina 2005 16 days No Chadwick et al. (2011); Bagnardi and Amelung (2012)With no eruption Fernandina 2006 1 day No Bagnardi and Amelung (2012)With no eruption Fernandina 2007 3 days No Bagnardi and Amelung (2012)With eruption Fernandina 2009 18 days Yes Bagnardi et al. (2013)With eruption Kilauea 2007 2 days No Montgomery-Brown et al. (2010)With eruption Kilauea 2011 4 days Yes Lundgren et al. (2013)With eruption Kizimen 2009-2010 >1 yr Yes Ji et al. (2013)With no eruption Mauna Loa 2002-2005 3.5 days No Amelung et al. (2007)With eruption Piton de la Fournaise 1998 3 months No Sigmundsson et al. (1999); Fukushima et al. (2010)With 4 eruptions Piton de la Fournaise 1999-2000 12 days-1 month No Fukushima et al. (2005, 2010)With eruption Piton de la Fournaise 2003 4 days No Froger et al. (2004)With eruption Tungurahua 2008 1 month No Biggs et al. (2010)With no eruption Yellowstone 1995-2000 few years No Wicks et al. (2006)

Table 2: Studies of magmatic intrusions though InSAR. If the event is classified as ”with eruption”, it means that at least one eruptive event associatedwith the intrusion emplacement is observed during the period of activity. For temporal evolution description, ’No’ means that the temporal evolutionof magma intrusion emplacement has not been described based on SAR data. List of references can be found in Supplementary Material.

3

References

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Amelung, F.and Yun, S.H., Walter, T.R., Segall, P., Kim, S.W., 2007. Stresscontrol of deep rift intrusion at Mauna Loa volcano, Hawaii. Science 316,1,026–1,030. DOI: 10.1126/science.1140035.

Baer, G., Hamiel, Y., 2010. Form and growth of an embryonic continentalrift: InSAR observations and modelling of the 2009 western Arabia riftingepisode. Geophysical Journal International 182, 155–167. doi:10.1111/j.1365-246X.2010.04627.x.

Baer, G., Hamiel, Y., Shamir, G., Nof, R., 2008. Evolution of a magma-driven earthquake swarm and triggering of the nearby Oldoinyo Lengai erup-tion, as resolved by InSAR, ground observations and elastic modeling, EastAfrican Rift, 2007. Earth and Planetary Science Letters 272, 339 – 352.doi:http://dx.doi.org/10.1016/j.epsl.2008.04.052.

Bagnardi, M., Amelung, F., 2012. Space-geodetic evidence for multiple magmareservoirs and subvolcanic lateral intrusions at Fernandina Volcano, Galapa-gos Islands. J. Geophys. Res. 117. Doi:10.1029/2012JB009465.

Bagnardi, M., Amelung, F., Poland, M.P., 2013. A new model for thegrowth of basaltic shields based on deformation of Fernandina volcano, Gal-pagos Islands. Earth and Planetary Science Letters 377378, 358 – 366.doi:http://dx.doi.org/10.1016/j.epsl.2013.07.016.

Biggs, J., Amelung, F., Gourmelen, N., Dixon, T.H., Kim, S.W., 2009. InSARobservations of 2007 Tanzania rifting episode reveal mixed fault and dykeextension in an immature continental rift. Geophys. J. Int. 179, 549–558.Doi: 10.1111/j.1365-246X.2009.04262.x.

Biggs, J., Mothes, P., Ruiz, M., Amelung, F., Dixon, T. H.and Baker, S.,Hong, S.H., 2010. Stratovolcano growth by co-eruptive intrusion: The2008 eruption of Tungurahua Ecuador . Geophysical Research Letters 37.L21302,doi:10.1029/2010GL044942.

Chadwick, W.W.J., Jonsson, S. Geist, D.J., Poland, M., Johnson, D.J., Batt, S.,Harpp, K.S., Ruiz, A., 2011. The May 2005 eruption of Fernandina volcano,Galapagos: The first circumferential dike intrusion observed by GPS andInSAR. Bull. Volcanol. 73, 679–697.

Froger, J.L., Fukushima, Y., Briole, P., Staudacher, T., Souriot, T., Villeneuve,N., 2004. The deformation field of the August 2003 eruption at Piton de laFournaise, Reunion Island, mapped by ASAR interferometry. GeophysicalResearch Letters 31. L14601, doi:10.1029/2004GL020479.

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Fukushima, Y., Cayol, V., Durand, P., 2005. Finding realistic dyke mod-els from interferometric synthetic aperture radar data: The February 2000eruption at Piton de la Fournaise. J. Geophys. Res. 110. B03206,doi:10.1029/2004JB003268.

Fukushima, Y., Cayol, V., Durand, P., Massonnet, D., 2010. Evolution ofmagma conduits during the 19982000 eruptions of Piton de la Fournaise vol-cano, Reunion Island. J. Geophys. Res. Doi:10.1029/...

Gonzalez, P.J., Samsonov, S.V., Pepe, S., Tiampo, K.F., Tizzani, P., Casu,F., Fernndez, J., Camacho, A.G., Sansosti, E., 2013. Magma storage andmigration associated with the 20112012 El Hierro eruption: Implications forcrustal magmatic systems at oceanic island volcanoes. Journal of GeophysicalResearch: Solid Earth doi:10.1002/jgrb.50289.

Grandin, R., Socquet, A., Binet, R., Klinger, Y., Jacques, E., de Chabalier,J.B., King, G.C.P., Lasserre, C., Tait, S., Tapponnier, P., Delorme, A.,Pinzuti, P., 2009. September 2005 Manda Hararo-Dabbahu rifting event,Afar (Ethiopia): Constraints provided by geodetic data. J. Geophys. Res.114. B08404,doi:10.1029/2008JB005843.

Grandin, R., Socquet, A., Doin, M.P., Jacques, E., de Chabalier, J.B., King,G., 2010a. Transient rift opening in response to multiple dike injections inthe Manda Hararo rift (Afar, Ethiopia) imaged by time-dependent elasticinversion of interferometric synthetic aperture radar data. J. Geophys. Res.115. B09403,doi:10.1029/2009JB006883.

Grandin, R., Socquet, A., Jacques, E., Mazzoni, N., , de Chabalier, J.B., King,G.C.P., Lasserre, C., Tait, S., Tapponnier, P., Delorme, A., Pinzuti, P., 2010b.Sequence of rifting in Afar, Manda-Hararo rift, Ethiopia, 2005-2009 : Timespace evolution and interactions between dikes from interferometric syntheticaperture radar and static stress change modeling. J. Geophys. Res. 115.Doi:10.1029/2009JB000815.

Hamling, I.J., Wright, T.J., Calais, E., Bennati, L., Lewi, E., 2010. Stress trans-fer between thirteen successive dyke intrusions in Ethiopia. Nature Geoscience3, 713–717.

Hooper, A., Ofeigsson, B., Sigmundsson, F., Lund, B., Einarsson, P., Geirsson,H., Sturkell, E., 2011. Increased crustal capture of magma at volcanoes withretreating ice cap. Nature Geoscience DOI: 10.1038/NGEO1269.

Ji, L., Lu, Z., Dzurisin, D., Senyukov, S., 2013. Pre-eruption deformation causedby dike intrusion beneath Kizimen volcano, Kamchatka, Russia, observedby InSAR. Journal of Volcanology and Geothermal Research 256, 87 – 95.doi:http://dx.doi.org/10.1016/j.jvolgeores.2013.02.011.

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Fernandina Volcano, Galapagos, observed by satellite Radar Interferometry.Geophysical Research Letters 26, 1,077–1,080.

Lundgren, P., Poland, M., Miklius, A., Orr, T., Yun, S.H., Fielding, E., Liu,Z., Tanaka, A., Szeliga, W., Hensley, S., Owen, S., 2013. Evolution of dikeopening during the March 2011 Kamoamoa fissure eruption, Klauea Vol-cano, Hawai‘i. Journal of Geophysical Research: Solid Earth 118, 897–914.doi:10.1002/jgrb.50108.

Lundgren, P., Rosen, P.A., 2003. Source model for the 2001 flank erup-tion of Mt. Etna volcano. Geophysical Research Letters 30, n/a–n/a.doi:10.1029/2002GL016774.

Montgomery-Brown, E.K., Simnett, D.K., Poland, M., Segall, P., Orr, T.,Zbker, H., Miklius, A., 2010. Geodetic evidence for en echelon dike emplace-ment and concurrent slow slip during the June 2007 intrusion and eruption atKilauea volcano, Hawaii. J. Geophys. Res. 115. Doi:10.1029/2009JB006658.

Pedersen, R., Sigmundsson, 2004. InSAR based sill model links spatiallyoffset areas of deformation and seismicity for the 1994 unrest episodeat Eyjafjallajokull volcano, Iceland. Geophysical Research Letters 31,L14610,doi:10.1029/2004GL0202368.

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Volcanology: Lessons learned from Synthetic Aperture

Radar imagery-Supplementary material

V. Pinela, M. P. Polandb, A. Hooperc

aISTerre, Universite de Savoie, IRD, CNRS, F73376 Le Bourget du Lac, FrancebU.S. Geological Survey–Hawaiian Volcano Observatory, PO Box 51, Hawaii National

Park, HI 97818-0051, USAcUniversity of Leeds, School of Earth and Environment

1. List of satellite SAR Studies applied to volcanoes

Figure 1 is only based on scientific papers included in the ISI Web ofScience. It takes into account only studies based on satellite data, such thatstudies using SIR-C data have been omitted (e.g., Rosen et al., 1996; Zebkeret al., 1997). We also haven’t considered the earliest studies performed withSEASAT data (e.g., Zebker and Villasenor, 1992).

Studies of volcanoes using only data provided by ESA (ERS-1,ERS-2 andENVISAT) appearing in figure 1 are listed below:Ali and Feigl (2012); Aly et al. (2009), Amelung et al. (2000a,b); Amelungand Day (2002); Anderssohn et al. (2009); Avallone et al. (1999); Bagnardiand Amelung (2012); Bathke et al. (2011, 2013); Beauducel et al. (2000);Bjornsson et al. (2001); Bonaccorso et al. (2004, 2006); Bonforte et al. (2007,2011, 2013b,a); Borgia et al. (2005); Biggs et al. (2009b, 2010a, 2011, 2013a),Biggs et al. (2013b); Briole et al. (1997); Brunori et al. (2013); Calais et al.(2008); Carnec and Fabriol (1999); Casu et al. (2011); Catalano et al. (2013);Chadwick et al. (2011); Chang et al. (2007, 2010); Clifton et al. (2002);D’Auria et al. (2012); Delacourt et al. (1998); de Zeeuw-van Dalfsen et al.(2004); Dietterich et al. (2012); Dzurisin et al. (2006, 2009); Feigl et al.(2000); Fernandez et al. (2003); Fernndez et al. (2005); Fernandez et al.(2009); Fialko and Simons (2000, 2001); Fialko and Pearse (2012); Frogeret al. (2001, 2004, 2007); Gonzalez et al. (2010); Gonzalez and Fernandez(2011); Grandin et al. (2009, 2010a),Grandin et al. (2010b); Hamling et al.

Email address: Virginie.Pinel@univ-savoie.fr (V. Pinel)

Preprint submitted to Journal of Volcanology and Geothermal Research October 6, 2014

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1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

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only ESA data

not only ESA data

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1994

a) b)

Figure 1: Temporal evolution of the number of peer reviewed scientific papersbased on satellite SAR data and applied to the study of volcanoes publishedbefore 2014. We make a distinction between studies using only data providedby ESA (ERS-1, ERS-2 and ENVISAT). A significant increase is observedsince 2010 due to the broad exploitation of L- and C-band data. The list ofscientific paper is from ISI Web Of Science. In insert, for comparison, thelist of scientific papers obtained a) with the two topics “volcanoes” and “de-formation” and b) with the topics “SAR” and (“earthquake” or “landslide”or “subsidence”) using ISI Web Of Science.

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(2009); Heleno et al. (2010); Henderson and Pritchard (2013); Henriot et al.(2001); Henriot and Villemin (2005); Hole et al. (2007); Hooper et al. (2004,2007a,b); Hooper (2008); Hooper et al. (2011); Jonsson et al. (1999, 2005);Jonsson (2009); Keiding et al. (2010); Kwoun et al. (2006); Lagios et al.(2005); Lanari et al. (1998, 2002, 2004); Lee et al. (2008, 2013); Lu et al.(1997); Lu and Freymueller (1998); Lu et al. (2000a,b,c, 2002a,b,c, 2003a,b,2004, 2005, 2010); Lundgren et al. (2001, 2003); Lundgren and Rosen (2003);Lundgren et al. (2004); McAlpin and Meyer (2013); Mann et al. (2002); Man-coni et al. (2010); Manconi and Casu (2012); Massonnet et al. (1995, 1997);Masterlark and Lu (2004); Masterlark et al. (2006, 2010, 2012); Navarro et al.(2009); Newman et al. (2006); Neri et al. (2007, 2009); Nobile et al. (2012);Ofeigsson et al. (2011); Pagli et al. (2006, 2012); Palano et al. (2008); Pa-pageorgiou et al. (2012); Papoutsis et al. (2013); Pearse and Fialko (2010);Pearse and Lundgren (2013); Pedersen and Sigmundsson (2004, 2006); Peltieret al. (2010); Pepe et al. (2008); Perlock et al. (2008); Pinel et al. (2011);Poland et al. (2006); Poland and Lu (2008); Price (2004); Pritchard and Si-mons (2002); Remy et al. (2003); Riddick et al. (2012); Romero et al. (2003);Rowland et al. (1994, 2003); Ruch et al. (2008, 2009, 2010); Sachpazi et al.(2002); Salvi et al. (2004); Scharrer et al. (2007); Shirzaei and Burgmann(2012); Shirzaei et al. (2013); Sigmundsson et al. (1997); Solaro et al. (2010);Stevens et al. (2001a), Stevens et al. (2001b); Stevens and Wadge (2004);Sykioti et al. (2003); Tizzani et al. (2007); Thatcher and Massonnet (1997);Toombs and Wadge (2012); Yun et al. (2006, 2007); Vadon and Sigmundsson(1997); Vasco et al. (2002); Velez et al. (2011); Wadge et al. (2002b, 2006);Webley et al. (2002); Wicks et al. (1998, 2002, 2006); Wright et al. (2006).

Studies of volcanoes using acquired by other satellites, eventually alsoincluding data from ESA are listed below:Amelung et al. (2007); Aoyama et al. (2009); Baer et al. (2008); Baer andHamiel (2010); Baker and Amelung (2012); Bagnardi et al. (2013); Biggs et al.(2009a, 2010b); Bignami et al. (2013); Carn (1999); Chaussard and Amelung(2012); Chaussard et al. (2013); Chen et al. (2008); Clarke et al. (2013);Currenti et al. (2012); De Michele et al. (2013); Negro et al. (2013); de Zeeuw-van Dalfsen et al. (2012); Di Martino et al. (2012); Ebmeier et al. (2010,2012, 2013a), Ebmeier et al. (2013b); Foumelis et al. (2013); Fournier et al.(2010); Fukushima et al. (2005, 2009, 2010); Furuya (2004); Gonzalez et al.(2013); Guglielmino et al. (2011); Ji et al. (2013); Jung et al. (2011); Kanekoet al. (2001); Kerle et al. (2003); Koike et al. (2002); Kozono et al. (2013);

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Lu and Dzurisin (2010); Lundgren and Lu (2006); Lundgren et al. (2013);Matthews et al. (2003); Montgomery-Brown et al. (2010); Moran et al. (2006);Myer et al. (2008); Nishimura et al. (2001); Ohkura (1998); Ozawa and Ueda(2011); Ozawa and Fujita (2013); Ozawa and Kozono (2013); Pagli et al.(2012); Pallister et al. (2013); Parks et al. (2011, 2012); Patrick et al. (2003);Pavez et al. (2006); Philibosian and Simons (2012); Pritchard and Simons(2004a), Pritchard and Simons (2004b); Pritchard et al. (2013); Richter et al.(2013); Riddick and Schmidt (2011); Ruch et al. (2013); Saepuloh et al.(2010, 2013); Samsonov et al. (2011); Sandwell et al. (2008); Scharrer et al.(2008); Sigmundsson et al. (1999, 2010); Smets et al. (2010); Solaro et al.(2011); Takada and Fukushima (2013); Tobita et al. (2001); Terunuma et al.(2005); Toombs and Wadge (2012); Torres et al. (2004); Wadge et al. (2011,2012),Wadge et al. (2002a); Wauthier et al. (2012, 2013); Whelley et al.(2012); Wiart et al. (2000); Wicks et al. (2011); Yulianto et al. (2013).

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