## 5 Discussion
### 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. Improve MOUNTS' 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. Incorporate additional data types in the processing chain to provide further insights into the volcanic activity. A priority is to analyze Sentinel-3 (S3) TIR data routinely, crucial to monitor ground thermal anomalies at high sampling rate (sensor characteristics similar to MODIS), but also to detect ash plumes in the atmosphere. Ash detection is commonly achieved using the brightness temperature difference (BTD) procedure [88], applied to two channels centered around 11 and 12 \(\upmu\)m. This approach is easily applicable, but also prone to generate false alarms. A number of methods have been developed to overcome this issue [20], including 3-band algorithms [48], the BTD algorithm with water vapor correction (BTD-WVC), the Robust Satellite Technique (RST) specifically configured for volcanic ash, and shallow neural networks [89; 90]. Future developments should therefore implement automated S3 processing to monitor volcanic ash propagation in the atmosphere, which poses a major threat for air traffic in particular. 3. Incorporate modeling tools to predict the propagation of volcanic eruptive products, using the recovered multiparametric data as input source terms for the models. Such strategies are not new, and are now, thanks to increasing computing power and data availability, becoming achievable in NRT to forecast the propagation of lava flows [91] or the dispersion of ash plumes into the atmosphere (e.g., [92; 93]), as well as to predict the geometry and depth of magma bodies responsible for volcano deformation (e.g., [94; 95; 96]). Monitoring platforms such as MOUNTS should not necessarily include such modeling routines, but should at least strive to provide parameters than can be fed to such models. 4. Migrate processing tasks on cloud platforms where data is archived. The development of MOUNTS was done by automatizing data download and processing, using free and open-source data and software. In doing so, we were able to keep the costs of this proof-of-concept platform very low (i.e., a single desktop computer manages data download, processing, and hosting of the web server).