The main objective of the project SCIA (Sviluppo di algoritmi per lo studio della Criosfera mediante Immagini PrismA) is the development and optimization of methods for generating products related to the cryosphere. The project foresees the development of a robust processing chain of PRISMA hyperspectral data for the estimation of snow and glacier parameters in Alpine areas, through a combined use of satellite images, field data and radiative transfer models (RTMs). The image spectroscopy measurements provided by PRISMA will make possible to investigate radiometrically complex surfaces and obtain geophysical parameters currently only achievable through airborne hyperspectral sensors.

This research aims to exploit the potentialities of multi-mission SAR data at X-, C- and L-band for the monitoring of snowpack and alpine soils. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism have been monitored and different methods are proposed for their retrieval. In order to gather consistent datasets, experimental activities have been conducted in two selected sites in Northern Italy, which are covered by alpine snow during winter and spring periods and are in some cases characterized by the presence of permafrost. Microwave responses of snow and soil have been then simulated by using electromagnetic (i.e., AIEM, Oh, SFT and DMRT-QCA), and physical models (SNOWPACK). Finally, machine learning approaches, as Artificial Neural Networks and Random Forest, were implemented for retrieving snow parameters; whereas interferometric techniques were used in case of snow and soil displacement as rock glaciers. Preliminary and consistent results have been obtained in terms of estimate of snow parameters and soil displacement. This multi-frequency/multi-mission approach enhances the ability of SAR sensors to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

The mapping of the snow cover from optical remote sensing is a largely investigated topic that still present some challenges especially in mountainous areas when high resolution time series are exploited. Providing reliable maps in all the acquisition conditions is limited by topographic effects such as shadows, sunglint on snow and atmospheric disturbances. If these effects are not corrected, the state of the art methods are generally failing. Support vector machine (SVM) proved to be an effective tool for dealing with classification problems, even when data are noisy and the classes are not linearly separable. In this work, we propose an unsupervised approach to snow cover fraction retrieval that collect "snow" and "snow free" samples to train a model tailored for the specific acquisition conditions of each image. The method has been applied in different test sites in the Alps. For a quantitative evaluation of the result, we used a snow maps derived by very high resolution images acquired in challenging situations of fractional snow cover and low sun elevation.

The backscattered signal collected by space-born radars, such as Sentinel-1, for area covered by snow is affected by the condition of the snow itself, in particular during melting. However, the potential evidence of a relationship between the different melting phases and the amplitude of the backscattered signal still remains a difficult phenomenon to be described quantitatively. This paper proposes a preliminary approximated model, built upon i) a first-order simulation based on plane-wave incidence on stratified media that account for the bulky physical parameters of the snowpack, such as depth, liquid water content, density, and ii) a full-wave simulation to include the effect of the surface roughness. The model is tested against experimental data for a site in the Italian Alps (Malga Fadner), where data from Sentinel-1, as well as in-situ data about the composition of the snowpack, are available for the winter season 2017/18, showing good general agreement.

The monitoring of the snow cover area (SCA) from optical sensors on board of satellites is affected by the trade-off between spatial and temporal resolution provided by the current operational missions. This limits the possibility to exploit satellite SCA for hydrological purposes. In this paper, we propose a novel downscaling approach driven by the low resolution (LR) information that takes advantage of i) all the high resolution (HR) images acquired in the past over a catchment; and ii) the geomorphometric features that drive the snow redistribution process. Possible applications of the proposed method are time-series gap-filling and snow pattern detection. The downscaled scenes have been validated across existing HR scenes showing an accuracy of about 90%.

In this paper, we present a system for snow roughness measurement. The system is made up of digital camera and a black Forex panel. The whole system has been designed for being low cost and portable; and will be distributed as an open-source Python software. The system is based on the coregistration of the photos taken at the panel on the field with a reference scaled image with pixel size of 0.05 mm. In detail, the coregistration process is done by computing the homography, whose parameters are estimated exploiting automatic key-points matching. Among all methods forkey-points extraction, we used the scale invariant feature transform (SIFT) because of its robustness and recognized efficiency. After key-points extraction, the matching has been performed using a brute force matching (BFM) approach. The proposed system has been tested under different conditions, showing to be an effective and easy approach to snow surface roughness measurement, as well as robust.

The characterization of snow conditions and the estimation of snow water equivalent (SWE) are the main goals of this paper, achieved through the exploitation of multi-frequency SAR data at both C- and X-bands from Sentinel-1 (S-1) and COSMO-SkyMed (CSK) satellites, respectively. Dry/wet snow conditions have first been assessed using C-band S-1 images. Subsequently, a sensitivity analysis was carried out by using datasets of in-situ snow measurements (i.e. snow depth, density, snow grain radius, temperature and wetness) collected in South Tyrol region, in north-eastern Italy. Simulations based on the Dense Medium Radiative Transfer (DMRT) forward electromagnetic model were considered to interpret and assess the experimental findings. Two retrieval algorithms for SWE estimation from X-band SAR data were implemented. These algorithms are based on machine learning approaches, i.e. Artificial Neural Networks (ANN) and Support Vector Regression (SVR). The training of the algorithms accounts for experimental data and DMRT model simulations and, then is applied to a selection of X-band CSK StripMap HIMAGE scenes collected over the test area. The results are promising, and pave the way for further analysis and validation to exploit the potential of SAR for snow parameter retrieval.

The characterization of snow conditions and the estimate of snowpack parameters have been investigated in this paper, by taking into account both C- and X-band SAR data collected from Sentinel-1 (S-1) and Cosmo-SkyMed (CSK) satellites, respectively. Although C- and X-band are not the most suitable frequency for the retrieval of snow parameters due to the high penetration power inside snowpack, some results concerning the wet/dry snow status and both Snow Depth (SD) and Snow Water Equivalent (SWE) estimate can be achieved by using appropriate algorithms and models.A sensitivity analysis was carried out by exploiting datasets of in situ measurements (snow depth, density, snow grain radius, temperature and wetness) collected on two test site in South Tyrol (Ulten Valley and Val Senales). This analysis provided indications on the sensitivity of C- and X-band backscattering to the target snow parameters. As a second step of the analysis, simulations based on the Dense Medium Radiative Transfer (DMRT) forward electromagnetic model have been considered for interpreting and assessing the experimental findings. Finally, an attempt of implementing a retrieval algorithm for estimating SWE from these frequencies is carried out. The algorithm is based on Artificial Neural Networks (ANN). The training of the algorithm accounts for experimental data and DMRT model simulations and, successively, it is applied to time series of CSK images collected on both test areas. The obtained results are encouraging, although more analysis and validation is needed for exploiting the potential of SAR in snow parameter retrieval.

In this work, we present a framework to integrate physically based hydroclimatological models and remote sensing products, by exploiting their advantages and overcoming their limitations, for an improved understanding and estimation of the alpine glacier accumulation and ablation processes. The capability of remote sensing to well represent the spatial variability of the snow cover over the glaciers is used to correct possible errors in the model simulations, thus obtaining a more reliable estimation of annual glacier mass balance. The proposed approach is tested on the glaciers in the Rofen Valley (Austria) by employing the AMUNDSEN model for accumulation and ablation processes simulation and Landsat-5/7/8 data for glacier zone mapping from 1998 to 2016.

The main idea of this study is the development of an innovative data fusion method through which state-of-the-art remotely sensed products and hydrological modelling simulations can be integrated to improve the retrieval and the reliability of snow cover and snow water equivalent mapping. The proposed method is based on a machine learning technique, Support Vector Machine (SVM), and on exploitation of two well-instrumented test-sites in EUREGIO region for the validation. Results show an improvement of performances with respect to single products from remote sensing and model. On EUREGIO scale the accuracy of snow cover mapping obtained from fusion reaches 0.95.

In this paper, a novel approach for the retrieval of snow wetness is presented for Sentinel-1 (S-1) data. The approach uses the information on snow proprieties provided by the hydroclimatological model AMUNDSEN and confirmed by comparisons performed at different sematic level to train a regressor that is able to exploit the dual-polarimetric information provided by S-1. The preliminary results obtained for the Rofental in Austria are discussed.

In this work, the characterization and extraction of snowpack parameters from X-band SAR imagery is investigated. X-band is not yet the most suitable frequency for the retrieval of snow parameters, namely Snow Depth (SD) and Snow Water Equivalent (SWE); however, it is the higher frequency available in the existing SAR systems. Previous research demonstrated that retrieval of SD/SWE can be achieved at this frequency too for . A sensitivity analysis is carried out by exploiting datasets of in situ measurements (snow depth, density, snow grain radius, temperature and wetness) collected on two test sites in the Eastern Italian Alps: the Cordevole plateau (Dolomites) and the Ulten valley (South Tyrol). This analysis provides indications on the sensitivity of X band backscattering to the target snow parameters. As a second step of the analysis, simulations based on a forward electromagnetic model, namely the Dense Medium Radiative Transfer (DMRT), are considered for interpreting and assessing the experimental findings. Finally, an attempt of implementing a retrieval algorithm for estimating SWE from X-band data is carried out. The algorithm is based on machine learning approaches, namely Supported Vector Regressions (SVR) and Artificial Neural Networks (ANN). The training of both algorithms accounts for experimental data and DMRT model simulations. These algorithms are applied to time series of COSMO-SkyMed (CSK) images collected on both test areas. The obtained results are encouraging, although more analysis and validation is needed for exploiting the potential of SAR X band in snow parameter retrieval.

The increasing availability of very high resolution (VHR) images regularly acquired over urban areas opens new attractive opportunities for monitoring human settlements at the level of individual buildings. This paper presents a novel approach to building change detection in multitemporal VHR synthetic aperture radar (SAR) images. The proposed approach is based on two concepts: 1) the extraction of information on changes associated with increase and decrease of backscattering at the optimal building scale and 2) the exploitation of the expected backscattering properties of buildings to detect either new or fully demolished buildings. Each detected change is associated with a grade of reliability. The approach is validated on the following: 1) COSMO-SkyMed multitemporal spotlight images acquired in 2009 on the city of L'Aquila (Italy) before and after the earthquake that hit the region and 2) TerraSAR-X multitemporal spotlight images acquired on the urban area of the city of Trento (Italy). Results demonstrate that the proposed approach allows an accurate identification of new and demolished buildings while presents a low false-alarm rate and a high reliability.

In this paper an approach that exploits and combines the acquisition modes offered by satellite SAR systems is presented that: i) quickly and automatically identifies the areas severely affected by a catastrophic event (i.e., hotspots), such as an earthquake by analyzing images characterized by a large coverage and a medium to high geometrical resolution; and ii) analyzes images characterized by very high geometrical resolution acquired over hot-spots in order to detect collapsed buildings. Experimental results obtained on a dataset made up of COSMO-SkyMed (CSK) data acquired before and after the 2009 L'Aquila earthquake (Italy) demonstrate the effectiveness of the proposed approach.

This paper presents a novel approach to multitemporal detection of primitives in very high resolution (VHR) SAR images. The proposed approach aims at exploiting the monotemporal as well as the multitemporal information in order to both: i) identify transitions in the state of primitives (i.e., detect changes); and ii) improve the monotemporal detection of primitives taking explicitly advantage of the temporal correlation. The performance of the multitemporal detector is evaluated on a time series of four TerraSAR-X images acquired over the city of Lüneburg in Germany. Experimental results confirm the effectiveness of the proposed approach.

This paper presents an approach to the detection of changed buildings using multitemporal Very High Resolution (VHR) Synthetic Aperture Radar (SAR) images. The proposed approach is based on two concepts i) the extraction of information on changes associated with increase and decrease of backscattering at the optimal building scale; and ii) the exploitation of the expected backscattering proprieties of buildings to detect new and fully destroyed buildings with their grade of reliability. Experimental results obtained on a dataset made up of two COSMO-SkyMed (CSK©) spotlight images acquired in 2009 over the city of L'Aquila (Italy) before and after an earthquake demonstrated that the proposed approach allows an effective identification of destroyed buildings.

The availability of very high resolution (VHR) synthetic aperture radar (SAR) images, which can be acquired by satellites over the same geographical area with short repetition interval, makes the development of effective unsupervised change detection (CD) techniques very important. This paper proposes a hierarchical approach to CD in VHR SAR images for addressing surveillance applications, where VHR data are acquired with high temporal resolution (e.g., one image every few days). The proposed approach is based on two concepts: exploitation of a multiscale technique for a preliminary detection of areas containing changes in backscattering at different scales (hot spots) and explicit modeling of the semantic meaning of changes by using both the intrinsic SAR image properties (e.g., acquisition geometry and scattering mechanisms) and the available prior information. In order to illustrate the effectiveness of the proposed approach, a problem of freight traffic surveillance is addressed considering two data sets. Each of them is made up of a pair of multitemporal VHR SAR images acquired by the COSMO-SkyMed (COnstellation of small Satellites for the Mediterranean basin Observation) constellation in spotlight mode. Each data set defines a complex CD problem due to both the presence of a variety of changes on the ground and the complexity of object backscattering. Experimental results point out the effectiveness of the proposed approach.

This paper proposes an approach to change detection in multitemporal very high geometrical resolution (VHR) SAR images for surveillance applications. The approach takes advantage of 3 concepts: i) multiscale representation for a preliminary detection of areas showing significant changes in backscattering between the two images (hot spots); ii) exploitation of prior information about typical usage of zones of interest in the area under control; and iii) definition of features and change detectors optimized for an effective detection of specific changes in each zone of interest. Here the proposed approach is designed for the solution of surveillance problems. A data set made up of a pair of multitemporal VHR SAR images acquired by the COSMO-SkyMed (CSK®) constellation in spotlight mode on the commercial port of Livorno (Italy) was used. Experimental results point out the effectiveness of the proposed approach.

In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project “Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures,” funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.