PEOPLEValentina Premier

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Valentina Premier

Senior Researcher
Institute for Earth Observation

T +39 0471 055 282

valentina.premier@eurac.edu

https://orcid.org/0000-0002-4629-2235

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About

Snow Remote Sensing
Snow Modelling
Hydrology
Data Analysis
Machine Learning

Short Bio

Valentina Premier received in 2022 her Ph.D. degree in Information Engineering and Computer Science at the University of Trento, Trento, Italy, within the Remote Sensing Laboratory and with Eurac Research, Bolzano, Italy, within the Institute for Earth Observation. Previously, she held a Master's in Environmental Engineering in 2016. Her activities focus on snow cover and snow water equivalent retrieval using remote sensing data. She is currently involved in different projects of the group for Mountain Cryosphere, such as SNOWCOP, Snowtinel and Open Earth Monitor.

Links

LinkedIn https://www.linkedin.com/in/valentina-premier-03078811b/

Google Scholar https://scholar.google.com/citations?user=8PHSfQ4AAAAJ&hl=en&oi=ao

ResearchGate https://www.researchgate.net/profile/Valentina-Premier

Scientific Publications

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

Database2025

Daily Snow Cover Fraction (SCF) for the nine river basins across Europe (1st October 2017 - 30th September 2023) [Dataset]

Premier V, Moschini F, Casado Rodríguez J, Bavera D, Marin C, Pistocchi A

Carlo Marin, Valentina Premier

The Snow Cover Fraction (SCF) product is based on high-resolution snow cover.

The product is generated through a data fusion method that relies on a low-resolution SCF product and a high-resolution product, both based on optical data. In detail, the high-resolution snow cover is derived from Sentinel-2. For the Adige river basin the SnowFLAKES classification method is used to produce snow cover maps - see Barella, R., Marin, C., Gianinetto, M., & Notarnicola, C. (2022, July). A novel approach to high resolution snow cover fraction retrieval in mountainous regions. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 3856-3859). IEEE.) For the other basins, the Copernicus Fractional Snow Cover (FSC) product is used (see https://land.copernicus.eu/en/products/snow/fractional-snow-cover). Regarding the low-resolution SCF product, MOD10A1 product derived from MODIS is used (see https://nsidc.org/data/mod10a1/versions/61). For details about the data fusion approach, see the method proposed by Premier et al., 2021.

The dataset contains daily SCF information (0 - 100 %) per pixel. The product is aggregated at the spatial resolution of the LISFLOOD model (i.e., approximately 1.4 km). Water bodies are masked out (no data are marked here as -999). The dataset is used to evaluate the snow module of the LISFLOOD model and to calibrate the snowmelt coefficient.

DOI Source

DOI: 10.5281/zenodo.14961639

Journal article2025

Evaluating Sentinel-1/-2 and MODIS fractional snow cover products for applications in alpine cosmic ray neutron snow monitoring

Krebs N, Schattan P, Premier V, Mejia-Aguilar A, Fey C, Bremer M, Rutzinger M

Valentina Premier

Project: Assimilating Cosmic-Ray Neutron and Remote Sensing Data for Improved Water Resource Management

Integrating hydrological models, satellite observations and ground-based measurements remains challenging due to different spatial resolutions. Further, field-based techniques are commonly subject to placement bias in complex mountain topography. Cosmic Ray Neutron Sensing (CRNS) offers an alternative, providing monitoring of soil moisture (SM) and snow water equivalent (SWE) over several hectares. However, the mixed signal requires good knowledge of the research site. Many influencing factors remain unexplored, causing that snow is often perceived as a disturbance in SM observations and partial snow cover remains difficult to interpret. This study evaluates the complementary use of four Sentinel-1/-2 and MODIS based fractional snow cover (FSC) products at the footprint scale of five alpine CRNS sites to identify snow seasonality and corresponding neutron count (NC) ranges. After validating the products against topographic lidar, we (i) establish, (ii) test, and (iii) apply a CRNS footprint-wide classification into snow-free, partial and full snow conditions. We find that all products perform well in comparison to lidar with an overall FSC deviation of 11.8 ± 14.7%. Partial snow cover is best differentiated using product-specific FSC thresholds of approximately 2% and 97.6% (Sentinel) and 0% and 74.8% (MODIS). Identifying seasonal NC ranges allows us to evaluate the station-specific Neutron Level Index (NLI) as an inverse proxy for CRNS uncertainty, ranging from 1.2 to 2.1, and the Hydro-pool Ambiguity Index (HAI), as a proxy for SWE separability after NC rates. Our findings demonstrate that remote FSC observations enhance decision-making a-priori and a-posteriori to the installation and operation of CRNS probes

DOI Source

DOI: 10.1016/j.rsase.2025.101812

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

Database2024

Daily 50-m SWE dataset for the Maipo, Aconcagua, Mendoza and Tunuyan river basins (01/04/2018-31/03/2023) based on remote sensing data (1.0.0) [Data set]. Zenodo

Premier V, Marin C

Carlo Marin, Valentina Premier

This dataset is derived by combining multi-source remote sensing observations (e.g., multi-resolution optical images) with in-situ observations. The time-series are provided for the four adjacent basins of Rio Maipo and Aconcagua (Chile) and the upper Rio Mendoza and Tunuyàn (Argentina). The SWE time-series are provided for the last five hydrological seasons, i.e., from 01/04/2018 until 31/03/2023. SCA can be derived as well by simply thresholding pixels with SWE>0. The time-series are provided as monthly NetCDF files with EPSG:32719-WGS 84/UTM, spatial resolution 50 m and daily maps.

DOI Source

DOI: 10.5281/zenodo.10580094

Database2024

Daily Snow Water Equivalent (SWE) time-series for the Venosta Valley at 50 m spatial resolution - 2017 to 2022

Premier V, Ciapponi N, Zvolenský J, Zellner PJ, Marin C

Carlo Marin, Juraj Zvolenský, Valentina Premier

Project: Snowtinel: Sentinel-1 SAR assisted catchment hydrology: toward an improved snow-melt dynamics for alpine regions., Open-Earth-Monitor Cyberinfrastructure

General Description

The dataset contains daily Snow Water Equivalent (SWE) time-series at 50 m spatial resolution for five hydrological seasons spanning from 1st October 2017 to 30th September 2022. The dataset is based on remote sensing data (Sentinel-2, MODIS) that are merged together to achieve high spatial and temporal detail, as well as in-situ data (temperature and snow depth). The SWE is reconstructed by using a degree-day model. The dataset can be used in many applications like hydrological modeling, water resource management or hydropower planning.

Data Details

Time period: 1st October 2017 - 30th September 2022
Type of data: Snow Water Equivalent (SWE) [mm]
How the data was collected or derived: Derived from Sentinel-2, MODIS and in-situ temperature and snow depth data
Statistical methods used: Generalized Additive Model (GAM)
Limitations or exclusions in the data: The dataset does not include glacierized areas
Coordinate reference system: EPSG:3035
Bounding box (Xmin, Ymin, Xmax, Ymax): (4344230.449, 2592049.728, 4405893.303, 2640759.882)
Spatial resolution: 50 m
Image size: 1233 x 974
File format: Cloud Optimized Geotiff (COG) format.

DOI Source

DOI: 10.5281/zenodo.10686757

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

Journal article2024

Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow

Barella R, Bavay M, Carletti F, Ciapponi N, Premier V, Marin C

Carlo Marin, Valentina Premier

Melting calorimetry, a classic experiment often conducted in high-school chemistry laboratories, holds significant untapped potential for scientific applications beyond its educational context. Traditionally, this technique has been applied to measure the liquid water content in snow using two different formulations: melting calorimetry and freezing calorimetry. In contrast to freezing calorimetry, which is considered the reference method for measuring liquid water content, melting calorimetry has been perceived as prone to generating significant inaccuracies. This research revisits the formulations for both melting and freezing calorimeters to assess volumetric liquid water content in snow. By incorporating the calorimetric constant, we account for heat exchange with the calorimeter, a critical factor often neglected in melting-calorimetry experiments. This paper identifies the most effective and least uncertain method for determining this constant. A central contribution of this work is the introduction of a framework for estimating uncertainty in volumetric liquid water content measurements, adhering to established guidelines for uncertainty expression. This novel framework allows us to revisit past mathematical analyses and demonstrate that melting calorimetry delivers reliable measurements with an uncertainty 0.25 % greater than freezing calorimetry. Notably, despite this slightly higher uncertainty, melting calorimetry offers significant practical advantages for field applications. Moreover, we show how the proposed uncertainty framework can be expanded beyond instrumental uncertainty and also take into account the variability from environmental factors and operators, providing a more comprehensive characterization of the uncertainty. By exploiting the proposed uncertainty framework, we finally conduct an in-depth analysis for the optimal tuning of the experiment parameters. This analysis culminates in a robust field protocol for melting calorimetry that transcends commonsense procedural guidelines. Strict adherence to this protocol will maximize measurement accuracy. Applied in field tests in Italy and Switzerland, the melting calorimetry was demonstrated to accurately track the wet front penetration in the snowpacks, producing results comparable to independent dielectric measurements. These findings highlight the accuracy and the practical advantages of melting calorimetry as a reliable field tool for quantifying snowpack liquid water content. Melting calorimetry can potentially serve as a valuable tool for the independent calibration and validation of proximal and remote sensing techniques used for liquid water content retrieval.

DOI

DOI: 10.5194/tc-18-5323-2024

Journal article2023

Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments

Premier V, Marin C, Bertoldi G, Barella R, Notarnicola C, Bruzzone L

Giacomo Bertoldi, Valentina Premier, Claudia Notarnicola, Carlo Marin

Project: Snowtinel: Sentinel-1 SAR assisted catchment hydrology: toward an improved snow-melt dynamics for alpine regions.

The hydrological cycle is strongly influenced by the accumulation and melting of seasonal snow. For this reason, mountains are often claimed to be the “water towers” of the world. In this context, a key variable is the snow water equivalent (SWE). However, the complex processes of snow accumulation, redistribution, and ablation make its quantification and prediction very challenging. In this work, we explore the use of multi-source data to reconstruct SWE at a high spatial resolution (HR) of 25 m. To this purpose, we propose a novel approach based on (i) in situ snow depth or SWE observations, temperature data and synthetic aperture radar (SAR) images to determine the pixel state, i.e., whether it is undergoing an SWE increase (accumulation) or decrease (ablation), (ii) a daily HR time series of snow cover area (SCA) maps derived by high- and low-resolution multispectral optical satellite images to define the days of snow presence, and (iii) a degree-day model driven by in situ temperature to determine the potential melting. Given the typical high spatial heterogeneity of snow in mountainous areas, the use of HR images represents an important novelty that allows us to sample its distribution more adequately, thus resulting in highly detailed spatialized information. The proposed SWE reconstruction approach also foresees a novel SCA time series regularization technique that models impossible transitions based on the pixel state, i.e., the erroneous change in the pixel class from snow to snow-free when it is expected to be in accumulation or equilibrium and, vice versa, from snow-free to snow when it is expected to be in ablation or equilibrium. Furthermore, it reconstructs the SWE for the entire hydrological season, including late snowfall. The approach does not require spatialized precipitation information as input, which is usually affected by uncertainty. The method provided good results in two different test catchments: the South Fork of the San Joaquin River, California, and the Schnals catchment, Italy. It obtained good agreement when evaluated against HR spatialized reference maps (showing an average bias of −22 mm, a root mean square error – RMSE – of 212 mm, and a correlation of 0.74), against a daily dataset at coarser resolution (showing an average bias of −44 mm, an RMSE of 127 mm, and a correlation of 0.66), and against manual measurements (showing an average bias of −5 mm, an RMSE of 191 mm, and a correlation of 0.35). The main sources of error are discussed to provide insights into the main advantages and disadvantages of the method that may be of interest for several hydrological and ecological applications.

DOI Source

DOI: 10.5194/tc-2022-146

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

Database2023

Snow Water Equivalent Dataset for the South Fork of the San Joaquin River (2018/2021) and Senales (2019/2021)

Premier V, Marin C, Bertoldi G, Barella R, Notarnicola C, Bruzzone L

Giacomo Bertoldi, Claudia Notarnicola, Carlo Marin, Valentina Premier

The dataset is related to: Premier, V., Marin, C., Bertoldi, G., Barella, R., Notarnicola, C., & Bruzzone, L. (2022). Exploring the Use of Multi-source High-Resolution Satellite Data for Snow Water Equivalent Reconstruction over Mountainous Catchments. The Cryosphere Discussions, 1-42.

It contains three hydrological seasons - from 1st of October 2018 to 30th of September 2021 - of snow water equivalent (SWE) for the South Fork of the San Joaquin river in California (USA) and two hydrological seasons - from 1st of October 2019 to 30th of September 2021 - for the Schnals/Senales basin in South Tyrol (Italy). The product is daily and with a spatial resolution of 25 m. SWE values are in mm. Snow cover area (SCA) can be derived from the same by thresholding pixel containing SWE greater than 0 mm. Further information about the reference system is contained in the attributes of the netcdf files. Please, contact the authors for further questions. Information about the methodology and the input data for producing these time series is contained in the related article.

DOI Source

DOI: 10.5281/zenodo.8036793

Journal article2022

Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment

Hofmeister F, Arias-Rodriguez L F, Premier V, Marin C, Notarnicola C, Disse M, Chiogna G

Valentina Premier, Claudia Notarnicola, Carlo Marin

Modelling runoff generation in high-elevation Alpine catchments requires detailed knowledge on the spatio-temporal distribution of snow storage. With Sentinel-2 MultiSpectral Instrument (MSI), it is possible to map snow cover with a high temporal and spatial resolution. In contrast to the coarse MODIS data, Sentinel-2 MSI enables the investigation of small-scale differences in snow cover duration in complex terrains due to gravitational redistribution (slope), energy balance and wind-driven redistribution (aspect). In this study, we describe the generation of high-resolution spatial and temporal snow cover data sets from Sentinel-2 images for a high-elevation Alpine catchment and discuss how the data contribute to our understanding of the spatio-temporal snow cover distribution. The quality of snow and cloud detection is evaluated against in-situ snow observations and against other snow and cloud products. The main problem was in the false detection of snow in the presence of clouds and in topographically shaded areas. We then seek to explore the potential of the generated high-resolution snow cover maps in calibrating the gravitational snow redistribution module of a physically based snow model, especially for an area with a very data-scarce point snow observation network. Generally, the calibrated snow model is able to simulate both the mean snow cover duration with a high F1 accuracy score of > 0.9 and the fractional snow-covered area with a correlation coefficient of 0.98. The snow model is also able to reproduce spatio-temporal variability in snow cover duration due to surface energy balance dynamics, wind and gravitational redistribution.

DOI Source

DOI: 10.1016/j.hydroa.2022.100123

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Journal article2023

Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments

Premier V, Marin C, Bertoldi G, Barella R, Notarnicola C, Bruzzone L

Giacomo Bertoldi, Valentina Premier, Claudia Notarnicola, Carlo Marin

Project: Snowtinel: Sentinel-1 SAR assisted catchment hydrology: toward an improved snow-melt dynamics for alpine regions.

DOI Source

DOI: 10.5194/tc-2022-146

Valentina Premier

About

Scientific Publications

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

Daily Snow Cover Fraction (SCF) for the nine river basins across Europe (1st October 2017 - 30th September 2023) [Dataset]

Evaluating Sentinel-1/-2 and MODIS fractional snow cover products for applications in alpine cosmic ray neutron snow monitoring

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

Daily 50-m SWE dataset for the Maipo, Aconcagua, Mendoza and Tunuyan river basins (01/04/2018-31/03/2023) based on remote sensing data (1.0.0) [Data set]. Zenodo

Daily Snow Water Equivalent (SWE) time-series for the Venosta Valley at 50 m spatial resolution - 2017 to 2022

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

Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow

Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments

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

Snow Water Equivalent Dataset for the South Fork of the San Joaquin River (2018/2021) and Senales (2019/2021)

Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment

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Scientific Publications

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

Daily Snow Cover Fraction (SCF) for the nine river basins across Europe (1st October 2017 - 30th September 2023) [Dataset]

Evaluating Sentinel-1/-2 and MODIS fractional snow cover products for applications in alpine cosmic ray neutron snow monitoring

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

Daily 50-m SWE dataset for the Maipo, Aconcagua, Mendoza and Tunuyan river basins (01/04/2018-31/03/2023) based on remote sensing data (1.0.0) [Data set]. Zenodo

Daily Snow Water Equivalent (SWE) time-series for the Venosta Valley at 50 m spatial resolution - 2017 to 2022

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

Unlocking the potential of melting calorimetry: a field protocol for liquid water content measurement in snow

Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments

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

Snow Water Equivalent Dataset for the South Fork of the San Joaquin River (2018/2021) and Senales (2019/2021)

Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment