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Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.

On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards.

Previous studies have shown contrasting glacier elevation and mass changes in the sub-regions of high-mountain Asia. However, the elevation changes on an individual catchment scale can be potentially influenced by supraglacial debris, ponds, lakes and ice cliffs besides regionally driven factors. Here, we present a detailed study on elevation changes of glaciers in the Lahaul-Spiti region derived from TanDEM-X and SRTM C-/X-band DEMs during 2000–2012 and 2012–2013. We observe three elevation change patterns during 2000–2012 among glaciers with different extent of supraglacial debris. The first pattern (<10% debris cover, type-1) indicates maximum thinning rates at the glacier terminus and is observed for glaciers with no or very low debris cover. In the second pattern (>10% debris cover, type-2), maximum thinning is observed up-glacier instead of glacier terminus. This is interpreted as the insulating effect of a thick debris cover. A third pattern, high elevation change rates near the terminus despite high debris cover (>10% debris cover, type-3) is most likely associated with either thinner debris thickness or enhanced melting at supraglacial ponds and lakes as well as ice cliffs. We empirically determined the SRTM C- and X-band penetration differences for debris-covered ice, clean ice/firn/snow and correct for this bias in our elevation change measurements. We show that this penetration bias, if uncorrected, underestimates the region-wide elevation change and geodetic mass balance by 20%. After correction, the region-wide elevation change (1712 km 2 ) was estimated to be −0.65 ± 0.43 m yr − 1 during 2000–2012. Due to the short observation period, elevation change measurements from TanDEM-X for selected glaciers in the period 2012–2013 are subject to large uncertainties. However, similar spatial patterns were observed during 2000–2012 and 2012–2013, but at different magnitudes. This study reveals that the thinning patterns of debris-covered glaciers cannot be generalized and spatially detailed mapping of glacier elevation change is required to better understand the impact of different surface types under changing climatic conditions.

Permanent scatterer interferometric synthetic aperture radar (PSInSAR) processing requires parameter selection that can significantly impact results, yet these parameters are often not fully disclosed in scientific publications. To demonstrate how different parameter settings in PSInSAR processing affect results, our study analyzed PSInSAR processing results using varying parameters. Results were evaluated both with and without temporal coherence filtering (threshold ≥ 0.8). Parameter variations produced differences that exceeded previously stated accuracy ranges for PSInSAR methods, while overall deformation trends remained similar across parameter sets. This shows that even seemingly minor parameter variations can lead to significant differences in PSInSAR results, exceeding what would be considered acceptable with respect to previously published accuracies. These findings emphasize the need for complete parameter disclosure in scientific publications and suggest more careful interpretation of small velocity differences in PSInSAR results.

Interferometric data processing (InSAR) for extraction of information about the Earth terrain heights is one of the general guidelines in development of contemporary spacebased radar systems. The InSAR processing for building the digital elevation models (DEM) and terrain displacement maps includes a phase noise suppression step, which is usually performed by different image filters, as well as spectral and adaptive spectral filters (mean/boxcar, spectral domain filtration with different window functions, Wiener, Goldstein, etc.), and an analysis of filtration algorithms efficiency is one of important issues of InSAR processing. For ALOS PALSAR data, different phase noise filters were researched, and the processing results were compared with the ground control data reprojected into the radar coordinate system. It is shown, that the gaussian spectral filter with some definite parameters shows the best results in the phase noise suppression, and a dependence of filter parameters on interferogram spectral characteristics was obtained.

Interferometric data processing (InSAR) for extraction of information about the Earth terrain and its changes is one of the general guidelines in development of contemporary space-based radar systems. The InSAR processing for building the digital elevation models (DEM) and terrain displacement maps includes a phase noise suppression step, which is usually performed by adaptive spectral filers (Goldstein filter). Such filters need an interferometric coherence value as a parameter, and a method of coherence estimation may affect the quality of the resulting DEM. For ALOS PALSAR data, different coherence estimation techniques (classical, Fourier, slope compensation etc.) were researched as an adaptive filter parameter. The filtered and unwrapped interferograms were compared with the ground control data reprojected into the radar coordinate system. It is shown, that coherence estimation method affects the quality of the phase noise suppression, and the Fourier estimate shows the better results in a Goldstein filtration.

Precise interferometric synthetic aperture radar (InSAR) is a new intelligent photogrammetric technology that uses automatic imaging and processing means. Precise InSAR has become the most efficient satellite surveying and mapping (SASM) method that uses the interferometric phase to create a global digital elevation model (DEM) with high precision. In this paper, we propose the application of systematic InSAR technologies to SASM. Three key technologies are proposed: calibration technology, data processing technology and post-processing technology. First, we need to calibrate the geometric and interferometric parameters including the azimuth time delay, range time delay, and atmospheric delay, as well as baseline errors. Second, we use the calibrated parameters to create a precise DEM. One of the important procedures in data processing is the determination of phase ambiguities. Finally, we improve the DEM quality through the joint use of the block adjustment method, long and short baseline combination method and descending and ascending data merge method. We use 6 sets of TanDEM-X data covering Shanxi to conduct the experiment. The root mean square error of the final DEM is 5.07m in the mountainous regions. In addition, the low coherence area is 0.8km 2. The result meets the China domestic SASM accuracy standard at both the 1∶50000 and 1∶25000 measurement scales.

With advancements in radar sensors, communications, and computer technologies, alongside an increasing number of ground observation tasks, Synthetic Aperture Radar (SAR) remote sensing is transitioning from being theory and technology-driven to being application-demand-driven. Since the late 1960s, Interferometric Synthetic Aperture Radar (InSAR) theories and techniques have continued to develop. They have been applied significantly in various fields, such as in the generation of global topography maps, monitoring of ground deformation, marine observations, and disaster reduction efforts. This article classifies InSAR into repeated-pass interference and single-pass interference. Repeated-pass interference mainly includes D-InSAR, PS-InSAR and SBAS-InSAR. Single-pass interference mainly includes CT-InSAR and AT-InSAR. Recently, China has made significant progress in the field of SAR satellite development, successfully launching several satellites equipped with interferometric measurement capabilities. These advancements have driven the evolution of spaceborne InSAR systems from single-frequency to multi-frequency, from low Earth orbit to higher orbits, and from single-platform to multi-platform configurations. These advancements have supported high precision and high-temporal-resolution land observation, and promoted the broader application of InSAR technology in disaster early warning, ecological monitoring, and infrastructure safety.

For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (~km) relative displacements with an accuracy of <1 cm/epoch and ~2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit.

La Fossa Caldera at Vulcano (Italy) has been showing signs of unrest since September 2021. To investigate this phenomenon, we conducted an analysis of geodetic and seismological data from July to December 2021. In particular, we analyzed Multi Temporal Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data, showing a pronounced elliptical uplift signal, which we elaborated using analytical source modeling. Additionally, seismic data were used to identify seismicity associated with hydrothermal system activity and assess its temporal evolution. The results indicate that the observed deformation is consistent with the expansion of the hydrothermal system within the La Fossa Caldera. These findings align with the analysis of seismic data, revealing signals indicative of hydrothermal activity, such as Very Long Period events. The results suggest that the ongoing phenomenon since 2021 represents a hydrothermal unrest, similar to the one observed during the late 1970s to early 1990s. Plain Language Summary La Fossa Caldera at Vulcano Island, part of the Aeolian Islands archipelago in Italy, has shown an increased volcanic activity since September 2021. This activity is characterized by an increase in fumarole temperatures, massive gas emissions, as well as a marked uplift of the crater area, accompanied by an increase in seismicity. To investigate the nature of these phenomena, an analysis of ground deformation data obtained from Multi Temporal Interferometric Synthetic Aperture Radar and Global Navigation Satellite System measurements is presented. Additionally, a detailed analysis of data recorded by the seismic network on Vulcano Island has been conducted. The results indicate that these anomalies can be attributed to the expansion of the hydrothermal system, a phenomenon previously observed in the late 1970s and early 1990s. Key Points Multi Temporal Interferometric Synthetic Aperture Radar enabled investigating localized ground deformation in the La Fossa Caldera The analysis of local seismicity indicates it is associated with the injection of fluids into conduit‐like structures The modeled source of ground deformation associated with the 2021 unrest is consistent with the pressurization of the hydrothermal system