Monitoring surface and vegetation conditions is crucial for analyzing the impact of climate change on natural resources, especially in regions susceptible to extreme events like land and forest dryness caused by summer heatwaves. Traditional satellite indices, including NDVI, have limitations in distinguishing between barren soil and distressed vegetation. This study shows the potential of two recently validated indices, the Emissivity Contrast Index (ECI) and the Water Deficit Index (WDI), to assess vegetation stress and woodland degradation. These indices, derived from Infrared Atmospheric Sounding Interferometer (IASI) data, utilize an Optimal Interpolation scheme for upscaling and remapping. The effectiveness of ECI and WDI has been validated through a comparison with Surface Soil Moisture (SSM).The methodology allows for simultaneous assessment of surface hydric stress, identifying regions at risk of drought and forest fires. This approach has been applied to southern Italy during year 2023, an area which has been impacted by strong heatwaves in the last decade. These indices could demonstrate significant effectiveness when estimated using high-resolution sounders, such as the Surface Biology and Geology Observing Terrestrial Thermal Emission Radiometer (SBG OTTER). This would allow for more effective monitoring of small, heterogeneous areas.

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.

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.

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.

Over the last three decades interferometric synthetic aperture radar (InSAR) techniques have attained a fundamental role in surface deformation monitoring. The main importance is given by two key factors: the exploitement of rich SAR data archives and the development of processing techniques able to extract information from these datasets. InSAR time series techniques such as permanent scatterers (PS) or SBAS have proven capable of estimating displacements to with 1 mm precision for targets that show a stable electromagnetic signature. At the same time the availability of SAR constellations with a reduced revisit time, such as the X band COSMO-SkyMed (CSK) and TerraSAR-X/PAZ satellites, has reduced the minimum detectable deformation gradient between two neighboring points and improved the theoretical precision given for a network of selected stable pixels. Independently from the applied processing methodology, the main drawback at X-band is the shorter time interval to temporal decorrelation. This effect can be partially mitigated by a short repeat time acquisitions plan. A technique able to deal with distributed scatterers is fundamental for extending the spatial coverage of the coherent area. In this paper we analyze the potential of X-band COSMO-SkyMed short repeat pass interferometry over a rural area in the southern part of Italy for assessing the capabilities and limitations of the Quasi-PS (QPS) technique in an X-band non-optimal scenario.

PRE-EARTHQUAKES (Processing Russian and European EARTH observations for earthQUAKE precursors Studies) EU-FP7 project is devoted to demonstrate - integrating different observational data, comparing and improving different data analysis methods - how it is possible to progressively increase reliability of short term seismic risk assessment. Three main testing area were selected (Italy, Turkey and Sakhalin) in order to concentrate observations and integration efforts starting with a learning phase on selected events in the past devoted to identify the most suitable parameters, observations technologies, data analysis algorithms. For these areas, different ground (80 radon and 29 spring water stations in Turkey region, 2 magneto-telluric in Italy) and satellite (18 different systems) based observations, 11 data analysis methods, for 7 measured parameters, have been compared and integrated. A specific integration platform (PEG, Pre-Earthquakes Geoportal) based on OGC (Open Geospatial Consortium) standards, was developed to operate a products integration, cross-validation and scientific interpretation.

Radiances observed by the Interferometric Monitor for Greenhouse gases sounder have been used to retrieve temperature, water vapor, and ozone profiles. It is shown that the sounder allows one to simultaneously retrieve stable solutions for temperature and water vapor. Once water vapor and temperature have been retrieved, ozone profile may be estimated on the same fine vertical mesh as water vapor and temperature. Comparison among calculated and observed radiances shows good agreement in several parts of the thermal band which is sensed by the sounder. A slight discrepancy is observed in the wing region of the 720-cm/sup -1/ CO/sub 2/ Q-branch, whereas the most severe form of disagreement is seen in the 6.7-/spl mu/m vibrational H/sub 2/O absorption band. Nevertheless, suitable spectral ranges may be identified which yield accurate and stable inversions for temperature, water vapor, and ozone.