PEOPLEClaudia Notarnicola

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Claudia Notarnicola

Head of Institute
Institute for Earth Observation

T +39 0471 055 375

claudia.notarnicola@eurac.edu

https://orcid.org/0000-0003-1968-0125

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About

Research topics

Retrieval of biophysical variables (soil moisture, vegetation water content, snow cover fraction, snow water equivalent, albedo)
Development of innovative retrieval approaches by exploiting synergy of multi-sensor data (optical and radar) and physical based models
Analysis of snow cover changes at global scale by merging Earth Observation and reanalysis datasets.
Remote sensing of cryosphere and water resources in mountain areas

Short bio

Claudia Notarnicola is the Head of the Institute for Earth Observation since 2022.

As background, she received the Degree in Physics, summa cum laude, and the PhD in Physics from the University of Bari (Italy) in 1995 and 2002. From 2002 to 2004, she had a postdoctoral position at the at Polytechnic of Bari and University of Bari (Italy). From 2005 to 2007 she worked for the company Carlo Gavazzi Space in Milano (now called Compagnia Generale per lo Spazio) as project manager of the project “PROSA: Satellite Observation Products for Meteorological Alert” funded by the Italian Space Agency. In 2008, she joined as senior researcher Eurac Research- Institute for Earth Observation (Bolzano, Italy) and from 2012 to 2022, she was Deputy Director in the same institute. She conducts her research activities within the frameworks of several national and international projects. Since 2006, she serves as Conference Chairs for SPIE Europe International Conference on “Microwave Remote Sensing: Data Processing and Applications”. She has been member of the Cassini RADAR Team in the framework of the Cassini-Huygens Mission for the exploration of the Saturn system. The mission was a joint initiative among NASA-ESA-ASI. In 2020 she was winner of the Women in Science Award Südtirol-South Tyrol.

Links

ORCID https://orcid.org/0000-0003-1968-0125

ResearchGate https://www.researchgate.net/profile/C-Notarnicola?ev=hdr_xprf

Scientific Publications

Conference proceedings article2025

Assessing snow cover changes in global mountain regions by exploiting MODIS time series from 2000 to 2024

Notarnicola C

Claudia Notarnicola

Snow cover extent and related variables are key elements to understand many processes in mountain regions. To constantly monitor and assess the changes in these areas, consistent and accurate data sets are of utmost importance. In this perspective, MODIS sensors offer an unprecedented possibility in terms of time availability from 2000 to present and ground resolution (500 m) (Bormann et al., 2018).

This work presents a unique time series of snow cover extent and snow phenology (snow cover duration-SCD, first snow day-FSD, and last snow day-LSD) for the period 2000-2024 with a ground resolution of 500 m (Notarnicola, 2024). The main input data is the MODIS product, MOD10A1.061, from which the Normalized Difference Snow Index (NDSI) layer was considered and converted to SCF by exploiting the Salomonson and Appel formulation (2004). The snow phenology parameters (SCD, FSD, LSD) were derived from MOD10A1.061 daily maps. The SCD values were obtained from daily snow cover maps by exploiting an auto-regressive approach to reduce the gaps due to cloudiness (Dietz et al., 2012). In this time series, FSD and LSD represent the first and the last date in the hydrological year with snow presence. The whole dataset is available here: https://zenodo.org/records/11181638

Preliminary analysis of the whole datasets indicate that reduction in snow cover duration can reach up to 55 days while the snow cover extent declines up to 13%. These results were obtained on regions showing changes with significance level at 5% in the Mann-Kendall statistics. Interestingly there are some areas in eastern Russia which show a snow cover extent increase up to 15% while snow cover duration indicates an increase as well but not significant in the adopted statistics. When considering FSD and LSD variables, both mainly indicates a shortening of the snow season with an average of 15 days for both delayed start of the season and anticipated end of the season. These preliminary results on the trends in the period 2000-2024 provide confirmation of behaviour found in the shorter period 2000-2018 (Notarnicola, 2020), highlighting a general decline for main snow variables but as well with a high variability among the different investigated regions.

References

Bormann, K. J., Brown, R. D., Derksen, C., Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Change 8, 924–928, 2018.

Dietz, A.J., Wohner C., Kuenzer, C. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sens. 4, 2432-2454, 2012.

Notarnicola, C., Hotspots of snow cover changes in global mountain regions over 2000-2018. Rem. Sen. Environ. 243, 111781, 2020. https://doi.org/10.1016/j.rse.2020.111781.

Notarnicola, C. Snow cover phenology dataset over global mountain regions from 2000 to 2023,Data in Brief, Volume 56, 2024, 110860, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110860.

Salomonson, V.V., Appel, I. Estimating the fractional snow covering using the normalized difference snow index. Remote Sens Environ 89, 351-360, 2004.

How to cite: Notarnicola, C.: Assessing snow cover changes in global mountain regions by exploiting MODIS time series from 2000 to 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14994, https://doi.org/10.5194/egusphere-egu25-14994, 2025.

DOI

DOI: 10.5194/egusphere-egu25-14994

Journal article2025

Assessing the Impact of Terrestrial Photography-Derived Roughness Lengths on Evaposublimation from Seasonal Snow in the Mediterranean Mountains of Sierra Nevada (Spain)

Scheidt KT, Pimentel R, Premier V, Marin C, Polo MJ, Notarnicola C

Claudia Notarnicola, Carlo Marin, Valentina Premier, Katharina Scheidt

1. Study Region The Sierra Nevada mountain range in Spain.

2. Study focus We estimate time series of snow aerodynamic roughness length from daily terrestrial photographs, leveraging textural features to analyze its influence on evaposublimation estimates in our regional snow model under an isotropic fetch assumption. Over a long-term study (2010–2021), we compare our derived roughness length time series with a constant roughness length, examining their implications for model performance and evaposublimation trends.

3. New hydrological insights for the region Our comparison reveals that the estimated contribution of evaposublimation to total annual snow ablation using roughness length time series is significantly higher (up to (48.8 ± 3.6)%) than previously reported values (30 - 35%) and improve performance of our regional snow model upon using roughness length time series. In our study period spanning twelve years, cumulative evaposublimation decline (-7.23 mm/yr), likely linked to decreasing snow cover duration (-4.57 d/yr) and decreasing annual solid precipitation (-24.81 mm/yr), though no trend in evaposublimation rates is observed. The Sierra Nevada’s shallow snowpacks and multiple accumulation and ablation periods create mixed surface types with patchy snow, rocks, and vegetation influencing roughness lengths. Consequently, we identify a relationship between roughness length and snow cover fraction, with the potential to facilitate evaposublimation rate estimates on a spatio-temporal scale for this region in the future.

DOI

DOI: 10.1016/j.ejrh.2025.102431

Journal article2025

Asymmetry in snow-water nexus in mountain areas mainly governed by meteorological seasonal changes

Pimentel R, Notarnicola C

Claudia Notarnicola

Despite the importance of snow cover and related streamflow changes in mountain catchments worldwide, hitherto the quantification of their mutual variability has not been addressed at this scale. This work utilizes satellite and ground observations spanning the last two decades to investigate the nexus between snow cover and streamflow across 548 mountain catchments globally. The results show that around 5% of these catchments are simultaneously affected by significant trends in both variables. This happened mainly in the Andes Cordillera, where snow season ends 28 days earlier, with a snow cover area reduction of about 15%, and a decrease in annual streamflow of 67 m³/s. Moreover, in 16% of the catchments, significant changes occur only in one of the variables. This asymmetric behaviour was connected with significant changes in a specific season. When the most affected season is summer, with marked temperature increase, snow changes are not reflected in streamflow, as the latter mainly depends on spring snowmelt, as observed in the European Alps. When there is an increase in winter solid precipitation, this may not be reflected in yearly snow cover changes but can impact the resulting streamflow, as shown in the western North American catchments.

DOI

DOI: 10.1038/s41598-025-29597-9

Journal article2025

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

Patriarca E, Stendardi L, Dorigatti E, Sonnenschein R, Ventura B, Claus M, Castelli M, Tufail B,

Ruth Sonnenschein, Bartolomeo Ventura, Claudia Notarnicola, Mariapina Castelli, Michele Claus, Emanuela Patriarca

Mountain grasslands are increasingly threatened by climate change, land abandonment, and overexploitation. Remote sensing is a valuable tool for monitoring these changes through vegetation mapping. However, challenges such as frequent cloud cover, short growing seasons, and limited field data can reduce the accuracy of results. In this study, we evaluated the effectiveness of different remote sensing data for classifying mountain grasslands in the Sciliar-Catinaccio Nature Park, Italy. We compared classification results using a hyperspectral PRISMA image (Sept 29, 2023), multispectral data from a single-date Sentinel-2 image (Sept 25, 2023), and Spectral-Temporal Metrics (STM) derived from a Sentinel-2 time series from 2021 to 2023. Additionally, we assessed the impact on accuracy of combining optical datasets with Synthetic Aperture Radar (SAR) data, including a time series of 2023 Sentinel-1 backscatter and coherence metrics. Using the Recursive Feature Elimination algorithm (RFE), we selected the most relevant features for classification and applied both Random Forest (RF) and Support Vector Machines (SVM) classifiers. SVM outperformed RF, performing better with the limited training data available. SAR data did not significantly enhance classification and was therefore excluded by the RFE algorithm. PRISMA-based classification achieved up to 74 % accuracy, while single-date Sentinel-2 imagery reached 52 %. The use of STM improved classification performance, yielding an overall accuracy of 77 %. The highest accuracy (87 %) was achieved by combining PRISMA and STM features. These findings suggest that while individual optical datasets may not provide optimal classification accuracy, integrating data from multiple optical sensors significantly enhances the mapping of mountain grasslands.

DOI

DOI: 10.1016/j.rsase.2025.101666

Conference proceedings article2025

More than the sum of its parts: Acting to better observe, understand, forecast and react to climate change in a combined Network of European High-Altitude Research Stations: The Virtual Alpine Observatory (VAO)

Kraushaar S, Stammberger V, Krautblatter M, Einhellinger B, Zeitler R, Chilingarian A, Fischer A, Keuschnig M,

Claudia Notarnicola

The Alpine region experiences climate change at an accelerated pace compared to the rest of Europe, leading to profound and measurable impacts across all geospheres. To monitor, understand, and forecast these developments, European alpine observatories and research facilities have formed the interdisciplinary and cross-border Virtual Alpine Observatory Network (VAO). This collaborative network aims to unify and amplify individual research efforts, focusing on the comprehensive analysis and prediction of climate change effects throughout the Alpine Arc.

By exploring individual monitoring datasets for transnational patterns, the VAO creates a collective knowledge base that transcends the limitations of isolated understanding. This approach fosters innovative insights into the interconnected dynamics of the Alpine environment and enhances the ability to address climate challenges at a regional and global scale.

This study highlights the VAO network's expansion, its extensive data availability across Europe, and its potential for facilitating groundbreaking spatial analyses of geodata from various observatory stations. The findings illustrate the power of collaborative research in advancing climate science and informing strategies for environmental resilience.

The VAO network is substantially funded by the Bavarian State Ministry of the Environment and Consumer Protection.

DOI

DOI: 10.5194/egusphere-egu25-20113

Database2025

OEMC project use case: High resolution Snow Water Equivalent in selected mountain regions

Eurac Research, Premier V, Claus M, Jacob A, Notarnicola C, Marin C, Singh A, Zvolenský J

Alexander Jacob, Claudia Notarnicola, Carlo Marin, Juraj Zvolenský, Michele Claus, Valentina Premier, Abhishek Singh

We propose to produce a snow water equivalent (SWE) product at a spatial resolution of 20 meters. The product will be available for selected mountain basins in South Tyrol on a daily temporal resolution, updated on a yearly basis. We rely on a multi-sensor EO SWE reconstruction method using Sentinel-2, MODIS, potentially Sentinel-1 and in-situ data. The information will be of value for different applications such as earth system models, irrigation planning and hydro power planning (e.g. Eumetsat/ZAMG). The Province of South Tyrol is a local stakeholder planning to evaluate the data for their hydrological planning activities.

Stakeholder needs:

High-resolution and daily data over some selected hydrological catchments that are relevant for the Hydrological Office of the Province of Bolzano;
Open access data that respect the FAIR principle, easy to access and download.

Planned implementation:

Implementation steps include:

High-resolution snow cover classification (Sentinel-2 or Landsat data);
Data fusion algorithm to obtain daily high-resolution snow cover data (merging high-resolution snow cover maps with low-resolution snow cover maps, such as MODIS);
SWE reconstruction using a degree-day model forced with available in-situ temperature or re-analysis data;
Implementation in OpenEO to upscale the algorithm.

DOI Source

DOI: 10.60566/z84j1-yjc55

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

Project: PNRR iNEST - Interconnected Nord-Est Innovation Ecosystem

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

Conference proceedings article2024

Combining Terrestrial Photography and Sentinel-1 Imagery for Assessing Wet Snow Dynamics in Ephemeral Snowpack over Semiarid Mountain Areas

Pimentel R, Torralbo P, Polo MJ, Notarnicola C

Claudia Notarnicola

Snowmelt dynamics in Mediterranean mountains differs from the ones found in higher latitudes. This work assesses wet snow dynamics in semiarid environments combining proxy and remote sensing databases over a pilot area in the Sierra Nevada Mountain Range (southern Spain). A linear relationship was found between the minimum backscattering signal and the maximum snow depth achieved within a melting cycle. The slope of this relationship depends on the maximum snow depth reached and is linked to the contribution of the ground to the backscattering signal. These results delve into the understanding of wet-snow dynamics in Mediterranean mountains and can constitute the basis for an easy tool to compute maximum snowpack depth from the Sentinel 1 backscattering signal in these environments.

DOI

DOI: 10.1109/IGARSS53475.2024.10641534

Database2024

Global mountain snow cover phenology from MODIS/Terra imagery [dataset]

Notarnicola C

Claudia Notarnicola

The snow cover phenology dataset was generated by exploiting the MODIS snow product MOD10A1.061 and contains the following variables: yearly averaged snow cover area (SCA), snow cover duration (SCD), first snow day (FSD), last snow day (LSD). The dataset has a ground resolution of 500 m and encompasses the global mountain ranges as defined by the Global Mountain Biodiversity Assessment (GMBA) shapefile (v1.1). The dataset timeframe covers the hydrological years from 2000 to 2023.

DOI Source

DOI: 10.48784/1zvv-nw59

Conference proceedings article2024

Merging Earth Observation imagery and model simulations to monitor long-term global snow cover dynamics

Notarnicola C

Claudia Notarnicola

Remote sensing imagery offers a unique tool to monitor the snow cover evolution both at global and regional scales. However, the availability of the time series of normally 3-4 decades limits the applicability of the data to understand the dynamics of snow processes at longer time scale (Pulliainen et al., 2020).

In this perspective the possibility to generate hybrid dataset merging satellite records with model simulations is offering the chance to both cross-validate the model simulations and to extent the time series (Lenton et al 2024).

In this work, an approach proposed by Notarnicola 2022, with the aim to merge earth observation imagery and modelled data was adapted to generate longer time series. The method is a machine learning approach based on Artificial Neural Network (ANN), where the uncertainties on the ANN trained model were obtained through a bootstrap procedure with a resampling technique. As modelled data, the NOAA-CIRES-DOE 20th Century Reanalysis V3 contains land surface and meteorological maps and their uncertainty available in the period 1806-2015 was addressed (Slivinski et al., 2019). For satellite data, the longest time series on snow parameters were used derived from ESA Climate Change Initiative (CCI) Snow project, namely Snow Water Equivalent (SWE) data available from 1979 to 2020 (https://climate.esa.int/en/odp/#/project/snow). As predictors for the ANN training and test, dynamics variables such as air temperature and precipitation were considered as they regulate the snow dynamics and rainfall events. As static parameters, the location in terms of latitude and longitude, mean elevation, the land cover type, and percentage of vegetation cover were inserted. The target variable to be improved is the snow water equivalent value at pixel level.

Before the application of the ANN approach, a comparison between the SWE values in two data sets for the overlapping period (1979-2015) indicated an averaged correlation coefficient of around 0.52, a bias in the range 35-42 mm, the latter one being strongly depending on the different months. After the application of the model derived by the trained ANN applied to the test dataset, the correlation coefficients raised on average to 0.87, and the RMSE is reduced to around 20 mm.

The merged data set will be further compared with ground data where available and then used to derive long-term trends for the SWE variable.

DOI

DOI: 10.5194/egusphere-egu24-20329

Conference proceedings article2024

Multisensor Validation of Snow Albedo and Grain Size Retrieval in Mountain Areas, IGARSS 2024

Notarnicola C, Santos C, Barella R, Claus M, Cremonese E, De Gregorio L, Di Mauro B, Schwaizer G,

Claudia Notarnicola, Michele Claus

This work presents the results of an algorithm for the retrieval of snow surface albedo and grain size by exploiting Sentinel-3 OLCI data. The algorithm is a hybrid approach based on spectral indices and radiative transfer theory and it was tested and intercompared with ground data from 6 field stations and other sensors in the European Alps for the period 2017-2023. The results indicate that for albedo the algorithm agrees well with ground measurements showing an unbiased Root Mean Square Error (ubRMSE) between 0.05 and 0.13 and a correlation coefficient ranging from 0.63 to 0.78 depending on the locations. For grain size, even though a general underestimation is found, the estimates reflect well the typical grain size metamorphosis from winter to spring. To further test the algorithm, these results from Sentinel-3 data were also confronted with ECOSTRESS thermal data specifically useful to show the metamorphosis of the grain size. For albedo, the algorithm was further applied to PRISMA hyperspectral images, showing consistent results with the values obtained by multispectral Sentinel-3 imagery.

DOI

DOI: 10.1109/IGARSS53475.2024.10641957

Conference proceedings article2024

Theresa Project: Study of Algorithms for SGB-TIR Mission

Silvestri M, Buongiorno MF, Laneve G, Colombo R, Notarnicola C, Pignatti S, Romaniello V, Venafra S

Claudia Notarnicola

The THERESA (THErmal infRarEd SBG Algorithms) project aims to enhance algorithms for processing Thermal InfraRed data from the SBG-TIR (Surface Biology and Geology – Thermal InfraRed) mission. Starting from state-of-the-art algorithms, THERESA takes in account the mission's technical features to develop algorithms exploiting diverse spectral channels. During the two years lifetime of the project, THERESA will contribute to enhance the investigation of terrestrial phenomena by using both visible and thermal images. The thematic areas that will benefit from SBG-TIR data range from the vegetation analysis to the volcanic eruptions and fires monitoring. Several parameters will be achieved such as the estimation of ash and SO 2 emissions from volcanoes, the surface temperature, the detection of hotspots as well as the FRP (Fire Radiative Power) in case of HTE’s (High Temperature Events). THERESA's innovation lies in algorithms advancements; the project offers a 360-degree support to the SBG-TIR mission.

DOI

DOI: 10.1109/IGARSS53475.2024.10642352

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Conference proceedings article2025

Assessing snow cover changes in global mountain regions by exploiting MODIS time series from 2000 to 2024

Notarnicola C

Claudia Notarnicola

References

Bormann, K. J., Brown, R. D., Derksen, C., Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Change 8, 924–928, 2018.

Dietz, A.J., Wohner C., Kuenzer, C. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sens. 4, 2432-2454, 2012.

Notarnicola, C., Hotspots of snow cover changes in global mountain regions over 2000-2018. Rem. Sen. Environ. 243, 111781, 2020. https://doi.org/10.1016/j.rse.2020.111781.

Notarnicola, C. Snow cover phenology dataset over global mountain regions from 2000 to 2023,Data in Brief, Volume 56, 2024, 110860, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110860.

Salomonson, V.V., Appel, I. Estimating the fractional snow covering using the normalized difference snow index. Remote Sens Environ 89, 351-360, 2004.

DOI

DOI: 10.5194/egusphere-egu25-14994

Claudia Notarnicola

About

Scientific Publications

Assessing snow cover changes in global mountain regions by exploiting MODIS time series from 2000 to 2024

Assessing the Impact of Terrestrial Photography-Derived Roughness Lengths on Evaposublimation from Seasonal Snow in the Mediterranean Mountains of Sierra Nevada (Spain)

Asymmetry in snow-water nexus in mountain areas mainly governed by meteorological seasonal changes

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

More than the sum of its parts: Acting to better observe, understand, forecast and react to climate change in a combined Network of European High-Altitude Research Stations: The Virtual Alpine Observatory (VAO)

OEMC project use case: High resolution Snow Water Equivalent in selected mountain regions

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

Combining Terrestrial Photography and Sentinel-1 Imagery for Assessing Wet Snow Dynamics in Ephemeral Snowpack over Semiarid Mountain Areas

Global mountain snow cover phenology from MODIS/Terra imagery [dataset]

Merging Earth Observation imagery and model simulations to monitor long-term global snow cover dynamics

Multisensor Validation of Snow Albedo and Grain Size Retrieval in Mountain Areas, IGARSS 2024

Theresa Project: Study of Algorithms for SGB-TIR Mission

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Scientific Publications

Assessing snow cover changes in global mountain regions by exploiting MODIS time series from 2000 to 2024

Assessing the Impact of Terrestrial Photography-Derived Roughness Lengths on Evaposublimation from Seasonal Snow in the Mediterranean Mountains of Sierra Nevada (Spain)

Asymmetry in snow-water nexus in mountain areas mainly governed by meteorological seasonal changes

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

More than the sum of its parts: Acting to better observe, understand, forecast and react to climate change in a combined Network of European High-Altitude Research Stations: The Virtual Alpine Observatory (VAO)

OEMC project use case: High resolution Snow Water Equivalent in selected mountain regions

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

Combining Terrestrial Photography and Sentinel-1 Imagery for Assessing Wet Snow Dynamics in Ephemeral Snowpack over Semiarid Mountain Areas

Global mountain snow cover phenology from MODIS/Terra imagery [dataset]

Merging Earth Observation imagery and model simulations to monitor long-term global snow cover dynamics

Multisensor Validation of Snow Albedo and Grain Size Retrieval in Mountain Areas, IGARSS 2024

Theresa Project: Study of Algorithms for SGB-TIR Mission