This study investigates the potential of a statistical-based, adaptive approach to unwrapping sequences of differential synthetic aperture radar (SAR) interferograms that cover a large swath of the terrain. The proposed method adopts a multiscale decomposition strategy to identify efficiently and then process sets of coherent points at different spatial scales. The coherent point selection process is performed considering the statistical properties of the stack of wrapped multilooked SAR interferograms generated at various scales. Overall, the adopted procedure allows automatically recognizing the areas in large swath interferograms where significant and reliable phase changes occur while moving from neighboring spatial scales. Over these regions, multiscale phase unwrapping (PhU) operations are performed efficiently, with a computational improvement and without losing significant information. To this aim, the implementation of a conditioned space-time PhU scheme that operates sequentially at different spatial grids is detailed. Then, the unwrapped interferograms are inverted to generate ground displacement time series through advanced multitemporal interferometric SAR (MT-InSAR) approaches, recovering information at different scales (from local to regional/continental). Experimental results have been obtained by applying the developed scheme to large-swath SAR datasets collected at the C band by Sentinel-1 sensors. The results demonstrate the feasibility and soundness of the developed multiscale PhU method.

This study investigates the efficacy of incoherent and coherent SAR descriptors for filling spatial and temporal gaps in optical-driven Leaf Area Index (LAI) time series. Within this context, an artificial intelligence (AI) algorithm based on Multi-Output Gaussian Process (MOGP) [1], [2] demonstrated its effectiveness in handling the different information derived from SAR signatures in a unified corpus. The study utilizes sequences of Sentinel-2 imagery to derive Leaf Area Index (LAI) maps, while Sentinel-1 observations over the same area are utilized to obtain SAR backscatter coefficients and interferometric coherence data. This comprehensive dataset is then employed as input for training the MOGP model. Experimental tests demonstrate the usefulness of the MOGP model in obtaining accurate LAI time series even during very cloudy periods.

In this work, a Change Detection (CD) analysis was conducted based on the assessment of ground deformation (subsidence) in the Venice Lagoon over recent years. The multi-temporal interferometric Small Baseline Subset (SBAS) technique [1], [2] was employed to analyze the interconnections between the subsidence in the area and the occurrence of extreme flood events, with a specific focus on the floods that occurred in November 2019. Examining the time series of backscattered signals from the Sentinel-1 (S-1) synthetic aperture radar (SAR) sensor, we identified the extent of the flooded regions and evaluated the impact of the floods on the city. The potential of a newly developed Artificial Intelligence (AI) method [3] based on Random Forest [4], [5] was exploited. This methodology leverages the capability of several coherent/incoherent SAR change detection indices (CDIs) and their mutual interaction in a single corpus for rapid mapping of surface changes. This method [3] has shown great success in rapidly mapping land surface changes of areas in Sardinia and Sicily that were affected by large wildfires in the summer of 2021 and flooded areas in Houston and GalvestonBay as a result of Hurricane Harvey in 2017. In conclusion, the comprehensive CD/SBAS analysis provided valuable insights into the relationship between subsidence and recent extreme flood events in the Venice Lagoon, revealing the dynamics of the lagoon and its vulnerability to such events.

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.

This study proposes an Artificial Neural Network approach for the detection of optically thin cirrus using observations from the Infrared Atmospheric Sounding Interferometer - New Generation (IASI-NG) and from its predecessor, IASI. The Thin Cirrus Detection Algorithm applies a Feedforward Neural Network (NN) to IASI/IASI-NG samples previously declared as clear by a cloud detection algorithm. The NN training, test and validation datasets are generated from a set of ECMWF 5-generation reanalysis (ERA5) processed with the σ-IASI radiative transfer model to simulate IASI/IASI-NG radiances. The IASI and IASI-NG Thin Cirrus detection algorithms were validated against an independent dataset showing better performances for the IASI-NG thin-cirrus-detection algorithm. Moreover, IASI thin-cirrus-detection algorithm outputs were compared against Cloudsat/CPR and SEVIRI cloud products, showing good probability of detection: 0.84 for SEVIRI and 0.77 for CPR/Cloudsat.

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth’s system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer—new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of “clear-sky” or “thin-cirrus” detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the$\sigma $-IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus.

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.

In this work, a novel approach for the correction of misregistration errors in sequences of Terrain Observation with Progressive Scan (TOPS) Sentinel-1 SAR data is presented. The method represents a further evolution of the Enhanced Spectral Diversity (ESD) approaches. Remarkably, the developed algorithm is almost insensitive to the presence of large azimuth ground displacements due, for instance, to massive earthquakes, volcanic eruptions or glacier movements. Indeed, in such non-stationary contexts, the conventional ESD and network ESD approaches for the SAR TOPS data co-registration reveals problematic being co-registration errors and azimuth ground deformation components mixed out. Preliminary experiments conducted on a set of TOP SAR data related to the area hit by the Ridgecrest earthquake MW 7.1, California, on July 04 2019 confirm the validity of the theoretical framework.

In this article, we present an innovative space–time adaptive multilooking technique that operates on a sequence of multitemporal, differential synthetic aperture radar interferograms. The developed approach relies on the application of the fundamentals of directional statistics theory. At variance with other methods that identify the set of statistically homogenous pixels (SHPs) within a multilooking (complex averaging) window based on the statistics of the single-look-complex (SLC) SAR images, the proposed method is exclusively based on the analysis of the multitemporal sequence of full resolution DInSAR interferograms. The SHPs are then used to generate spatially adaptive multilooked interferograms both at the native, full-scale grid of the SLC images and at the multilooked resolution scale. The algorithm is effective and simple to implement, only requiring the availability of a sequence of full-scale differential SAR interferometry (DInSAR) interferograms. The interferograms can then be used to generate ground displacement time-series through advanced multitemporal interferometric SAR (MTInSAR) approaches. Experimental results obtained by applying the adopted technique to two SAR data sets acquired at X- and L-band, respectively, demonstrate the validity of the developed method.

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.

In this work, a new method for the noise filtering and the adaptive multi-looking of a sequence of multi-temporal differential SAR interferograms, which relies on the use of directional (circular) statistics, is presented. At variance with other similar approaches, which identify homogenous distributed scatterers (DS) in a resolution cell by analyzing the statistics of the complex-valued single-look-complex (SLC) SAR images, our method is exclusively based on the analysis of statistics of the phase interferograms, thus making no other assumptions. The developed technique can be applied to generate adaptive multi-look interferograms both at the native grid of full-resolution images and/or at the multi-look resolution scale. The preliminary experimental results, achieved by applying the proposed approach to a dataset consisting in 93 SAR data acquired by the ERS-1/2 radar sensors from 1992 to 2008 over the Gulf of Napoli and its surrounding area (South Italy) confirm the effectiveness of the proposed method.