HS6.6 | Remote Sensing of Seasonal Snow
EDI PICO
Remote Sensing of Seasonal Snow
Co-organized by CR2
Convener: Rafael Pimentel | Co-conveners: Claudia Notarnicola, Ilaria Clemenzi
PICO
| Mon, 24 Apr, 08:30–12:30 (CEST)
 
PICO spot 4
Mon, 08:30
Snow constitutes a freshwater resource for over a billion people world-wide. High percentage of this water resource mainly comes from seasonal snow located in mid-latitude regions. The current warming situation alerts that these snow water storages are in high risk to be dramatically reduced, affecting not only the water supply but also the ecosystem over these areas. Therefore, understanding seasonal snow dynamics, possible changes and implications have become crucial for water resources management. Remote sensing has proven to be the main technique used to monitor the snow properties across mid-large extensions and their hydrological implications, for decades now. Moreover, the recent advances, which are mainly focused on the study of snow properties at higher spatio-temporal scales (e.g., small-scale snow-topography interactions, snow-vegetation interaction, diurnal variation of snow, rain over snow events), are helping to understand better snow accumulation, distribution and ablation dynamics.
This session is focused on studies linking the use of remote sensing of seasonal snow in hydrological applications: techniques and data from different technologies, such as time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies, or other new applications, with the aim of quantifying and better understanding snow characteristics (i.e. snow grain size, snow depth, albedo, pollution load, snow specific area and snow density), snow related processes (snowfall, melting, evaporation and sublimation), snow dynamics, snow modelling, snow hydrological impacts and snow environmental effects. Works covering different spatial scales, from the plot to the global, and temporal scales, from instantaneous to multiyear, are welcome.

PICO: Mon, 24 Apr | PICO spot 4

Chairpersons: Rafael Pimentel, Claudia Notarnicola, Ilaria Clemenzi
08:30–08:35
Optical
08:35–08:37
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PICO4.1
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EGU23-1920
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ECS
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Highlight
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On-site presentation
Di Liu and Qingling Zhang

The Arctic region has been experiencing significant climate change, with the loss of snow and ice accelerating at an alarming rate. Accurate monitoring of the spatiotemporal dynamics of snow and ice is essential for understanding and predicting the impacts of climate change on Arctic ecosystems and their feedback on global climate. In this paper, we use the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite to monitor the spatiotemporal dynamics of snow and ice in polar regions. The VIIRS/Day/Night Band (DNB) is a unique instrument that can provide high-resolution imagery of the Earth's surface at night, with a spatial resolution of 750 m and a sensitivity of 0.01 nW/cm2/sr. This enables the detection of faint moonlight and artificial light and allows for mapping snow and ice in polar winter when no sunlight is available for months.  Our aims demonstrate the potential of moonlight remote sensing for continuous monitoring of snow/ice in the Arctic region and analyse the importance of continuous monitoring and research on the impacts of climate change on the Arctic ecosystem and the potential for Arctic seaway.

How to cite: Liu, D. and Zhang, Q.: Monitoring the Spatiotemporal Dynamics of Arctic Winter Snow/Ice with MoonlightRemote Sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1920, https://doi.org/10.5194/egusphere-egu23-1920, 2023.

08:37–08:39
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PICO4.2
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EGU23-16000
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Highlight
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On-site presentation
David Gustafsson, Jude Musuuza, Katharina Klemeth, Denica Bozhinova, Andrea Popp, Liudmila Lebedeva, and Tetsuya Hiyama

The study investigates the role of snow for the climate change impacts in hydrological regimes across the Lena river basin in Yakutia, Eastern Siberia using a hydrological model constrained by in-situ and satellite-based snow and river discharge observations. The river runoff observations in large and medium sized rivers show an increase over recent decades that can be associated with increasing air temperature and precipitation, as well as changes in snow, glaciers, and permafrost. We assessed the relation between changes in snow and streamflow using the satellite-based ESA CCI snow data and the hydrological model HYPE. The streamflow trend analysis showed a general pattern of increasing monthly mean and minimum stream flows from October to April, but more frequent in larger river basins, and especially if the last 20 years are included in the trend analysis. This can be explained by the increasing autumn precipitation, but the absence of change in annual maximum flow and streamflow in June also suggests relation to changes in the snow. The snow data shows a pattern of decreasing maximum snow water equivalent in the western part of the study area, and a corresponding decreasing trend of number of days with snow cover. These results are in line with the trends in observed streamflow; a short snow cover period (and increasing amount of autumn and winter rainfall, not shown here) as well as a lower maximum snow water equivalent could contribute both to the increasing winter runoff, and the absence of increasing streamflow in early summer. 

This work was conducted as part of the HYPE-ERAS project funded by FORMAS (project DNR: 2019-02332), RFBR (project No. 20-55-71005), and JST (Grant No. JPMJBF2003) through the Belmont Forum Collaborative Research Action: Resilience in the Rapidly Changing Arctic.

How to cite: Gustafsson, D., Musuuza, J., Klemeth, K., Bozhinova, D., Popp, A., Lebedeva, L., and Hiyama, T.: Role of snow for changes hydrological regimes in the Lena river basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16000, https://doi.org/10.5194/egusphere-egu23-16000, 2023.

08:39–08:41
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PICO4.3
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EGU23-11702
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ECS
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On-site presentation
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Lucia Ferrarin, Franziska Koch, Karsten Schulz, and Daniele Bocchiola

The spatiotemporal distribution of snow cover affects several processes at different scales, such as the Earth’s energy balance, the hydrological cycle and ecosystems functions, with important implications on many aspects of human life. Topography, meteorological conditions in general and wind in particular affect the evolution of seasonal snow cover patterns during snow accumulation and ablation. With the help of remote sensing techniques, such as Sentinel-2 imagery, it is feasible to study snow cover patterns also in complex terrain. Satellite based morphological analysis of snow cover patterns may provide i) information on snow cover and its connection to morphology and alpine topography ii) a valuable complement to ground-based data and snow-hydrological simulations. In this study, we evaluate the effectiveness of two types of geometric indexes, i) MN, Minkowsky numbers (representing area, perimeter and Euler characteristic), and ii) CL, Average chord length, in quantitatively describing the morphology of Sentinel-2 derived snow cover patterns within the high-alpine area of Zugspitze at the boarder of Germany and Austria for a five-year period. MN and CL have been used previously in different fields, e.g. soil sciences, but to the authors’ knowledge, these measures have never been applied in the field of snow cover pattern monitoring before. We present the seasonal evolution of MN and CL, as well as their correlation to topographic features (e.g., aspect, slope, curvature) and meteorological and snow variables. The individual indexes show distinct differences during snow accumulation and ablation and a clear annual periodicity. MN and CL can effectively quantify some aspects of the dynamic of snow cover patterns, although further analysis are necessary to conclude if such morphologic pattern descriptors can substantively improve the accuracy of the understanding and the modelling of snow-related processes.

How to cite: Ferrarin, L., Koch, F., Schulz, K., and Bocchiola, D.: Seasonal evaluation of morphological indexes in quantifying snow cover patterns in the Zugspitze area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11702, https://doi.org/10.5194/egusphere-egu23-11702, 2023.

08:41–08:43
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PICO4.4
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EGU23-11618
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ECS
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On-site presentation
Andreas Kollert, Andreas Mayr, Martin Rutzinger, and Stefan Dullinger
Recently, snow cover has gained a lot of interest as an important driver of plant species distribution in arctic and alpine environments, especially on small spatial scales . However, variation of snow cover at this scale is hardly resolved by open satellite data. Hence, linking remotely sensed snow cover and critical patterns and processes in vegetation can be challenging due to a mismatch in spatial resolution.
We present a study based on a high alpine network of three webcams for the validation of snow cover products covering an entire year. Satellite based snow cover products (Landsat, Sentinel-2, downscaled MODIS products) are benchmarked on webcam-derived snow cover. While optical satellite remote sensing is a valuable tool for characterizing snow cover dynamics at the scale of tens of meters, cloud cover causes considerable data gaps. As a temporally and spatially more continuous estimate, we additionally produce meter-scale snow cover using the openAmundsen model, and we compare this to the webcam derived snow cover as well. For all datasets, ecologically relevant indicators like snow cover duration and the number of snow-free days are aggregated and validated both for the entire year and on a sub-seasonal scale.

How to cite: Kollert, A., Mayr, A., Rutzinger, M., and Dullinger, S.: Validation of high resolution remotely sensed and modeled snow cover with webcam imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11618, https://doi.org/10.5194/egusphere-egu23-11618, 2023.

08:43–08:45
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PICO4.5
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EGU23-7343
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ECS
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On-site presentation
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Pau Wiersma, Fatemeh Zakeri, and Grégoire Mariéthoz

Snowmelt can vary largely across time and space, especially in complex terrain. However, hydrological models often represent snowmelt using a single static degree-day factor that relates the melt runoff with air temperature. Seasonally or spatially varying degree-day factors have been shown to better capture the snowmelt heterogeneity, but still rely on simplified parameterizations. One interesting solution proposed in the literature is to use MODIS satellite imagery to capture the true snowmelt heterogeneity, and use it to inform hydrological models on the temporal and spatial evolution of the degree-day factor on a near-daily basis. However, the limited spatial resolution of MODIS makes this process difficult to apply in complex mountainous terrain. Meanwhile, Landsat or Sentinel 2 satellite imagery could be an interesting alternative as they have a much higher spatial resolution but fall short in terms of temporal resolution. In this study, we overcome both these obstacles with a synthetically generated daily snow cover time series based on Landsat resampling. We use the daily synthetic snow cover maps to derive the snow cover depletion in each coarse resolution hydrological model grid cell, which in turn defines the degree-day factor for each cell using a transfer function. To capture the inherent uncertainty of this methodology, we run an ensemble of models using different meteorological forcings and different stochastic realizations of the synthetic snow cover maps. The resulting degree-day factors are evaluated through the skill of the modeled streamflow and snow water equivalent, using different transfer functions in several snow-influenced catchments in Switzerland. 

How to cite: Wiersma, P., Zakeri, F., and Mariéthoz, G.: Using synthetic snow cover maps to determine the degree-day snowmelt factor of a distributed hydrological model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7343, https://doi.org/10.5194/egusphere-egu23-7343, 2023.

08:45–08:47
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PICO4.6
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EGU23-12126
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ECS
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On-site presentation
Hyunho Jeon, Jaehwan Jeong, Yangwon Lee, and Minha Choi

In the past decade, heavy snow has recorded the third-highest disaster damage in Korea after typhoons and heavy rain. In addition, snowfall is one of the important factors in the water cycle, and it directly affects hydrological factors such as evapotranspiration and soil moisture. Due to the topographical features of Korea, snowfall occurs heterogeneously, so it has limitations to use only in-situ data for snow monitoring. Although grid data such as remote sensing and model simulated data has been suggested as an alternative to this, it is also difficult to use only grid data due to the characteristics of snow that influence spectral behavior depending on grain size, age, etc. In this study, snow depth data was evaluated using model simulated data and ground observation data over the South Korea. For data, Local Data Assimilation and Prediction System [LDAPS] (provided with 3 hours of temporal resolution and 1.5 km of spatial resolution), Modern-Era Retrospective analysis for Research and Applications, version 2 [MERRA-2] (provided with 1 hour of temporal resolution and 0.5° × 0.623° of spatial resolution) and Automated Synoptic Observing System [ASOS] (provided with 1 hour of temporal resolution) were used. The applicability of each data was evaluated with topographic data, and long-term trend of snow depth was analyzed. This study can help to predict snow information, with the combination of various reanalysis data and model simulated forecast dataset.

 

Keywords: Snow Depth, LDAPS, MERRA-2, ASOS

 

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF-2021R1A6A3A01087645).

How to cite: Jeon, H., Jeong, J., Lee, Y., and Choi, M.: Evaluation of LDAPS Snow information by MERRA-2 and ASOS over the South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12126, https://doi.org/10.5194/egusphere-egu23-12126, 2023.

08:47–08:49
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PICO4.7
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EGU23-7537
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ECS
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On-site presentation
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López Moreno

The unprecedented precision of the altimetry satellite ICESat-2 and the increasing availability of high-resolution elevation datasets open new opportunities to measure snow depth in the mountains, a critical variable for ecosystems and water resources monitoring. We retrieved snow depth over the upper Tuolumne basin (California, USA) for three years by differencing ICESat-2 ATL06 snow-on elevations and various snow-off elevation sources, including ATL06 and external digital elevation models. The snow presence of each ATL06 segment (i.e. point measurements regularly spaced every 20 m) can be determined from the number of photons returned by the ground surface. Snow depth derived from ATL06 data only (snow-on and snow-off) provided a poor temporal and spatial coverage, limiting its utility. However, using airborne lidar or satellite photogrammetry elevation models as snow-off elevation source yielded an accuracy of ~0.2 m (bias), a precision of ~0.5 m for low slopes and ~1.2 m for steeper areas, compared to eight reference airborne lidar snow depth maps. The snow depth derived from ICESat-2 ATL06 will help address the challenge of measuring the snow depth in unmonitored mountainous areas.

How to cite: Deschamps-Berger, C., Gascoin, S., Shean, D., Besso, H., Guiot, A., and López Moreno, J. I.: A new opportunity to measure snow depth from space: evaluation of retrievals from ICESat-2 using airborne laser-scanning data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7537, https://doi.org/10.5194/egusphere-egu23-7537, 2023.

08:49–08:51
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PICO4.8
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EGU23-32
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ECS
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On-site presentation
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Joschka Geissler, Lars Rathmann, and Markus Weiler

Snow plays a crucial role in the hydrological cycle as it serves as an intermediate storage of winter precipitation and renews groundwater reserves. It is therefore of central importance for, among others, our drinking water supply and agriculture. Snow interacts with its environment in many ways, is constantly changing with time, and thus has a highly heterogeneous spatial and temporal distribution. Therefore, modelling snow variability is difficult, especially when additional components such as forests add complexity. To increase our understanding of the spatiotemporal variability of snow as well as to validate snow models, we need reliable validation data. For these purposes, airborne LiDAR surveys or time series derived from snow sensors on the point scale are commonly used. However, these are disadvantageously limited to one point either in space or in time. In this study, we profited from current advances in LiDAR and drone technology, as well as machine learning, to bridge this gap. We present a new dataset on snow variability in forests for the Alptal, a sub-alpine, forested valley in the pre-alps, Switzerland. The core dataset consists of a dense sensor network, repeated UAV-based LiDAR flights and manual snow height and density measurements. Using modern machine learning algorithms, we determine four clusters of similar spatiotemporal behaviour regarding their snowheight. These clusters are characterized and further used to derive daily snow depth and snow water equivalent maps. By using the latter, we obtain spatially continuous key hydrological variables. The results suggest that snow occurs in clusters that reoccur in space. These clusters underline the relation between canopy cover and spatial snow accumulation patterns and (the much more complex) spatial ablation patterns. The presented dataset and derived products are the first to our knowledge that provide daily, high-resolution snow height and hydrologic variables based on field data. The results of this study can therefore improve our understanding of the spatiotemporal variability of snow in forested environments. Moreover, they are ideally suited for the validation of modern snow models.

How to cite: Geissler, J., Rathmann, L., and Weiler, M.: Machine Learning and LiDAR Snowheight Maps from UAVs Reveal Clusters of Snow Variability in a Sub-Alpine Forest., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-32, https://doi.org/10.5194/egusphere-egu23-32, 2023.

08:51–08:53
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PICO4.9
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EGU23-3304
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ECS
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On-site presentation
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Sophie Rosenburg, Charlotte Lange, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch

The melting snow layer, as a composition of ice, liquid water, and air, supplies meltwater in the runoff phase inducing the melt pond formation. These melting processes of Arctic sea ice alter the surface reflection properties and thereby affect the energy budget. Such sea ice surface reflection properties were surveyed by airborne hyperspectral imagery within the framework of an Arctic field campaign performed in May/June 2017. A retrieval approach based on different absorption indices of pure ice and liquid water in the near infrared spectral range is applied to the campaign data retrieving the spatial distribution of snow layer liquid water fraction and effective radius of snow grains. For the same sceneries the melt pond depth was retrieved based on an existing approach that isolates the dependence of a melt pond reflectance spectrum on the pond depth by eliminating the reflection contribution of the pond ice bottom. The presented retrieval methods show the potential of airborne hyperspectral imagery to map the transition phase of the Arctic sea ice surface examining the snow layer composition and melt pond bathymetry.

How to cite: Rosenburg, S., Lange, C., Jäkel, E., Schäfer, M., and Wendisch, M.: Retrieval of snow layer and melt pond properties based on airborne hyperspectral imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3304, https://doi.org/10.5194/egusphere-egu23-3304, 2023.

08:53–08:55
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PICO4.10
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EGU23-15479
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Virtual presentation
Alexander Kokhanovsky, Maximillian Brell, Sabine Chabrillat, Saskia Förster, and Karl Segl

Cryosphere is an integral part of the terrestrial ecosystem with important linkages and feedbacks generated through its influence on moisture fluxes, hydrology, and climate change due to temporal changes in snow/ice extent, impurity load and albedo. Therefore, snow and ice properties including ice and snow albedo and extent are monitored using ground and satellite instrumentation. The measurements are performed at various temporal and spatial scales using passive and active remote sensing instrumentation in a broad spectral range. The high spatial resolution is highly relevant for studies of cryosphere due to the rapid horizontal variability of snow properties and impurity load (dust outbreaks, algae blooms, structures on the snow surface/sastrugi). The instruments with low spatial resolution are not capable to resolve fine scales of snow variability. The accurate information on specific snow features (e.g., spatial distribution of algae blooms) can be hardly detected using measurements performed on the scale 0.3-1.0km.  The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission (Guanter et al., 2015), which provides information on evolution of aquatic and terrestrial ecosystems including cryosphere on the spatial scale of 30m, is capable to resolve the fine scale variability of snow properties. The measurements of EnMAP can be also used to assess the sub-pixel snow variability for coarse spatial resolution satellite missions and assess the accuracy of satellite products derived on the coarse spatial grid (e.g., snow fraction). This paper is aimed at the adaptation of the previously proposed snow remote sensing technique (Kokhanovsky et al., 2023) to EnMAP measurements. The retrievals are based on the asymptotic radiative transfer theory valid for weakly absorbing multiply light scattering turbid media (Kokhanovsky, 2021). The local optical properties of snow are calculated using the geometrical optics approximation, which is a valid technique for snow due to large size of ice grains as compared to the wavelength of the incident solar light in the spectral range under study. In particular, the spectral snow albedo and snow grain size are retrieved using EnMAP measurements performed by the SWIR EnMAP detector in the spectral range 900 - 1283nm. The snow specific surface area (SSA) and broadband albedo (BBA) are also derived using EnMAP measurements. The example of retrievals over Concordia station in Antarctica is given. It has been found that the effective ice grain diameter is around 0.23mm, SSA=28m*m/kg, and BBA=0.81, which is similar to the values of snow parameters measured at this location at the same season using both ground and satellite instrumentation.

References

Guanter, L., H. Kaufmann, K. Segl, et al., 2015: The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sensing 7: 8830-8857.

Kokhanovsky, A., 2021: Snow Optics, Cham: Springer.

Kokhanovsky, A., B. Vandecrux, A. Wehrlé, O. Danne, C. Brockmann, and J. E. Box, 2023: Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation. Remote Sensing 15: 1-25.

How to cite: Kokhanovsky, A., Brell, M., Chabrillat, S., Förster, S., and Segl, K.: Retrieval of snow grain size and albedo using EnMAP spaceborne observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15479, https://doi.org/10.5194/egusphere-egu23-15479, 2023.

08:55–10:15
Chairpersons: Rafael Pimentel, Claudia Notarnicola, Ilaria Clemenzi
Microwave
10:45–10:47
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PICO4.1
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EGU23-4196
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Virtual presentation
Jean-Pierre Dedieu, Joep Van Noort, Benoit Montpetit, Manon Levistre, Simon Vauclare, Anna Wendleder, Julia Boike, Eric Bernard, Jean-Charles Gallet, and Hans-Werner Jacobi

Arctic snow cover dynamics exhibit modification in terms of extent and duration due to recent changes in climate, i.e. increasing temperatures and changing precipitation patterns, i.e. winter rain-on-snow events (WROS). Remote sensing methods based on active radar images (SAR) have demonstrated a significant advantage for snow monitoring, (i) capturing physical and dielectric properties, (ii) overcoming the weakness of optical images limited by cloud cover and polar night.

The aim of this study is dedicated to the analysis of the spatial and temporal variability of snow cover in the Ny-Ålesund area on the BrØgger peninsula, Svalbard (N 78°55’ / E 11° 55’). In-situ snow measurements from two automated weather stations (Ny-Ålesund, and Bayelva), regular snowpits around the village and in spring on the Austre Loéven, were compared with the spaceborne dataset.

The RADARSAT Constellation Mission (RCM) is comprised of three satellites into closely coordinated orbits operating in C-band (5.4 GHz, 5.5 cm). The high temporal (4-day repeat cycle) and spatial resolution of the sensors in Quad-Pol mode (9-m) or Compact-Pol mode (5-m) provide a helpful performance for detecting the spatial variability of snow properties. RCM data are also compared to images of the TerraSAR-X satellite (DLR, Germany) operated in X-band (9.6 GHz, 3.1 cm) at 5-m spatial resolution. Both RCM QP mode and TSX data were acquired with medium incidence angles (33° to 39°) providing better snow penetration for volume backscattering. The RCM CP data were only available under low (23°) and high (53°) beam angles.

The following two snow properties were analyzed:

WROS detection: the focus was set on the 16-17 March 2022 event (+ 5.5 °C, 62 mm). RCM data at cross-polarization VH or HV can clearly detect the impact of rain on snow, indicating an intensity drop of -10 dB, even on the glacier at high elevation.

Snow depth retrieval: the study covers spring 2021 (March-June) and the complete winter season 2021-2022 (November-June).

- Concerning QP mode, better correlation between snow depth and SAR backscattering is observed by the cross-pol VH component, retrieving more volume backscattering information than co-pol configuration or total backscattering power (Span). We observe also that descending orbit images (06 :30 AM) provide a better correlation with snow depth than ascending orbit (15 :30 PM) data.

- Concerning CP mode and Span (RH+RV), the low incidence images (23°) do not match the snow depth observations due to main surface backscattering, contrariwise the high incidence images correlate better with in-situ observations. The analyses of the Stokes vector elements showed a satisfying correlation for the g3 element and the Relative Phase polarimetric decomposition.

Finally, a comparison of Span temporal values between RCM at C-band and TSX at X-band indicates similar time profiles, but clearly lower values of -5 to -10 dB at the C-band.

How to cite: Dedieu, J.-P., Van Noort, J., Montpetit, B., Levistre, M., Vauclare, S., Wendleder, A., Boike, J., Bernard, E., Gallet, J.-C., and Jacobi, H.-W.: Snow cover monitoring in the Arctic (Svalbard) with RADARSAT Constellation Mission (RCM). Comparison with in-situ measurements and TerraSAR-X data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4196, https://doi.org/10.5194/egusphere-egu23-4196, 2023.

10:47–10:49
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PICO4.2
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EGU23-17234
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ECS
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On-site presentation
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Isis Brangers, Hans-Peter Marshall, Grabielle J.M. De Lannoy, and Hans Lievens

Measuring snow from space is still a significant challenge in hydrology. Work by Lievens et al. (2019) for the first time showed the potential of the Sentinel-1 C-band radar mission to measure snow depth from space. However, the physical interactions between snow grains and the comparatively long C-band waves are not sufficiently understood. To improve this understanding, a tower based C-band radar experiment was set up in Idaho’s Rocky mountains starting from January 2020. The ultra-wideband radar system recorded the reflections in the time-domain, allowing to study the return throughout the different layers of the snowpack at a fine resolution. Reference data of the stratigraphy and snow properties were collected during ~weekly site visits. Our results indicate that some volume scattering is present at C-band for dry snow, and that the backscatter return increases substantially after melt-freeze cycles and with the appearance of ice features within the snowpack.

How to cite: Brangers, I., Marshall, H.-P., De Lannoy, G. J. M., and Lievens, H.: Tower based C-band measurements of an alpine snowpack, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17234, https://doi.org/10.5194/egusphere-egu23-17234, 2023.

10:49–10:51
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PICO4.3
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EGU23-998
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ECS
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On-site presentation
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Adriano Lemos and Aku Riihelä

Seasonal snow in the northern regions plays an important role providing water resources for both consumption and hydropower generation. Moreover, the snow depth in the northern Finland during winter exceeds 1 m, impacting local agriculture, vegetation, tourism and recreational activities. The objective of this study is to estimate snow depth using an empirical methodology applied to synthetic aperture radar (SAR) images and compare with in situ measurements collected by automatic weather stations (AWS) and snow courses in northern Finland. Snow depth estimates with high spatiotemporal resolution can improve our understand of seasonal snow mass in complex access areas. Here, we use an adapted version of the empirical methodology developed by Lievens et al. (2019) to estimate snow depth using Sentinel-1 constellation (C-band). The algorithm utilizes changes in the cross-polarized backscatter measurements of SAR images repeatedly acquired on the same orbit to avoid geometry distortions. We use SNAP toolbox, combined with the Copernicus digital elevation model (DEM), posted at 30 meters, in the pre-processing stage.  The snow retrievals between 2019 and 2022 are compared to three automatic weather stations and four snow courses measurements collected over the same period. The ongoing Sentinel-1 snow depth retrievals during the winter 2021/2022 demonstrate a correlation of 0.76, when compared to in situ measurements, supporting the potential ability to derive snow changes in regions where in situ measurements of snow are currently lacking. Despite the good agreement between the empirical algorithm and the collected datasets on land, further investigation is still necessary to better understand the backscatter response over frozen lake areas. Thanks to the effort of international space agencies, we have available currently, and in a near future, global coverage at high resolution SAR imagery and, combined with installed automatic weather stations, opens the possibility of a wide spatial monitoring of snow variations.

How to cite: Lemos, A. and Riihelä, A.: Snow Depth derived from Sentinel-1 compared to in-situ observations in northern Finland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-998, https://doi.org/10.5194/egusphere-egu23-998, 2023.

10:51–10:53
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PICO4.4
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EGU23-10436
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On-site presentation
Narae Kang, Seokhwan Hwang, and Jungsoo Yoon

In order to more quantitatively compare the differences between radar reflectivity and snowfall intensity against ground-observed snow depth, snow depth ground observation data and weather radar observation data were analyzed. For radar observation data, cumulative reflectivity and precipitation intensity derived from reflectivity, differential reflectivity, and specific differential phase (Quantitative Precipitation Estimation) were compared. As a result of the analysis, it was found that the precipitation intensity was similar to the variation according to the snow depth and time compared to the radar reflectivity. However, although the initial accumulation tendency of snow fall was very well matched due to the characteristics of snow cover, which is sensitive to temperature and has accumulation and melting characteristics, the melting tendency from daytime showed a difference. Therefore, it is judged that more accurate snow depth can be estimated only when precipitation intensity estimation method according to temperature is derived and used in addition to methods such as accumulation of reflectivity.

 

Acknowledgement

This research was supported by a grant(2022-MOIS61-003) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).

 

How to cite: Kang, N., Hwang, S., and Yoon, J.: Comparison of correlations between radar reflectivity and radar precipitation intensity for snow depth, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10436, https://doi.org/10.5194/egusphere-egu23-10436, 2023.

10:53–10:55
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PICO4.5
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EGU23-6176
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ECS
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On-site presentation
Jonas-Frederik Jans, Ezra Beernaert, Hans Lievens, and Niko Verhoest

Satellite information concerning the snow water equivalent (SWE) stored in the world’s mountain ranges is still lacking. This observation gap hinders the accurate estimation of total seasonal water storage in snow. Therefore, the SNOW-MODE project aims to address this gap by improving and developing two satellite retrieval methods to estimate SWE. Firstly, a recently developed empirical change detection algorithm for SWE retrieval from Sentinel-1 (S1) backscatter observations will be thoroughly analyzed and, if possible, improved. Secondly, a snow (Bic-DMRT), soil (Oh) and vegetation (WCM) radiative transfer model (RTM) will be coupled and inverted to estimate SWE using S1 radar backscatter observations and auxiliary snow, soil and vegetation properties. This method will be applied at the point- and grid-scale. The point-scale approach will make use of detailed in-situ measurements and novel tower-mounted radar measurements for RTM development and validation of the retrievals, whereas the grid-scale approach will utilize data generated from a land surface and a snow model. The inclusion of the grid-scale approach allows to investigate whether spatial patterns in SWE can be accurately represented by the S1 retrievals.

Subsequently, both S1 retrieval methods (i.e., change detection and RTM) will be compared over several mountain regions in the Northern Hemisphere (High-Mountain Asia and European and western United States mountains) to assess their uncertainties, validity conditions and main strengths as well as shortcomings. Furthermore, a physics-based snow model (e.g., SnowClim) will also be utilized to simulate snow depth and SWE on a daily basis. To improve the simulation results, the meteorological forcings will be downscaled to a resolution of 500 meter. Further improvements will be aspired by assimilating the mountainous snow depth retrievals (either from the RTM or change detection method) into the snow model. Finally, the generated SWE dataset will be related to modes of climate variability and will be translated into basin-scale water resources availability for society. 

How to cite: Jans, J.-F., Beernaert, E., Lievens, H., and Verhoest, N.: Dynamics in mountain SNOW water resources by MODEs of climate variability assessed from satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6176, https://doi.org/10.5194/egusphere-egu23-6176, 2023.

10:55–10:57
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PICO4.6
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EGU23-7050
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ECS
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Highlight
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On-site presentation
Lucas Boeykens, Hans Lievens, Ezra Beernaert, Jonas-Frederik Jans, and Niko Verhoest

Seasonal snow is an essential source of water, especially in mountainous regions. However, accurate satellite observations of the snow water equivalent (SWE), i.e., snow depth multiplied by the snow density, are still lacking. Therefore, new and robust remote sensing techniques are urgently needed. This study presents a novel method for SWE retrieval in mountainous regions at sub-weekly temporal and 500-m spatial resolution, based on snow depth observations from the ESA and Copernicus Sentinel-1 (S1) satellite mission and model simulations of snow density. The snow depth observations rely on a change detection algorithm which translates the temporal changes in the S1 radar backscatter measurements into the accumulation or ablation of snow. The snow density estimates are obtained from different modeling approaches, including empirical methods (e.g., based on the day of the year, the snow depth, snow climate class, etc.) and a physics-based mass and energy balance model. The performance of the different snow density modeling approaches is here compared, both with respect to their ability to accurately simulate in situ measurements of snow density, as well as their ability to accurately simulate in situ measurements of SWE after combination with the S1 snow depth observations. The performance is evaluated over the European Alps, using a large dataset of in situ time series measurements for the period 2015-2022. The results show that the physics-based snow density modeling approach outperforms the empirical approaches, yielding high spatio-temporal correlation between S1 SWE retrievals and in situ measurements. Therefore, the study demonstrates the capability of the Sentinel-1 satellite mission, in combination with a physics-based snow model, to accurately represent the spatio-temporal distribution of SWE in mountainous regions, which can benefit a large range of applications, including hydropower generation, water management, flood forecasting, and numerical weather prediction.

How to cite: Boeykens, L., Lievens, H., Beernaert, E., Jans, J.-F., and Verhoest, N.: SWE retrieval in the European Alps based on Sentinel-1 snow depth observations and modeled snow density, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7050, https://doi.org/10.5194/egusphere-egu23-7050, 2023.

10:57–10:59
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PICO4.7
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EGU23-15295
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ECS
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Highlight
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On-site presentation
Jorge Jorge Ruiz, Juha Lemmetyinen, Ioanna Merkouriadi, Juval Cohen, Anna Kontu, Jouni Pulliainen, and Jaan Praks

The mass of seasonal snow is a challenging parameter to measure from space. This is a significant observational gap as information on snow mass would be required by diverse applications such as flood prevention, and water resource management. Snow Water Equivalent (SWE) describes the amount of liquid water that is stored in the snowpack. A promising technique to measure changes in SWE over time is repeat-pass Interferometric SAR (InSAR), since it provides high spatial resolution and reasonable temporal resolution. The retrieval technique relies on the phase difference induced by the increase in propagation path due to snow accumulation since snow has a higher permittivity than air [1]. The retrieval has been demonstrated using a wide range of sensors [1-3]. In a recent work [4], the usability of L-, S-, C-, and X- frequency bands (1-10 GHz) was analysed in the context of coherence conservation and SWE retrieval. L-band emerged as a solid candidate, as this band appeared more resilient against temporal decorrelation in snow while enabling retrieval of large amounts of SWE.

The Copernicus Radar Observation System for Europe in L-band (ROSE-L), estimated to be launched in 2028, is one of the six Copernicus high-priority Sentinel Expansion missions selected for implementation. The mission will consist of two satellites with a 180 degrees orbit phasing, allowing a temporal baseline of 6 days. We present an analysis of L-band ALOS2 imagery over Sodankylä, in northern Finland, applied for SWE retrieval using the InSAR method. The landscape is dominated by coniferous forest, presenting a challenge for large-scale retrieval of SWE. Due to ALOS2 revisit time of 14 days, it is prone to suffer from temporal decorrelation. We analysed the coherence conservation considering environmental events, land cover, canopy cover and topography. We introduce SnowModel [5], a high-resolution, spatially distributed physical snow evolution model, for comparison to InSAR SWE retrievals. SnowModel simulations were used to calibrate the interferometric phase, allowing a comparison between the two and demonstrating in which areas and under which conditions the retrieval works.

 

[1] T. Guneriussen, K. A. Hogda, H. Johnsen and I. Lauknes, "InSAR for estimation of changes in snow water equivalent of dry snow," in IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 10, pp. 2101-2108, Oct. 2001, doi: 10.1109/36.957273.

[2] T. Nagler et al., "Airborne Experiment on Insar Snow Mass Retrieval in Alpine Environment," IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 4549-4552, doi: 10.1109/IGARSS46834.2022.9883809.

[3] S. Leinss, A. Wiesmann, J. Lemmetyinen and I. Hajnsek, "Snow Water Equivalent of Dry Snow Measured by Differential Interferometry," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 8, pp. 3773-3790, Aug. 2015.

[4] J. J. Ruiz et al., "Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 4306715, doi: 10.1109/TGRS.2022.3223760.

[5] Liston, Glen E.; Elder, Kelly. 2006. A distributed snow-evolution modeling system (SnowModel). Journal of Hydrometeorology. 7(6): 1259-1276

 

How to cite: Jorge Ruiz, J., Lemmetyinen, J., Merkouriadi, I., Cohen, J., Kontu, A., Pulliainen, J., and Praks, J.: Analysis of ALOS2 L-band repeat-pass InSAR for the retrieval of Snow Water Equivalent over boreal forest., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15295, https://doi.org/10.5194/egusphere-egu23-15295, 2023.

10:59–11:01
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PICO4.8
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EGU23-16822
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On-site presentation
Eric A. Sproles, Ross T. Palomaki, Madison Woodley, and Samual E. Tuttle

In a low-relief, agricultural landscape we integrate detailed measurements from plane-based L-band SAR (UAVSAR), drone-based LiDAR and photogrammetry, cosmic ray neutron sensor (CRNS), and field assessments to disentangle and quantify how topography, wind, and vegetation influence the spatial distribution snow cover and water equivalent. Seasonal snow in prairie and temperate grasslands environments helps sustain agriculture, socio-environmental systems, and aquifers while also exacerbating flooding in wetter years. Because these expansive landscapes cover roughly 10% of the earth’s surface, quantifying snow and snow water equivalent (SWE) is critical to better resolve water and energy budgets from local to global scales. Present day, remotely-sensed observations and conventional automated ground-based observations (e.g. SWE scales) of seasonal snow in these biomes contain considerable uncertainty. Optical imagery can detect the presence/absence of snowpack, but lacks the capacity to provide estimates of SWE. Synthetic Aperture Radar (SAR) provides a potential path forward to quantify SWE in grassland and agricultural environments, but current measurements are poorly constrained, especially in prairie environments. The Central Agricultural Research Center (CARC) in central Montana, USA (47ºN, 110ºW) served as field site for NASA’s SnowEx 2021 Mission and was distinct from other campaign locations due to its prairie landscape, controlled agricultural vegetation patterns, and ephemeral snow cover. The CRNS measures an integrated snow signal over several hectares, allowing for continuous estimations of SWE that are less influenced than smaller scale observations by the significant spatial heterogeneity of prairie snow. Initial results show that CRNS effectively quantifies an integrated SWE signal at the study site (R2 ≥ 0.90).  Interferometric UAVSAR products and drone flights provide complementary high resolution snow information for narrow time periods that effectively identify snow presence across areas with different crop types (wheat, barley, peas) and stubble heights (0-0.6 m) . The limited number of UAVSAR flights in 2021 preclude a full season or multi-year analysis. However our integrated sensing approach and analysis provides a framework to reduce uncertainty in future efforts, and better constrain measurements from the upcoming L-band NISAR mission that is expected to be launched in January 2024.   

How to cite: Sproles, E. A., Palomaki, R. T., Woodley, M., and Tuttle, S. E.: An integrated remote-sensing approach for prairie snowpack, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16822, https://doi.org/10.5194/egusphere-egu23-16822, 2023.

11:01–11:03
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PICO4.9
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EGU23-15926
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ECS
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Highlight
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On-site presentation
Greta Cazzaniga, Ali Nadir Arslan, and Carlo De Michele

The spatial and temporal quantification of the liquid water content (LWC) of the snowpack in alpine regions provides information on the short-term availability of water, which could eventually lead to wet snow avalanches or river floods. The monitoring and forecasting of snow wetness are hence of paramount importance in many fields, from operational avalanche forecasting to hydropower production and flood prediction, when combined with hydrological models. 
Remote sensing is an essential tool for snow monitoring as it offers observations of the snowpack's physical properties. For instance, Sentinel-1 satellites provide C-band synthetic aperture radar (SAR) data at high temporal and spatial resolutions and are capable to detect the presence of wet snow.  On the other side, many snow models were built in literature to simulate snowpack mass dynamics in space and time (see e.g., Crocus and HyS model) and can provide predictions of variables of interest in snow hydrology, such as the LWC. 
In the present work, we aim at identifying and quantifying the differences between satellite products and model snow estimates. In particular, the comparison is led among (1) Sentinel-1-based wet-snow products, (2) HSAF products, coming from the processing of data from Earth observation satellites and revealing the wet or dry status of the snow mantle, and (3) simulations of the liquid water content from HyS model, a temperature-index model, leveraged in both its one-layer and two-layer version. The case study is the Mallero basin, a middle-size alpine basin, whose flow regime is strongly influenced by snow melting and glacier ablation in the spring and summer seasons. 
The comparison returns a good agreement between Sentinel-1 products and HyS simulations. The short period of mismatches between the two outputs is analyzed to identify the physical processes that the model is not able to reproduce. On the other side HSAF products have a coarser resolution if compared to Sentinel-1 products and for this, they can just provide a qualitative overview of the snow mantle status, over a middle-size basin. Moreover, such products are also limited by the effect of cloud covering that makes it impossible to have information on the snow wetness when it is present.

How to cite: Cazzaniga, G., Arslan, A. N., and De Michele, C.: Liquid water content in a seasonal snowpack: a comparison between satellite products and model simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15926, https://doi.org/10.5194/egusphere-egu23-15926, 2023.

11:03–11:05
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PICO4.10
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EGU23-8530
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Highlight
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On-site presentation
Chris Derksen, Benoit Montpetit, Vincent Vionnet, Vincent Fortin, Juha Lemmetyinen, Richard Kelly, and Aaron Thompson

Environment and Climate Change Canada (ECCC) and the Canadian Space Agency (CSA) continue to advance a new satellite Ku-band radar mission focused on providing moderate resolution (500 m) information on seasonal snow mass. Like many regions of the northern hemisphere, estimates of the amount of water stored as seasonal snow are highly uncertain across Canada. To address this gap, a technical concept capable of providing dual-polarization (VV/VH), moderate resolution (500 m), wide swath (~250 km), and high duty cycle (~25% SAR-on time) Ku-band radar measurements at two frequencies (13.5; 17.25 GHz) is under development. Parallel to engineering studies to address the technical readiness, a range of activities are in progress to advance scientific readiness. In this presentation, we will review how recent progress within the mission science team and across the snow community has provided a sound science foundation for the mission, and identify risks to meeting the required level of readiness within the required timeline for full mission implementation. Key areas include:

  • Implementation of computationally efficient SWE retrieval techniques, including parameterized forward model simulations for prediction of snow volume scattering, physical snow modeling to provide initial estimates of snow microstructure, and consideration of background characteristics;
  • Incorporation of land surface model SWE estimates to infill gaps with no radar-derived SWE information due to dense forest, wet snow, and swath gaps;
  • Direct assimilation of Ku-band backscatter into environmental prediction systems (analogous to how SMOS and SMAP data have improved soil moisture analysis through radiance-based assimilation);
  • Segmentation of wet from dry snow;
  • Continued advancement of the understanding of the physics of Ku-band backscatter response to variations snow through new experimental tower and airborne measurements.

Ku-band radar is a viable approach for a terrestrial snow mass mission because these measurements are sensitive to SWE through the volume scattering properties of dry snow and can discriminate the wet versus dry state of snow cover. To justify investment in such a mission, however, the scientific pieces must be in place. Balanced and honest assessments of the state of scientific readiness, the likelihood for emerging capabilities, and the level of engagement across the snow community are essential to ensure a healthy mission development process.

How to cite: Derksen, C., Montpetit, B., Vionnet, V., Fortin, V., Lemmetyinen, J., Kelly, R., and Thompson, A.: Advancing science readiness for a new snow mass radar mission, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8530, https://doi.org/10.5194/egusphere-egu23-8530, 2023.

11:05–11:07
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PICO4.11
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EGU23-12702
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On-site presentation
Shiyi Li, Lanqing Huang, Philipp Bernhard, and Irena Hajnsek

Wet snow is a critical component of the cryosphere, and its spatial and temporal distribution has important implications for water resources, natural hazards, and the regional climate. However, mapping wet snow in alpine regions such as the Karakoram is challenging due to complex topography, harsh weather conditions, and limited in-situ observations.

Previous studies have shown that synthetic aperture radar (SAR) can effectively detect wet snow surfaces using the backscattering ratio between the current and reference images (e.g. the average of summer acquisitions). However, its regional application on a large-scale and complex terrain is hampered, as the ratio value is easily affected by the land cover, local topography, surface roughness, and snow wetness.

In this study, we present a new approach for mapping wet snow in the Karakoram using a combination of SAR data and topographic information. The SAR data used in the analysis were obtained from Sentinel-1, and the topographic data included a digital elevation model (DEM), slope angle, and slope aspect ratio. We first used a Gaussian Mixture Model to classify the ratio image of Sentinel-1 into wet snow (WS) and non-wet snow (NWS) classes, then transformed the two classes into a logistic function to characterize the probability of WS based on the backscattering ratio. Secondly, we categorized the image based on the topography and calculated the likelihood of WS for each topographic bin using the WS probability. The joint WS likelihood map was finally obtained by multiplying the WS probability on the backscattering ratio with the WS likelihood on topography, and a binary WS map was generated by setting a threshold on the joint likelihood map.

The proposed method was validated using snow maps generated from Sentinel-2 images. Compared with the traditional method of using only the SAR backscattering ratio, our method significantly reduced false negative detections and preserved the high true positive rate, indicating an improvement of robustness and accuracy by combining SAR and topographic data for regional wet snow mapping.

This study demonstrates a practical method of merging SAR backscattering features and topographic information for robust regional wet snow mapping in complex mountain ranges. It also provides new insights into the incorporation of different datasets using a probabilistic framework. With the proposed method, the operational monitoring of wet snow distribution in the Karakoram using SAR becomes feasible and reliable.

How to cite: Li, S., Huang, L., Bernhard, P., and Hajnsek, I.: Wet Snow Mapping in the Karakoram using SAR and Topographic Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12702, https://doi.org/10.5194/egusphere-egu23-12702, 2023.

11:07–11:09
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PICO4.12
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EGU23-8018
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ECS
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On-site presentation
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Morgane De Breuck, Hans Lievens, Jonas-Frederik Jans, Ezra Beernaert, and Niko Verhoest

Remote sensing can offer important information on snow properties at the global scale. However, the sensitivity of satellite radar measurements at C-band (5.4 GHz) from the ESA and Copernicus Sentinel-1 (S1) mission to snow properties still requires further investigation. This study provides the first results of a detailed sensitivity analysis, carried out over the European Alps at 1-km spatial resolution. It includes three processing types of radar measurements: radar backscatter observations in vertical-vertical (VV) and vertical-horizontal (VH) polarizations, polarimetric decomposition parameters (e.g., H-Alpha dual polarization decomposition), and interferometric phase change and coherence between successive S1 acquisitions from the same relative orbit. The sensitivity of the different radar measurements is investigated with respect to snow properties (snow depth, snow water equivalent, wet-dry snow state), soil properties (surface soil moisture, soil temperature), and vegetation properties (LAI), and furthermore stratified by snow climatology, land cover, and elevation. Preliminary results suggest that, in regions with significant snowfall and limited vegetation, the VH backscatter correlates strongest with snow depth and SWE, whereas the VV backscatter is more strongly correlated with soil properties. The Alpha polarimetric decomposition parameter increases with snow accumulation, indicating increased contributions of volume scattering and multiple scattering. The often low interferometric coherence is confounding the interpretation of interferometric phase changes in mountainous regions. In conclusion, the first results of this sensitivity study indicate the usefulness of S1 radar backscatter and polarimetric decomposition parameters for snow retrieval algorithm development.

How to cite: De Breuck, M., Lievens, H., Jans, J.-F., Beernaert, E., and Verhoest, N.: Sensitivity of Sentinel-1 observations to snow properties, comparing radar backscatter, polarimetric decomposition parameters, and interferometric phase changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8018, https://doi.org/10.5194/egusphere-egu23-8018, 2023.

11:09–11:11
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PICO4.13
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EGU23-8142
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On-site presentation
Annett Bartsch, Helena Bergstedt, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Marietta Soininen

Rain-on-Snow (ROS) events change snow pack properties and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. Active as well as passive microwave sensors have been used in the past to document ROS on regional scale. Either wet snow during a ROS event or the formation of crust afterwards are identified in most cases. The fusion of both approaches is promising for circumpolar monitoring.

C-band radar is of special interest due to good data availability including a range of nominal spatial resolution (10 m–12.5 km). Previous studies indicated that radar backscatter is suitable to identify snow structure change. As an example L-band passive microwave observations from SMOS and C-band backscatter from Metop ASCAT have been jointly analysed and compared to snowpit observations in Scandinavia and Northwestern Siberia.

A circumpolar dataset of potential ROS has been created. The gridded information has been eventually aggregated for events. Larger mid-winter events have been eventually extracted for 2012-2021. They occur mostly in the NE part of northern Eurasia (mostly November) and across Alaska (mostly December). The spatiotemporal patters of these events and the magnitude of snow structure change will be presented and discussed.

How to cite: Bartsch, A., Bergstedt, H., Muri, X., Rautiainen, K., Leppänen, L., Joly, K., Sokolov, A., Orekhov, P., Ehrich, D., and Soininen, E. M.: Monitoring the impact of rain-on-snow events across the Arctic with satellite data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8142, https://doi.org/10.5194/egusphere-egu23-8142, 2023.

11:11–12:30