*5.2. Limitations of MOUNTS and Future Developments*

The quantity of data available for volcano monitoring is increasing exponentially, but so is the difficulty to transform it into knowledge. Indeed a number of limitations arise, related to the extraction of meaningful parameters (are we looking at the right variables?), resolution issues (is the sampling in time and space accurate enough?), and data handling issues (how do we deal with the growing mass of data?). MOUNTS is at this stage still a proof-of-concept, which has large potential for improvement. We hereafter discuss the main limitations and development directions.

1. ImproveMOUNTS' capability to recover parameters informing on the state of volcanic activity, eruptive precursors in particular. While IR and UV spectroradiometry is able to provide rather straightforward parameters (i.e., heat and gas flux respectively), recovering parameters from SAR in a robust and automated fashion is more challenging. In this paper, we show how trained neural networks can achieve complex tasks in a timely and reliable manner, and can be easily implemented in operational processing chains. In particular, strong deformation typically imprint on interferograms as many colored fringes, which are successfully detected. Further development however is needed to detect slow deformation mechanisms, which do not generate deformation patterns with numerous fringes. Future developments should also focus on designing and training neural networks to recover from SAR data other relevant parameters that can inform on volcanic activity. For example, efficient change detection able to exclude changes non related to volcanic activity (e.g., snow fall, vegetation growths, etc.) would prove extremely useful during both pre-eruptive and syn-eruptive phases.


#### **6. Conclusions**

We present an operational volcano monitoring system, based on the automated download and processing of multisensor satellite-based data (Sentinel-1 SAR, Sentinel-2 SWIR, Sentinel-5P TROPOMI). The recovered data aim at providing key parameters able to inform on the state of volcanic activity, namely: surface deformation, surface reflectivity changes, surface heat anomalies, and SO2 gas emissions. The results are disseminated in NRT on a public website (www.mounts-project.com), where both geocoded images and multi-parametric time series help understand the activity. Moreover,

we demonstrate how artificial intelligence can be used in such monitoring system to solve complex tasks. In particular, we designed and trained a convolutional neural network to detect large deformation signals in wrapped interferograms with no atmospheric corrections. The training was done on synthetically generated interferograms, and evaluated on >1360 real interferograms produced by MOUNTS. Due to the very good performances of the network, it is now incorporated into the operational processing chain, which delivers automatic email alerts to dedicated users when strong deformation is recorded at the monitored volcanoes.

In addition to the set of parameters recovered from spaceborne sensors, we incorporate information available from global earthquake catalogues (GEOFON and USGS) to inform on the seismicity located in the vicinity of the volcano. The utility of integrating both satellite-based parameters (deformation, heat and gas) and ground-based parameters (seismicity) are demonstrated through a number of recent eruptions: Erta Ale 2017, Piton de la Fournaise 2018–2019, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, and Ambrym 2018. We show how this interdisciplinary approach allows for assessment of a variety of volcanic phenomena, ranging from subsurface magma migration, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and SO2 gas emission into the atmosphere. The data processed by MOUNTS is providing insights into the eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of such multiparametric datasets can help better monitor volcanic hazards.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2072-4292/11/13/1528/s1, S1: Details on DInSAR, SAR, and SWIR processing chains; S2: Details on neural network architecture, training, and synthetic data generation; S3: Video of the summit crater morphological changes detected from SAR imagery during the 2018 eruptive episodes at Kilauea (Hawai'i); S4: Video of the island growth and destruction before/after the December 2018 eruption of Krakatau (Indonesia); S5: Kilauea (Hawai'i) East Rift Zone flank eruption monitored by MOUNTS on large spatial scale.

**Author Contributions:** Conceptualization: (S.V., T.R.W., A.L.), inspired by MIROVA (D.C., M.L.); Methodology: system architecture and management (S.V., A.L.), website development (S.V.), Convolutional Neural Network (A.L.), SWIR hot pixel detection (F.M.), SAR speckle filtering and change detection, (O.D.), SO2 emission analysis (S.V., M.L.); Writing: original draft preparation (S.V.), review and editing by (A.L., D.C., D.L., F.M., O.D., M.L., T.R.W., O.H.).

**Funding:** This publication was financially supported by Geo.X, the Research Network for Geosciences in Berlin and Potsdam (Project Number: SO\_087\_GeoX).

**Acknowledgments:** We thank four anonymous reviewers for their comments which helped improve the manuscript. We also wish to acknowledge N. Anantrasirichai for providing details regarding the training dataset used in their study [34], as well as M. Gouhier, F. Amelung and F.J. Meyer for sharing details on the monitoring systems compiled in Table 1. Sentinel data are made freely available online by Copernicus Open Access Hub (https://scihub.copernicus.eu/), and are partially processed with the free SNAP toolboxes (https://step.esa.int/main/toolboxes/snap/). Earthquake catalogs are provided by GEOFON (GFZ Potsdam) and USGS, and interrogated using the Pyrocko Toolbox (https://pyrocko.org/, [103]).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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