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 Massive Open Online Course – Earth Observation Open Data Science (MOOC EOODS) teaches the concepts of data cubes, cloud platforms, and open science in the context of Earth Observation (EO). The course is designed to bridge the gap towards the recent cloud native advancements in EO by offering a MOOC as an integrated and open learning experience relying on a mixture of animated lecture content and hands-on exercises hosted on the EO themed e-learning platform EO College.

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 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.

With this work, we present a method for the detection of alpine permafrost surface deformations by using DInSAR (Differential SAR Interferometry) technique, integrating RADARSAT-2 and COSMO-SkyMed data through Support Vector Machine (SVM). On our test dataset, the combination of the two sensors produces an increase of classification accuracy equal to 8.5% with respect to the case in which only one sensor is employed, leading to an overall accuracy equal to 86.9%. We are also showing here how COSMO-SkyMed data time series acquired in the snow-free period are well suited to estimate surface deformations on some particular alpine rock glaciers. The velocities estimated with the SBAS (Small BAseline Subset) algorithm are well correlated with velocity measurements obtained by means of a ground based total station, showing a root mean squared error (RMSE) and R square value equal to 3.5 cm and to 0.67 respectively.

The prediction of monthly mean discharge is critical for water resources management. Statistical methods applied on discharge time series are traditionally used for predicting this kind of slow response hydrological events. With this paper we present a Support Vector Regression (SVR) system able to predict monthly mean discharge considering discharge and snow cover extent (250 meters resolution obtained by MODIS images) time series as inputs. Additional meteorological and climatic variables are also tested as inputs for the SVR approach. The prediction system has been evaluated on 14 catchments in South Tyrol (Northern Italy). Considering as a reference the estimates based on the average discharge computed on the past 10 years, which is a common practice for water resources management in the study region, the percentage root mean square error (RMSE%) is reduced of 11% and 6% for a prediction lag of 1 and 3 months respectively.

On mountain areas, one main important pre-processing step for optical satellite imagery is the topographic correction. The illumination condition strongly depends on the area topography, sun elevation and azimuth and acquisition geometry of the sensor. These elements generate unequal light distribution over the observed surface. This effect needs to be corrected to improve classification and parameters retrieval performances. In this paper, a novel empirical approach for topographic correction is presented using the combined (direct and diffuse) solar radiation. The combined solar radiation has the advantage to take into account the brightening effect of topography on scene radiance. The approach aims at defining correction coefficients depending on the different land cover. We tested our approach using both high and medium resolution images and terrain effects in both data sets were considerably reduced.

This paper presents a study on the retrieval of snowpack biophysical parameters in mountain areas from satellite remote sensing imagery. More in detail, the integration of new generation X-band Cosmo SkyMed SAR imagery and land surface temperature (LST) information derived from optical thermal remote sensing is investigated. First, a sensitivity analysis is carried out, in order to understand whether and to what extent the investigated remote sensing signals are sensitive to variations in different snowpack target parameters. Then, an advanced retrieval system based on the Support Vector Machine approach in a multilevel architecture is developed. Experiments carried out in a small valley in the eastern Alps during the winter 2010/11 point out the effectiveness of the combined use of X-band SAR and thermal satellite imagery for the characterization of snowpack parameters in terms of both accuracy on reference point measurements and capability to reproduce spatial patterns of the target variables.