PEOPLECarlo Marin

Carlo Marin

Research Group Leader
Institute for Earth Observation

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About

Short bio: received the M.Sc. in telecommunication engineering (summa cum laude), and the Ph.D. in remote sensing both from the University of Trento, Italy in 2011 and 2015, respectively. Currently he is the group leader of the mountain cryosphere group at the Institute for Earth Observation at Eurac Research, where he conducts research related to the multi-temporal analysis and change detection of SAR and optical images for alpine cryosphere monitoring within the context of several national and international projects.

Main areas of interest: I leverage advanced remote sensing techniques to monitor and understand the mountain cryosphere, focusing on: i) Interaction of radar signals with the snowpack, particularly during melt periods; ii) Development of AI models that combine remote sensing data and ground measurements with physically-based snow models to accurately estimate SWE and iii) Development of innovative in-situ instrument tools for snow property measurements. This data is essential for validating the accuracy of satellite-derived products.

ORCID: https://orcid.org/my-orcid?orcid=0000-0001-6987-9445

Linkedin: it.linkedin.com/in/carlo-marin-234200294

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 Quelle

DOI: 10.5281/zenodo.14961639

Journal article2025

Mixed Analytical-Numerical Modeling of Radar Backscattering for Seasonal Snowpacks

Lodigiani M, Marin C, Pasian M

Carlo Marin

The intensity of the backscattered signal collected by active radars over wet, seasonal snowpacks depends on numerous variables related to the snowpack, which are often difficult to determine accurately. In recent years, thanks to the increased availability of spaceborne synthetic aperture radars (SARs), a temporal relationship between wet-snow metamorphism and microwave backscattering has been demonstrated. However, a precise quantitative description of this phenomenon has yet to be fully determined. In this article, we propose a new mixed analytical-numerical model to describe the effect of the physical parameters related to the wet snowpack metamorphism on the intensity of the backscattering at L, C, and X bands, with a focus on high alpine snowpacks. Particular attention was paid to integrate the effects of the snow superficial roughness and the snow scattering. The model is first applied to several simulated snowpacks and then validated against a real multitemporal SAR signature acquired by Sentinel-1 over the snow station of Malga Fadner (South Tyrol, Italy) and of Torgnon (Aosta Valley, Italy). The comparison between the model outcomes and the satellite data were in good agreement, leading to the possibility of using such method for operational identification of the run-off phase from remote locations.

DOI Quelle

DOI: 10.1109/JSTARS.2024.3521612

Journal article2025

Multitemporal analysis of Sentinel-1 backscattering during snow melt using high-resolution field measurements and radiative transfer modeling

Carletti F, Marin C, Ghielmini C, Bavay M, Lehning M

Carlo Marin

The spatiotemporal evolution of snowmelt is fundamental for water resources management and risk mitigation in mountain catchments. Synthetic aperture radar (SAR) images acquired by satellite systems such as Sentinel-1 (S1) are promising for monitoring wet snow due to their high sensitivity to liquid water content (LWC) and ability to provide spatially distributed data at a high temporal resolution. While recent studies have linked multitemporal S1 backscatter to snowmelt phases, a correlation with detailed snowpack properties is still missing. To address this, we collected the first dataset of comprehensive wet-snow properties tailored for SAR applications over two consecutive snow seasons at the Weissfluhjoch field site near Davos, Switzerland. First, we tested previous methods which use multitemporal S1 backscatter to characterize melting phases and demonstrated that the observed monotonous increase in backscatter following the local minimum is due to the development of surface roughness. Then, we used the measured snow properties as input to the Snow Microwave Radiative Transfer (SMRT) model to reproduce S1 backscatter signals. Our simulations showed that rather than melting phases, time series of backscatter identify regimes dominated by either LWC, early in the season, or surface roughness, later on. The results also highlight several key challenges for reconciling S1 signals with radiative transfer simulations of wet snow: (i) the discrepancy in spatiotemporal variability of LWC as seen by the satellite and validation measurements, (ii) the lack of fully validated permittivity, microstructure and roughness models for wet snow in the C-band, and (iii) the difficulty of capturing wet-snow features potentially generating stronger scattering effects on a large scale – such as internal snowpack structures, soil features in case of low LWC and surface roughness – which are not necessarily captured by pointwise measurements.

DOI Quelle

DOI: 10.5194/tc-19-5579-2025

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 Quelle

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 Quelle

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

Projekt: Sentinel-1 SAR-unterstützte Einzugsgebietshydrologie: für eine bessere Modellierung der Schneeschmelze in alpinen Regionen., 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 Quelle

DOI: 10.5281/zenodo.10686757

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

Journal article2024

Retrieval of snow liquid water content from radiative transfer model, field data and PRISMA satellite data

Ravasio C, Garzonio R, Di Mauro B, Matta E, Giardino C, Pepe M, Cremonese E, Pogliotti P,

Carlo Marin

The amount of liquid water (LWC) present in the snowpack is critical for predicting wet snow avalanches, forecasting meltwater release, and assessing water availability in river basins. However, measuring this variable using traditional in situ methods is challenging. Space imaging spectroscopy is emerging as a promising approach to map the spatial and temporal variations of snow parameters. While some studies suggest the potential of hyperspectral remote sensing to infer liquid water content, field validation is still lacking. In this context, we propose a new spectral index, namely Snow Surficial Water Index (SSWI), which is designed to be sensitive to the percentage of surficial liquid water content in snow. We developed the index using the BioSNICAR radiative transfer model and then we tested it on both field spectral data and satellite PRISMA imagery. Validation was performed using field data collected with a Snow Sensor during four campaigns in alpine environments, one of which simultaneously with PRISMA. Through a k-fold cross-validation analysis, we achieved a coefficient of determination of 0.7 and a Root Mean Square Error equal to 3%, demonstrating the effectiveness of the proposed index in retrieving LWC from field data and mapping LWC from PRISMA data. A spatial analysis at the catchment level reinforced the results, showing an LWC distribution consistent with orography. The proposed method can be easily applied to other space imaging spectroscopy missions.

DOI Quelle

DOI: 10.1016/j.rse.2024.114268

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

Projekt: Sentinel-1 SAR-unterstützte Einzugsgebietshydrologie: für eine bessere Modellierung der Schneeschmelze in alpinen Regionen.

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 Quelle

DOI: 10.5194/tc-2022-146

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 Quelle

DOI: 10.5281/zenodo.8036793

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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

Projekt: Sentinel-1 SAR-unterstützte Einzugsgebietshydrologie: für eine bessere Modellierung der Schneeschmelze in alpinen Regionen.

DOI Quelle

DOI: 10.5194/tc-2022-146

Carlo Marin

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]

Mixed Analytical-Numerical Modeling of Radar Backscattering for Seasonal Snowpacks

Multitemporal analysis of Sentinel-1 backscattering during snow melt using high-resolution field measurements and radiative transfer modeling

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

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

Retrieval of snow liquid water content from radiative transfer model, field data and PRISMA satellite data

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

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

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Externe Inhalte

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]

Mixed Analytical-Numerical Modeling of Radar Backscattering for Seasonal Snowpacks

Multitemporal analysis of Sentinel-1 backscattering during snow melt using high-resolution field measurements and radiative transfer modeling

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

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

Retrieval of snow liquid water content from radiative transfer model, field data and PRISMA satellite data

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

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