PEOPLEGiovanni Cuozzo

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Giovanni Cuozzo

Senior Researcher
Institute for Earth Observation

T +39 0471 055 943

giovanni.cuozzo@eurac.edu

https://orcid.org/0000-0001-9120-7426

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About

Remote sensing SAR data specialist with application in:

- Cryosphere (snow and permafrost)

- Soil Moisture Content estimation

- SAR interferometry

- Machine learning techniques

Giovanni Cuozzo is senior researcher at EURAC Institute for Earth Observation in the Mountain Cryosphere group since 2010. He received his M.Sc. in telecommunication engineering from the University Federico II in Naples, Italy and his Ph.D. degree in Electric Engineering and Information Technology from the University of Cassino, Italy. From 2006 to 2010 he worked as researcher and operator of the ground segment of the Mediterranean Agency for Remote Sensing and Environmental Control (MARSec) in Benevento. At Eurac Research his main research activities focus on SAR and optical data processing and analysis with applications to SAR interferometry, study of permafrost and snow dynamics and retrieval of biophysical parameters like soil moisture. He contributed to a several projects as project manager and/or senior researcher like for example ALPSMOTION and ACR_WATER (funded by the Province of Bolzano), CRIOSAR and ALGORITMI-SAR (funded by the Italian Space Agency) and HERCULES (EU Horizon 2020).

ORCID https://orcid.org/0000-0001-9120-7426

ResearchGate https://www.researchgate.net/profile/Giovanni-Cuozzo

LinkedIn https://www.linkedin.com/in/giovanni-cuozzo-11a8323b/

Academia https://independent.academia.edu/GCuozzo

Scientific Publications

Journal article2026

A Transformer-based Convolutional Regressor to include SAR Backscatter Signals in Monitoring Alpine Grasslands

Singh A, Göhner CS, Stendardi L, Claus M, Cuozzo G, Jacob A, Castelli M

Giovanni Cuozzo, Alexander Jacob, Mariapina Castelli, Michele Claus, Abhishek Singh

Projekt: Innovative Methoden zur Verbesserung der Überwachung von Agrarumweltbedingungen unter Verwendung von Sensordaten auf verschiedenen Skalen

Monitoring grasslands over the phenological season in the alpine environment using Synthetic Aperture Radar (SAR) backscatter data requires innovative approaches that can combine domain-specific ancillary information with advanced deep learning techniques. Alpine grasslands are characterized by complex topography, seasonal variations, and heterogeneous agricultural management, making traditional monitoring methods less effective. The Leaf Area Index (LAI) can generally be used to study the above ground biomass of grasslands, which is estimated rapidly and accurately from the Sentinel-2 multispectral data. However, the frequent unavailability of cloud-free imagery poses a significant challenge, creating temporal gaps. To address this limitation, there is growing interest in incorporating Sentinel-1 SAR backscatter data to perform temporal gap-filling, enhancing the temporal resolution of grassland LAI derived from Sentinel-2 and enabling continuous monitoring of grasslands throughout the growing season. In this paper, we propose a domain-specific transformer-based convolutional network that extracts features from SAR data and its derivatives to capture long-range dependencies, and we preliminarily investigate its application to temporal gap-filling of Sentinel-2 LAI. The effectiveness of the proposed method for predicting LAI has been validated by testing it against Sentinel-2-derived LAI. In the results, we achieved an R2 around 0.59 on the test sets, between the related predicted LAI and Sentinel-2 LAI.

DOI Quelle

DOI: 10.1109/LGRS.2025.3645675

Journal article2025

Optimizing rock glacier activity classification in South Tyrol (northeastern Italy): integrating multisource data with statistical modelling

Crippa C, Steger S, Cuozzo G, Bearzot F, Mair V, Notarnicola C

Giovanni Cuozzo, Claudia Notarnicola, Chiara Crippa

Projekt: PNRR iNEST - Vernetztes Nord-Ost-Innovations-Ökosystem

As a consequence of atmospheric warming, high-altitude periglacial and glacial environments exhibit clear signs of cryosphere degradation, and the Alps serve as a natural laboratory for studying the primary effects on permafrost-related features. Our research in South Tyrol, northeastern Italy, aimed to develop an updated classification system, based on remote sensing data and statistical models, for rock glacier activity, categorizing it as active, transitional, or relict according to the new Rock Glacier Inventories and Kinematic (RGIK) guidelines. While the current regional inventory includes activity attributes based on morphological observations and differential interferometric synthetic aperture radar (DInSAR) coherence, it lacks a comprehensive classification that also considers climatic drivers, displacement rates, and morphometric parameters. To fill this gap, we utilized the Alaska Satellite Facility's interferometric synthetic aperture radar (InSAR) cloud computing, employing the Small Baseline Subset (SBAS) and Miami InSAR time-series software in Python (MintPy) algorithms to extract velocity data for each rock glacier investigated in this study. Additionally, we analysed geomorphological and climatic maps derived from in situ and remote sensing data to obtain descriptive parameters influencing rock glacier development and activity. From a wide range of potential variables, we selected eight key predictors, representing physical (e.g. temperature), morphological (e.g. roughness), and dynamic attributes (e.g. velocity and coherence indicators). These predictors were integrated in a multiclass generalized additive model (GAM) classifier to categorize the mapped landforms. Applying this model to the entire dataset (achieving an area under the curve (AUC) over 0.9) allowed us to address gaps in previous classification methods and provided activity attributes for previously unclassified rock glaciers, along with associated uncertainty values. Our approach enhanced the previous classification, leaving only 3.5 % of features unclassified compared to 13 % in morphological classification and 18.5 % in the DInSAR-based method. The results revealed a predominance of relict features (∼75 %) and a smaller number of active ones (∼10 %). The result of the distribution of active, transitional, and relict classes suggests that the transition from active to relict states is not direct. Instead, an intermediate transitional phase is commonly observed. This comprehensive approach refines the categorization of mapped features and improves our understanding of the factors influencing rock glacier activity in the alpine environment in South Tyrol.

DOI

DOI: 10.5194/tc-19-3493-2025

Inbook2024

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Notarnicola C, Callegari M, Castelli M, Cuozzo G, De Gregorio L, Greifeneder F, Jacob A, Marin C,

Giovanni Cuozzo, Ruth Sonnenschein, Mattia Callegari, Massimiliano Pittore, Alexander Jacob, Claudia Notarnicola, Mariapina Castelli, Carlo Marin, Marc Zebisch

Mountain regions are biodiversity hotspots, support a great diversity of habitats and species, and provide numerous ecosystem services for lowland areas. At the same time, mountain areas are very sensitive environments because of their peculiarities, and are considered as sentinel of climate changes. In fact, despite large uncertainties (Kohler et al., 2014; Stocker et al., 2013), the temperature increase in mountains doubles the global average (Kohler et al., 2014) and several parameter changes (e.g., albedo, snow cover) intensify with altitude (Notarnicola, 2020; Pepin et al., 2015). The importance of mountains is acknowledged and supported by numerous global initiatives. The United Nations included mountain ecosystem conservation in its Sustainable Development Goals and in 2019 the GEO Mountain called for the identification of Essential Climate Variables (ECVs) and Biodiversity Variables (EBVs) for high altitudes (Adler, 2018a, 2018b; Shahgedanova et al., 2021; Thornton et al., 2021). Moreover, GEO Mountain is aiming at supporting the different UN frameworks and has a relevant role in several policy agendas such as the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services (IPBES), the Sendai Framework for Disaster Risk Reduction, the Paris Agreement, and assessments of the Intergovernmental Panel on Climate Change (IPCC). With the advent of the Copernicus era, Earth Observation (EO) tools in terms of imagery, platforms, and software offer a unique opportunity to continuously monitor the mountain environment and provide valuable information for its protection and safety. This chapter addresses the current and future challenges of EO observations in the different sectors, going from the cryosphere to water resources and agriculture, to ecosystems, and to gravity-driven natural hazards. Current data and processing platforms are presented to provide an overview of the plethora of information and tools now made available to researchers and practitioners. In this chapter, we selected these sectors based on our experience in several national and international projects, where EO data play a major role. Moreover, the analysis of these sections can provide a connection thread on how the changes in snow and glaciers impact the water resources and then consequently agriculture and ecosystems. It is out of the scope of this chapter to draw a complete picture of all the sectors that can be affected by climate changes.

DOI

DOI: 10.1016/B978-0-12-822095-5.00014-0

Conference proceedings article2024

Integrating Sentinel-1 radar and Sentinel-2 multispectral data for detecting freshly exposed supraglacial deposits

Crippa C, Cuozzo G, Callegari M, Notarnicola C

Giovanni Cuozzo, Mattia Callegari, Claudia Notarnicola, Chiara Crippa

Projekt: PNRR iNEST - Vernetztes Nord-Ost-Innovations-Ökosystem

We propose a tool that integrates spaceborne radar data from Sentinel-1 and multispectral data from Sentinel-2 satellites to detect fresh debris accumulation or rock exposure on alpine glaciers. The tool leverages the VH cross-polarized backscatter difference between Sentinel-1 radar images to pinpoint clusters of pixels exhibiting prominent changes over time. It then integrates the NDSI information from Sentinel-2 multispectral images to discriminate between potential causes of the observed backscatter variations, such as snow cover changes, rock exposure or debris accumulation. The correlation between time series of VH values further allows to distinguish between localized phenomena like landslides and extensive ones like glacial surface changes due to snow metamorphism or accumulation. The tool is implemented in Google Earth Engine (GEE) and leverages open-source libraries and datasets, making it readily adaptable to other glacial environments.

DOI

DOI: 10.1117/12.3037075

Journal article2024

Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions

De Gregorio L, Cuozzo G, Barella R, Corvalán F, Greifeneder F, Grosse P, Mejia-Aguilar A, Niedrist G,

Georg Niedrist, Giovanni Cuozzo, Alessandro Zandonai, Claudia Notarnicola, Valentina Premier

In this work, we present two datasets for specific areas located on the Alpine arc that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of water supply over five defined test areas over the South Tyrol (Italy) and Tyrol (Austria) areas in alpine environments. The snow cover fraction (SCF) and Soil Moisture Content (SMC) datasets are derived from machine learning algorithms based on remote sensing data. Both SCF and SMC products are characterized by a spatial resolution of 20 m and are provided for the period from October 2020 to May 2023 (SCF) and from October 2019 to September 2022 (SMC), respectively, covering winter seasons for SCF and spring–summer seasons for SMC. For SCF maps, the validation with very high-resolution images shows high correlation coefficients of around 0.9. The SMC products were originally produced with an algorithm validated at a global scale, but here, to obtain more insights into the specific alpine mountain environment, the values estimated from the maps are compared with ground measurements of automatic stations located at different altitudes and characterized by different aspects in the Val Mazia catchment in South Tyrol (Italy). In this case, an MAE between 0.05 and 0.08 and an unbiased RMSE between 0.05 and 0.09 m³·m⁻³ were achieved. The datasets presented can be used as input for hydrological models and to hydrologically characterize the study alpine area starting from different sources of information.

DOI

DOI: 10.3390/data9110136

Conference proceedings article2023

Multi-frequency SAR images for investigations of the Cryosphere: Preliminary results of CRIOSAR Project

Pettinato S, Santi E, Paloscia S, Baroni F, Pilia S, Santurri L, Palchetti E, Bovenga F,

Giovanni Cuozzo, Mattia Callegari, Valentina Premier, Claudia Notarnicola, Carlo Marin

This research intends to exploit the potentialities of multimission SAR data at X-, C- and L-band to monitor the snowpack and alpine soil. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism were monitored and different algorithms are proposed for the retrieval of these parameters. Several methods and techniques have been used depending on the parameter to be returned: machine learning approaches as Artificial Neural Networks and Random Forest were implemented in case of snow parameter retrieval; interferometric techniques are also involved in case of snow and soil displacement as rock glaciers. Microwave models are used to better explain the snow and soil behavior in the microwave range (AIEM, Oh, SFT and DMRT-QCA), and SNOWPACK physical model for corroborate experimental data. Moreover, experimental activities have been conducted in two selected sites in Northern Italy, which are characterized, in some cases, by the presence of permafrost and they are covered by alpine snow during the winter and spring period. Preliminary and consistent results have been obtained in terms of snow parameters and soil displacement. This comprehensive approach enhances the ability to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

DOI

DOI: 10.1109/IGARSS52108.2023.10281448

Journal article2023

Unprecedented Observation of Hourly Rock Glacier Velocity With Ground‐Based SAR

Bertone A, Seppi R, Callegari M, Cuozzo G, Dematteis N, Krainer K, Marin C, Notarnicola C,

Giovanni Cuozzo, Mattia Callegari, Claudia Notarnicola, Carlo Marin

The kinematic acceleration of rock glaciers observed in recent decades shows that the behavior of these landforms is related to climate change. Velocity variations on yearly to seasonal time scales are frequently investigated, but velocity changes measured on shorter time scales (i.e., on hourly resolutions) are as yet poorly investigated. We used a ground based synthetic aperture radar to investigate, on an hourly time scale, the displacement of a rock glacier located in Val Senales (European Alps, northern Italy). We observed velocity fluctuations occurring at a very regular pace, characterized by phases of sharp acceleration (up to 0.9 mm/hr) lasting 4–11 hr followed by long phases of stagnation lasting 13–20 hr. This study describes an unprecedented observation of an hourly velocity rhythm of an active rock glacier and opens up new perspectives in the analysis and interpretation of rock glacier kinematics.

DOI Quelle

DOI: 10.1029/2023GL102796

Conference proceedings article2022

Forest height estimation using Sentinel-1 Interferometry: a phase unwrapping-free method based on least squares adjustment

De Petris S, Cuozzo G, Notarnicola C, Borgogno-Mondino E

Giovanni Cuozzo, Claudia Notarnicola

Forest height is a fundamental parameter in forestry. SAR interferometry (InSAR) has been widely used to retrieve digital elevation models (DEM), which are designed to provide a continuous representation of Earth topography, including forests. The ordinary InSAR framework requires a further phase unwrapping step in order to recover unambiguously the actual topography over the entire scene. The latter was proved to fail over vegetation due to low coherence values and therefore all algorithms tend to avoid these areas during the unwrapping, making InSAR-derived DEM over vegetation very unreliable. In this work, an alternate technique was coupled to least squares adjustment (LSA) with the aim of retrieving accurate forest heights avoiding phase unwrapping. It was computed entirely using free available Sentinel-1 data and SNAP ESA software. A mean absolute error equal to 2.6 m was found and it is consistent to the one estimated by LSA theoretical uncertainty. Preliminary outcomes suggest that proposed approach could be a valid alternative to retrieve forest height based on free data/software constituting an example of technological transfer of SAR technology into forest operative sector.

DOI Quelle

DOI: 10.1007/978-3-031-17439-1_18

Conference proceedings article2022

Monitoring of snow water equivalent and snowmelt through space-borne synthetic aperture radar techniques

Pettinato S, Bovenga F, Santi E, Paloscia S, Baroni F, Belmonte A, Refice A, Argentiero I,

Giovanni Cuozzo, Mattia Callegari, Claudia Notarnicola, Carlo Marin

Projekt: Multi-Frequenz-SAR-Anwendungen in der Kryosphäre

This work aims to consider the use of different SAR operating at X-, C- and L-band in order to improve the monitoring of snow covered areas and soil in Alpine areas. A preliminary dataset of X-, C- and L-band is acquired and analysed in order to exploit the potentialities in snow and permafrost monitoring. The intensity and phase of the SAR signal at different frequencies shows sensitivity for estimating Snow Water Equivalent and permafrost characteristics.

DOI Quelle

DOI: 10.23919/EuMC54642.2022.9924399

Journal article2022

On the Use of COSMO-SkyMed X-Band SAR for Estimating Snow Water Equivalent in Alpine Areas: A Retrieval Approach Based on Machine Learning and Snow Models

Santi E, De Gregorio L, Pettinato S, Cuozzo G, Jacob A, Notarnicola C, Günther D, Strasser U,

Giovanni Cuozzo, Claudia Notarnicola, Alexander Jacob

This study aims at estimating the dry snow water equivalent (SWE) by using X-band synthetic aperture radar (SAR) data from the COSMO-SkyMed (CSK) satellite constellation. The time series of CSK acquisitions have been collected during the dry snow period in the Alto Adige test site, in the Italian Alps, during the winter seasons from 2013 to 2015 and from 2019 to 2021. The SAR data have been analyzed
and compared with the in situ measurements to understand the X-band SAR sensitivity to SWE, which has been further assessed by dense media radiative transfer (DMRT) model simulations. The sensitivity analysis provided the basis for addressing the SWE retrieval from the CSK data, by exploiting two different machine learning (ML) techniques, namely, artificial neural networks (ANNs) and support vector regression (SVR). To ensure statistical independence of training and validation processes, the algorithms are trained and tested using SWE predictions of the fully distributed snow model AMUNDSEN as reference data and are subsequently validated on the experimental dataset. Due to its influence on the CSK estimates, the effect of forest canopy was accounted for in the analysis. Depending on the algorithm, the validation resulted in a correlation coefficient 0.78 < R < 0.91 and a root-mean-square error (RMSE) 55.5 mm < RMSE < 87.4 mm between estimated and in situ SWE. Further analysis and validation are needed; however the obtained results seem suggesting the CSK constellation as an effective tool for the retrieval of the dry SWE in alpine areas.

DOI

DOI: 10.1109/TGRS.2022.3191409

Journal article2021

Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data.

Chiarito E, Cigna F, Cuozzo G, Fontanelli G, Mejia Aguilar A, Paloscia S, Rossi M, Santi E,

Giovanni Cuozzo, Claudia Notarnicola

Projekt: Entwicklung von Algorithmen zur Bestimmung und Beobachtung hydrologischer Parameter mit Satelliten- und Drohnendaten

A general framework for the integration of multi-sensor data for dry and fresh biomass retrieval is proposed and tested in Alpine meadows and pastures. To this purpose, hyperspectral spectroradiometer (as simulation of hyperspectral imagery) and biomass samples were collected in field campaigns and Copernicus Sentinel-1 Interferometric Wide (IW) swath SAR backscattering coefficients were used. First, a genetic algorithm feature selection was performed on hyperspectral data, and afterwards the resulting most sensitive bands where combined with SAR data within a support vector regression (SVR) model. The most sensitive hyperspectral bands were mainly located in different regions of the SWIR range for both fresh and dry biomass, and in the red and near-infrared regions mainly for dry biomass, but with less influence for fresh biomass. The R² correlation values between the sampled and the estimated biomass range from 0.24 to 0.71. The relatively low performances are mainly related to the saturation effect in the optical bands, as well as to the paucity of points for high values of biomass. The methodology allows a better understanding of the interaction between grassland systems and the electromagnetic spectrum by offering a model with a reduced number of narrow bands in the context of a multi-sensor integration.

DOI Quelle

DOI: 10.1080/22797254.2021.1901063

Conference proceedings article2021

Snow water equivalent retrieval from COSMO-SkyMed observations through machine learning algorithms and model simulations

Santi E, Paloscia S, Pettinato S, Notarnicola C, Cuozzo G, De Gregorio L, Cigna F, Tapete D

Giovanni Cuozzo, Claudia Notarnicola

Projekt: Entwicklung von Algorithmen zur Bestimmung und Beobachtung hydrologischer Parameter mit Satelliten- und Drohnendaten

The monitoring of snow conditions in Alpine areas to support water management and avalanche warning applications would require the estimate of snow parameters, such as the snow water equivalent (SWE). In this research, COSMOSkyMed (CSK) X-band SAR data were exploited to estimate the SWE. In-situ snow measurements (depth, density, snow grain radius, temperature) collected in South Tyrol (Italy), were used to simulate the X-band backscatter with the Dense Medium Radiative Transfer (DMRT) electromagnetic model.
Two SWE retrieval algorithms based on machine learning approach were implemented. The algorithms are based on Artificial Neural Networks (ANN) and Support Vector Regression (SVR) and have been trained with both experimental data and DMRT model simulations. These algorithms were applied to a selection of CSK StripMap HIMAGE HH-polarized scenes collected over the test area.
The obtained results are promising and they confirm the potential of SAR data at X-band to retrieve snow parameters, although the algorithm validation should be improved in the future, with more consistent measurement dataset.

DOI Quelle

DOI: 10.1109/IGARSS47720.2021.9553985

Technische
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Journal article2025

Optimizing rock glacier activity classification in South Tyrol (northeastern Italy): integrating multisource data with statistical modelling

Crippa C, Steger S, Cuozzo G, Bearzot F, Mair V, Notarnicola C

Giovanni Cuozzo, Claudia Notarnicola, Chiara Crippa

Projekt: PNRR iNEST - Vernetztes Nord-Ost-Innovations-Ökosystem

DOI

DOI: 10.5194/tc-19-3493-2025

Giovanni Cuozzo

About

Scientific Publications

A Transformer-based Convolutional Regressor to include SAR Backscatter Signals in Monitoring Alpine Grasslands

Optimizing rock glacier activity classification in South Tyrol (northeastern Italy): integrating multisource data with statistical modelling

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Integrating Sentinel-1 radar and Sentinel-2 multispectral data for detecting freshly exposed supraglacial deposits

Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions

Multi-frequency SAR images for investigations of the Cryosphere: Preliminary results of CRIOSAR Project

Unprecedented Observation of Hourly Rock Glacier Velocity With Ground‐Based SAR

Forest height estimation using Sentinel-1 Interferometry: a phase unwrapping-free method based on least squares adjustment

Monitoring of snow water equivalent and snowmelt through space-borne synthetic aperture radar techniques

On the Use of COSMO-SkyMed X-Band SAR for Estimating Snow Water Equivalent in Alpine Areas: A Retrieval Approach Based on Machine Learning and Snow Models

Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data.

Snow water equivalent retrieval from COSMO-SkyMed observations through machine learning algorithms and model simulations

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Externe Inhalte

Scientific Publications

A Transformer-based Convolutional Regressor to include SAR Backscatter Signals in Monitoring Alpine Grasslands

Optimizing rock glacier activity classification in South Tyrol (northeastern Italy): integrating multisource data with statistical modelling

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Integrating Sentinel-1 radar and Sentinel-2 multispectral data for detecting freshly exposed supraglacial deposits

Two Datasets over South Tyrol and Tyrol Areas to Understand and Characterize Water Resource Dynamics in Mountain Regions

Multi-frequency SAR images for investigations of the Cryosphere: Preliminary results of CRIOSAR Project

Unprecedented Observation of Hourly Rock Glacier Velocity With Ground‐Based SAR

Forest height estimation using Sentinel-1 Interferometry: a phase unwrapping-free method based on least squares adjustment

Monitoring of snow water equivalent and snowmelt through space-borne synthetic aperture radar techniques

On the Use of COSMO-SkyMed X-Band SAR for Estimating Snow Water Equivalent in Alpine Areas: A Retrieval Approach Based on Machine Learning and Snow Models

Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data.

Snow water equivalent retrieval from COSMO-SkyMed observations through machine learning algorithms and model simulations