This study briefly overviews the methodologies employed at the mining pilot site in Austria, to demonstrate continuous monitoring of raw material extraction (progress, stability, waste dumps) under the scope of S34I project. Several unmanned aerial vehicle (UAV) flights were performed, and tri-stereo digital elevation models (DEMs) were computed and compared to estimate the volume of the material extracted and deposited. The low-cost global navigation satellite system (GNSS)-based monitoring system was installed in April 2023 and provided near real-time data. In addition, state-of-the-art Interferometric synthetic aperture radar (InSAR) processing provided dense displacement times series in 3D to create the deformation model.

This study briefly overviews the methodologies employed for generating ground displacement time series of areas subject to severe phenomena, emphasizing the azimuthal (i.e., about north-south) components not seen from conventional interferometric SAR analyses. To face this problem efficiently, multiple aperture interferometry (MAI) and, more recently, burst overlapped interferometry (BOI) have been proposed. Here, the authors of this investigation would like to provide the readers with some experiments conducted in heterogeneous contexts to demonstrate the validity of BOI but also to point out its evident limitations (in terms of the coverage areas and the expected precision of the ground measurements) and shade lights on potential further developments of such techniques. The presented results refer to two selected regions, i.e., the Galapagos Island and the Ridgecrest (U.S.) earthquake area.

In this study, we show the potential of a synergistic use of COSMO-SkyMed (CSK) and Sentinel-1 (S-1) synthetic aperture radar (SAR) data for the analysis of ground deformations in coastal regions potentially subjected to flood risk. The global climate change and the inherent increase of the mean sea level exacerbate the risk. In this work, we want to focus on the capability of high-resolution CSK images, combined with medium-resolution Sentinel-1 data, to jointly provide a more comprehensive view of the changes occurring in highly urbanized coastal regions, such as the one represented by the megacity of Shanghai. Specifically, we have applied a split-spectrum method to identify a set of highly coherent SAR pixels. The location and spatial density of such permanent (and the semi-permanent scatters) have revealed helpful to generate a flood risk map for the coastal area of Shanghai in a worst-case scenario. To this aim, we have used the LISFLOOD-FP hydrodynamic model to forecast possible inundation patterns and used the estimated density of high-coherent SAR pixels into the inundated areas to quantify the coastal flood impacts. Similar analyses could also be extended to other coastal regions in the World, as the West Coast of U.S.

Recent investigations have shown that multi-look (ML) SAR interferograms with very short baselines are seriously affected by undesired short-lived phase artefacts, which can negatively influence the reliability of the InSAR products (e.g., ground displacement time-series, mean displacement velocity maps). In this work, we present a strategy to estimate and mitigate such phase bias effects, by making exclusive use of ML InSAR phase triplets. Specifically, we present a stationary (time-invariant) method that keeps the simplification that the InSAR phase bias signal in an ML interferogram exclusively depends on the temporal baseline of the considered interferogram, and not on the specific acquisition times of the interfering SAR images. The results demonstrate the validity of the proposed method using both simulated and real SAR data.

This work proposes a multigrid differential synthetic aperture radar (SAR) interferometry (InSAR) technique for the detection of ground displacements at different spatial scales. The method relies on efficient phase-unwrapping (PhU) operations performed at the native spatial scales. In particular, a set of multi-look interferograms are first unwrapped using conventional (or advanced) PhU algorithms at the regional scale. Subsequently, ML unwrapped interferograms are used to facilitate the PhU operations performed at the local scale (single-look). Specifically, the unwrapped multi-look interferograms are resampled to the single-look grid and modulo-$2\pi $subtracted to the single-look interferograms. These phase residuals are then unwrapped and added back to the multi-look resampled interferograms. To accomplish these operations, at variance with alternative multiscale methods, no (linear/nonlinear) models are used to fit the spatial high-pass phase residuals. Finally, the unwrapped single-look interferograms are properly inverted to retrieve the ground displacement time series using any small baseline (SB)-oriented multitemporal InSAR tool. Experimental results are performed by processing a set of SAR data acquired by the X-band COSMO-SkyMed sensor over the coastal area of Shanghai, China.

This work explores the properties characterizing the phase non-closure of multi-look synthetic aperture radar (SAR) interferograms. Specifically, we study the implications of multi-look phase time incongruences on the generation of ground displacement time-series through small baseline (SB) multi-temporal InSAR (Mt-InSAR) methods. Our research clarifies how these phase inconsistencies can propagate through a time-redundant network of SB interferograms and contribute, along with phase unwrapping (PhU) errors, to the quality of the generated ground displacement products. Moreover, we analyze the effects of short-lived phase bias signals that could happen in sequences of short baseline (SB) interferograms and propose a strategy for their mitigation. The developed methods have been tested using both simulated and real SAR data. The latter were collected by the Sentinel-1A/B (C-band) sensors over the study areas of Nevada state, U.S., and Sicily Island, Italy.

This work proposes a method for estimating and compensating the atmospheric phase screen (APS) in sets of synthetic aperture radar (SAR) interferograms generated with a ground-based SAR (GB-SAR) instrument. We address the presented approach’s physical, statistical, and mathematical framework by discussing its potential and limitations. In contrast with other existing algorithms that estimate the APS from the unwrapped phase signals, our methodology is based on the straightforward analysis of the wrapped phases, directly. Therefore, the method is not affected by any potential phase unwrapping mistake, and it is suitable for multitemporal interferometric SAR (InSAR) applications. The effects of the local topography, the decorrelation noise, and the ground deformation on the APS estimates are deeply studied. Experiments performed on simulated and real GB-SAR InSAR data corroborate the validity of the theory. In particular, the simulated results show that the method is beneficial in zones with medium-to-high topographic slopes (e.g., for Alpine and mountainous regions).

Tropospheric excess path delays afflict surface motion measurements when Ground-based SAR (GB-SAR) interferometry is adopted. In this work, we propose an adaptive frequency-domain methodology to estimate the atmospheric phase screen (APS) components using wrapped GB-SAR interferometric data pairs, thus avoiding any possible phase unwrapping mistake. The experimental results show that the developed technique can detect and compensate for tropospheric fluctuations found in steep mountain areas without using any external digital elevation model of the investigated area. The paper addresses the potential of the developed technique by applying it on a set of simulated data.

The present paper aims to illustrate new indices of vegetation-soil dryness based on the surface emissivity complemented with atmospheric water vapor mixing ratio or related parameters, such as the dew point temperature. The indices are based on satellite measurements and they have been built using the hyperspectral infrared sensor IASI (Infrared Atmospheric Sounder Interfemoter) flying onboard the European Meteorological Platforms (MetOp). With the IASI instrument, we can retrieve simultaneously the surface emissivity and temperature, and thermo-dynamical parameters of air, such as temperature and water vapor mixing ratio profiles. Infrared surface emissivity (ε) is more closely related to surface type and coverage concerning the commonly used normalized differential vegetation index (or NDVI). By properly using surface emissivity in the infrared we defined a set of channels that are particularly sensitive to bare soil, green and senescent vegetation. IASI capability to sense the thermodynamic state of the atmosphere enables to retrieve both surface temperature (Ts) and dew point temperature (Td) close to the surface. The difference between these two quantities (Ts-Ts) is a direct measure of the hydric stress at the surface. Emissivity indices complemented with the last one, obtained from the same measurements, enable the individuating region to be subject to a risk of drought, hence and forest fire, and allow us to overcome the problem of lacking space and temporal consistency. We applied this methodology to the region of Balgarska Polyana in southern Bulgaria which was hit by intense fires in August 2016.

This work is based on the use of weighted Least-squares (WLS) approaches for the generation of ground line-of-sight (LOS) displacement time-series through differential interferometric SAR methods. More precisely, in this paper, a WLS method that extends the usability of the Multi-Temporal InSAR (MT-InSAR) Small Baseline Subset (SBAS) algorithm in regions with medium-to-low coherence is presented. The proposed method relies on the adaptive selection and exploitation, pixel-by-pixel, of the medium-to-high coherent interferograms, such as to discard the noisy phase measurements. In such a way, for each pixel, it is possible to obtain several disjoined sets of interferograms, which are then connected by exploiting the weighted singular value decomposition (WSVD) method. As a consequence, the interferogram networks reduction may lead to rejecting some SAR acquisitions from the used dataset, this results in the generation of so called variable-length displacement time-series. The experimental results have been performed considering by applying the Weighted Adaptative Variable-lEngth (WAVE) technique to a dataset composed of 50 SAR images, gathered by the Italian Space Agency Cosmo-SkyMed (CSK) constellation sensors over the Basilicata region, Southern Italy.

In this study, we show the potential of an adaptive quad-tree-based decomposition method applied to Differential Synthetic Aperture Radar (DInSAR) data. Specifically, the proposed method exploits a multi-resolution scheme for the phase unwrapping of sequences of DInSAR interferograms and allows one to produce DInSAR deformation products at different scales of resolution. The selection of the used multi-grid is based on the analysis of the statistical properties of a sequence of interferometric phase, allowing to recognizing major deformation areas where phase unwrapping operations can be performed more efficiently, with a computational improvement and without losing significant information.