## 3 Materials and Methods
### Processing Techniques  
#### 3.3.1 Sentinel-1  
SAR Processing  
The amplitude component of a SAR image measures the energy backscattered to the radar antenna after the electromagnetic wave hits the surface. It depends on the surface slope (i.e., incidence angle of the wave front onto the surface), the surface roughness (i.e., specular or diffuse reflector), and the surface material (i.e., the dielectric constant, particularly affected by moisture content). For instance, a terrain with a slope facing towards the radar sensor will backscatter a lot of energy to the radar (high amplitude signal), which will appear as bright pixels on the SAR image. Conversely, a slope facing away from the radar will appear as dark pixels (low amplitude). Volcanic ash will tend to smooth the terrain on which it is deposited, thereby acting like a specular reflector, which will tend to backscatter more energy if the radar viewing angle is orthogonal to the slope, but less energy if looking at an oblique angle [23]. As soon as an S1 product is downloaded, it goes through the SAR processing chain which creates a geocoded SAR intensity image (see Supplementary Material S1 for details). Visual inspection of this image can be useful for volcano monitoring even for non-experts, especially when optical images are not available because of cloud coverage. In particular, large surface topography changes and eruptive deposits are usually easily identifiable (see Section 4.3). Retrieving parameters which can help characterize volcanic activity over time in a robust and automated fashion is however a difficult task. Various approaches have been used to detect and visualize changes in SAR imagery. \"Low-cost\" approaches usually involve \"incoherent change detection\", which identifies surface intensity changes in successive SAR images. These changes can be visualized in a number of ways: (i) as difference maps, whereby the arithmetic difference, logarithmic ratio, or normalized change index (NCI) between two images is plotted [58; 59; 60], or (ii) as composite RGB image, where two bands are assigned to the first and second SAR image, and the third band to one of the difference map described above (e.g., [23; 24]). More advanced techniques track features in the radar image prior to geocoding (i.e., radar coordinates in range and azimuth) to quantify morphological changes. Tracking radar shadows, for example, has proven useful to detect crater deepening [23], infilling of valleys by pyroclastic flows [24], and lava lake level variations [61]. Tracking the position of specific reflectors on the other hand, has proven capable of quantifying volcanic dome growths [62]. Such techniques unfortunately require a lot of manual tuning to adapt to each volcano, and are consequently a difficult approach for global monitoring. In this paper we instead favor the logarithmic ratio approach, in which we color code areas where the intensity has increased and decreased to red and blue respectively (see Section 4.2.2). SAR interferometry exploits the phase difference between two SAR images having similar viewing geometries, in order to measure both surface topography and displacements. More specifically, InSAR processing is commonly used to generate digital elevation maps, whereas DInSAR processing (Differential InSAR) successively subtracts the terrain elevation to retrieve surface deformation between the two acquisitions [63; 64; 65; 66]. While these techniques typically exploit only two images, more advanced techniques known as \"time series processing\" exploit multiple images to retrieve more accurate deformation time series (i.e., Permanent Scatters PS and Small Baseline SBAS, see [67] for a review). The accuracy difference between DInSAR and time series approaches is about one order of magnitude, i.e., from centimeter to millimeter accuracy [68]. However, time series techniques require a lot of computing power and are therefore not performed by MOUNTS, which instead focuses on the two-image DInSAR approach.